______ 140 ..... the plant leaf through which gases and water vapor pass (Figure 3). The vaporization occurs within the leaf, in the intercellular spaces, and the ...
Tanta University Faculty of Engineering Irrigation and Hydraulics Engineering Department
Modeling of Water Resources in the Nile Delta Using GIS and Remote Sensing A THESIS Submitted in the partial fulfillment of the requirement for the Degree of Master of science in Engineering (Irrigation and Hydraulics Engineering)
Prepared by
Sobhy Rezk Sobhy Emara B.Sc. Civil Engineering, Tanta University, 2013 Demonstrator at Irrigation and Hydraulics Engineering Department
Faculty of Engineering, Tanta University Supervised by
Prof. Dr. Bakenaz A. Zeidan Professor of Water Resources, Head of Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University
& Assoc. Prof. Dr. Mosaad Khadr Associate Professor - Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University
2018
I
The Supervisors Committee Name
Position
Prof. Dr. / Bakenaz Abdelazim Zeidan
Professor of Water Resources Head of Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University.
Assoc. Prof. Dr. / Mosaad Khadr
Associate Professor, Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University.
The Supervisors Committee Signature Name Prof. Dr. / Bakenaz Abdelazim Zeidan
Assoc. Prof. Dr. / Mosaad Khadr
Signature
II
The Examining Committee Name
Position
Prof. Dr. / Osama Khairy Saleh
Professor of Hydraulics, Water and water Structures Engineering Department, Faculty of Engineering, Zagazig University
Prof. Dr. / Noha Samir Donia
Professor of Environmental Hydraulics, Institute of Environmental Studies and Research, Ain Shams University
Prof. Dr. / Bakenaz Abdelazim Zeidan
Assoc. Prof. Dr. / Mosaad Khadr
Professor of Water Resources Head of Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University. Associate Professor, Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University.
The Examining Committee Signature Name Prof. Dr. / Osama Khairy Saleh Prof. Dr. / Noha Samir Donia Prof. Dr. / Bakenaz Abdelazim Zeidan Assoc. Prof. Dr. / Mosaad Khadr
Signature
III
Acknowledgement First and foremost, I would like to thank my Lord, ALLAH, who enabled me to complete this work. I would like to express my sincere gratitude to my main supervisor Prof. Dr. Bakenaz A. Zeidan, Professor of Water Resources, Head of Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University, who encouraged me and gave me the expert guidance, time, motivation, support and constructive comments throughout this work. Also, I wish to thank Assoc. Prof. Dr. Mosaad Khadr, Associate Professor, Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University, my co-supervisor. I owe gratitude to him for his time and continuous help and encouragement which without it, this work would not come to reality. As an indication and a clue of sincerest thanks and appreciation to my indispensable family members, my Father and Mother, owners of the greatest favor regarding bringing me up to value science. My deep gratitude is due to my dear wife Dr. Magy Monier for her continuous support and encouragement. I would also like to express my thanks for my brother and sister. Finally, I express my great thanks to my dear colleagues in the Irrigation and Hydraulics Engineering Department for their help. My thanks extend to all, who assisted me in the fulfillment of this thesis.
IV
Abstract Water has become a critical issue because of its scarcity all over the world. Water scarcity obstacles the development of urban expansion. Egypt is one of the most countries that suffer from water shortage problems. Egypt is one of the riparian States on the Nile River as a downstream country with a fixed share of the Nile water reaching about 55.5 billion cubic meters (BCM)/year. Evapotranspiration (ET) constitutes a large portion of the hydrologic cycle and considered as the important parameter in the water budget in the arid areas. Estimation of evapotranspiration is a major component of water resources management. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. In this study, earth observation satellite sensors are used in conjunction with Surface Energy Balance Algorithm for Land (SEBAL) model to overcome difficulties in obtaining evapotranspiration measurements on a regional scale. The estimation of pixel-scaled ET was conducted via SEBAL using Landsat-8 images and meteorological data. Compared with the recorded pan evaporation, the estimated evapotranspiration calculated by SEBAL agreed well with the results derived from pan observations with correlation coefficient equal to 0.8927. Calculation of evapotranspiration depends on the weather parameters like air temperature, relative humidity, solar radiation, and wind speed. Taking into consideration the water used by the potable water treatment plants and the water loss through evaporation and seepage from the canals and drains, total water available for irrigation can be calculated. Finally, assessment of the performance of the irrigation system in the study area was performed. Results of this research show that the irrigation efficiency for the study area is about 61.07% and the distribution efficiency for the study area is about 59.61%. The research demonstrates the considerable potential of SEBAL model for estimation of spatial ET with little ground-based weather data over large areas.
V
Contents ABSTRACT……………………………………………………………………....….……………
IV
CONTENTS………...…………………………………………………………..…….…….…......
VI
LIST OF FIGURES……………...………………………….………………….........…..……….
XII
LIST OF TABLES…………..…………...…………………………………………….…..…….. XVI LIST OF SYMBOLS………………...…...…...…………………………………………..….….. XVIII
CHAPTER (1) ...................................................................................................... 1 INTRODUCTION ................................................................................................. 1 1.1
Introduction ............................................................................... 1
1.2
The Global Water Resources ......................................................... 2
1.3
Water Resources in Egypt ............................................................. 4
1.4
Significance of Irrigation in Agriculture ........................................... 5
1.5
Evapotranspiration....................................................................... 7
1.6
Application of Geographic Information System in Irrigation Management
................................................................................... 9 1.7
Remote Sensing Techniques and Capabilities.................................. 10
1.8
Problem Statement .................................................................... 11
1.9
Objectives of the Study
1.10
Organization of the Thesis .......................................................... 12
.............................................................. 11
CHAPTER (2) .................................................................................................... 13 LITERATURE REVIEW ....................................................................................... 13 2.1
Introduction ............................................................................. 13
2.2
Evapotranspiration Estimation ..................................................... 13
VI 2.2.1
Empirical Methods .................................................................... 13
2.2.2
Simplified Energy Balance Methods ............................................. 14
2.2.3
Biophysical Estimation Evapotranspiration Model ........................... 15
2.2.4
Surface Temperature and Vegetation Index Method ......................... 16
2.2.5
Full Energy Balance Method ........................................................ 18
2.3
Different Surface Energy Balance Algorithms ................................. 18
2.3.1
Surface Energy Balance Index (SEBI) ........................................... 18
2.3.2
Surface Energy Balance System (SEBS) ........................................ 20
2.3.3
Simplified Surface Energy Balance Index (S-SEBI) ......................... 22
2.3.4
Surface Energy Balance Algorithm for Land (SEBAL). .................... 25
2.3.5
Mapping Evapotranspiration at High Resolution and with Internalized Calibration (METRIC) ............................................................... 28
2.3.6
Two-Source Models (TSM) ......................................................... 30
2.3.7
Distinction between SEBAL and METRIC ..................................... 31
2.4
Performance Assessment Indicators for Irrigation and Drainage System ...
................................................................................. 35 CHAPTER (3) .................................................................................................... 36 METHODOLOGY ............................................................................................... 36 3.1
Introduction ............................................................................. 36
3.2
Layout of the Study Area ............................................................ 36
3.2.1
Irrigation System in the Study Area .............................................. 38
3.2.2
The Drainage System in the Study Area ......................................... 39
3.3
Data Requirements .................................................................... 39
3.3.1
Remote Sensing Data ................................................................. 40
VII 3.3.2
Weather Data ........................................................................... 40
3.4
Data Preparations ...................................................................... 45
3.4.1
Downloading of Landsat 8 Images. ............................................... 45
3.5
Weather Data Preparation and Calculation of Reference ETr
3.6
Operation of the Model
3.6.1
The Net Surface Radiation Flux (Rn) ............................................ 47
3.6.1.1
Surface Albedo ......................................................................... 47
3.6.1.2
Incoming Shortwave Radiation (RS↓)
3.6.1.3
Outgoing Longwave Radiation (RL↑) ............................................ 52
3.6.1.4
Choosing the “Hot” and “Cold” Pixels
3.6.1.5
Incoming longwave Radiation (RL↓) ............................................. 61
3.6.1.6
Solving the Surface Radiation Balance Equation for Rn .................... 63
3.6.2
Soil Heat Flux (G) ..................................................................... 64
3.6.3
Sensible Heat Flux (H) ............................................................... 65
3.6.4
Latent Heat Flux (λET), Instantaneous ET (ETinst), and Reference ET
............. 45
.............................................................. 46
........................................... 51
.......................................... 61
Fraction (ETrF). ........................................................................ 79 3.6.4.1
24-Hour Evapotranspiration (ET 24) ............................................... 81
3.6.4.2
Seasonal Evapotranspiration (ET seasonal) ..................................... 82
CHAPTER (4) .................................................................................................... 90 RESULTS AND DISCUSSION ............................................................................... 90 4.1
Introduction ............................................................................. 90
4.2
The Net Surface Radiation Flux (Rn) ............................................ 90
4.2.1
Estimation of Surface Albedo (α) ................................................. 90
VIII 4.2.2
Estimation of Vegetation Indices .................................................. 90
4.2.3
Estimation of Surface Temperature Ts ........................................... 93
4.2.4
The Outgoing Longwave Radiation (RL↑) ...................................... 95
4.2.5
The Incoming Long Wave Radiation (RL↓) .................................... 96
4.3
Soil Heat Flux (G) ..................................................................... 99
4.4
Sensible Heat Flux (H) ............................................................... 99
4.5
Latent Heat Flux (λET), Instantaneous ET (ETinst), Reference ET Fraction (ETrF), and 24-Hour Evapotranspiration (ET 24).............................. 101
4.6
Seasonal Evapotranspiration (ET seasonal) .................................... 103
4.7
Validation of SEBAL Model ...................................................... 104
4.8
Total Amount of Water Lost by Evapotranspiration ........................ 106
4.9
Irrigation Water Supply ............................................................. 115
4.10
Irrigation Water Performance
4.10.1
Irrigation Efficiency (Ei ) ........................................................... 119
4.10.2
Distribution Efficiency (Ed) ........................................................ 119
.....................................................117
CHAPTER (5) ................................................................................................... 121 CONCLUSIONS AND FUTURE WORK .................................................................. 121 6.1
Introduction ............................................................................ 121
6.2
Conclusions ............................................................................ 121
6.3
Recommendations for Future Work ............................................. 124
REFERENCES ................................................................................................... 125 APPENDIX A .................................................................................................... 139 Importing Landsat 8 Data into ERDAS IMAGINE 2014 (into .img format) ............ 139
IX APPENDIX B ................................................................................................... 142 Weather Data Preparation and Calculation of Reference ETr ............................... 142 APPENDIX C .................................................................................................. 147 Weather Data and REF-ET Software Output ..................................................... 147
Arabic summary…………………………….…………………………………..160
X
List of Figures Figure 1The Global Water Cycle. (Values in 103 km3/yr). ________________________ 1
____________________________ 3 Figure 3 Schematic representations of stomata. ____________________________ 8 Figure 4 Surface Energy Balance ______________________________________ 26 Figure 5 Surface Radiation Balance ___________________________________ 26 Figure 6 Layout of the study area in Nile delta _____________________________ 37 Figure 7 Detailed layout of the study area ________________________________ 37 Figure 8 Irrigation system in the study area _______________________________ 38 Figure 9 Drainage system network in the study area _________________________ 38 Figure 10 Air Temperature (oc) _______________________________________ 42 Figure 11 Dew Point Temperature (oc) __________________________________ 42 Figure 12 Relative Humidity (%) ______________________________________ 43 Figure 13 Wind Speed (km/hr) _______________________________________ 43 Figure 14 Solar Radiation (w/m2) ______________________________________ 44 Figure 15 REF-ET output file _________________________________________ 45 Figure 2 the distribution of the total water world
Figure 16 Flowchart of computational steps used to obtain the seasonal evapotranspiration.
____________________________________________________________ 46 Figure 17 Flowchart of the Rn calculation ________________________________ 47 Figure 18 Surface Albedo Model ______________________________________ 50 Figure 19Calculation incoming Shortwave Radiation model. ____________________ 52 Figure 20 Vegetation Indices Model ____________________________________ 54 Figure 21 Surface Emissivity Model ____________________________________ 55 Figure 22 Flowchart of the algorithm to be performed during Ts estimation using TIRS Band
__________________________________________ 56 Figure 23 Surface temperature Model __________________________________ 60 Figure 24 Outgoing longwave radiation Model. ____________________________ 60 Figure 25"cold/hot pixel” estimation procedure in SEBAL for the image (25/5/2014). ___ 61 Figure 26 Incoming longwave radiation model _____________________________ 62 Figure 27Incoming longwave radiation calculations using excel spreadsheet _________ 63 10 and 11 and OLI sensor.
XI Figure 28 Rn calculating Model _______________________________________ 63 Figure 29 G/Rn and G Calculation Model _________________________________ 64 Figure 30 land-use map ____________________________________________ 66 Figure 31 𝒁𝒐𝒎 calculating Model _____________________________________ 67 Figure 32 Aerodynamic heat transfer ___________________________________ 67
___________________ 69 Figure 34 Iterative Process to Compute H ________________________________ 70 Figure 35 Friction Velocity Model______________________________________ 70 Figure 36 rah Calculation Model ______________________________________ 71 Figure 37 Relationship between dT and Surface Temperature ___________________ 72 Figure 38 coefficients “a” and b” by excel spreadsheet _______________________ 73 Figure 39dT Calculation Model _______________________________________ 73 Figure 40 H computation Model ______________________________________ 74 Figure 41 Stability Correction Model ___________________________________ 76 Figure 42 Corrected friction velocity model _______________________________ 77 Figure 43 Corrected rah model _______________________________________ 79 Figure 44 ET24 Model Calculator ______________________________________ 81 Figure 45 Seasonal ET Model Calculator _________________________________ 89 Figure 46 Estimated Albedo values, by Landsat 8 image(28/7/2014) ______________ 91 Figure 47 NDVI, SAVI, and LAI for the image acquired in 28/7/2014 _______________ 92 Figure 48 Difference LSE layer between Band 10 and 11 _______________________ 93 Figure 49 Mean of LSE layer between band 10 and 11 ________________________ 93 Figure 50 Surface Temperature Layerof "Handaset Tanta" on 28/7/2014. ___________ 94 Figure 51 the outgoing longwave radiation (RL↑) __________________________ 95 Figure 52"cold/hot pixel” estimation procedure in SEBAL for image (28/7/2014) ______ 96 Figure 33Iterative Process to Compute Sensible heat flux (H)
Figure 53Incoming long wave radiation calculations using excel spreadsheet for Landsat 8
________________________________________ 97 Figure 54 The net surface radiation flux for Landsat 8 image acquired in 28/7/2014 ____ 98 Figure 55 G/Rn for Landsat 8 image acquired in 28/7/2014 ____________________ 99 Figure 56Soil Heat Flux G for Landsat 8 image acquired in 28/7/2014 ______________ 99 image acquired in 28/7/2014
XII Figure 57 Surface roughness zom for each pixel ____________________________ 100 Figure 58 Sensible Heat Flux (H)for Landsat 8 image acquired in 28/7/2014
________ 101
Figure 59 Latent heat flux λET, Instantaneous ET, Reference ET Fraction ETrF, and 24-Hour Evapotranspiration ET24for Landsat 8 image acquired in 28/7/2014 ______________ 102 Figure 60 Spatial variation of seasonal evapotranspiration for "Handaset Tanta"- summer 2014
_______________________________________________________ 103
Figure 61 Comparison of daily ETa (mm/d) estimated via SEBAL and daily ETpan calculated from pan evaporation.____________________________________________ 105
______________________ 107 Figure 63 ArcMap 10.3 spatial tool (clip) ________________________________ 107 Figure 62 The net cultivated areas in "Handaset Tanta
Figure 68 a) ET for 16 days period represented by the image in (25/5/2014), [from 17/5/2014 to 1/6/2014], b) ET for 16 days period represented by the image in (10/6/2014), [from 2/6/2014 to 17/6/2014] c) ET for 16 days period represented by the image in (26/6/2014), [from 18/6/2014 to 3/7/2014]
_____________________________ 108
Figure 69 a) ET for 16 days period represented by the image in (12/7/2014), [from 4/7/2014 to 19/7/2014], b) ET for 16 days period represented by the image in (28/7/2014), [from 20/7/2014 to 4/8/2014] c) ET for 16 days period represented by the image in (13/8/2014), [from 5/8/2014 to 20/8/2014]
______________________________________ 109
Figure 70 a) ET for 16 days period represented by the image in (29/8/2014), [from 21/8/2014 to 5/9/2014], b) ET for 16 days period represented by the image in (14/9/2014), [from 6/9/2014 to 21/9/2014] c) ET for 16 days period represented by the image in
____________________________ 110 Figure 67 Cumulative ET for the summer season [from 17/5/2014] to [7/10/2014] ____ 111 Figure 68 Layer Properties window (ArcMap 10.3) _________________________ 112 Figure 69 Actual evapotranspiration (ET) for summer 2014 ___________________ 112 (30/9/2014), [from 22/9/2014 to 7/10/2014]
Figure 70 The cumulative evapotranspiration for the period under consideration (from 17/5/2014 to 7/10/2014) __________________________________________ 113 Figure 71 Histogram showing the distribution of seasonal evapotranspiration (ET) of the
___________________________________________________ 115 Figure 72 Importing Landsat 8 Data into ERDAS IMAGINE 2014 - Import Data. _______ 139 study area.
XIII Figure 73 Importing Landsat 8 Data into ERDAS IMAGINE 2014 -Select format. ______ 140 Figure 74 Importing Landsat 8 Data into ERDAS IMAGINE 2014 - Select the Input File. __ 140 Figure 75 Importing Landsat 8 Data into ERDAS IMAGINE 2014 - Import Multispectral and
_________________________________________________ 141 Figure 76 Weather Data in an Excel spreadsheet __________________________ 142 Figure 77 Saving the excel spreadsheet in a CSV (Comma delimited) format. ________ 142 Figure 78 Starting window of REF-ET software ____________________________ 143 Figure 79 REF-ET Data File Window ___________________________________ 143 Figure 80 Open or create definition file window ___________________________ 144 Figure 81 Order of weather parameters window __________________________ 144 Figure 82 Description of weather station and used file window _________________ 145 Figure 83 Output models and Reference equations window ___________________ 146 Thermal Data
XIV
List of Tables Table 1 World freshwater resources .......................................................................... 3 Table 2 Water Budget of Egypt (2010), and all Sources and Allocation/Usage (MWRI 2010) 5 Table 3 Comparisons of the different remote sensing ET models ................................... 33 Table 4 Landsat 8 Bands Designations ..................................................................... 39 Table 5 Monthly mean values of the precipitation and the effective precipitation of the studied area during the year 2014 .......................................................................... 41 Table 6 Metadata of Satellite Images ...................................................................... 44 Table 7 Values for the weighting coefficient, ωλ ........................................................ 49 Table 8 Typical Albedo values ................................................................................ 51 Table 9 Split-Window Coefficient Values .................................................................. 56 Table 10 Rescaling Factor ..................................................................................... 57 Table 11 K1 and K2 Values .................................................................................... 58 Table 12 NDVI for Soil and Vegetation ..................................................................... 58 Table 13 Emissivity Values ..................................................................................... 59 Table 14 Daily ETr values over the period of image .................................................... 83 Table 15 Hot and Cold pixels characteristics for Landsat 8 image acquired in 28/7/2014 ... 96 Table 16 Hot and Cold pixels characteristics for all Landsat 8 image .............................. 97 Table 17 Comparison of daily ETa (mm/d) estimated via SEBAL and daily ETpan calculated from pan evaporation ......................................................................................... 105 Table 18 The average value of evapotranspiration for each image ............................... 113 Table 19 The cumulative evapotranspiration for the period under consideration (from 17/5/2014 to 7/10/2014) ..................................................................................... 114 Table 20 The distribution of seasonal evapotranspiration (ET) of the study area. ............ 115 Table 21 Actual water supply for the Qanat Tanta Al-Melahia before the study area in the year 2014 ......................................................................................................... 116 Table 22 Actual water supply for the Qanat Tanta Al-Melahia before the study area for the period under consideration................................................................................... 117 Table 23 water budget for the study area................................................................ 118 Table 24 Irrigation efficiency for the study area ........................................................ 119
XV Table 25 Distribution efficiency for the study area .................................................... 120 Table 26 A sample of Weather Data and REF-ET software output ................................ 147
XVI
List of Symbols and Abbreviations Symbols
Description
AL
Band specific additive rescaling factor. (Table 10)
Aρ
Band-specific additive rescaling factor from the metadata
“a” and “b”
Coefficients derived by utilizing the anchor pixel concept
B
A constant is depending primarily on surface roughness and wind speed.
C0 to C6
Split-Window Coefficient values (Table 9)
Cp
Air specific heat (1004 J/kg/K)
CWSI
Crop Water Stress Index
dr
The inverse squared relative earth-sun distance.
dT
The difference (dT = T1 – T2) between two heights (z1 and z2)
E
The actual vapor pressure
ea
Actual vapor pressure (kPa)
es
Saturation vapor pressure (kPa)
ESUNλ
Mean solar exoatmospheric irradiances
ETINST
the instantaneous ET (mm/hr)
ETc
The crop evapotranspiration
XVII ETo
The reference evapotranspiration for 0.12 m clipped, cool-season grass (mm/day)
ETr
The reference ET at the time of the image (mm/hr).
ETr-24
The cumulative ETr for the day of the image.
ETrF
The Reference ET Fraction
ETrFperiod
The representative ETrF for the period.
FAO
Food and Agriculture Organization
FVC
Fractional Vegetation Cover
G
The soil heat flux (W/m2)
g
The gravitational acceleration (9.81 m/s2)
Gd
The daily value of the soil heat flux
GIS
Geographic Information System
GISci
Geographic Information Science
Gsc
The solar constant (1367 W/m2)
H
The sensible heat flux (W/m2)
Hcold
The sensible heat flux at the cold pixel
Hdry
The sensible heat flux at dry limit
Hhot
The sensible heat flux at the hot pixel
Hwet
The sensible heat flux at wet limit
k
Von Karman’s constant (0.41)
Kc
The crop coefficient
XVIII K1 & K2
The thermal constant of Bands 10 and 11 from metadata image file. (Table 11)
l
Constant for SAVI
L
The Monin - Obukhov length
LAI
Leaf Area Index
Lλ
Top of Atmospheric Radiance
ML
Band specific multiplicative rescaling factor. (Table 10)
Mρ
Band-specific multiplicative rescaling factor from the metadata
n
The number of days in the period
NDVI
The Normalized Difference Vegetation Index
NOAA
National Oceanic and Atmospheric Administration
Qcal
Quantized standard product pixel values (DN)
ra, max
The maximum aerodynamic resistance (s/m)
ra, min
The minimum aerodynamic resistance to sensible heat transfer (s/m)
rah
Aerodynamic resistance (s/m)
Rd
The daily value of the net radiation
RL↑
The outgoing longwave radiation (W/m2)
RL↓
The incoming longwave radiation (W/m2)
XIX Rn
The net radiation (W/m2)
Rs↓
The incoming short-wave radiation (W/m2)
SAVI
Soil Adjusted Vegetation Index
Ta
The near-surface air temperature (K)
TB10 & TB11
Brightness temperature of bands 10 and 11 (K)
TH
The land surface temperature of dry condition
TLE
the land surface temperature of to wet condition
Ts
The surface temperature (oC)
u*
The friction velocity (m/s)
u2
The wind speed at 2 m height (m/s)
u200
wind speed m/s at a blending height assumed to be 200 m
ux
Wind speed m/s at height 𝑧𝑥
VIs
Vegetation Indexes
VImax
The maximum value of the vegetation index
VImin
The minimum value of the vegetation index
W
Atmospheric water vapor content
z
The elevation above sea level (m)
z1 and z2
The heights in meters above the zero-plane displacement of the vegetation
Zom
The momentum roughness length (m)
XX α
The surface albedo (dimensionless)
αpath_radiance
The portion of the incoming solar radiation across all bands that is backscattered to the satellite before it reaches the earth’s surface.
αtoa
Albedo at the top of the atmosphere
γ
the psychrometric constant in k·Pa/°C
Δ
The slope of saturated vapor pressure k·Pa/°C
ε̅
Mean LSE of TIR bands
εa
The atmospheric emissivity (dimensionless)
εo
The surface thermal emissivity (dimensionless)
∈S
Emissivity for soil. (Table 13)
∈V
Emissivity for vegetation. (Table 13)
θSE
Sun elevation angle in degrees is provided in the metadata
λ
The latent heat of vaporization (2257000 J/kg).
λET
The latent heat flux (W/m2)
ρair
The density of air (kg/m3)
ρ4
Landsat 8 red band
ρ5
Landsat 8 near – infrared band
ρw
The density of water (kg/m3)
ρλ
The reflectivity for each band
XXI σ
The Stefan – Boltzmann constant (5.67 ∗ 10−8 𝑚2 𝑘 4).
τsw
The atmospheric transmissivity.
ψh(z1)
The stability correction for heat transport at Z1 height
𝑊
(equations 57 or 63).
ψh(z2)
The stability correction for heat transport at Z2 height (equations 56 or 62).
ψm(200m)
Stability correction for momentum transport (at 200 meters).
ωλ
is a weighting coefficient for each band
pbl
The average planetary boundary layer temperature (K)
∆ε
Difference in LSE
Chapter (1) Introduction 1.1 Introduction Apart from precipitation, the most significant component of the hydrologic budget is evapotranspiration (Figure 1). Evapotranspiration varies regionally and seasonally according to surrounding environmental conditions, such as climate condition, land use, land cover, soil moisture, and available radiation. Due to this variability, thorough understanding of the evapotranspiration process is needed in the research for integrated water resources modeling, dynamic crop-weather modeling, and drought monitoring.
Figure 1The Global Water Cycle. (Values in 103 km3/yr). Source:https://globalchange.umich.edu/globalchange1/current/lectures/kling/water_nitro/wat er_nitro.html
Evaporation is the primary process of removing water from a watershed and transpiration is the process of removing water from vegetation or any other
Chapter (1)
2
Introduction
moisture containing living surface. Therefore, evapotranspiration(ET), which is a combined process of evaporation and transpiration, is a main factor in the hydrological cycle. ET is the largest outgoing water flux from the Earth’s surface. Accurate quantifying ET is critical to developing a greater understanding of a range of hydrological, climatic, and ecosystem processes, and beneficial in numerous applications, e.g., water resources management, drought monitoring, improvement of hydrological modeling, weather forecasts, and vulnerability of forest to fire (Bastiaanssen et al. 2002, Anderson et al. 2007). In the last few decades, the theoretical and applied analysis of evapotranspiration and its components evaporation and transpiration have received much attention. A physically based equation for reference evapotranspiration was derived by Penman to estimate open-water evaporation (Penman 1948) and extended by Monteith in 1965 to estimate evaporation from vegetation-covered surfaces directly (Monteith 1965). It is nowdays the recommended method by the Food and Agriculture Organization (FAO) to calculate reference crop evapotranspiration (Allen et al. 1998). 1.2 The Global Water Resources The total volume of earth water is about 1.4 billion km³ Haden (2006). Freshwater represents 2.5% of earth's water (or about 35 million km3), about 70% of fresh water (about 24 million km3) is in the form of ice and permanent snow. Groundwater (shallow and deep) is around 30% of world's freshwater. The total available fresh water for ecosystems is about 200000 km³ of water, less than 1% of all freshwater resources (Gleick 2014). There are 263 international river basins which covering 45.3% of the earth’s land surface (Haden 2006). Figure 2 represents the distribution of the total water world.
Chapter (1)
Introduction
3
Figure 2 the distribution of the total water world
Source: https://water.usgs.gov/edu/watercycle.html Table 1 World freshwater resources Water source
Water volume, in cubic kilometers
Oceans, Seas, & Bays
1,338,000,000
Ice caps, Glaciers, & Permanent Snow Groundwater
Percent of fresh
Percent of total
water (%)
water (%)
--
96.5379
24,064,000
68.697
1.7362
23,400,000
--
1.6883
Fresh
10,530,000
30.061
0.7597
Saline
12,870,000
--
0.9286
Soil Moisture
16,500
0.047
0.0012
Ground Ice & Permafrost
300,000
0.856
0.0216
Lakes
176,400
--
0.0127
Fresh
91,000
0.260
0.0066
Saline
85,400
--
0.0062
Atmosphere
12,900
0.037
0.0009
Swamp Water
11,470
0.033
0.0008
Rivers
2,120
0.006
0.0002
Biological Water
1,120
0.003
0.0001
Total water
1,385,984,510
---
100
Fresh water
35,029,110
100
2.5274
https://water.usgs.gov/edu/earthhowmuch.html
Chapter (1)
4
Introduction
1.3 Water Resources in Egypt Egypt lies in the north of Africa continent and it is bordered by the Mediterranean in the north, by Gaza Strip in the east, by Sudan in the south and by Libyan Arab Jamahiriya in the west. The total area of Egypt is about one million Km². It is a vast desert plateau except for the Nile valley and delta which considered about 4 % of the total country area (Wang 2001). Most of the cultivated land is about 3 % of total area of the country and located close to the Nile river valley and its Delta (Attia 2009). The Nile River is the most important water source in Egypt, as it supplies Egypt by 94% of its water needs. Other sources have a weak participation in supply Egypt with water. There is a reduction in the per capita share of limited freshwater resources, and the degradation of water quality comes from the rapid increase of population growth and their economic activities (Sherif M. Abdelgawad1 2010). The total population was 33 million in 1965 and rose to 76 million in 2007 (Khan et al. 2011), then rose to 80 million in 2010 (MWRI 2010), then rose to 97.55 million in 2017 (http://www.worldometers.info/world-population/egypt-population/). To manage water in Egypt under this stressed situation, water resources and water demands must be well analyze and managed. So the 2050 water strategy of the Ministry of Water Resources and Irrigation the water budget of Egypt 2010 had illustrated as follows (MWRI 2010). Table 2 summarizes this budget to conventional and non-conventional sources as well as water allocation or usage and consumption in Egypt. In Table 2, the water demands for irrigation, industry, and domestic consumption are more than the Nile supply.This problem has been resolved by recycling fresh water more than once, which reflects the efficiency of the system as well as the sensitivity of the system to the water quality (MWRI 2010).
Chapter (1)
Introduction
5
Table 2 Water Budget of Egypt (2010), and all Sources and Allocation/Usage (MWRI 2010) Volume Water supply
(billion m³/
Consumption Demand by Sector
year) Conventional Water Source Nile River Deep Groundwater
55.5 2
year)
Industry
1.4
2
Agriculture
40.4
67
12.2
--
3
3
0.2
0.2
Desalination
0.2
Evaporation losses
Total supply conventional
59
Environment Balance
Re-Use OF Ag. Drainage Water
16
Total supply nonconventional
22.2
Total Water Supply
81.2
year) 9
Drainage to sea
6.2
(billion m³/
1.8
1.3
Shallow groundwater (delta)
Allocation
Drinking (fresh water only)
Rainfall & Flash Floods
Non-conventional water Sources
(billion m³/
Usage
Total consumption
59
Total Water usage or allocation
81.2
1.4 Significance of Irrigation in Agriculture Irrigation is a process that uses more than two-thirds of the Earth’s renewable freshwater resources and feeds one-third of the Earth’s population (Stanhill 2002). About 2.4 billion people depend directly on irrigated agriculture for food and employment. Irrigated agriculture thus plays an essential role in meeting the basic needs of billions of individuals in developing countries (FAO 1996). There is a need to focus attention on the growing problem of water scarcity in relation to food production. The World Food Summit of November 1996, drew attention to the importance of water as a vital resource for future development (FAO 1996). A major part of the developed global water
Chapter (1)
6
Introduction
resources is used for food production. The estimated minimum water requirement per capita is 1200 m3 annually (1150 m3 for food production and 50 m3 for domestic consumption) (FAO 1996). Sustainable food production depends on wise use of water resources as fresh water for agriculture, and human consumption becomes increasingly scarce. To meet future food demands and growing competition for clean water, a more efficient use of water in irrigated agriculture will be essential (Smith 2000) Options to increase water-use efficiency include reducing irrigation water losses, harvesting rainfall, and adopting cultural practices that increase A major constraint to the understanding of the use of water is the difficulty associated with its measurement and quantification. Measurement and data collection of discharge in canals is difficult and fraught with potential errors. Water use for crop production depends on the interaction of climatic parameters that determine crop evapotranspiration and water supply from rain (Smith 2000). Compilation, processing, and analysis of meteorological information for crop water use and crop production are therefore key elements in developing strategies to optimize the use of water for crop production and to introduce effective water management practices. Estimating crop water use from climatic data is essential to better water-use efficiency. Because most of the Earth’s irrigated land is in the under developed world, it is important to use the simplest, cheapest, and most practical meteorological method to improve crop water-use efficiency in irrigation. Stanhill (2002) reported that in these regions use of standard, correctly sited and maintained evaporation pans operating within a national network can provide the basis for a scheduling method in which the use of empirical crop coefficients is
Chapter (1)
7
Introduction
accepted. These coefficients reflect the local economic as well as agronomic, climatologic and hydrological (water quality) situation (Stanhill 2002). Smith (2000) reported that Agro-meteorology would play a vital role in the looming global water crisis. Appropriate strategies and policies need to be defined, including the strengthening of national use of climatic data for planning and managing of sustainable agriculture and drought mitigation (Smith 2000). 1.5 Evapotranspiration The physical process whereby water flows from evaporating surfaces into the atmosphere is referred to as actual evapotranspiration (ЕТа). This water flux occurs via canopies through stomata as actual transpiration (Ta) and directly from the soil surface as actual evaporation (Ea). Stomata are small openings on the plant leaf through which gases and water vapor pass (Figure 3). The vaporization occurs within the leaf, in the intercellular spaces, and the stomatal aperture controls the vapor exchange with the atmosphere. The stomatal aperture can be open and closed, depending on the pressure of the guard cell. Ta loses nearly all soil water taken up by roots, and a negligible fraction is used within the plant. Not only the type of crop but also the crop development, environment, cultural management, and irrigation system should be considered when assessing Ta.
Chapter (1)
Introduction
8
Figure 3 Schematic representations of stomata.
Distinctions are made between reference crop evapotranspiration ETo, potential evapotranspiration ЕТр and actual evapotranspiration ЕТа. Several definitions of reference evapotranspiration ETo have been formulated. Jensen (Jensen 1974) defined ETo as the rate at which water, if available, would be removed from the soil and plant surface. Pereira (Pereira et al. 1999) defined ETo as “the water used by a well-watered reference crop, such alfalfa, which fully covers the soil surface.” The modified Penman combination equation is used to compute ETo, as it is a satisfactory estimation equation when daily estimates of ETo are desired (Jensen et al. 1990). Studies showed the superior performance of the Penman-Monteith approach, in both arid and humid climates, and convincingly confirmed the sound underlying concepts of the method (equation 1). Based on these findings, the method was recommended by the FAO Panel of Experts for adoption as a standard for reference crop evapotranspiration estimates (Hall 1999):
𝐸𝑇𝑜 =
0.408 ∆(𝑅𝑛 − 𝐺) + 𝛾 𝑇
900
𝑎 +273.15
𝑢2 (𝑒𝑠 − 𝑒𝑎 )
∆ + 𝛾(1 + 0.34𝑢2 )
(1)
Chapter (1)
9
Introduction
Where, ETo
The reference evapotranspiration for 0.12 m clipped, cool-season grass (mm/day),
Rn
The net radiation at the crop surface (MJ/m2/day)
G
The ground heat flux density (MJ/m2/day)
Ta
The mean daily air temperature at 2 m height (°C)
u2
The wind speed at 2 m height (m/s)
es
saturation vapor pressure (kPa),
ea
actual vapor pressure (kPa)
∆
Slope of the vapor pressure-temperature curve (kPa/°C)
γ
The psychometric constant (kPa/°C)
ETp may be referred as the water flux from crops that are grown in large fields under optimum soil moisture, excellent management, and environmental conditions, and achieve full production under the given climatic conditions. ЕТа involves all conditions of the vegetated surface. Due to sub-optimal crop management and environmental constraints that affect crop growth and limit evapotranspiration, ЕТа is generally smaller than ETp (Allen et al. 1998). 1.6 Application of Geographic Information System in Irrigation Management A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. GIS technology is about 50 years old. However, for the most part, it is still often used just to make maps. However, GIS can do much more. Using GIS databases, more up-to-date information can be obtained, or information that was unavailable before can be estimated and complex analyses performed. This information can result in a better understanding of a place, can help make
Chapter (1)
10
Introduction
the best choices, or prepare for future events and conditions (Mitchell 1999)(Mitchel 1999). The most common geographic analyses that can be done with a GIS are (Johnston et al. 2001): •
Mapping where things are
•
Mapping the maximum and minimum values
•
Mapping density
•
Finding what is inside (intersection analysis)
•
Finding what is nearby (proximity analysis)
•
Mapping change (overlay analysis)
GIS have potentially important application to irrigation water management, especially in regions where there are poorly defined procedures for irrigation water management data collection, processing, and analysis. The possibility of using GIS to identify crop areas, plan irrigation schedules and quantify performance offer exciting possibilities for research (Ray and Dadhwal 2001). The tools necessary to create a useful GIS in irrigation are the availability of weather data and how it is spatially distributed over the study area. 1.7 Remote Sensing Techniques and Capabilities Remote Sensing is defined as the acquisition of information about an object without being in physical contact with the object. In current usage, the term "remote sensing" generally refers to the use of satellite- or aircraft-based sensor technologies to detect and classify objects on Earth, including on the surface and in the atmosphere and oceans (Campbell and Wynne 2011). Remote sensing technology can provide a large and continuous spatial coverage within a few minutes. It costs less than when the same spatial information is obtained with conventional measurements, and it is the only
Chapter (1)
11
Introduction
approach for ungauged areas where human-made measurements are complicated to conduct (Rango 1994, Engman and Schultz 2000) Remote sensing with its large coverage and high data frequency should be able to provide ample data and information to determine regional ET. Satellite remote sensing equipped with suitable sensor provides suitable information for the ET estimation (ALMHAB 2009). Remote sensing is identified as an important tool supporting the management of natural resources and agricultural practices for wider spatial coverage. This remote sensing based daily evapotranspiration models better suit the estimation of crop water use at a regional agriculture scale (Allen et al. 2007, Muthuwatta et al. 2010) 1.8 Problem Statement This research work focuses on the satellite-derived evapotranspiration to characterize the spatial and temporal variability of evapotranspiration in the study area. The result is important for water balance studies and water resources management, which will be of great importance for future sustainable water use in the study area. Saving irrigation water is the main motivation to conduct this study. 1.9 Objectives of the Study The main objective of this study is to estimate evapotranspiration in the study area which is named "Handaset Tanta" using a combination of remote sensing techniques and meteorological observations. In this study, the following objectives are addressed:
Estimation of actual evapotranspiration through the application of the Surface Energy Balance Algorithm for Land (SEBAL) model.
Chapter (1)
12
Introduction
Determine the spatial-temporal distribution patterns of the actual evapotranspiration in the study area.
Assess the performance of the irrigation system in the study area to evaluate the losses in the irrigation system at regional level.
1.10 Organization of the Thesis This thesis is divided and organized into five chapters as follows: Chapter (1) is an introduction to this thesis which includes the statement of the problem, objectives, and organization of the study. Chapter (2) describes the literature review of remote sensing application in evapotranspiration estimation and different surface energy balance algorithms. Chapter (3) presents the methodology of the study, the description of the study, the collected data, and it also presents the construction of Surface Energy Balance Algorithm for Land (SEBAL) model. Chapter (4) presents the results of SEBAL model, model validation, the total amount of water lost in the form of evapotranspiration, and assessment of irrigation efficiency. Chapter (5) includes the conclusions and the recommendations of this thesis. Finally, the thesis is ended by a list of references for the related handbook and papers Appendices (A) which contains the importing of Landsat 8 data into ERDAS IMAGINE 2014. Appendices (B) which contains the weather data preparation and calculation of reference ET using REF-ET software Appendices (C) which contains weather data and REF-ET software output.
Chapter (2) Literature Review 2.1 Introduction The key for efficient water resources management for a regional scale is the estimation of accurate and reliable water requirements for irrigation purposes. Evapotranspiration is the major consumptive use of irrigation water in agriculture. Any attempt to improve the efficiency of the water supply system should be based on reliable estimates of daily evapotranspiration, which includes evaporation from land and water surfaces and transpiration by vegetation (Muthuwatta et al. 2010). Daily evapotranspiration is recognized as an essential process in determining the surface and mass-energy interaction for any water resources management related to agriculture practices (Sellers et al. 1996). Daily evapotranspiration varies regionally and seasonally according to weather and wind conditions (Su et al. 2003). Understanding these variations in evapotranspiration is essential for managers responsible for planning and management of water resources, especially in arid and semi-arid regions 2.2
Evapotranspiration Estimation
2.2.1 Empirical Methods The empirical relationship between the quantities that can be measured from a satellite and evapotranspiration has been recognized early. Idso (Idso et al. 1975) found a linear relationship between evaporation and net thermal radiation. Seguin and Itier (Seguin and Itier 1983) showed that, at a given location, there exists a good correlation between the midday surface temperature and daily evapotranspiration. Menenti (Menenti 1984) obtained evapotranspiration as a bilinear function of the remotely estimated temperature
Chapter (2)
14
Literature Review
and surface Albedo. Kerr (Kerr et al. 1989) found a close relationship between the Normalized Difference Vegetation Index (NDVI). derived from (National Oceanic and Atmospheric Administration) NOAA High Resolution Picture Transmission (HRPT) data, and the actual evapotranspiration with a time lapse of 20 days. 2.2.2 Simplified Energy Balance Methods Various researchers have investigated the significance of satellite-derived surface temperature in a simplified energy balance equation for ET estimation. Heliman (Heilman et al. 1976) used airborne sensors derived surface temperature in combination with ground-measured solar radiation, wind speed, air temperature, and crop growth condition parameters to calculate daily ET based on an energy balance equation. Jackson (Jackson et al. 1977) simplified the energy balance equation in which the daily evapotranspiration value is given as a function of the instantaneous value of the difference between the surface temperature and the air temperature both measured near midday (Ts Ta) as follows: 𝐸𝑇 = 𝑅𝑑 − 𝐺𝑑 − 𝐵(𝑇𝑠 − 𝑇𝑎 )
(2)
Where, Rd
The daily value of the net radiation
Gd
The daily value of the soil heat flux
B
A constant is depending primarily on surface roughness and wind speed.
This approach was used in various studies (Seguin and Itier 1983, Moran et al. 1994). The soil heat flux Gd was assumed to be zero when the daily average was used. Using equation (2) with instantaneous remote sensing imagery requires further assumptions because Gd can be relatively large and Rd may
Chapter (2)
15
Literature Review
vary from location to location. As a first approximation, equation (2) was rewritten in the following format: 𝐸𝑇 = 𝐴 − 𝐵(𝑑𝑇)
(3)
Where A = Rd - Gd, which is sometimes termed the "available energy.". Parameters A and B can be determined empirically using ground data. The advantage of Equation 2 is its simplicity, which requires minimal amounts of ground-based meteorological data (net radiation and temperature). 2.2.3 Biophysical Estimation Evapotranspiration Model Reference ET (ETo) was calculated from meteorological data using FAO Penman-Monteith equation 1 (Allen et al. 1998). ETo was calculated as a daily total ET (mm/d) from an imaginary grass reference crop. Potential evapotranspiration was also calculated using Blaney-Criddle method for comparison purposes (Brouwer and Heibloem 1986). This method is based mainly on mean monthly temperature. Empirical methods based on Vegetation Indexes (VIs) for estimating ET are modifications of the crop coefficient method for estimating water demand by irrigated crops (Jensen and Haise 1963). Crop coefficients (Kc) are empirical ratios relating crop ET (ETc) to a calculated reference-crop ET (ETo) which is based on atmospheric water demand over a crop cycle or to actual ET measurements. A Kc curve gives the seasonal distribution of Kc as a function over time or a time-related index, such as growing degree-days. In this form, however, Kc could not account for variations in crop growth from field to field, as affected by soil type, nutrition, uneven water distribution, or other agronomic factors. As an alternative, Kc can be adjusted throughout the crop cycle to take into account changes in the fraction of absorbed solar radiation
Chapter (2)
16
Literature Review
(FARs) by the plant canopy (estimated by VIs) as the crop develops. A timeseries of VI measurements is correlated with measured ETc or ETo to develop a VI–Kc curve over the crop cycle. Once calibrated, these VI-based Kc curves can provide close estimates of ETc within 10% of measured values among fields with different growth characteristics (Hunsaker et al. 2003). Choudhury (Choudhury et al. 1994) used a heat balance and irradiative transfer model to study relations among transpiration coefficients (Tc) and VIs. They provided a theoretical basis for estimating transpiration from no stressed crops from VI and Ta data. Based on the relationship between ET and Leaf Area Index (LAI) and the relationship between LAI and VI, and an equation was developed as following: 𝐸𝑇𝑐 = 𝐸𝑇𝑜 [ 1 − 𝑉𝐼𝑚𝑎𝑥 −𝑉𝐼
The term [1 − 𝑉𝐼
𝑚𝑎𝑥 −𝑉𝐼𝑚𝑖𝑛
𝑉𝐼𝑚𝑎𝑥 − 𝑉𝐼 𝑛 ] 𝑉𝐼𝑚𝑎𝑥 − 𝑉𝐼𝑚𝑖𝑛
(4)
𝑛
] converts VI to a scaled value (0–1) and is derived
from the light extinction curve through a canopy as estimated by VIs. the VImax is the maximum value of the vegetation index and VImin is the minimum value of the vegetation index. The exponent n depends on the crop and the VI used. The effects of soil evaporation and crop stresses added scatter and uncertainty into the ET estimates. 2.2.4 Surface Temperature and Vegetation Index Method Many studies on radiometric surface temperature have focused on the widely observed negative correlation between surface temperature and remotely sensed measurements of actively transpiring vegetation such as NDVI (Nemani and Running 1989, Hope and McDowell 1992, Moran et al. 1994). Shuttle worth (Shuttleworth and Wallace 1985) adapted the Penman-Monteith
Chapter (2)
17
Literature Review
equation (Monteith 1981) to account for energy partitioning between crop and soil. Gillespie (Gillespie et al. 1999) used a relation between NDVI and surface temperature derived from multispectral aircraft measurements to define surface fluxes. Over a large area, a plot of NDVI versus surface temperature forms a triangular distribution that is due to the distribution of soil moisture and vegetative cover. Schmugge (Schmugge et al. 1991) also observed the NDVI and surface temperature relationship with ET. Humes (Humes et al. 1994) showed that points of both low NDVI and surface temperature correspond to areas of high soil moisture. Price (Price 1990) developed a method for relating contextual information (the slope of the vegetation index surface temperature line and the slope of the wet soil - dry soil line) in AVHRR data to large area evapotranspiration. Carlson (Carlson et al. 1990) found that spatial variations in surface radiometric temperature are related to variations in the vertical variation of soil water content modulated by fractional vegetation cover. Based on theoretical and experimental evidences, Moran (Moran et al. 1994) proposed a concept named the vegetation index/temperature trapezoid, which combines vegetation indices with composite surface temperature measurements to allow application of Crop Water Stress Index (CWSI) theory to partially vegetated fields without knowledge of foliage temperature. Much effort is now concentrated on increasing the accuracy of radiant fluxes. Although surface albedo can easily be estimated by common sensors (enabling the calculation of the shortwave net radiation), it takes more specific sensors to estimate the longwave component of the radiation balance. Surface Albedo and temperature can also be the basis for estimates of the upwelling components, while the downwelling components are based on meteorological data (Moran et al. 1989). The parameterization of turbulent fluxes is having a large part of research in itself; two main parameters are used [generally, the
Chapter (2)
18
Literature Review
Leaf Area Index (LAI, inferred from NDVI) and the aerodynamic resistance (rah, for momentum and heat transport)]. 2.2.5 Full Energy Balance Method The Earth system is operated close to an energy balance, which implies that an equal amount of energy enters into the Earth system and emerges out of it. Consequently. The variations in surface conditions affect the amount of energy retained and distributed in and within the Earth system. Some of these variations result from the changes in surface conditions, such as whether the surface is land/water, covered by snow/ice. Such variations in surface conditions lead to changes in the surface energy balance. The surface energy balance at the land-air interface can be written as shown in Equation (5) where the net radiation is considered as a residual of the soil heat flux, the sensible heat flux, and the latent heat flux: 𝑅𝑛 = 𝐺 + 𝐻 + 𝜆𝐸𝑇
(5)
Where, Rn
The net radiation (W/m2)
G
The soil heat flux (W/m2)
H
The sensible heat flux (W/m2)
λET
The latent heat flux (W/m2)
2.3
Different Surface Energy Balance Algorithms
2.3.1 Surface Energy Balance Index (SEBI) Based on the contrast between dry and wet regions,(Menenti and Choudhury 1993) proposed the Surface Energy Balance Index (SEBI) method to derive the evapotranspiration from the evaporative fraction. This method is based on the Crop Water Stress Index (CWSI) (Jackson et al. 1981). In this approach,
Chapter (2)
Literature Review
19
relative evaporation is determined by scaling an observed surface temperature in a maximum range of surface temperature, denoted by extremes in the surface energy balance suggesting a theoretical lower and upper bound on the surface and air temperature difference. Here under dry-condition, evaporation is assumed to be zero due to the limitation of water availability in the soil for a particular set of boundary layer characteristics so that the sensible heat flux density takes its maximum value Ts,max (maximum surface temperature). Ts,max is inverted from the bulk transfer equation, which is expressed as follows (Van den Hurk 2001):
𝑇𝑠.𝑚𝑎𝑥 = 〈𝑇〉𝑃𝑏𝑙 + 𝑟𝑎.𝑚𝑎𝑥 (
𝐻 ) 𝜌𝑎𝑖𝑟 𝐶𝑃
(6)
Where, pbl
The average planetary boundary layer temperature (K)
ra, max
The maximum aerodynamic resistance to sensible heat transfer (s/m)
ρair
The density of air (kg/m3)
Cp
The air specific heat (1004 J/kg/K)
The minimum surface temperature is obtained for the wet region from Equation (7): 𝑟𝑎.𝑚𝑖𝑛(𝑅𝑛 −𝐺)
𝑇𝑠.𝑚𝑖𝑛 = 〈𝑇〉𝑃𝑏𝑙 +
𝜌𝐶𝑃
− ∆
(𝑒𝑠 −𝑒𝑎 ) 𝛾
(7)
1+𝛾
Where, ra, min
The minimum aerodynamic resistance to sensible heat transfer (s/m)
ea
The actual vapor pressure
es
The saturation vapor pressure
Chapter (2)
Literature Review
20
Δ
The slope of saturated vapor pressure as a function of Ta(air temperature measured at a reference height) in k·Pa/°C
γ
the psychrometric constant in k·Pa/°C
ByInterpolating the observed surface temperature with the maximum and minimum surface temperatures, the relative evaporative fraction can then be calculated from the equation given below (Van den Hurk 2001): ∆𝑇
𝐿𝐸 = 1− 𝐿𝐸𝑃
−
𝑟𝑎 ∆𝑇𝑚𝑎𝑥
𝑟𝑎.𝑚𝑎𝑥
∆𝑇𝑚𝑖𝑛 𝑟𝑎.𝑚𝑖𝑛 ∆𝑇𝑚𝑖𝑛
−𝑟
(8)
𝑎.𝑚𝑖𝑛
Where, ∆T
= Ts − Tpbl
∆Tmin
= Ts, min − Tpbl
∆Tmax
= Ts, max − Tpbl
Ts
is determined by using image data in the thermal infrared region for each pixel,
2.3.2 Surface Energy Balance System (SEBS) Another well-known model is the Surface Energy Balance System (SEBS). Su (Su 2002, Su 2002) and Su.(Su et al. 2003, Su 2005) described a modified form of SEBI for the estimation of land surface energy balance using remotely sensed data, which has been named SEBS. SEBS estimates sensible and latent heat fluxes from satellite data and routinely available meteorological data. Computations of land surface physical parameters, calculation of roughness length for heat transfer, and estimation of the evaporative fraction based on energy balance at limiting cases are the main bases of SEBS (Choudhury 1989). In SEBS, the latent heat flux is considered to be zero at the dry limit, which means sensible heat flux reaches its maximum value (i.e., Hdry= Rn − G). On
Chapter (2)
21
Literature Review
the other hand, at the wet limit, ET takes place at a potential rate (LEwet), (i.e., the evaporation is restricted only by the energy available for a particular surface and atmospheric condition) and the sensible heat flux attains its minimum value, Hwet. The sensible heat flux at dry and wet limits can be expressed as: 𝐻𝑑𝑟𝑦 = 𝑅𝑛 − 𝐺 𝐻𝑤𝑒𝑡 =
(𝑅𝑛 − 𝐺)𝛾 𝜌𝐶𝑃 (𝑒𝑠𝑎𝑡 − 𝑒) − (𝛾 − ∆) 𝑟𝑎 (𝛾 + ∆)
(9) (10)
Where ra is dependent on the Obukhov length, which in turn is a function of the friction velocity and sensible heat flux. The relative evaporative fraction (EFr) and evaporative fraction (EF) then can be expressed as: 𝐸𝐹𝑟 =
𝐸𝐹 =
𝐻𝑑𝑟𝑦 − 𝐻 𝐻𝑑𝑟𝑦 − 𝐻𝑤𝑒𝑡
(11)
𝐸𝐹𝑟 ∗ 𝐿𝐸𝑤𝑒𝑡 𝑅𝑛 − 𝐺
(12)
By utilizing similarity theory, a distinction is made in SEBS between the PBL/Atmospheric Boundary Layer (ABL) and the Atmospheric Surface Layer (ASL). Such distinction is made to take the ABL height as a reference of potential air temperature to calculate the heat fluxes. Here a distinction is made between surface temperature and potential air temperature. Remote sensing data derived land parameters, and ground-based meteorological measurements are used as inputs in SEBS. Using remote sensing data from ATSR and ground data from a Numerical Weather Prediction model, Jia (Jia et al. 2003) proposed a modified version of SEBS and validated the estimated sensible heat flux with large aperture scintillometers. Wood. (Wood et al. 2003) applied SEBS to the Southern Great Plains region of the United States and compared the latent heat
Chapter (2)
22
Literature Review
fluxes with the measurements from the Energy Balance Bowen Ration (EBBR) sites. Their results indicate the potential usefulness of SEBS approach in estimating surface heat flux from space for data assimilation purposes. Daily, monthly, and annual estimation of evaporation in a semi-arid environment have been done by SEBS (Su et al. 2003). SEBS can be even used for both local scaling and regional scaling under all atmospheric stability regimes as shown by Su (Su 2002).The accuracy of ET value estimated from SEBS could reach 10%–15% of that of in-situ measurements even when evaporative fraction ranged from 0.5 to 0.9 as shown by Su. (Su et al. 2005).Key advantages of the SEBS include: (1) consideration of the energy balance at the limiting cases, which minimizes the uncertainty involved in surface temperature or meteorological variables. (2) a new formulation of the roughness height for heat transfer instead of using constant values. (3) characterizing actual turbulent heat fluxes without any prior knowledge, and (4) representativeness of parameters associated with surface resistance. Note that SEBS has been widely applied over large heterogeneous areas fed with MODIS data with thermal band information of 1 km (McCabe and Wood 2006, Gao and Long 2008). However, are a latively complex solution of the turbulent heat fluxes and too many required parameters can often cause inconveniences in SEBS when data are not readily available. 2.3.3 Simplified Surface Energy Balance Index (S-SEBI) A simplified new method derived from SEBI, called Simplified Surface Energy Balance Index (S-SEBI), has been developed to estimate the surface
Chapter (2)
Literature Review
23
flux from remote sensing data (Roerink et al. 2000).The contrast between a reflectance (albedo) dependent maximum and minimum surface temperature for dry and wet conditions, respectively, is the main base of this method to partition available energy into sensible and latent heat fluxes. No additional meteorological data is needed if the surface extremes are accessible on the scene studied. By assuming steady global radiation and air temperature, a physical explanation to the observed surface reflectance and temperature in the S-SEBI approach can be given when surface characteristics within the observed image changes between dark/wet and dry/bright pixels. At low reflectance, surface temperature remains almost constant with increasing reflectance because of the presence of sufficient water under these conditions. At higher reflectance, surface temperature increases to some value with the increase of reflectance and is designated as “evaporation controlled” because the change in temperature at this stage is solely controlled by the decrease of evaporation resulting from the less soil moisture availability. Beyond the inflection of reflectance, the surface temperature declines with the increase of surface reflectance. At this point, soil moisture shrinks to such a level that evaporation cannot occur. Therefore, the available energy is completely utilized for surface heating. Thus, an increase in surface reflectance yields a net radiation decrease, which in turn produces less surface heating and the corresponding surface temperature, which is referred as “radiation-controlled” (Roerink et al. 2000, Liou et al. 2002, Li et al. 2009) (Figure 2). Here, evaporative fraction (EF) is constrained by the dry and wet regions and formulated by interpolating the reflection-dependent surface temperature between
the
reflection-dependent
maximum
temperatures as shown in Equation (13):
and
minimum
surface
Chapter (2)
24 𝐸𝐹 =
Literature Review
(𝑇𝐻 −𝑇𝑆 ) (𝑇𝐻 −𝑇𝐿𝐸 )
(13)
Where, TH
The land surface temperature corresponding to dry condition and represents the minimum latent heat flux (LEdry = 0) and maximum sensible heat flux (Hdry = Rn − G)
TLE
the land surface temperature corresponding to wet condition and represents the maximum latent heat flux (LEwet = (Rn − G)) and minimum sensible heat flux (Hwet = 0)
Using the following regression equation, TH and TLE can be, respectively, calculated: 𝑇𝐻 = 𝑐𝑚𝑎𝑥 + 𝑑𝑚𝑎𝑥 ∝
(14)
𝑇𝐿𝐸 = 𝑐𝑚𝑖𝑛 + 𝑑𝑚𝑖𝑛 ∝
(15)
Where the empirical coefficients cmax, dmax, cmin, and dmin are estimated from the scatter plot of Ts and α over the study area. Finally, the EF is calculated from Equation (13) using Equations (14) and (15). The major advantages of S-SEBI are that: (1) Additional ground-based measurement is not needed to derive the EF except the surface temperature and reflectance (albedo) derived from remote sensing data if the surface extremes are present in the remotely sensed imagery; and (2) Extreme temperatures for the wet and dry conditions vary with changing reflectance (albedo) values, but in other methods like SEBAL, a fixed temperature is determined for wet and dry conditions.
Chapter (2)
25
Literature Review
2.3.4 Surface Energy Balance Algorithm for Land (SEBAL). Surface Energy Balance Algorithm for Land (SEBAL), an image-processing model for calculating evapotranspiration (ET) as a residual of the surface energy balance, was developed in the Netherlands by Bastiaanssen. (Bastiaanssen et al. 1998, Bastiaanssen et al. 1998). Within the most promising approaches currently available to estimate evapotranspiration, the SEBAL has been designed to calculate the energy balance components, at both local and regional scales with minimum ground data. This model is an intermediate approach using both empirical relationships and physical parameterization. It requires digital imagery data collected by any satellite sensor measuring visible, near-infrared, and thermal infrared radiation, Ts, NDVI, and albedo maps. Latent heat flux (LE) is estimated as a residual of the energy balance equation on a pixel-by-pixel basis. Net radiation (Rn) is computed from the balance of short and longwave radiation. Soil heat flux (G) is calculated utilizing the equation proposed by Bastiaanssen, which is applicable to all sorts of vegetation cover and soil type (Bastiaanssen et al. 1998, Bastiaanssen et al. 1998). SEBAL has been verified at many places around the world including Spain, Italy, Turkey, Pakistan, India, Sri Lanka, Egypt, Niger, and China (Bastiaanssen 2000, Pelgrum et al. 2005). ETa is estimated from satellite images and weather data in SEBAL model using the surface energy balance as shown in Figure 4. Satellite images only provide information for the overpass time, so SEBAL computes an instantaneous ETa flux for that time. The ETa flux is calculated for each pixel of the image as a “residual” of the surface energy budget equation 18
Chapter (2)
26
Literature Review
Figure 4 Surface Energy Balance
https://www.researchgate.net/figure/Surface-Energy-Balance-12_301679158 According to the radiation balance, the net radiation can be considered as a balance between incoming and outgoing short-wave, and long-wave radiation under the steady atmospheric condition. It is computed by subtracting all outgoing radiant fluxes from all incoming radiant fluxes (Figure 5), this is given in the surface radiation balance equation 16.
Rn= (1-α) RS↓ + RL↓ - RL↑ - (1-εo) RL↓
(16)
Figure 5 Surface Radiation Balance
https://www.researchgate.net/figure/228898906_Surface-radiation-balance-2 Where, α
The surface albedo (dimensionless)
Rs↓
The incoming short-wave radiation (W/m2)
RL↓
The incoming longwave radiation (W/m2)
Chapter (2) RL↑
27
Literature Review
The outgoing longwave radiation (W/m2)
The soil heat flux (G) is empirically calculated using vegetation indices, surface temperature, and surface albedo The sensible heat flux (H) is the rate of heat loss to the air by convection and conduction due to a temperature difference, and can be computed using the following equation: 𝐻 =
𝜌𝑎𝑖𝑟 ∗ 𝐶𝑝 ∗ 𝑑𝑇 𝑟𝑎ℎ
(17)
Where, ρair
The density of air (kg/m3)
Cp
The air specific heat (1004 J/kg/K)
dT
The difference (dT = T1 – T2) between two heights (z1 and z2)
rah
The aerodynamic resistance
Latent heat flux (λET) is the rate of latent heat loss from the surface due to evapotranspiration. According to the Equation (5), the latent heat can be calculated as follows: λET = Rn– G – H
(18)
Once (λET) is computed for each pixel, an equivalent amount of instantaneous ETa (mm/hr) is readily calculated by dividing by the latent heat of vaporization (λ). These values are then extrapolated using a ratio of ETa to reference crop ET to obtain daily or seasonal levels of ETa. Reference crop ET, termed ETr, is the ET rate expected from a well-defined surface of full-cover alfalfa or clipped grass and is computed in the SEBAL process using ground weather data.
Chapter (2)
28
Literature Review
2.3.5 Mapping Evapotranspiration at High Resolution and with Internalized Calibration (METRIC) Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) is a variant of SEBAL, an energy balance model developed in The Netherlands. It is also an image-processing tool for mapping regional ET over more complex surfaces as a residual of the energy balance at the Earth’s surface. METRIC has been extended from SEBAL through integration with reference ET, which is computed using ground-based weather data. The fundamental principle underlying METRIC is that evaporating liquid drops absorbs heat as indicated by Allen (Allen et al. 2005, Allen et al. 2007) to derive ET from remotely sensed data in visible, near-infrared, and thermal infrared spectral regions along with ground-based measurements of wind speed and near-surface dew point temperature. Two anchor conditions are selected within an observed scene to calibrate the sensible and latent heat flux computation internally and to fix boundary conditions for the energy balance. Such internal calibration eliminates the need for an in-depth atmospheric correction of surface temperature or reflectance (albedo) measurements using the radiative transfer model (Tasumi et al. 2005). The internal calibration, similar to SEBAL, also reduces impacts of any biases in the estimation of aerodynamic stability correction or surface roughness. The calibration is done by choosing manually a hot and a cold pixel to define the range of vertical temperature gradients (dT) above the surface. The cold condition is typically a well-irrigated alfalfa field where ET = ETr (reference ET in mm/h). The hot condition is typically a dry, bare agricultural field where ET = 0. 𝑑𝑇 = 𝑏 + 𝑎 𝑇𝑠
(19)
Once surface temperature, Ts, and dT are calculated corresponding to hot and cold conditions, the linear relationship as indicated in Equation (19) is defined.
Chapter (2)
29
Literature Review
However, the context-dependency of SEBAL, METRIC, and triangular models has been indicated in a recently conducted study (Long and Singh 2013). They reported that the wet/dry pixels (edges) required to trigger these models might not necessarily exist within a specific extent of an image. As the extent of satellite image and/or spatial resolution of satellite vary, the wet/dry limits of ET could change significantly, thereby resulting in differing model outputs, i.e., the ET estimates from these models are not deterministic. It is unknown, particularly in SEBAL, exactly how large extent of a study site of interest would be appropriate for the operator to properly select the so-called hot/wet pixels that can satisfy the assumptions made in these models so that the linear correlation between the near surface temperature difference and remotely sensed surface temperature holds true. In many cases, even the very large extent would not necessitate the existence of both hot and wet extremes. For instance, one would not be able to select a hot pixel from a large homogeneous forest. Also, there is no other alternative for the SEBAL/METRIC models to automatic selection of extreme pixels from images with varying extents, spatial resolutions, and clouds (Long and Singh 2010, Long et al. 2011), Furthermore, even though the extremes can be appropriately selected from relatively large images that probably entail hot and cold extremes reflecting surface conditions after cloud and terrain effects are favorably reduced/removed, the SEBAL-type algorithms appear to be limited to providing reasonable ET patterns due mostly to constant coefficients “a” and “b” in the SEBAL algorithm that do not accommodate the effect of variations in fractional vegetation cover on ET extremes (Long and Singh 2012, Long and Singh 2013). The performance of the METRIC model was tested by Gowda. (Gowda et al. 2008) in the Texas High Plains on two different days in 2005 using Landsat 5
Chapter (2)
30
Literature Review
TM data by comparison of resultant daily ET estimates with measured values derived from soil moisture budget. Integration of water balance model with METRIC estimated ET could provide significant improvements in the irrigation schedules as found in Spain by Santos. (Santos et al. 2008). Tatsumi. (Tasumi et al. 2005) pointed out the high potential for successful ET estimates of SEBAL/METRIC models by comparing the derived ET with lysimeter measured values in the semi-arid US. 2.3.6 Two-Source Models (TSM) Norman (Norman and Becker 1995) proposed a new model named two-source model, also known as duel-source model to improve the accuracy of LE estimates using satellite remote sensing data, especially over sparse surfaces (Blyth and Harding 1995, Huntingford et al. 1995, Norman and Becker 1995, Kabat et al. 1997, Wallace 1997). The basic principle of this model is to partitioning the composite radiometric surface temperature into soil and vegetation components and considered sensible and latent heat fluxes are transferred to the atmosphere from both surface components. Dispensability of ground-based information or any prior calibration has made the applicability of duel source model wider without resorting to any additional input data. In the duel source model, satellite-derived surface temperature (Ts) is considered to be a composition of the soil (Tsoil) and canopy temperatures (Tveg), and H and LE are also divided into soil and vegetation contributions, respectively. Canopy latent heat flux is computed using the Priestley-Taylor equation (Priestley and Taylor 1972). An iterative method is used to obtain the soil (Tsoil) and canopy temperatures (Tveg) from satellite-derived Ts setting an initial value of 1.3 for the Priestley-Taylor parameter α (Anderson et al. 2008, Kustas and Anderson 2009). This nominal choice of α overestimates canopy latent heat
Chapter (2)
31
Literature Review
flux under moisture-stressed conditions and yield negative soil evaporation (LEsoil) and is regarded as a nonphysical solution during the daytime. The α is therefore iteratively reduced until LEsoil approaches zero to obtain a final α as well as Tsoil and Tveg. The LE and H are then calculated from these estimates. Both the one- and Two-source models are sensitive to their use of the temperature differences to estimate H. Dispensability of precise atmospheric corrections, emissivity estimations and high accuracy in sensor calibration are the main advantages of the duel source method. Coupling of the duel source models with PBL eliminates the need for ground-based measurement of Ta (Kustas and Norman 1996) and, thus, is much better suitable for applications over large-scale regions than other algorithms (Anderson et al. 1997). Effects of view geometry are normally incorporated, while the empirical corrections for the “excess resistance” are eliminated in the duel-source models. More details of these Two-source models are found in Li. (Li et al. 2009), while the revision and recent advancements of these Two-source models are found in the literature (Kustas and Norman 1999, Norman et al. 2000, Kustas et al. 2004, Li et al. 2005, Kalma et al. 2008, Sánchez et al. 2008, Sánchez et al. 2008, Li et al. 2009, Wang and Dickinson 2012). 2.3.7 Distinction between SEBAL and METRIC Distinctions between SEBAL and METRIC can be summarized as follows: (1) At wet pixel, METRIC does not assume Hwet = 0 or LEwet = (Rn − G). Instead, a daily surface soil water balance is used to assure that ET is zero and set to 1.05ETr at hot and wet pixels, respectively. (2) In METRIC, wet pixels are selected in an agricultural setting, while on the other hand the cold pixels are selected based on biophysical characteristics similar to the reference crop (like alfalfa); and
Chapter (2)
32
Literature Review
(3) Instead of the actual evaporative fraction, the interpolation (extrapolation) of instantaneous ET to daily value is based on the alfalfa ETrF (ratio of instantaneous ET to the reference ETr and is computed from meteorological station data at satellite overpass time). Comparisons of the different remote sensing ET models mentioned above are summarized in Table 1for quick reference.
Chapter (2)
Literature Review
33
Table 3 Comparisons of the different remote sensing ET models
Algorithms
Input
Main
Parameters
assumptions
Merits
Demerits
(ET)dry limit = 0; SEBI
pbl, hpbl,
(ET)wet limit →
Relating the effects of
v,Ts, Rn, G
evaporates
Ts and radirectly on LE
potentially
(ET)dry limit = 0; SEBS
Tair, ha, v,Ts,
(ET)wet limit →
Rn, G
takes place at a potential rate
(EF)α = (TH − S-SEBI
Ts, αs, Rn, G
TS)/ (TH − TLE) TH= (LE)min TLE= (LE)max dT = cTs + d (ET)dry pixel =
SEBAL
v, ha,Ts, VI, Rn, G
0; (ET)wet → considered as the surface available energy
Requires ground based measurements
Uncertainty in SEBS
Requires
from
many
Ts
and
meteorological
parameters;
parameters can partially be solved; Roughness height for heat transfer
Relatively complex
is computed explicitly
derivation
instead of using fixed
turbulent
values
fluxes
Ground
too
based
of heat
Extreme
measurements are not
temperatures are
required
location specific
Requires
minimum
Ground-based
plain
measurements; Equipped
with
automatic
internal
calibration;
Exact
atmospheric corrections are not required
Applied
over
surfaces;
Possesses uncertainties in the determination of anchor pixels
Chapter (2)
Literature Review
34
Possesses
METRIC
v, ha, Ts, VI, Rn, G
(ET)hot pixel = 0
Similar to SEBAL, but
uncertainties in
(LE)wet pixel =
surface slope and aspect
the
1.05ETr
can be considered
determination of anchor pixels
(1) Component
v, ha, Tair, Ts, TSM
Tc, Fr or LAI, Rn, G
fluxes are parallel
(1) Includes the view
to each other;
geometry;
Many
(2) Priestly-
(2) Eliminates the need
measurements
Taylor equation
for empirical corrections
and components
is used to
for
compute canopy
resistance.”
the
“excess
ground
are needed.
transpiration.
Where, pbl
Average planetary boundary layer temperature
hpbl
Height of the PBL
v
Wind speed
Ts
Surface temperature
Tc
Vegetation canopy temperature
Rn
Surface net radiation
G
Soil heat flux density
ha
Measurement height of wind speed and air temperature
VI
Vegetation Index
LAI
Leaf Area Index
Fr
Fractional vegetation cover
αs
Surface shortwave albedo
Tair
Air temperature measured at a reference height.
Chapter (2)
35
Literature Review
2.4 Performance Assessment Indicators for Irrigation and Drainage System (Lenton 1986) defined the irrigation performance indicators as the achieving the objectives of an irrigation system. (Abernethy 1986) presented a review of the performance indicators of the irrigation system at symposium and concluded that the performance indicators should not only consider the output indicators of the irrigation water delivery system but some indicators of that take the effects of the output into consideration. So the assessment of an irrigation system is the measurement of achieving levels of the irrigation system.
Chapter (3) Methodology 3.1
Introduction
In this study, Surface Energy Balance Algorithm for Land (SEBAL) was used to estimates of the spatial and temporal distribution of Evapotranspiration ET over "Handaset Tanta.".SEBAL requires spatially distributed, visible, nearinfrared and thermal infrared data together with routine weather data. The algorithm computes net radiation (Rn), sensible heat flux (H) and soil heat flux (G) for every pixel and the latent heat flux (λET) is acquired as a residual in energy balance equation. 3.2
Layout of the Study Area
The study area which was selected for this study is "Handaset Tanta", which is located in El-Gharbia governorate in the central Nile Delta. It is one of administration districts in Gharbia governorate, with command area about (56317 feddan) (Emara et al. 2016). It is located between the latitudes of 30°43ʹ41.33ʺ N - 30°58ʹ23.79ʺ N and longitudes of 30°53ʹ43.60ʺ E - 31°5.76ʺ E. It has an elevation of about 11 m above sea level. The human's activities in the study area are mainly agriculture and industry (El-Halim 2003) Figure 6 shows the study area in the Nile Delta While, Figure 7 presents the detailed layout of the study area.
37
Methodology
Handasa Tanta
El-Gharbia governorate
Nile Delta
Chapter (3)
Figure 6 Layout of the study area in Nile delta
Figure 7 Detailed layout of the study area
Chapter (3)
Methodology
38
3.2.1 Irrigation System in the Study Area The irrigation system in Egypt is one of the oldest irrigation systems in the world. The classification of the irrigation system could be the diversions (Rayah) that supply main canals which are considered the feeder to branch canals. The branch canals feed distributaries canals which irrigate command area through mesqas and marwas by rotation system (AMIN 1999).Qanat Tanta Al-Melahia stems from Bahr Shibin and it divided into two main canals; Al-Qasid canal and Qanat Tanta Al-Melahia canal at Km16500. These two canals are the main components of the irrigation system in the study area. (See Figure 8) 31°0'0"E
31°4'0"E
¯
30°54'0"N
30°54'0"N 30°54'0"N
5
10 Kilometers
Legend
30°45'0"N 30°45'0"N
31°0'0"E
0
5
10 Kilometers
canals
Legend drains agriculture buildings
agriculture buildings
30°56'0"E
30°48'0"N
30°48'0"N 30°48'0"N
30°51'0"N
30°51'0"N 30°51'0"N
30°54'0"N 30°51'0"N 30°48'0"N 30°45'0"N
0
30°57'0"N
¯
30°57'0"N
30°56'0"E
31°4'0"E
30°45'0"N
31°0'0"E
30°57'0"N 30°57'0"N
30°56'0"E
30°56'0"E
31°0'0"E
31°4'0"E
31°4'0"E
Figure 8 Irrigation system in the study area
Figure 9 Drainage system network in the study area
Chapter (3)
Methodology
39
3.2.2 The Drainage System in the Study Area The drainage system network in the study area consists of open drains (main and secondary) and sub-surface drains (collectors and laterals). The main open drains in the study area for instance are Samtay drain. The secondary drains from Samtay are Sibirbay and Mahalt-Monof. Figure 9 shows the drainage network in the study area. 3.3 Data Requirements For SEBAL to be operated, a satellite images are needed with some weather data from the nearby weather station. A land use map is helpful to apply SEBAL. Table 4 Landsat 8 Bands Designations Landsat 8
Wavelength (micrometers) 0.43 - 0.45
Resolution (meters) 30
Band 2 - Blue
0.45 - 0.51
30
Band 3 - Green
0.53 - 0.59
30
Band 4 - Red
0.64 - 0.67
30
Band 5 - Near Infrared (NIR)
0.85 - 0.88
30
Band 6 - Shortwave Infrared (SWIR) 1
1.57 - 1.65
30
Band 7 - Shortwave Infrared (SWIR) 2
2.11 - 2.29
30
Band 8 - Panchromatic
0.50 - 0.68
15
Band 9 - Cirrus
1.36 - 1.38
30
Band 10 - Thermal Infrared (TIRS) 1
10.60 - 11.19
100 * (30)
Band 11 - Thermal Infrared (TIRS) 2
11.50 - 12.51
100 * (30)
Bands Band 1 - Ultra Blue (coastal/aerosol)
Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)
Chapter (3)
Methodology
40
3.3.1 Remote Sensing Data To complete the estimation of ET in the present study, the main remote sensing data that were used are Landsat 8 TIRS (Thermal Infrared Sensor) Band 10, 11, and OLI (Operational Land Imaginer) sensor Band (1-9) (Table 4). All bands were used to derive multi-temporal images of the normalized difference vegetation index (NDVI), leaf area index (LAI), surface albedo (α), net radiation (Rn) and all components in a sequence of calculations required to estimate ET of the summer season 2014. Landsat 8 is one of the Landsat series of NASA. The data of Landsat 8 is available in Earth Explorer website (https://earthexplorer.usgs.gov/ ). 3.3.2 Weather Data Many weather data are required for the application of SEBAL the following data
are
collected
from
meteorological
website
(http://www.wunderground.com) for El-Gharbia governorate, Egypt location Hourly wind speed which is needed for the computation of (H) and the ETr calculations. 1
Hourly humidity data such as vapor pressure or dew point temperature which is used for the completion of the ETr calculations.
2
Hourly solar radiation which is useful for the estimation of the cloudiness of the image and the ETr calculations.
3
Hourly air temperature which is required for the computation of (H) and the ETr calculations. [Source https://midcdmz.nrel.gov/solpos/solpos.html ]
4
Rainfall data, the total monthly values of precipitation were collected from meteorological website station at 30.8 N and 30.9 E. The effective monthly precipitation were calculated by assuming the effective precipitation is
Chapter (3)
Methodology
41
80% of total precipitation (Doorenbos 1975). Table 5 shows the total and effective precipitation for the year 2014. Table 5 Monthly mean values of the precipitation and the effective precipitation of the studied area during the year 2014
month
Average monthly Precipitation in (mm)
Effective Precipitation during the period under consideration
Total P
Effective P
January
13
10.4
February
8
6.4
march
7
5.6
April
3
2.4
may
2
1.6
June
0
0
July
0
0
0
august
0
0
0
September
0
0
0
October
2
1.6
November
4
3.2
December
12
9.6
Total Effective Precipitation during the period under consideration
1.6 ∗
1.6 ∗
15 = 0.774 31 0
7 = 0.361 31
1.135 mm
Source http://www.tanta.climatemps.com/precipitation.php
As shown in Figure 10 Air Temperature (oc), Figure 11 Dew Point Temperature (oc), Figure 12 Relative Humidity (%), Figure 13 Wind Speed (km/hr),Figure 14 Solar Radiation (w/m2), and [Table 26 Appendix (C)]
Chapter (3)
Methodology
42 AIR TEMPERATURE (OC)
DEW POINT TEMPERATURE (OC)
Max Dewp T max T avg T min
Figure 10 Air Temperature (oc)
Avg Dewp Min Dewp
Figure 11 Dew Point Temperature (oc)
Chapter (3)
Methodology
43 RELATIVE HUMIDITY (%)
Max Humidity Avg Humidity" Min Humidity"
Figure 12 Relative Humidity (%)
WIND SPEED (KM/HR)
Max wind speed
Figure 13 Wind Speed (km/hr)
Solar radiation (w/m2)
Chapter (3)
Methodology
44
5 4 3 2 1 0
Avg solar radiation
Max solar radiation
Figure 14 Solar Radiation (w/m2) Table 6 Metadata of Satellite Images
No. 1 2 3 4 5 6 7 8 9
Sensor OLI TIR OLI TIR OLI TIR OLI TIR OLI TIR OLI TIR OLI TIR OLI TIR OLI TIR
No. of Bands 9 2 9 2 9 2 9 2 9 2 9 2 9 2 9 2 9 2
Resolution (m) 30 100 30 100 30 100 30 100 30 100 30 100 30 100 30 100 30 100
Path / Row
Date of Acquisition
177/39
25/05/2014
177/39
10/06/2014
177/39
26/06/2014
177/39
12/07/2014
177/39
28/07/2014
177/39
13/08/2014
177/39
29/08/2014
177/39
14/09/2014
177/39
30/09/2014
Chapter (3) 3.4
45
Methodology
Data Preparations
3.4.1 Downloading of Landsat 8 Images. Nine Landsat 8 images from 25 May 2014 to 30 September 2014 were downloaded. The path/row for the images is 177/39 as shown in Table 6. The downloaded images were imported into ERDAS IMAGINE 2014 as briefly shown in appendix A 3.5 Weather Data Preparation and Calculation of Reference ETr Collected weather data was prepared for ETr calculation using the Ref – ET software provided by Idaho University. Many steps are performed to get a final text file ready to be used within the REF-ET software to calculate the hourly ETr for 17 may 2014 to 7 October 2014. Now it is ready to use the software to complete the ETr. The steps are briefly shown in appendix B. Figure 15shows the output of the REF-ET software.
Figure 15 REF-ET output file
Chapter (3)
46
Methodology
3.6 Operation of the Model The flowchart shown in Figure 16 summarize the computational steps used to obtain the seasonal evapotranspiration for the study area.
Figure 16 Flowchart of computational steps used to obtain the seasonal evapotranspiration.
Chapter (3)
47
Methodology
3.6.1 The Net Surface Radiation Flux (Rn) The first step in the SEBAL procedure is to compute the net surface radiation flux (Rn) using the surface radiation balance (equation 16 and Figure 5) through a series of steps using EDRAS spatial modeler to compute the terms in equation (16). A flow chart of the process is shown (Figure 17).
Figure 17 Flowchart of the Rn calculation
The computation steps begins by computing the reflectivity for each band (𝜌𝜆 ) and continue downward for the calculation of Rn. 3.6.1.1 Surface Albedo 1. The reflectivity for each band (𝜌𝜆 ) is computed. The reflectivity of a surface is defined as the ratio of the reflected radiation flux to the incident radiation flux. It is computed using the following equation given for Landsat images:
Chapter (3)
48 𝜌𝜆 =
Methodology
𝑀𝜌 ∗ 𝑄𝑐𝑎𝑙 +𝐴𝜌 sin 𝜃𝑆𝐸
(20)
Where, 𝜌𝜆
The reflectivity for each band Band-specific multiplicative rescaling factor from the
𝑀𝜌
metadata Band-specific additive rescaling factor from the
𝐴𝜌
metadata Quantized and calibrated standard product pixel values
𝑄𝑐𝑎𝑙
(DN)
𝜃𝑆𝐸
Local sun elevation angles provided in the metadata
Source:http://www.pancroma.com/downloads/Using%20the%20USGS%20L andsat%208%20Product.htm 2.
The albedo at the top of the atmosphere (𝛼𝑡𝑜𝑎 ) is computed. This is the albedo unadjusted for atmospheric transmissivity and is computed as follows: 𝛼𝑡𝑜𝑎 = ∑(𝜔𝜆 ∗ 𝜌𝜆 )
(21)
Where, 𝛼𝑡𝑜𝑎
The albedo at the top of the atmosphere
𝜌𝜆
The reflectivity for each band
𝜔𝜆
is a weighting coefficient for each band 𝜔𝜆 =
𝐸𝑆𝑈𝑁𝜆 ∑ 𝐸𝑆𝑈𝑁𝜆
Where, 𝐸𝑆𝑈𝑁𝜆
mean solar exoatmospheric irradiances (Table 7)
(22)
Chapter (3)
Methodology
49 Table 7 Values for the weighting coefficient, ωλ Band 2 3 4 5 6 7
ΣESUN
ESUN
ωλ
2067 1893 1603 972.6 245 79.72
0.3012979 0.27593465 0.23366257 0.14177181 0.03571262 0.01162045
6860.32
Source: http://www.gisagmaps.com/landsat-8-atco/
3.
The final step is to compute the surface albedo. Surface albedo (α) is defined as the ratio of the reflected radiation to the incident shortwave radiation. Surface albedo is calculated by correcting the 𝛼𝑡𝑜𝑎 for atmospheric transmissivity: 𝛼=
𝛼𝑡𝑜𝑎 − 𝛼path_radiance 2 𝜏𝑠𝑤
(23)
Where, 𝛼path_radiance 𝜏𝑠𝑤
The average portion of the incoming solar radiation across all bands that is back scattered to the satellite before it reaches the earth’s surface. The atmospheric transmissivity.
Values for 𝛼path_radiance range between 0.025 and 0.04 and for SEBAL we recommend a value of 0.03 based on (Bastiaanssen 2000). Atmospheric transmissivity 𝜏𝑠𝑤 is defined as the fraction of incident radiation that is transmitted by the atmosphere and it represents the effects of absorption and reflection occurring within the atmosphere. This effect occurs to incoming radiation and to outgoing radiation and is thus squared. τsw includes transmissivity of both direct solar beam radiation and diffuse (scattered)
Chapter (3)
50
Methodology
radiation to the surface. We calculate τsw assuming clear sky and relatively dry conditions using an elevation-based relationship from FAO-56: 𝜏𝑠𝑤 = 0.75 + 2 ∗ 10−5 ∗ 𝑧
(24)
Where, z is the elevation above sea level (m). This elevation should best represent the area of interest in our case the elevation of the study area is 11 m above sea level. Then 𝜏𝑠𝑤 = 0.75022 A model in ERDAS spatial modeler was developed to calculate surface albedo (Figure 18)
Figure 18 Surface Albedo Model
Compare the values for various known surfaces with typical albedo values given in Table 1
Chapter (3)
Methodology
51 Table 8 Typical Albedo values
Fresh snow
0.80 – 0.85
Old snow and ice
0.30 – 0.70
Black soil
0.08 – 0.14
Clay
0.16 – 0.23
White-yellow sand
0.34 – 0.40
Gray-white sand
0.18 – 0.23
Grass or pasture
0.15 – 0.25
Corn field
0.14 – 0.22
Rice field
0.17 – 0.22
Coniferous forest
0.10 – 0.15
Deciduous forest
0.15 – 0.20
Water
0.025 – 0.348
3.6.1.2 Incoming Shortwave Radiation (RS↓) Incoming shortwave radiation is the direct and diffuse solar radiation flux that actually reaches the earth’s surface (W/m2). It is calculated, assuming clear sky conditions, as a constant for the image time using the following equation: Rs ↓ = 𝐺𝑠𝑐 ∗ sin 𝜃𝑆𝐸 ∗ 𝑑𝑟 ∗ 𝜏𝑠𝑤
(25)
Where, 𝐺𝑠𝑐
The solar constant (1367 W/m2)
𝜃𝑆𝐸
Local sun elevation angle in degrees is provided in the metadata.
dr
The inverse squared relative earth-sun distance.
𝜏𝑠𝑤
The atmospheric transmissivity.
A model in ERDAS spatial modeler was developed to calculate Incoming shortwave radiation (Figure 19).
Chapter (3)
52
Methodology
Figure 19Calculation incoming Shortwave Radiation model.
3.6.1.3 Outgoing Longwave Radiation (RL↑) The outgoing long wave radiation is the thermal radiation flux emitted from the earth’s surface to the atmosphere (W/m2). It is computed through the several steps shown previously (Figure 17) 1. Many vegetation indices should be calculated; Normalized Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI). They are calculated using many equations in the ERDAS spatial modeler. The NDVI is the ratio of the differences in reflectivity for the near-infrared band (ρ5) and the red band (ρ4) to their sum in Landsat 7. For Landsat 8, one should use the bands; near – infrared band (ρ5) and the red band (ρ4)
Chapter (3)
Methodology
53 𝑁𝐷𝑉𝐼 =
𝑁𝐼𝑅 − 𝑅𝐸𝐷 𝜌5 − 𝜌4 = 𝑁𝐼𝑅 + 𝑅𝐸𝐷 𝜌5 + 𝜌4
(26)
NDVI is a sensitive indicator of the amount and condition of green vegetation. Values for NDVI range between -1 and +1. Green surfaces have a NDVI between 0 and 1 and water and cloud are usually less than zero. SAVI is an index that attempts to remove the effects of the background soil from NDVI so that impacts of soil wetness are reduced in the index. It is computed using the following equation: 𝑆𝐴𝑉𝐼 =
(1 + 𝑙)(𝜌5 − 𝜌4 ) (𝑙 + 𝜌5 + 𝜌4 )
(27)
Where, l is a constant for SAVI If l is zero, SAVI becomes equal to NDVI. A value of 0.5 frequently appears in the literature for l. However, a value of 0.1 is used for a better representation of soil. The LAI is the ratio of the total area of all leaves on a plant to the ground area represented by the plant. It is an indicator of biomass and canopy resistance. LAI is computed using the following empirical equation: 0.69−𝑆𝐴𝑉𝐼
𝐿𝐴𝐼 = −
ln (
0.59
)
(28)
0.91
Where, SAVI
is the SAVI calculated from Equation 28
To complete the whole steps, a vegetation indices model was developed in ERDAS spatial modeler (Figure 20)
Chapter (3)
Methodology
54
Figure 20 Vegetation Indices Model
2. Surface emissivity (ε) It is the ratio of the thermal energy radiated by the surface to the thermal energy radiated by a blackbody at the same temperature. 𝜀𝑜 is an emissivity that represents surface behavior for thermal emission in the broad thermal spectrum (6 to 14 μm). εο is used to calculate total long wave radiation emission from the surface. The surface emissivity 𝜀𝑜 is computed using the following equations: For NDVI > 0: 0.95 + 0.01 𝐿𝐴𝐼 𝜀𝑜 = { 0.98
𝐿𝐴𝐼 < 3 𝐿𝐴𝐼 ≥ 3
(29)
For water; NDVI < 0 and α < 0.47 𝜀𝑜 = 0.985 The developed model to calculate 𝜀𝑜 is shown in Figure 21
(30)
Chapter (3)
Methodology
55
Figure 21 Surface Emissivity Model
3. Surface temperature Ts was calculated by applying a structured mathematical algorithm viz., Split-Window (SW) algorithm. It uses brightness temperature of two bands of TIR, mean and difference in land surface emissivity for estimating Ts of an area. The algorithm is Ts = TB10 + C1 (TB10-TB11) + C2 (TB10-TB11)2 + C0 + (C3+C4W) (1- 𝜀̅) + (C5+C6W) ∆ ε
(31)
Where, Ts
Land Surface Temperature (K)
C0 to C6
Split-Window Coefficient values (Table 9) *
TB10& TB11
brightness temperature of band 10 and band 11 (K)
𝜀̅
mean LSE of TIR bands
W
Atmospheric water vapor content (0.013) **
∆ε
Difference in LSE
* (Skokovic et al, 2014; Sobrino et al, 1996; 2003; Shaouhua Zhao et al, 2009)
**[Source:
Meteorological Observatory of Dept. of Agricultural Meteorological, Ranchi
Agricultural College, Birsa Agricultural University, Ranchi] (Latif
2014)
Chapter (3)
Methodology
56 Table 9 Split-Window Coefficient Values Constant C0 C1 C2 C3 C4 C5 C6
Value -0.268 1.378 0.183 54.3 -2.238 -129.2 16.4
Figure 22 Flowchart of the algorithm to be performed during Ts estimation using TIRS Band 10 and 11 and OLI sensor.
Chapter (3)
Methodology
57
Step1: Estimation of Top of Atmospheric Spectral Radiance of TIRS Band 10 and 11 by using the algorithm given below (equation (32)). This algorithm transforms the raw image into spectral radiance image. Lλ = ML*Qcal + AL
(32)
Where, Lλ
Top of Atmospheric Radiance
Qcal
band 10/ 11 image
ML
Band specific multiplicative rescaling factor (radiance_mult_band_10/11). (Table 10)
AL
Band specific additive rescaling factor (radiance_add_band_10/11) . (Table
10) Table 10 Rescaling Factor
Rescaling Factor
Band 10
Band 11
ML
0.000342
0.000342
AL
0.1
0.1
Step2: Estimation of Brightness Temperature (TB) of Band 10 and 11. Brightness Temperature is the electromagnetic radiation traveling upward from the top of the Earth’s atmosphere. The thermal calibration process is done by converting thermal DN values of raw thermal bands of TIR sensor into TOA Spectral Radiance and after using Brightness Temperature equation shown in equation (33), we got Brightness Temperature (TB) band. 𝑇𝐵 =
Where,
𝐾2 𝐾1
𝑙𝑛 [(𝐿𝜆) + 1]
(33)
Chapter (3)
Methodology
58
Lλ
Top of Atmospheric Radiance
K1 & K2
The thermal constant of Bands 10 and 11from metadata image file. (Table 11) Table 11 K1 and K2 Values
Thermal Constant
Band 10
Band11
K1
777.89
480.89
K2
1321.08
1201.14
Thermal constant K1 and K2 and other image statistic are obtained from metadata file of the image. Step-3: Estimation of Fractional Vegetation Cover (FVC) for an image using NDVI image obtain earlier, NDVI for Soil and NDVI for Vegetation from (Table 12) using equation (34). Fractional Vegetation cover estimates the fraction of the area under vegetation. 𝐹𝑉𝐶 =
𝑁𝐷𝑉𝐼 − 𝑁𝐷𝑉𝐼(𝑆𝑂𝐼𝐿) 𝑁𝐷𝑉𝐼(𝑉𝐸𝐺𝐸𝑇𝐴𝑇𝐼𝑂𝑁) − 𝑁𝐷𝑉𝐼(𝑆𝑂𝐼𝐿)
(34)
Table 12 NDVI for Soil and Vegetation
NDVI for Soil
0.2
NDVI for Vegetation
0.574185
Step-4: Estimation of Land Surface Emissivity (LSE) from FVC layer obtain from step-4 using the algorithm in equation (35). Land Surface Emissivity measure the inherent characteristic of the earth surface. It measures its ability to convert thermal or heat energy into radiant energy. LSE estimation required emissivity of soil and vegetation of both Band 10 and 11 are given in Table 13 Emissivity Values. LSE of Band 10 and 11 are individually calculated.
Chapter (3)
Methodology
59 𝐿𝑆𝐸 = ∈𝑆 ( 1 − 𝐹𝑉𝐶) + ∈𝑉 𝐹𝑉𝐶
(35)
Where, ∈𝑆
Emissivity for soil. (Table 13)
∈𝑉
Emissivity for vegetation. (Table 13)
𝐹𝑉𝐶
Fractional Vegetation Cover Table 13 Emissivity Values
Emissivity
Band 10
Band 11
∈𝑆
0.971
0.977
∈𝑉
0.987
0.989
Step-5: Combination of LSE of Band10 and LSE of Band 11 obtain from step5 through Mean and Difference in between them as shown in equation (35) and (36) 𝐿𝑆𝐸10 + 𝐿𝑆𝐸11 2 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑜𝑓 𝐿𝑆𝐸 = ∆ ε = 𝐿𝑆𝐸10 − 𝐿𝑆𝐸11 𝑀𝑒𝑎𝑛 𝑜𝑓 𝐿𝑆𝐸 = ε̅ =
(36) (37)
Step-7: Estimation of Land Surface Temperature (Ts) using the algorithm in equation (31) Ts = TB10 + C1 (TB10-TB11) + C2 (TB10-TB11)2 + C0 + (C3+C4W) (1- ε̅) + (C5+C6W) ∆ ε
(31)
A model was developed to compute Ts in ERDAS IMAGINE 2014 Spatial Modeler (Figure 23).
Chapter (3)
60
Methodology
Figure 23 Surface temperature Model Now, all the required parameters are ready to be used to calculate the outgoing long wave radiation (RL↑). It is calculated using the Stefan – Boltzmann equation RL ↑ = ε𝑜 ∗ σ ∗ 𝑇𝑠4
(38)
Where, ε𝑜
The broadband surface emissivity (dimensionless).
σ
The Stefan – Boltzmann constant (5.67 ∗ 10−8 𝑚2 𝑘 4).
𝑇𝑠
The surface temperature in kelvin (K).
𝑊
A model was developed to complete the calculation in ERDAS IMAGINE Spatial modeler (Figure 24).
Figure 24 Outgoing longwave radiation Model.
Chapter (3)
61
Methodology
3.6.1.4 Choosing the “Hot” and “Cold” Pixels The SEBAL process utilizes two “anchor” pixels to fix boundary conditions for the energy balance. These are the “hot” and “cold” pixels and are located in the area of interest. The “cold” pixel is selected as a wet, well-irrigated crop surface having a full ground cover by vegetation. The surface temperature and near-surface air temperature are assumed to be the similer at this pixel. The “hot” pixel is selected as a dry, bare agricultural field where ET is assumed to be zero. Both “anchor” pixels should be in large and homogeneous areas that contain more than one thermal band pixel (see Figure 25 ).
Figure 25"cold/hot pixel” estimation procedure in SEBAL for the image (25/5/2014).
3.6.1.5 Incoming longwave Radiation (RL↓) The incoming long wave radiation is the downward thermal radiation flux from the atmosphere (W/m2). It is computed using the Stefan-Boltzmann equation: RL ↓ = ε𝑎 ∗ σ ∗ 𝑇𝑎4
Where,
(39)
Chapter (3)
62
Methodology
ε𝑎
The atmospheric emissivity (dimensionless)
σ
The Stefan – Boltzmann constant (5.67 ∗ 10−8 𝑚2 𝑘 4).
𝑇𝑎
The near surface air temperature (K)
𝑊
The following empirical equation for εa is applied the coefficients by (Bastiaanssen 1995), derived for western Egypt: ε𝑎 = 1.08 ∗ (− ln 𝜏𝑠𝑤 )0.265
(40)
Where, 𝜏𝑠𝑤
The atmospheric transmissivity calculated from Equation (24)
Substituting the second equation into the first one, and using Tcold for the cold pixel for𝑇𝑎 yield the following equation: 4 RL ↓ = 1.08 ∗ (− ln 𝜏𝑠𝑤 )0.265 ∗ σ ∗ 𝑇𝑐𝑜𝑙𝑑 =
(41)
A model was developed to complete the calculation in ERDAS IMAGINE Spatial Modeler (Figure 26). Or, these calculations can be done using excel spreadsheet
as shown in Figure 27
Figure 26 Incoming longwave radiation model
Chapter (3)
63
Methodology
Figure 27Incoming longwave radiation calculations using excel spreadsheet
3.6.1.6 Solving the Surface Radiation Balance Equation for Rn The net surface radiation flux (Rn) is now computed using Equation (16). A model was developed to complete the calculation in ERDAS IMAGINE Spatial Modeler (Figure 28)
Figure 28 Rn calculating Model
Chapter (3)
64
Methodology
This completes the first step of the SEBAL procedure. 3.6.2 Soil Heat Flux (G) The first step in calculating G is to compute the ratio 𝐺 ⁄𝑅𝑛 using the following equation developed by (Bastiaanssen 2000): 𝐺 = 𝑇𝑆 (0.0038 + 0.0074 ∝)(1 − 0.98 𝑁𝐷𝑉𝐼 4 ) 𝑅𝑛
(42)
Where, 𝑇𝑠
The surface temperature (oC)
𝛼
The surface albedo(dimensionless)
𝑁𝐷𝑉𝐼
The Normalized Difference Vegetation Index
G is then readily calculated by multiplying G/Rn by the value for Rn computed in equation (16). A model was developed to compute 𝐺 ⁄𝑅𝑛 and 𝐺 (Figure 29). Water filters was applied to the model as: If NDVI < 0; assume surface is water; 𝐺 ⁄𝑅𝑛 = 0.5
Figure 29 G/Rn and G Calculation Model
Chapter (3)
65
Methodology
3.6.3 Sensible Heat Flux (H) Before proceeding in the complicated steps in computing H, a momentum roughness length (𝑍𝑜𝑚 ) should be calculated. A land-use map is used in SEBAL for determining the momentum roughness length (𝑍𝑜𝑚 ). The user can develop one using satellite data. In this study supervised image classification method was used. Supervised classification is the process of using training samples (samples of known identity) to classify pixels of unknown identity. In the supervised classification technique the maximum likely hood algorithm classifies the image based on the training sets provided by the user based on his field knowledge. The training data given by the user guides the software as to what types of pixels are to be selected for certain land cover type. The classified images obtained after pre-processing and supervised classification which are showing the land use and land cover of the study area are Figure 30. This image provides the information about the land use of the study area. The red color represents the urban area, green color shows the agricultural area, blue color shows the water bodies. Now a land-use map is available, values of 𝑍𝑜𝑚 foragricultural areas, zom is calculated as a function of Leaf Area Index (LAI): 𝑍𝑜𝑚 = 0.018 ∗ 𝐿𝐴𝐼 For non-agricultural surface features, zom can be assigned as follows: Water
𝑍𝑜𝑚 = 0.0005 m
Cities
𝑍𝑜𝑚 = 0.2 m
A model was developed to calculate 𝑍𝑜𝑚 for each pixel (Figure 31)
(43)
Chapter (3)
66
Figure 30 land-use map
Methodology
Chapter (3)
67
Methodology
Figure 31 𝐙𝐨𝐦 calculating Model
The sensible heat flux (H) is defined as the rate of heat loss to the air by convection and conduction due to a temperature difference; it is computed using the equation for heat transport:
𝐻 =
𝜌𝑎𝑖𝑟 ∗ 𝐶𝑝 ∗ 𝑑𝑇 𝑟𝑎ℎ
(17)
Where, ρair
The density of air (kg/m3)
Cp
The air specific heat (1004 J/kg/K)
dT
The difference (dT = T1 – T2) between two heights (z1 and z2)
rah
The aerodynamic resistance to heat transport (s/m)
The sensible heat flux (H) is a function of the temperature gradient, surface roughness, and wind speed. The previous equation is hard to solve for the two unknowns, rah and dT (see Figure 32).
Figure 32 Aerodynamic heat transfer
Chapter (3)
Methodology
68
To overcome this complexity, the principle of choosing two anchor pixels was proposed and utilized, and the wind speed at a given height (where reliable values for H and dT can be estimated). The aerodynamic resistance to heat transport (rah) is computed for neutral stability as follows: 𝑧
𝑟𝑎ℎ =
ln (𝑧2 ) 1
(44)
𝑢∗ 𝑘
Where, z1 and z2
The heights in meters above the zero-plane displacement of the vegetation
𝑢∗
The friction velocity (m/s)
𝑘
Von Karman’s constant (0.41)
The friction velocity (𝑢∗ ) is computed during the first iteration using the logarithmic wind law for neutral atmospheric conditions: 𝑢∗ =
𝑘 𝑢𝑥 𝑧
ln (𝑧 𝑥 ) 𝑜𝑚
Where, 𝑘
Von Karman’s constant (0.41)
𝑢𝑥
Wind speed m/s at height 𝑧𝑥 (weather station)
𝑍𝑜𝑚
The momentum roughness length (m)
(45)
Chapter (3)
Methodology
69
To start the process of H computation, some weather data should be collected. Wind speed ux, 𝑍𝑜𝑚 at weather station, air temperature, and air pressure. These parameters are used in the computation of H. many steps to do (Figure 34) 1. Calculate 𝑢∗ at the weather station for neutral atmospheric conditions using the following equation: 𝑢∗ =
𝑘 𝑢𝑥 𝑧
ln (𝑧 𝑥 )
(45)
𝑜𝑚
2. Calculate wind speed at the blending height (200 m) using the following equation: 200
𝑢200 = 𝑢∗
ln (𝑧 ) 𝑜𝑚
𝑘
Figure 33Iterative Process to Compute Sensible heat flux (H)
(46)
Chapter (3)
70
Methodology
Figure 34 Iterative Process to Compute H
3. Calculate 𝑢∗ for each pixel using 𝑢200 calculated in the previous step. Use ERDAS Spatial Modeler to complete this step as shown in Figure 35
Figure 35 Friction Velocity Model
Chapter (3)
71
Methodology
4. Calculate aerodynamic resistance to heat transport (rah) for each pixel using ERDAS Spatial Modeler as shown in Figure 36
Figure 36 rah Calculation Model
5. To complete H calculation, a temperature difference (dT) must be defined. SEBAL computes dT for each pixel by assuming a linear relationship between dT and Ts. 𝑑𝑇 = 𝑏 + 𝑎 𝑇𝑠
(47)
“a” and “b” are coefficients derived by utilizing the anchor pixel concept. To do this, some parameters should be collected for the two anchor pixels which are:
Rn and G, and surface temperature for the hot pixel.
Rn and G, and surface temperature for the cold pixel.
ETr at overpass time of the satellite.
To compute H for the first round, ETa for the hot pixel is assumed to be zero, which is a bare soil with old olives orchard with no green vegetation pixel.
Chapter (3)
Methodology
72
Compute H for the two anchor pixels as follows: 𝐻𝑐𝑜𝑙𝑑 = 𝑅𝑛 − 𝐺 − 1.05 𝜆𝐸𝑇𝑟
(48)
𝐻ℎ𝑜𝑡 = 𝑅𝑛 − 𝐺
(49)
6. Calculate dT for the two anchor pixels as follows: 𝑑𝑇𝑐𝑜𝑙𝑑 =
𝐻𝑐𝑜𝑙𝑑 ∗ 𝑟𝑎ℎ−𝑐𝑜𝑙𝑑 𝜌𝑎𝑖𝑟 ∗ 𝑐𝑃
(50)
𝐻ℎ𝑜𝑡 ∗ 𝑟𝑎ℎ−ℎ𝑜𝑡 𝜌𝑎𝑖𝑟 ∗ 𝑐𝑃
(51)
𝑑𝑇ℎ𝑜𝑡 =
7. Plot a linear line between surface temperature and dT as shown (Figure
dT (k)
37).
d T a
2
1 dT 1
0 b
27 3
Ts1
Ts2
surface temperature (k)
Figure 37 Relationship between dT and Surface Temperature
From the line find the linear equation and the coefficients “a” and b” (equations (52&53)) to be used for computing dT for the image.
Chapter (3)
73 𝑎=
Methodology
𝑑𝑇ℎ𝑜𝑡 − 𝑑𝑇𝑐𝑜𝑙𝑑 𝑇𝑠 ℎ𝑜𝑡 − 𝑇𝑠 𝑐𝑜𝑙𝑑
𝑏 = 𝑑𝑇𝑐𝑜𝑙𝑑 − 𝑎 ∗ 𝑇𝑠 𝑐𝑜𝑙𝑑
(52) (53)
The calculations of these equations were done using excel spreadsheet as shown in Figure 38
Figure 38 coefficients “a” and b” by excel spreadsheet
8. Compute dT for the image using ERDAS Spatial Modeler (Figure 39)
Figure 39dT Calculation Model
Chapter (3)
74
Methodology
9. Compute H using ERDAS Spatial Modeler (Figure 40) using the equation 𝐻 =
𝜌𝑎𝑖𝑟 ∗ 𝐶𝑝 ∗ 𝑑𝑇 𝑟𝑎ℎ
(17)
From previous steps, all input files are computed. The computed H for the whole image is the output file
Figure 40 H computation Model
This is the first estimation of H assuming neutral conditions. Some stability correction should be applied inorder to account for buoyancy effects that generated by surface heating. This process is based on the Monin - Obukhov (MO) theory in an iterative process (Figure 34) through several steps: a. The Monin - Obukhov length (L) is used to define the stability conditions of the atmosphere in the iterative process. It is a
Chapter (3)
Methodology
75
function of the heat and momentum fluxes and is computed as follows: − 𝜌 𝐶𝑃 𝑢∗3 𝑇𝑠 𝐿= 𝑘𝑔𝐻
(54)
ERDAS Spatial Modeler is used to develop a to compute L Values
of the integrated stability corrections for momentum and heat transport 𝜓𝑚 𝑎𝑛𝑑 𝜓ℎ are computed using formulations by Paulson 1970 and Webb 1970, depending on the sign of L. When L0, the boundary layer is stable model as shown in Figure 41. For L0 stable condition. 𝜓𝑚(200𝑚) = −5 ( )
2 𝐿
(61)
2 𝜓ℎ(2𝑚) = −5 ( ) 𝐿
(62)
𝜓ℎ(0.1𝑚) = −5 (
0.1 ) 𝐿
(63)
Equation (61) uses a value of 2 m rather than 200 m for z because it is assumed that under stable conditions, the height of the stable, inertial boundary layer is on the order of only a few meters. Using a larger value than 2 m for z can cause numerical instability in the model. When L=0, neutral conditions: The stability values are set to 0. 𝜓ℎ = 0 and 𝜓𝑚 = 0
Figure 41 Stability Correction Model
Chapter (3)
77
Methodology
b. Calculate A corrected value of 𝑢∗ using the following equation: 𝑢∗ =
𝑢200 𝑘 200
ln (𝑧 ) − 𝜓𝑚(200𝑚)
(64)
𝑜𝑚
Where, 𝑢200
wind speed m/s at a blending height assumed to be 200 m
𝑘
von Karman’s constant (0.41)
zom
momentum roughness length (m)
𝜓𝑚(200𝑚)
is the stability correction for momentum transport at 200 meters (equation 55 or 61) ERDAS Spatial Modeler is used to develop a model for calculation (Figure 42)
Figure 42 Corrected friction velocity model
Chapter (3)
78
Methodology
c. Calculate a corrected value for aerodynamic resistance to heat transport (rah) using the following equation: 𝑧
𝑟𝑎ℎ =
ln (𝑧2 ) − 𝜓ℎ(𝑧2 ) + 𝜓ℎ(𝑧1 ) 1
(65)
𝑢∗ 𝑘
Where, Z2
2.0 meters
Z1
0.1 meters
𝜓ℎ(𝑧2 )
𝜓ℎ(𝑧1 )
The stability correction for heat transport at Z2 height (equations 56or62). The stability correction for heat transport at Z1 height (equations 57or63).
ERDAS Spatial Modeler is used to develop a model for calculation (Figure 43)
Chapter (3)
79
Methodology
Figure 43 Corrected rah model d. Compute dT for the image using the model maker tool (Figure 39) e. Compute H using the model maker tool (Figure 40)
This is the second estimation of H. repeat the stability correction steps four to five times until rah and dT stabilizes for hot pixel 3.6.4 Latent Heat Flux (λET), Instantaneous ET (ETinst), and Reference ET Fraction (ETrF). All the components of the energy balance equation are now computed. λET, ETinst and ETrF can be calculated to get an ET map for the target area. Compute λET using the equation λET = Rn– G – H
(18)
Chapter (3)
Methodology
80
Where, λET
Is an instantaneous value for the time of the satellite overpass (W/m2)
An instantaneous value of ET in equivalent evaporation depth is computed as follows:
𝐸𝑇𝐼𝑁𝑆𝑇 = 3600 ∗ 1000 ∗
𝝀𝐸𝑇 𝝀 ∗ 𝜌𝑤
(66)
Where, 𝐸𝑇𝐼𝑁𝑆𝑇
The instantaneous ET (mm/hr)
3600
The time conversion from seconds to hours
1000
The conversion from meters to millimeters
𝝀
The latent heat of vaporization or the heat absorbed when a kilogram of water evaporates (~2257000 J/kg).(Woodward et al. 2011)alternatively, can be calculated as 𝜆 = [2.501 − 0.00236(𝑇𝑠 − 273.15)] ∗ 106 (Allen et al. 2011)
𝜌𝑤
The density of water (~1000 kg/m3)
Substituting the values of 𝜌𝑤 and 𝝀 in equation (66) yield the following equation: 𝑚𝑚 𝑤 𝐸𝑇𝐼𝑁𝑆𝑇 ( ) = 1.595 ∗ 10−3 ∗ 𝝀𝐸𝑇 ( 2 ) ℎ𝑟 𝑚
(67)
The Reference ET Fraction (ETrF) is defined as the ratio of the computed instantaneous ET (ETinst) for each pixel to the reference ET (ETr) calculated from weather data using the following equation:
Chapter (3)
81 𝐸𝑇𝑟𝐹 =
Methodology
𝐸𝑇𝐼𝑁𝑆𝑇 𝐸𝑇𝑟
(68)
Where, 𝐸𝑇𝑟
The reference ET at the time of the image from the REF-ET software (mm/hr).
𝐸𝑇𝑟𝐹
The Reference ET Fraction
3.6.4.1 24-Hour Evapotranspiration (ET24) Daily values of ET (ET24) are often more useful than instantaneous ET. SEBAL computes the ET24 by assuming that the instantaneous ETrF computed in equation (68) is the same as the 24-hour average. Finally, the ET24 (mm/day) can be calculated as: 𝐸𝑇24 = 𝐸𝑇𝑟𝐹 ∗ 𝐸𝑇𝑟_24
(69)
Where, 𝐸𝑇𝑟_24
The cumulative 24-hour ETr for the day of the image.
ERDAS Spatial Modeler is used to develop a model for the calculation (Figure 44)
Figure 44 ET24 Model Calculator
Chapter (3)
Methodology
82
3.6.4.2 Seasonal Evapotranspiration (ET seasonal) A seasonal evapotranspiration map that covers an entire growing season is often valuable, this can be derived from the 24-hour evapotranspiration data by extrapolating the ET 24 proportionally to the reference evapotranspiration (ETr). Assume that the ET for the entire area of interest changes in proportion to the change in the ETr at the weather station. ETr was computed using REFET software for the target location.Moreover, therefore, does not represent the actual condition at each pixel. This does not matter, however, since ETr is used only as an index of the relative change in weather, and therefore ET, for the image area. Assume also that the ETrF computed for the time of the image is constant for the entire period represented by the image. The following steps show the process for computing seasonal ET: 1. Decide the length of the season for which ET is desired. 2. Determine the period represented by each satellite image within the chosen season. 3. Compute the cumulative ETr for the period represented by the image, this is simply the sum of daily ETr values over the period. These 24-hour values can be computed using the REF-ET software described before. The same ETr method must be applied through the SEBAL process. 4. Compute the cumulative ET for each period as follows: 𝑛
𝐸𝑇𝑝𝑒𝑟𝑖𝑜𝑑 = 𝐸𝑇𝑟𝐹𝑝𝑒𝑟𝑖𝑜𝑑 ∗ ∑ 𝐸𝑇𝑟_24 1
Where, 𝐸𝑇𝑟𝐹𝑝𝑒𝑟𝑖𝑜𝑑 𝐸𝑇𝑟_24 n
The representative ETrF for the period. The daily ETr The number of days in the period
(70)
Chapter (3)
Methodology
83
Table 14 Daily ETr values over the period of image
n
Date
ETr at the time of image mm/hr
𝑛
𝐸𝑇𝑟_24
∑ 𝐸𝑇𝑟_24
mm/d
mm/ (16 d)
1
1
17/05/2014
11.63
2
18/05/2014
11.04
3
19/05/2014
11.16
4
20/05/2014
13.25
5
21/05/2014
12.72
6
22/05/2014
9.68
7
23/05/2014
10.06
8
24/05/2014
10.04
9
25/05/2014
10
26/05/2014
11.36
11
27/05/2014
16.77
12
28/05/2014
10.51
13
29/05/2014
11.17
14
30/05/2014
13
15
31/05/2014
10.98
16
01/06/2014
10.37
17
02/06/2014
10.52
18
03/06/2014
16.53
19
04/06/2014
21.82
20
05/06/2014
11.85
21
06/06/2014
10.53
22
07/06/2014
10.89
23
08/06/2014
10.11
24
09/06/2014
10.86
0.54
11.25
184.99
Chapter (3)
n
Methodology
84
Date
ETr at the time of image 0.6
𝑛
𝐸𝑇𝑟_24
1
25
10/06/2014
26
11/06/2014
12.32
27
12/06/2014
11.74
28
13/06/2014
10.38
29
14/06/2014
10.51
30
15/06/2014
12.25
31
16/06/2014
15.21
32
17/06/2014
14.18
33
18/06/2014
12.45
34
19/06/2014
12.82
35
20/06/2014
12.82
36
21/06/2014
11.12
37
22/06/2014
11.77
38
23/06/2014
11.86
39
24/06/2014
11.54
40
25/06/2014
11.73
41
26/06/2014
42
27/06/2014
14.69
43
28/06/2014
16.14
44
29/06/2014
12.94
45
30/06/2014
11.06
46
01/07/2014
11.84
47
02/07/2014
13.02
48
03/07/2014
11.51
49
04/07/2014
11.92
50
05/07/2014
12.21
51
06/07/2014
11.4
0.56
∑ 𝐸𝑇𝑟_24
11.54
12.68
201.24
199.99
Chapter (3)
n
Methodology
85
Date
ETr at the time of image
𝑛
𝐸𝑇𝑟_24
1
52
07/07/2014
11.62
53
08/07/2014
11.28
54
09/07/2014
11.06
55
10/07/2014
11.7
56
11/07/2014
11.89
57
12/07/2014
58
13/07/2014
11.51
59
14/07/2014
11.07
60
15/07/2014
10.97
61
16/07/2014
10.97
62
17/07/2014
10.99
63
18/07/2014
10.91
64
19/07/2014
11.21
65
20/07/2014
11.28
66
21/07/2014
10.77
67
22/07/2014
10.38
68
23/07/2014
11.58
69
24/07/2014
11.97
70
25/07/2014
10.48
71
26/07/2014
10.55
72
27/07/2014
10.77
73
28/07/2014
74
29/07/2014
11.71
75
30/07/2014
11.45
76
31/07/2014
10.8
77
01/08/2014
10.76
0.75
0.56
∑ 𝐸𝑇𝑟_24
13.28
11.04
183.99
175.52
Chapter (3)
n
Methodology
86
Date
ETr at the time of image
𝑛
𝐸𝑇𝑟_24
1
78
02/08/2014
11.25
79
03/08/2014
10.38
80
04/08/2014
10.35
81
05/08/2014
12.08
82
06/08/2014
11.31
83
07/08/2014
12.49
84
08/08/2014
10.93
85
09/08/2014
10.14
86
10/08/2014
10.09
87
11/08/2014
9.6
88
12/08/2014
10.24
89
13/08/2014
90
14/08/2014
11.08
91
15/08/2014
10.78
92
16/08/2014
10.12
93
17/08/2014
10.47
94
18/08/2014
9.95
95
19/08/2014
10.06
96
20/08/2014
10.64
97
21/08/2014
10.47
98
22/08/2014
10.15
99
23/08/2014
10.42
100
24/08/2014
10.06
101
25/08/2014
9.98
102
26/08/2014
10.47
103
27/08/2014
10.3
0.5
∑ 𝐸𝑇𝑟_24
10.48
170.46
Chapter (3)
n
Methodology
87
Date
ETr at the time of image
𝑛
𝐸𝑇𝑟_24
∑ 𝐸𝑇𝑟_24 1
104
28/08/2014
9.33
105
29/08/2014
106
30/08/2014
9.87
107
31/08/2014
9.52
108
01/09/2014
9.35
109
02/09/2014
9.02
110
03/09/2014
8.89
111
04/09/2014
7.94
112
05/09/2014
9.52
113
06/09/2014
9
114
07/09/2014
8.95
115
08/09/2014
9.28
116
09/09/2014
8.53
117
10/09/2014
8.74
118
11/09/2014
8.87
119
12/09/2014
9.52
120
13/09/2014
9.4
121
14/09/2014
122
15/09/2014
8.61
123
16/09/2014
8.2
124
17/09/2014
8.86
125
18/09/2014
8.5
126
19/09/2014
8.96
127
20/09/2014
8.37
128
21/09/2014
8.29
129
22/09/2014
7.85
0.47
0.42
9.38
8.59
154.67
140.67
Chapter (3)
n
Methodology
88
Date
ETr at the time of image
𝑛
𝐸𝑇𝑟_24
1
130
23/09/2014
8.95
131
24/09/2014
9.69
132
25/09/2014
8.23
133
26/09/2014
8.49
134
27/09/2014
9.04
135
28/09/2014
8.68
136
29/09/2014
8.11
137
30/09/2014
138
01/10/2014
8.09
139
02/10/2014
6.9
140
03/10/2014
7.31
141
04/10/2014
7.22
142
05/10/2014
7.83
143
06/10/2014
8.16
144
07/10/2014
8.45
0.36
∑ 𝐸𝑇𝑟_24
7.2
130.2
5. Compute the seasonal ET by summing all of the ETperiod values for the length of the season. ERDAS Spatial Modeler is used to develop a model for the calculation of the seasonal ET as shown in Figure 45
Chapter (3)
89
Figure 45 Seasonal ET Model Calculator
Methodology
Chapter (4) Results and Discussion 4.1 Introduction The results obtained in this study are categorized into five parts as follows. The first part is the introduction. The second part is related to the derivation of parameters in SEBAL model. The third part includes the calculation of seasonal evapotranspiration The fourth part includes model validation which was done by comparing with measured pan evaporation ETpan with estimated ET using Landsat 8 imageries. Then the fifth includes the calculation of the total amount of water lost in the form of evapotranspiration. The sixth part discusses the assessment of irrigation water performance by calculating the irrigation efficiency Ei and the distribution efficiency Ed. 4.2
The Net Surface Radiation Flux (Rn)
4.2.1 Estimation of Surface Albedo (α) The estimated Albedo by the method described in section 3.6.1.1 has agreed well with the general values given in Table 8. Figure 46 shows estimated albedo in agricultural areas for the image acquired in 28/7/2014 as an example. 4.2.2 Estimation of Vegetation Indices Normalized Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI). Was calculated using the ERDAS spatial modeler. Figure 47shows [NDVI, SAVI, and LAI] for the image acquired in 28/7/2014 as
an example
Chapter (4)
91
Results & Discussion
Figure 46 Estimated Albedo values, by Landsat 8 image(28/7/2014)
Chapter (4)
92
Results & Discussion
Figure 47 NDVI, SAVI, and LAI for the image acquired in 28/7/2014
Chapter (4)
Results & Discussion
93
4.2.3 Estimation of Surface Temperature Ts Surface temperature Ts was calculated by applying a structured mathematical algorithm viz., Split-Window (SW) algorithm. It uses brightness temperature of two bands of TIR, mean and difference in land surface emissivity for estimating Ts of an area. Figure 48 shows difference LSE layer between Band 10 and 11, and Figure 49 shows mean of LSE layer between band 10 and 11for the image acquired in 28/7/2014 as an example.
Figure 48 Difference LSE layer between Band 10 and 11
Figure 49 Mean of LSE layer between band 10 and 11
Figure 50 represents the final Ts layer of "Handaset Tanta" on 28/7/2014.
Chapter (4)
94
Results & Discussion
Figure 50 Surface Temperature Layerof "Handaset Tanta" on 28/7/2014.
Chapter (4)
95
Results & Discussion
4.2.4 The Outgoing Longwave Radiation (RL↑) Now, all the required parameters are ready to be used to calculate the outgoing longwave radiation (RL↑). It is calculated using equation 38 which was implemented in ERDAS IMAGINE Spatial Modeler as shown in (Figure 24). The outgoing longwave radiation (RL↑)for the image acquired in 28/7/2014 is shown in Figure 51.
Figure 51 the outgoing longwave radiation (RL↑)
Chapter (4)
Results & Discussion
96
4.2.5 The Incoming Long Wave Radiation (RL↓) The first step to calculate the incoming long wave radiation is to choose the hot and cold pixels in the image. Figure 52 shows the hot and cold pixels in the Landsat 8 image acquired in 28/7/2014. The second step is to calculate the atmospheric emissivityε𝑎 using equation 40. The incoming longwave radiation (RL↓) is now computed using equation 39these calculations were done using excel spreadsheet as shown in Figure 53 The net surface radiation flux (Rn) is now computed using Equation (16). Figure 54 shows net surface radiation flux for Landsat 8 image acquired in
28/7/2014
Figure 52"cold/hot pixel” estimation procedure in SEBAL for image (28/7/2014) Table 15 Hot and Cold pixels characteristics for Landsat 8 image acquired in 28/7/2014 x
y
Ts
m
m
k
hot
321601.308
3411150.162
312.07732
cold
311944.973
3426087.495
303.752472
Chapter (4)
Results & Discussion
97
Table 16 Hot and Cold pixels characteristics for all Landsat 8 image Date 25/05/2014 10/06/2014 26/06/2014 12/07/2014 28/07/2014 13/08/2014 29/08/2014 14/09/2014 30/09/2014
Pixel
x
y
Ts
m
m
k
hot
305761.422
3425854.417
317.621
cold
322891.643
3412053.988
304.906
hot
308730.51
3419611.274
315.506
cold
322802.892
3413402.851
304.744
hot
298621.279
3428130.158
314.848
cold
324125.369
3405211.006
305.729
hot
295985.137
3398782.92
312.947
cold
294489.178
3416331.038
303.766
hot
321601.308
3411150.162
312.077
cold
311944.973
3426087.495
303.752
hot
322384.665
3413014.193
311.399
cold
313413.907
3422911.445
303.780
hot
306125.615
3412230.003
311.117
cold
312058.412
3425580.909
303.657
hot
310077.708
3429780.43
311.279
cold
309391.577
3424649.189
303.658
hot
310077.708
3429780.43
311.279
cold
309391.577
3424649.189
303.658
Figure 53Incoming long wave radiation calculations using excel spreadsheet for Landsat 8 image acquired in 28/7/2014
Chapter (4)
98
Results & Discussion
Figure 54 The net surface radiation flux for Landsat 8 image acquired in 28/7/2014
Chapter (4)
99
Results & Discussion
4.3 Soil Heat Flux (G) First the ratio 𝐺 ⁄𝑅𝑛 was calculated using equation (42).Then G was calculated by multiplying G/Rn by the value for Rn computed in equation (16) as shown in Figure 55 and Figure 56 .
Figure 55 G/Rn for Landsat 8 image acquired in 28/7/2014
Figure 56Soil Heat Flux G for Landsat 8 image acquired in 28/7/2014
4.4 Sensible Heat Flux (H) The first estimate of H, H(1st) for each pixel is calculated by Equation (17), using the first estimate for dT and rah. After the H (1st) calculation, second estimates for u* and rah are calculated with stability correction applied. The Monin-Obukov length parameter L is applied as the indicator of air stability. Figure 33 showing the flow chart of the steps used for computing sensible
heat flux (H) and their iterative process to make the atmospheric stability
Chapter (4)
100
Results & Discussion
corrections for momentum and heat transport. Figure 58 shows the Sensible Heat Flux (H) for Landsat 8 image acquired in 28/7/2014
Figure 57 Surface roughness zom for each pixel
Chapter (4)
101
Results & Discussion
Figure 58 Sensible Heat Flux (H)for Landsat 8 image acquired in 28/7/2014
4.5 Latent Heat Flux (λET), Instantaneous ET (ETinst), Reference ET Fraction (ETrF), and 24-Hour Evapotranspiration (ET24) Latent heat flux λET was calculated using Equation (18). Instantaneous ET (for satellite image time) was calculated using Equation (66) with ETrF calculated by Equation (68) the estimated instantaneous ET can be extrapolated to 24hour ET using Equations (69) as shown in Figure 59.
Chapter (4)
102
Results & Discussion
Figure 59 Latent heat flux λET, Instantaneous ET, Reference ET Fraction ETrF, and 24-Hour Evapotranspiration ET24for Landsat 8 image acquired in 28/7/2014
Chapter (4)
Results & Discussion
103
4.6 Seasonal Evapotranspiration (ET seasonal) A seasonal evapotranspiration map that covers an entire growing season is derived from the 24-hour evapotranspiration data by extrapolating the ET
24
proportionally to the reference evapotranspiration (ETr) for the nine images for the period under consideration (from 17/5/2014 to 7/10/2014) to obtain (ETperiod )ET for 16 days period represented by each the image 9
𝐸𝑇𝑆𝑒𝑎𝑠𝑜𝑛𝑎𝑙 = ∑ 𝐸𝑇𝑖 period
(71)
𝑖=1
Where 𝑖 is the number of the image. 31°0'0"E
31°4'0"E
Legend
30°51'0"N
30°51'0"N
30°54'0"N
30°54'0"N
30°57'0"N
¯
30°57'0"N
30°56'0"E
Seasonal-ET.tif mm 0 - 100 100 - 200 200 - 300 300 - 400
30°48'0"N
30°48'0"N
0
400 - 500 500 - 600
700 - 800 800 - 900 900 - 1,000 1,000 - 1,100
0
5
10 Kilometers
1,100 - 1,200
30°45'0"N
30°45'0"N
600 - 700
1,200 - 1,300 1,300 - 1,400 1,400 - 1,477
30°56'0"E
31°0'0"E
31°4'0"E
Figure 60 Spatial variation of seasonal evapotranspiration for "Handaset Tanta"- summer 2014
Chapter (4)
104
Results & Discussion
Figure 60 shows the optioned seasonal evapotranspiration map for the summer
season. Assuming that the ET for the entire area of interest changes in proportion to the change in the ETr calculated from the weather data. ETr was computed using REF-ET software for the target location. 4.7 Validation of SEBAL Model Actual evapotranspiration (ET) for summer 2014 was computed via SEBAL model using nine Landsat 8 images and routine meteorological data. Due to scarce direct fluxes measurements, the recorded pan evaporation was used to validate actual evapotranspiration calculated by SEBAL for each image (Sun et al. 2011). Pan evaporation (ETpan) is the amount of water evaporated during a period (mm/day) with an unlimited supply of water (potential evaporation) and can be calculated from direct observation of water loss from pan (Epan, mm/day) and the crop coefficient (𝑘𝑝 ). 𝐸𝑇𝑝𝑎𝑛 = 𝑘𝑝 ∗ 𝐸𝑝𝑎𝑛
(72)
Where, 𝑘𝑝 𝐸𝑝𝑎𝑛
pan evaporation in mm/day and represents the mean daily value of the period considered pan coefficient (0.85 under Egyptian conditions)(El
Afandi and Abdrabbo 2015) The pan evaporation (ETpan) was estimated by equation 72. Table 17 and Figure 61 show a comparison between the ET derived from Landsat 8 by SEBAL and
the ETpan calculation from pan evaporation at the meteorological station for each image. The correlation coefficient between the pan evaporation and ET derived from SEBAL equal to 0.8927. SEBAL overestimated with the mean deviation of 16.44% for the daily estimates and this was considered to be acceptable.
Chapter (4)
Results & Discussion
105
Table 17 Comparison of daily ETa (mm/d) estimated via SEBAL and daily ETpan calculated from pan evaporation Date 25/5 10/6 26/6 12/7 28/7 13/8 29/8 14/9 30/9
E pan
ET pan
ET (SEBAL)
3.6 4.7 8.3 12 11.2 8.8 7 8.9 5.88
mm/day 3.06 3.995 7.055 10.2 9.52 7.48 5.95 7.565 4.998
0.633445 3.624799 8.626986 11.340415 11.323862 10.130862 8.35605 8.878074 8.681702
correlation coefficient
0.8927
12
ET (mm/day)
10 8 6 ET (SEBAL)
4
ET pan
2
30/9
14/9
29/8
13/8
28/7
12/7
26/6
10/6
25/5
0
Date
Figure 61 Comparison of daily ETa (mm/d) estimated via SEBAL and daily ETpan calculated from pan evaporation.
Chapter (4)
Results & Discussion
106
4.8 Total Amount of Water Lost by Evapotranspiration The total volume of water lost from the agriculture area in the form of evapotranspiration for each image can be calculated using equation 73. 𝑛
𝑛
𝐸𝑇𝑣𝑜𝑙𝑢𝑚𝑒 = ∑(𝐸𝑇𝑖 ∗ 𝐴𝑖 ) = 𝑛 ∗ 𝐴𝑖 ∑ 𝑖=1
𝑖=1
𝐸𝑇𝑖 = 𝐴𝑡 ∗ ̅̅̅̅ 𝐸𝑇 𝑛
(73)
Where, 𝐸𝑇𝑣𝑜𝑙𝑢𝑚𝑒
The total volume of water lost in the form of evapotranspiration for each image (in cubic meter)
𝐸𝑇𝑖
Calculated evapotranspiration for each pixel (in agriculture land use only (in meter))
𝐴𝑖
Pixel area (30*30 = 900 square meter)
𝑛
Number of pixels (in agriculture land use only = 214977 pixels)
𝐴𝑡
Total agriculture area = 𝑛 ∗ 𝐴𝑖 = 214977*900 = 193479300 m2
̅̅̅̅ 𝐸𝑇
The average evapotranspiration for each image (in agriculture land use only (in meter))
The first step is to clip the ET raster by the agriculture land use polygon (Figure 62) using ArcMap 10.3 spatial tool (clip) [Arc Toolbox / Data Management tools / Raster / Raster processing/clip] as shown in Figure 63 Figure 64, Figure 65, and
Figure 66 Represent ET for 16 days period
represented by each the image. Figure 67 Represents the cumulative ET from the summer season [from 17/5/2014] to [7/10/2014].
Results & Discussion
107 30°56'0"E
31°0'0"E
31°4'0"E
30°45'0"N
30°45'0"N
30°48'0"N
30°48'0"N
30°51'0"N
30°51'0"N
30°54'0"N
30°54'0"N
30°57'0"N
¯
30°57'0"N
Chapter (4)
0
5
10 Kilometers
Legend agriculture
30°56'0"E
31°0'0"E
31°4'0"E
Figure 62 The net cultivated areas in "Handaset Tanta
Figure 63 ArcMap 10.3 spatial tool (clip)
Chapter (4)
a)
Results & Discussion
108
b)
c) Figure 64 a) ET for 16 days period represented by the image in (25/5/2014), [from 17/5/2014 to 1/6/2014], b) ET for 16 days period represented by the image in (10/6/2014), [from 2/6/2014 to 17/6/2014] c) ET for 16 days period represented by the image in (26/6/2014), [from 18/6/2014 to 3/7/2014]
Chapter (4)
Results & Discussion
109
a
b
)
)
c) Figure 65 a) ET for 16 days period represented by the image in (12/7/2014), [from 4/7/2014 to 19/7/2014], b) ET for 16 days period represented by the image in (28/7/2014), [from 20/7/2014 to 4/8/2014] c) ET for 16 days period represented by the image in (13/8/2014), [from 5/8/2014 to 20/8/2014]
Chapter (4)
a)
Results & Discussion
110
b)
c) Figure 66 a) ET for 16 days period represented by the image in (29/8/2014), [from 21/8/2014 to 5/9/2014], b) ET for 16 days period represented by the image in (14/9/2014), [from 6/9/2014 to 21/9/2014] c) ET for 16 days period represented by the image in (30/9/2014), [from 22/9/2014 to 7/10/2014]
Chapter (4)
111
Results & Discussion
Figure 67 Cumulative ET for the summer season [from 17/5/2014] to [7/10/2014]
Chapter (4)
Results & Discussion
112
The second step is to acquire the average value of evapotranspiration for each ̅̅̅̅) using ArcMap 10.3 [Layer Properties window / Source tap / image (𝐸𝑇 statistics] as shown in Figure 68
Figure 68 Layer Properties window (ArcMap 10.3)
The following table summarises the average value of evapotranspiration for each image for the day of the image and for the 16-day parade represented by
Actual evapotranspiration (ET) for summer 2014 110.584 106.22 102.137
600
200
651.7717 73.041 67.571 72.965
63.898
400
150
44.789
100 50
10.572
ET 16 day
Cumulative ET
30/9
14/9
29/8
13/8
28/7
12/7
26/6
0
10/6
0
Date
Figure 69 Actual evapotranspiration (ET) for summer 2014
ET 16 day (mm)
800
25/5
Cumulative ET (mm)
each image.(see Table 18 and Figure 69)
Chapter (4)
Results & Discussion
113
Table 18 The average value of evapotranspiration for each image
Date of the image
The ET average for the day of the image mm/day
The ET average for the 16 days peruse represented by each image mm/16day
25/5/2014 10/6/2014 26/6/2014 12/7/2014 28/7/2014 13/8/2014 29/8/2014 14/9/2014 30/9/2014
0.623099 2.542334 4.036086 7.922152 6.655354 6.249714 4.411967 4.103308 4.103308
10.57103 44.78834 63.89746 110.5832 106.2191 102.1366 73.04085 67.57037 72.96466
Cumulative ET mm 10.57103481 55.35937424 119.2568361 229.8400678 336.0591786 438.1958235 511.2366728 578.8070408 651.7717021
The next step is to calculate the total volume of water lost in the form of evapotranspiration for each image using equation 73 and to calculate the cumulative evapotranspiration for the period under consideration (from
21.396 20.552 19.762
100
126.1043327
20
14.132 13.074 14.118
12.363
25
8.666
15
10
50
5
2.046 30/9
14/9
Date
29/8
28/7
Series3
13/8
12/7
Series1
26/6
0
10/6
0
Figure 70 The cumulative evapotranspiration for the period under consideration (from 17/5/2014 to 7/10/2014)
ET 16 day (106 m3)
Volume of water lost in the form of evapotranspiration
150
25/5
Cumulative ET (106 m3)
17/5/2014 to 7/10/2014) as illustrated in Table 19 and Figure 70
Chapter (4)
Results & Discussion
114
Table 19 The cumulative evapotranspiration for the period under consideration (from 17/5/2014 to 7/10/2014) The ET average for the 16 days peruse represented by each image
𝐸𝑇𝑣𝑜𝑙𝑢𝑚𝑒 for each image
Cumulative 𝐸𝑇𝑣𝑜𝑙𝑢𝑚𝑒
10-3 m /16 day
106 m3
106 m3
25/5/2014
10.57103
2.0453
2.0453
10/6/2014
44.78834
8.6656
10.7109
26/6/2014
63.89746
12.3628
23.0737
12/7/2014
110.5832
21.3956
44.4693
28/7/2014
106.2191
20.5512
65.0205
13/8/2014
102.1366
19.7613
84.7818
29/8/2014
73.04085
14.1319
98.9137
14/9/2014
67.57037
13.0735
111.9872
30/9/2014
72.96466
14.1172
126.1043
Date of the image
Figure 70 shows the total volume of water lost in the form of evapotranspiration
for each image and the cumulative volume of water lost in the form of evapotranspiration for the period under consideration (from 17/5/2014 to 7/10/2014). The distribution of ET values is presented in Table 20 and Figure 71. The ET values between 500 and 1200 mm made up around 67.88% of the study area. The histograms in Figure 71 associated the higher ET to irrigated crop grown in the study area, while a low ET was observed from bare soil land.
Chapter (4)
Results & Discussion
115
Table 20 The distribution of seasonal evapotranspiration (ET) of the study area. Area (km2) 7.6446 11.1492 9.5418 11.5083 14.9553 16.9632 15.4647 13.4712 13.3875 15.0849 17.7858 20.5983 17.6211 6.408 0.4599 0.0135
ET 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500
Area (%) 3.98 5.81 4.97 5.99 7.79 8.83 8.05 7.01 6.97 7.85 9.26 10.73 9.17 3.34 0.24 0.01
10.73
Area (%)
12.00 10.00
7.79
8.00 6.00
5.81 3.98
4.97
5.99
8.83
8.05
9.26 7.01 6.97
9.17
7.85
3.34
4.00 2.00
0.24 0.01
0.00
ET (mm) Figure 71 Histogram showing the distribution of seasonal evapotranspiration (ET) of the study area.
4.9 Irrigation Water Supply Water supply in the command area is classified into two categories; the first one is the proposed water supply recommended by Ministry of Water Resources and Irrigation, Integrated Irrigation of El-Gharbia Administration
Chapter (4)
116
Results & Discussion
(MWRI-IIGA). It is proposed to meet the needs of the water requirements of the theoretical crop pattern which is proposed by the Agriculture Ministry. The proposed water supply is not applied as a result of ignoring the theoretical crop pattern by farmers. The second type is the actual water supply which is applied to the canals to satisfy the actual crop pattern requirements. In actual water supplies; the water velocity was measured by the current meter at the head regulator of Qanat Tanta Al-Melahia after divaricated from Bahr Shbin canal. Table 21 shows the actual water supply at the head of Qanat Tanta Al-Melahia and Qanat Tanta Al-Melahia at the head of Handaset Tanta. These discharges are factorized to the total discharge of each canal. Table 21 Actual water supply for the Qanat Tanta Al-Melahia before the study area in the year 2014 Month Discharge
At the head of Qanat
Qanat Tanta Al-Melahia at
(Mm³)
Tanta Melahia
the head of Handaset Tanta
January
61.415
20.404
February
79.170
26.303
march
86.679
28.899
April
86.984
28.798
may
115.011
38.211
June
157.500
52.327
July
155.903
51.796
august
153.684
51.059
September
108.059
35.901
October
87.268
28.993
November
80.627
26.787
December
78.925
26.222
Source: Ministry of Water Resources and Irrigation - Integrated Irrigation of El-Gharbia Administration (MWRI-IIGA).
Chapter (4)
Results & Discussion
117
The maximum actual water supply discharges were in June, July, and August. The summation of the actual water supply discharges for the period under consideration (from 17/5/2014 to 7/10/2014) is calculated in Table 22. Table 22 Actual water supply for the Qanat Tanta Al-Melahia before the study area for the period under consideration Month Discharge (Mm³) May (from 17/5 to
Qanat Tanta Al-Melahia at the head of Handaset Tanta 38.211 *
31/5)
15 31
Qanat Tanta Al-Melahia at the head of Handaset Tanta
= 18.489
(cumulative)
18.489
June
52.327
70.816
July
51.796
122.613
august
51.059
173.673
September
35.901
209.574
October (from 1/10 to 7/10)
Total
28.993 *
7 31
= 6.547
216.121 216.121 Mm³
4.10 Irrigation Water Performance Irrigation efficiency is a general term that indicates how well a water resource is used to produce a crop (El-Agha 2010). The basic concept of irrigation efficiency was set by (Israelsen 1950) as the ratio of the irrigation water consumed by crops to the irrigation water delivered from a surface or ground water source to the canals or farm head gates. The irrigation water performance indicators for the study area are developed to assess the use of fresh water resources for irrigation. In order to assess the performance of irrigation in the study area, the required data are collected and prepared. Data related to the
Chapter (4)
118
Results & Discussion
average monthly discharges at the head of Qanat-Tanta Al-Melahia at the head of Handaset Tanta were measured by the Water Resources and Irrigation Ministry- Integrated Irrigation of El-Gharbia Administration (MWRI-IIGA). The water consumption by crops is calculated in terms of actual evapotranspiration using Surface Energy Balance Algorithm for Land (SEBAL) Model (Table 19). The additional water demands such as drinking water and industrial water are given by (MWRI-IIGA). Table 23 shows water budget and water demands for the study. Table 23 water budget for the study area Water sector
Water amount (Mm³)
water supply to the study area
216.121
Crop water consumption
Water amount (%) 100%
Source: (MWRI-IIGA) 126.1043 58.35% Calculated by SEAL 0.219691876
precipitation amount
water for the industry sector
2.5
water required for drinking networks water losses through the conveyance system
0.102%
1.16% Source: (MWRI-IIGA) 9.988 4.62% Source: (MWRI-IIGA) 77.309 35.87% Source: ayat
Chapter (4)
119
Results & Discussion
4.10.1 Irrigation Efficiency (Ei ) Irrigation efficiency is the ratio of irrigation water consumed by the crop of an irrigated area to the water delivered from the source, (Suat Irmak 2011) 𝐸𝑖 =
𝑊𝑐 ∗ 100 𝑊𝑟
(74)
Where,
Ei
Irrigation efficiency (%) Irrigation water consumed by crop during its growth in an
𝑊𝑐
irrigation project
𝑊𝑟
Water delivered from canals during the growth period of crop
Table 24 shows the required calculations for the irrigation efficiency, it is clear that the irrigation efficiency is about 61.17 % as the irrigation efficiency depends on water distribution characteristics, weather and soil condition, and crop water uses(C. Brouwer 1989) Table 24 Irrigation efficiency for the study area parameters
In formula form
value
Wc
ETcrop precipitation
125.88 Mm³
Wr Ei
Water supply-( drinking water plants+ water used in industry) ( Wc / Wr )%
206.133 Mm³ 61.07%
4.10.2 Distribution Efficiency (Ed) Water losses occur from the point of diversion till it reaches the farmer's fields, so the water conveyance efficiency can be defined as the ratio of water delivered to fields at the outlet head to that is diverted into irrigation system from head works, (Bos 1979)
Chapter (4)
120
𝐸𝑑 =
Results & Discussion
𝑊𝑓 ∗ 100 𝑊𝑡
(75)
Where,
Ei
Distribution efficiency (%).
𝑊𝑓
Water introduced into the conveyance system from the point of diversion
𝑊𝑡
Water delivered to the farm by conveyance system
In Egypt, the irrigation water consumes about 85% of the total national water budget. In the study area, the water is not used as efficiently as it could be; about 59.61% of the water delivered to the farm gate is lost in the distribution channels, as shown in Table 25. The conveyance losses in the study area are estimated by assuming that all canals in the irrigation system, main, secondary and distributaries canals, are using as distribution canals. The conveyance losses in the open channel in the study area can be reduced by ditch lining or changing with closed pipeline. Table 25 Distribution efficiency for the study area parameters
Wf Wt Ed
In formula form Water supply-( conveyance losses+ drinking water plants+ water used in industry) The water supply to the study area
128.824 Mm³,
Wf / W
59.61%.
t
value
216.121 Mm³
Chapter (5) Conclusions and Future Work 6.1 Introduction A total of nine clouds free Landsat images during a summer season in 2014 were processed for the study area using a satellite remote sensing based SEBAL model. Other surface energy fluxes such as net radiation, sensible heat, soil heat flux, and surface albedo were estimated. Nine evapotranspiration ET maps generated by SEBAL (from May through October 2014) showed a reasonable progression of ET with time during the growing season in 2014 as the surface conditions continuously changed SEBAL uses digital image data collected by Landsat and other remote sensing satellites that record thermal infrared, visible and near-infrared radiation. ET is computed on a pixel-by-pixel basis for the instantaneous time of the satellite image. The process is based on a complete energy balance for each pixel, where ET is predicted from the residual amount of energy remaining from the classical energy balance, where ET = net radiation – heat to the soil – heat to the air 6.2 Conclusions This study is applied to a pilot area in the middle of Nile delta called “Handaset Tanta". This study aims to evaluate the actual evapotranspiration in "Handaset Tanta" through a combination of remote sensing and meteorological observations. In this study in order to assess the performance of the irrigation system in the study area to evaluate the losses in the irrigation system at regonal level. Analysis resulted in the following main conclusions:
Chapter (5)
122
Conclusion & Future work
1. In this study, the application of the SEBAL technique was conducted to map spatial variation in actual evapotranspiration (ETa) of the Nile Delta, using Landsat8 TM images of [17/5/2014-7/10/2014]. And the prediction of ETa was compared with the recorded pan evaporation. The results calculated by SEBAL were comparable with the values derived from pan observations. This implies the considerable practicability to an estimation of the spatial actual evaporation via SEBAL using satellite imagery with visible, near-infrared and thermalinfrared bands such as the Landsat TM remote sensing images and routine meteorological measurements of wind speed, solar radiation, humidity, and air temperature. 2. The spatial distribution of the ET was analyzed in combination with the land cover map. The estimated seasonal ET ranged from 0 for bare soil and town constructed land to 1477 mm for the high vegetated areas with the average ET value of about 651.77 mm for the whole area. The variation of estimated ET over different kinds of land use was accorded with the evapotranspiration theory, which hints the application of the SEBAL approach with some detailed field information such as land use type. 3. The correlation coefficient between the pan evaporation and ET derived from SEBAL equal to 0.8927. SEBAL overestimated with the mean deviation of 16.44% for the daily estimates and this was considered to be reasonable. 4. The major advantages of SEBAL for the estimation of land surface fluxes from thermal remote sensing data are (1) Minimum use of auxiliary ground-based data;
Chapter (5)
123
Conclusion & Future work
(2) Automatic internal correction, which prevents strict correction of atmospheric effects on surface temperature; and (3) Internal calibration, which is done within each analyzed image 5. Besides its several advantages, it has several drawbacks as well. Major disadvantages of this method are that (1) Subjective specifications of representative hot/dry and wet/cool pixels within the image are required (Long and Singh 2012, Long and Singh 2013) to determine model parameters a and b. The resulting H flux and ET estimates from SEBAL can vary with differing extreme pixels selected by the operator, domain size, and spatial resolution of satellite sensors (Long et al. 2011); and (2) Estimated H is greatly affected by the errors in surface-air temperature differences or surface temperatures measurements. 6. The water budget in the study area indicates that the total water supply in the summer season 2014 is about 216.1211Mm³, the crop consumption (calculated by SEBAL) is about 126.1043 Mm³ (58.35%) and the losses through the conveyance system is about 77.309 Mm³ (35.87%) 7. The irrigation efficiency for the study area is about 61.07% where the water that entered the fields is about 206.133 Mm³ and the water consumption by crops is about 126.1043 Mm³, this indicates that about 80 Mm³ of the total water supply is losses in surface runoff and/or percolation. 8. The distribution efficiency for the study area is about 59.61%, as the conveyance water losses is about 35.87% of the total water supplies in the study area
Chapter (5)
124
Conclusion & Future work
6.3 Recommendations for Future Work The future study may be including 1- ET estimation in agriculture using METRIC. 2- Estimation of ET using SEBAL in a regional scale 3- The use of numerical models to predict the spatial variation of ET in the area. 4- In order to establish reliable ratios of actual to potential ET, long term ET study of wetland vegetation is recommended. These parameters should be developed locally and are important for the accurate application of remote sensing methods for the determination of regional ET estimation in Egypt. 5- To Suggest a group of corrective actions to be applied by the government and highlighting their effect of them on the water saving and the agriculture revenue. 6- To use supervised calcification technique to obtain the crop pattern in the study area. Then, determine the evapotranspiration and irrigation requirement for the study area.
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Appendix A Importing Landsat 8 Data into ERDAS IMAGINE 2014 (into .img format) Images acquired from (https://earthexplorer.usgs.gov/) were downloaded as zipped TAR files (.tar.gz). They were unzipped as TIFF files (.tif), which IMAGINE can read. They must be unzipped first into the desired location before importing them into IMAGINE. • •
Start a new session of ERDAS IMAGINE 2014. On the Manage Data tab, click the Import Data button. (see Figure 72)
Figure 72 Importing Landsat 8 Data into ERDAS IMAGINE 2014 - Import Data.
•
In the Import window that appears, select Landsat-7 or Landsat-8 from USGS from the Format drop-down list as shown in Figure 73.
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Figure 73 Importing Landsat 8 Data into ERDAS IMAGINE 2014 -Select format.
•
For the Input File, navigate to the directory where the Landsat imagery is stored and select the (.tar.gz) file (see Figure 74)
Figure 74 Importing Landsat 8 Data into ERDAS IMAGINE 2014 - Select the Input File.
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•
Change the Output File name and directory as desired and click OK.
•
When you click OK, you are met with an import wizard (Figure 75) that stacks the multispectral bands for you and creates .img files for Thermal, Pan, QA, and Cirrus. Select Import Multispectral and Thermal Data and click ok.
Figure 75 Importing Landsat 8 Data into ERDAS IMAGINE 2014 - Import Multispectral and Thermal Data
Appendix B Weather Data Preparation and Calculation of Reference ETr 1. Prepare an excel sheet containing the collected weather data as shown in Figure 76
Figure 76 Weather Data in an Excel spreadsheet
2. Give abbreviations to the weather parameter header line. 3. Save the excel spreadsheet in a CSV (Comma delimited) (*.csv) format (Figure 77).
Figure 77 Saving the excel spreadsheet in a CSV (Comma delimited) format.
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A text file is ready to be used in the REF-ET software to calculate the hourly ETr for 17 may 2014 to 7 October 2014. Now it is ready to use the software to complete the ETr calculations: 1. Install the software on your device. 2. After installing, launch the software.
Figure 78 Starting window of REF-ET software 3. Click proceed to the window (Figure 78). 4. A new window is shown to choose the weather data text file (Figure 79)
Figure 79 REF-ET Data File Window
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Appendix B
5. Open or create new REF-ET definition file (Figure 80).
Figure 80 Open or create definition file window
6. A new window will be shown (Figure 81), the right side of the window shows the Parameter Identifier Data. Based on the sorting of weather parameters in the text file, double click on each parameter starting from left to right depending on your text file. 7. Note that every parameter you double click, it will be shown on the left-hand side of the window. 8. Click continue button at the right-hand side lower corner
Figure 81 Order of weather parameters window
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Appendix B
9. After that, a new window appears to enter information about the weather station and the used file (Figure 82). 10. Enter the required information about the weather station
Figure 82 Description of weather station and used file window
11. Click Continue to go to the next step. 12. A new window appears, letting you choose the required model or equation to estimate ETr (Figure 83).
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Figure 83 Output models and Reference equations window
13. After choosing the appropriate evapotranspiration model, click continue. 14. Then, you will get a new window asking for saving the definition file before proceeding to the calculation process. 15. After saving the definition file, the software will calculate ETr depending on the time step you used before. 16. Finally, the software will generate an output file containing the calculated ETr results. Note that the output file extension is (.out). You can change the extension to (.txt), so you can open the file using notepad or excel.
Appendix C Weather Data and REF-ET Software Output KEY for Headings: Rs
-- Solar radiation (w/m2)
Tem
-- hourly air temperature oc
Dew
-- hourly dew point temperature oc
Hum
-- relative humidity (%)
Wind
-- average wind speed (km/hr)
ASCE-stPM -ETr -- ASCE Penman-Monteith--Standardized Form (1999, 2004) in FAO-56 style reduced form for 0.12 m grass or 0.5 m alfalfa (mm/hr)
Table 26 A sample of Weather Data and REF-ET software output year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
RS 0 0 0 0 0 0.24 1.14 2.03 2.86 3.57 4.1 4.42 4.51 4.36 3.98 3.4
Tem Dew Hum 22 21 20 20 19 19 19 19 20 21 23 25 27 28 29 30
14 15 15 15 15 15 15 15 15 15 13 9 10 11 11 12
60 68 73 73 78 78 78 78 73 60 53 36 34 35 33 33
Wind
ASCE stPM
18.5 16.7 18.5 16.7 13 16.7 16.7 18.5 22.2 24.1 22.2 20.4 22.2 20.4 20.4 18.5
ETr 0.07 0.05 0.04 0.04 0.02 0.04 0.23 0.37 0.54 0.69 0.9 1.09 1.19 1.17 1.13 1.02
Appendix C 148
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
17 17 17 17 17 17 17 17 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
RS 2.67 1.81 0.91 0.05 0 0 0 0 0 0 0 0 0 0.26 1.15 2.04 2.87 3.57 4.1 4.42 4.51 4.36 3.99 3.41 2.67 1.82 0.92 0.06 0 0 0 0
Tem Dew Hum 30 30 29 28 27 25 23 22 21 20 19 19 18 18 17 18 19 21 21 23 24 26 27 27 28 28 28 27 26 24 22 21
11 9 9 8 9 9 10 11 13 13 13 13 13 13 13 14 14 12 11 9 8 6 8 7 5 5 6 8 10 10 12 12
31 27 29 28 32 36 44 50 60 64 68 68 73 73 77 77 73 56 53 41 36 28 30 28 23 23 25 30 36 41 53 56
Wind
ASCE stPM
20.4 20.4 22.2 20.4 22.2 22.2 22.2 20.4 18.5 16.7 16.7 16.7 14.8 9.3 11.1 9.3 14.8 18.5 14.8 18.5 18.5 18.5 18.5 16.7 20.4 20.4 18.5 18.5 20.4 20.4 20.4 20.4
ETr 0.92 0.77 0.61 0.19 0.18 0.15 0.12 0.1 0.07 0.05 0.04 0.04 0.03 0.04 0.2 0.36 0.52 0.75 0.84 1.01 1.07 1.13 1.08 0.96 0.92 0.76 0.55 0.17 0.15 0.13 0.09 0.08
Appendix C 149
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7
RS 0 0 0 0 0 0.27 1.16 2.05 2.88 3.58 4.11 4.43 4.51 4.37 3.99 3.42 2.68 1.83 0.94 0.07 0 0 0 0 0 0 0 0 0 0.28 1.17 2.06
Tem Dew Hum 20 19 19 19 19 18 17 18 19 21 23 25 26 28 29 30 30 30 30 29 28 28 26 25 23 21 21 20 20 20 20 22
12 11 12 12 12 12 12 13 13 13 9 9 9 8 8 9 9 9 10 10 11 11 13 12 13 12 13 14 13 13 13 10
60 60 64 64 64 68 72 73 68 60 41 36 34 28 27 27 27 27 29 30 35 35 44 44 53 56 60 68 64 64 56 46
Wind
ASCE stPM
20.4 18.5 16.7 13 13 13 13 13 13 16.7 11.1 11.1 11.1 9.3 14.8 18.5 16.7 14.8 18.5 16.7 14.8 11.1 18.5 18.5 22.2 18.5 14.8 14.8 14.8 11.1 11.1 13
ETr 0.07 0.06 0.05 0.04 0.04 0.05 0.23 0.38 0.54 0.73 0.9 1.02 1.06 1.06 1.09 1.05 0.88 0.67 0.56 0.18 0.14 0.11 0.13 0.12 0.1 0.08 0.06 0.04 0.05 0.06 0.26 0.51
Appendix C 150
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
RS 2.88 3.58 4.11 4.43 4.52 4.37 4 3.42 2.69 1.84 0.95 0.08 0 0 0 0 0 0 0 0 0 0.29 1.17 2.07 2.89 3.59 4.11 4.43 4.52 4.38 4 3.43
Tem Dew Hum 26 24 27 30 32 33 34 34 35 35 34 34 33 32 30 29 30 30 29 29 27 28 27 25 25 25 26 27 28 30 30 31
3 3 4 4 5 5 3 5 4 4 4 5 6 5 5 5 5 5 5 5 7 6 8 13 16 16 17 17 17 15 14 18
23 25 23 19 18 17 14 16 14 14 15 16 18 18 20 22 20 20 22 22 28 25 30 47 57 57 57 54 51 40 37 46
Wind
ASCE stPM
20.4 18.5 18.5 5.6 13 13 13 14.8 14.8 5.6 13 14.8 24.1 20.4 14.8 14.8 7.4 9.3 13 11.1 3.7 14.8 16.7 16.7 16.7 14.8 14.8 16.7 16.7 18.5 18.5 22.2
ETr 0.92 0.96 1.14 1.06 1.26 1.25 1.2 1.11 0.98 0.52 0.53 0.24 0.29 0.26 0.19 0.18 0.1 0.13 0.16 0.14 0.02 0.19 0.53 0.59 0.69 0.81 0.92 1.03 1.08 1.17 1.11 1.01
Appendix C 151
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
21 21 21 21 21 21 21 21 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
RS 2.7 1.85 0.96 0.09 0 0 0 0 0 0 0 0 0 0.3 1.18 2.07 2.89 3.59 4.12 4.44 4.53 4.38 4.01 3.44 2.71 1.86 0.97 0.1 0 0 0 0
Tem Dew Hum 31 31 30 28 28 27 28 25 23 22 22 21 21 20 20 20 21 21 23 25 26 28 29 30 29 29 29 29 27 25 24 24
13 14 13 13 12 13 13 14 16 16 16 16 16 16 17 17 15 16 16 15 14 14 11 9 10 10 10 12 15 15 14 14
33 35 35 39 37 30 39 50 65 69 69 73 73 78 83 83 68 73 65 54 47 42 33 27 30 30 30 35 48 54 53 53
Wind
ASCE stPM
20.4 20.4 22.2 25.9 14.8 24.1 25.9 16.7 16.7 13 9.3 14.8 11.1 5.6 9.3 9.3 11.1 0 5.6 9.3 5.6 5.6 7.4 1.9 0 11.1 13 16.7 25.9 20.4 16.7 11.1
ETr 0.93 0.75 0.59 0.19 0.14 0.16 0.18 0.1 0.06 0.04 0.03 0.03 0.02 0.04 0.21 0.37 0.57 0.69 0.83 0.96 0.99 0.99 0.97 0.75 0.54 0.58 0.44 0.17 0.15 0.1 0.09 0.06
Appendix C 152
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 24 24 24 24 24 24 24 24
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7
RS 0 0 0 0 0 0.31 1.19 2.08 2.9 3.6 4.12 4.44 4.53 4.38 4.01 3.44 2.71 1.87 0.98 0.11 0 0 0 0 0 0 0 0 0 0.31 1.2 2.09
Tem Dew Hum 23 22 22 21 20 20 19 20 20 21 24 25 26 28 28 30 30 30 30 30 29 27 23 24 23 22 21 21 20 20 19 20
15 15 15 16 15 15 15 15 15 17 17 16 15 15 15 11 11 11 11 11 11 16 17 17 17 17 17 17 17 17 17 16
61 64 64 66 73 73 78 73 73 78 65 57 51 45 45 21 31 31 31 31 33 51 69 65 69 73 78 78 83 83 88 78
Wind
ASCE stPM
11.1 9.3 9.3 9.3 9.3 9.3 7.4 9.3 9.3 0 0 0 7.4 7.4 7.4 11.1 11.1 13 14.8 11.1 13 22.2 16.7 13 16.7 16.7 13 13 9.3 13 9.3 7.4
ETr 0.05 0.03 0.03 0.02 0.02 0.08 0.21 0.39 0.54 0.69 0.84 0.92 0.99 1.01 0.93 0.92 0.77 0.63 0.48 0.14 0.14 0.13 0.05 0.05 0.05 0.04 0.02 0.02 0 0.07 0.19 0.38
Appendix C 153
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
RS 2.9 3.6 4.13 4.44 4.53 4.39 4.02 3.45 2.72 1.88 0.99 0.12 0 0 0 0 0 0 0 0 0 0.32 1.21 2.09 2.91 3.6 4.13 4.45 4.54 4.39 4.02 3.46
Tem Dew Hum 21 23 25 26 27 30 30 30 31 31 31 30 28 27 25 24 23 22 22 21 20 20 20 20 21 22 24 26 27 29 30 30
17 17 16 15 14 10 10 9 8 9 9 9 16 16 16 15 15 15 15 16 16 16 16 16 17 17 14 14 11 10 8 6
78 69 57 51 45 29 29 27 24 25 25 27 48 51 57 57 61 64 64 73 78 78 78 78 78 73 53 47 37 30 25 22
Wind
ASCE stPM
7.4 0 5.6 9.3 9.3 3.7 9.3 7.4 5.6 7.4 11.1 13 16.7 20.4 16.7 16.7 14.8 16.7 16.7 14.8 11.1 7.4 11.1 9.3 9.3 9.3 7.4 5.6 3.7 11.1 14.8 18.5
ETr 0.54 0.71 0.87 0.99 1.04 1.01 1.02 0.87 0.68 0.54 0.43 0.17 0.12 0.12 0.08 0.08 0.06 0.06 0.06 0.03 0.02 0.06 0.23 0.38 0.54 0.7 0.87 0.97 0.99 1.11 1.12 1.09
Appendix C 154
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
25 25 25 25 25 25 25 25 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
RS 2.73 1.89 1 0.13 0 0 0 0 0 0 0 0 0 0.33 1.21 2.1 2.91 3.61 4.13 4.45 4.54 4.4 4.03 3.46 2.73 1.9 1.01 0.14 0 0 0 0
Tem Dew Hum 30 31 31 30 29 27 25 24 24 23 23 22 21 21 21 21 22 24 25 27 29 31 32 32 32 33 33 32 32 31 31 29
6 7 5 8 11 11 13 13 13 13 14 14 14 14 15 15 15 14 13 11 10 8 9 9 9 9 9 9 9 10 8 11
22 22 19 25 33 37 47 50 50 53 57 60 64 64 68 68 64 53 47 37 30 24 24 24 24 23 23 24 24 27 24 33
Wind
ASCE stPM
16.7 16.7 14.8 14.8 20.4 22.2 18.5 13 11.1 11.1 7.4 5.6 7.4 5.6 0 0 3.7 0 9.3 11.1 11.1 11.1 11.1 16.7 13 13 11.1 13 13 9.3 9.3 13
ETr 0.91 0.76 0.55 0.19 0.19 0.17 0.11 0.08 0.07 0.06 0.03 0.01 0.02 0.07 0.19 0.37 0.57 0.72 0.91 1.06 1.14 1.17 1.1 1.09 0.86 0.7 0.46 0.19 0.18 0.12 0.13 0.14
Appendix C 155
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 28 28 28 28 28 28 28 28
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7
RS 0 0 0 0 0 0.34 1.22 2.1 2.92 3.61 4.13 4.45 4.54 4.4 4.03 3.47 2.74 1.91 1.02 0.15 0 0 0 0 0 0 0 0 0 0.35 1.22 2.11
Tem Dew Hum 28 27 26 26 26 26 26 27 30 32 34 35 36 37 38 39 39 39 39 36 33 31 30 28 27 25 25 23 20 20 20 20
11 10 10 9 7 8 8 9 9 6 5 5 5 5 6 5 5 3 3 11 11 6 9 10 15 16 14 16 16 16 16 15
35 34 36 34 30 32 32 32 27 20 16 15 15 14 14 12 12 11 11 22 26 21 27 32 48 57 50 65 78 78 78 73
Wind
ASCE stPM
14.8 13 9.3 5.6 14.8 14.8 13 14.8 9.3 9.3 16.7 14.8 16.7 20.4 20.4 27.8 24.1 25.9 24.1 20.4 18.5 18.5 13 16.7 9.3 9.3 13 11.1 13 11.1 11.1 9.3
ETr 0.14 0.13 0.09 0.05 0.14 0.16 0.45 0.66 0.79 0.98 1.3 1.36 1.44 1.52 1.47 1.56 1.31 1.19 0.95 0.3 0.23 0.23 0.16 0.16 0.07 0.05 0.08 0.04 0.02 0.08 0.23 0.4
Appendix C 156
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
RS 2.92 3.61 4.14 4.46 4.55 4.4 4.04 3.47 2.75 1.91 1.03 0.16 0 0 0 0 0 0 0 0 0 0.35 1.23 2.11 2.92 3.62 4.14 4.46 4.55 4.41 4.04 3.48
Tem Dew Hum 21 22 23 24 26 28 30 31 31 32 31 30 29 27 25 24 24 23 22 22 21 21 21 21 22 23 26 28 30 32 33 35
16 16 16 16 15 17 16 15 16 13 15 14 16 16 17 18 18 18 18 18 18 18 18 19 19 17 15 14 13 10 7 6
73 69 65 61 51 51 43 38 40 31 38 37 45 51 61 69 69 73 78 78 83 83 83 88 83 69 51 42 35 26 20 16
Wind
ASCE stPM
13 11.1 9.3 13 11.1 11.1 9.3 14.8 11.1 14.8 16.7 16.7 14.8 14.8 14.8 14.8 14.8 13 9.3 11.1 7.4 7.4 5.6 7.4 9.3 7.4 9.3 9.3 5.6 3.7 3.7 9.3
ETr 0.56 0.71 0.84 0.93 1.02 1.03 0.99 0.98 0.76 0.7 0.51 0.17 0.12 0.1 0.07 0.05 0.05 0.03 0.01 0.02 0 0.05 0.21 0.37 0.54 0.72 0.92 1.05 1.07 1.04 0.96 1
Appendix C 157
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
29 29 29 29 29 29 29 29 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
RS 2.76 1.92 1.04 0.17 0 0 0 0 0 0 0 0 0 0.36 1.24 2.11 2.93 3.62 4.14 4.46 4.55 4.41 4.05 3.48 2.76 1.93 1.05 0.18 0 0 0 0
Tem Dew Hum 35 35 34 33 32 31 30 28 27 27 26 26 27 26 26 28 31 33 36 37 38 40 41 42 42 41 41 41 39 39 33 30
5 6 8 9 9 8 10 14 11 11 11 9 7 6 8 8 9 9 7 7 5 5 2 2 2 2 0 2 3 4 11 12
15 16 20 23 24 24 29 42 37 37 39 34 28 28 32 28 25 23 17 16 13 12 9 8 8 9 8 9 11 11 26 33
Wind
ASCE stPM
11.1 14.8 14.8 13 11.1 14.8 14.8 20.4 18.5 18.5 18.5 18.5 18.5 18.5 7.4 0 0 0 0 0 0 0 3.7 7.4 14.8 13 20.4 9.3 9.3 13 22.2 22.2
ETr 0.89 0.8 0.58 0.2 0.15 0.19 0.17 0.15 0.15 0.15 0.14 0.15 0.17 0.19 0.35 0.39 0.6 0.77 0.91 1 1.02 1 1.04 1.03 1.11 0.84 0.89 0.22 0.18 0.25 0.25 0.2
Appendix C 158
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6
31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 1 1 1 1 1 1 1 1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7
RS 0 0 0 0 0 0.36 1.24 2.12 2.93 3.62 4.14 4.46 4.55 4.41 4.05 3.49 2.77 1.94 1.06 0.19 0 0 0 0 0 0 0 0 0 0.37 1.24 2.12
Tem Dew Hum 27 26 25 24 22 22 22 22 22 23 24 25 27 28 29 30 30 30 30 29 27 26 24 23 23 22 21 21 20 20 19 20
14 17 19 18 18 18 18 17 17 17 16 16 14 14 13 12 13 12 11 11 12 10 12 12 13 13 14 14 14 14 13 13
45 57 69 69 78 78 78 73 73 69 61 57 45 42 37 33 35 33 31 33 39 36 47 50 53 57 64 64 68 68 68 64
Wind
ASCE stPM
18.5 16.7 18.5 18.5 18.5 9.3 13 14.8 14.8 13 11.1 13 11.1 13 7.4 11.1 14.8 14.8 14.8 18.5 18.5 20.4 18.5 11.1 11.1 11.1 7.4 9.3 7.4 7.4 7.4 0
ETr 0.13 0.09 0.06 0.06 0.03 0.08 0.26 0.43 0.58 0.73 0.87 0.96 1.06 1.07 0.97 0.93 0.83 0.67 0.5 0.19 0.15 0.15 0.11 0.07 0.06 0.05 0.02 0.03 0.01 0.08 0.24 0.37
Appendix C 159
year 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014
Month day Time 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0
RS 2.93 3.62 4.14 4.46 4.56 4.42 4.05 3.49 2.77 1.95 1.06 0.2 0 0 0 0 0
Tem Dew Hum 21 22 23 24 26 27 28 28 29 29 29 28 27 26 24 24 23
14 13 13 12 12 11 11 9 11 10 9 10 9 11 12 11 11
64 57 53 47 42 37 35 30 33 30 29 32 32 39 47 44 47
Wind
ASCE stPM
0 9.3 9.3 13 0 0 7.4 13 11.1 13 18.5 16.7 20.4 18.5 16.7 9.3 14.8
ETr 0.55 0.74 0.86 0.98 0.94 0.92 0.96 0.93 0.76 0.64 0.57 0.18 0.17 0.14 0.1 0.07 0.09
الملخص العربي مقدمة المياه هي شريان الحياة الرئيسي ألي بلد ،وهي ضرورة أساسية للحفاظ على الحياة فهي مورد حيوي لبقاء اإلنسان وللتنمية االقتصادية ولتنمية المجتمع .تتعرض الموارد المائية في كثر من الدول لضغوط متزايدة بسبب النمو السكاني السريع والنمو االقتصادي .في مصر ،تعتبر الموارد المائية المحدودة من التحديات الرئيسية التي تواجه متطلبات التنمية المستدامة للزراعة .وبالتالي ،فإن الزيادة المستمرة في الطلب على المياه لتلبية االحتياجات البشرية والزراعية تتطلب إدارة فعالة للموارد المائية .وتعتبر النمذجة الهيدرولوجية تقنية فعالة في تخطيط وتطوير أساليب اإلدارة المتكاملة للموارد المائية فقد شهدت السنوات القليلة الماضية اهتمام كبير في تطبيق النمذجة الهيدرولوجية إلى جانب نظم المعلومات الجغرافية واالستشعار عن بعد .فإن تكنولوجيا نظم المعلومات الجغرافية لديها القدرة على التقاط وتخزين ومعالجة وتحليل وتصور البيانات الجغرافية وأصبحت أدوات االستشعار عن بعد اكثر استخداما ً مع ظهور تطور في أجهزة االستشعار عن بعد؛ حيث صار من الممكن الحصول على الصور بدقة عالية واصبحت عملية معالجة الصور سريعة وذات تكلفة محدودة .في هذه الدراسة تم انشاء نموذج يمكن استخدامه في حساب البخر نتح الذي يعتبر من اهم مكونات الدورة الهيدرولوجية وسيتم تطبيقه على منطقة الدراسة استنادا لبيانات االستشعار عن بعد ونظم المعلومات الجغرافية
الخالصة يعتبر البخر النتح من أهم عناصر الدورة الهيدرولوجية ويحل في المرتبة الثانية بعد االمطار .ويختلف البخر نتح إقليميا وموسميا وفقا للظروف البيئية المحيطة ،مثل حالة المناخ ،واستخدام األراضي، والغطاء األرضي ،ورطوبة التربة ،واإلشعاع المتاح .وبسبب هذا التباين فان هناك حاجة إلى اجراء العديد من األبحاث من أجل النمذجة المتكاملة للموارد المائية ،ونمذجة ديناميكية للمحاصيل والطقس ورصد الجفاف ،وفهم دقيق لعملية البخر النتح.
الهدف من البحث الهدف الرئيسي من هذه الدراسة هو حساب قيم البخر نتح الفعلي في منطقة الدراسة التي تم اختيارها و تسمي "هندسة طنطا" طبقا لتقسيم وزارة الري و الموارد المائية من خالل الجمع بين االستشعار عن بعد واألرصاد الجوية وظم المعلومات الجغرافية .في هذه الدراسة ،يتم تناول األهداف التالية:
2
الملخص العربي
)1تقدير البخر نتح الفعلي من خالل تطبيق خوارزمية توازن الطاقة السطحية لألرض ()SEBAL )2تحديد أنماط التوزيع المكاني والزماني للبخر نتح الفعلي في منطقة الدراسة )3تقييم أداء نظام الري في منطقة الدراسة لتقييم الفواقد في نظام الري على مستوى إقليمي
ملخص البحث لتحقيق أهداف البحث تم تجميع بيانات األرصاد الجوية لمنطقة الدراسة للفترة الزمنية من 2014/5/27 الي 2014/10/7من موقع ) (http:///www.wunderground.comلألرصاد الجوية وتم تحميل ما مجموعه تسعة صور للقمر الصناعي Landsat 8خالية من الغيوم خالل موسم الصيف في عام 2014لمنطقة الدراسة من موقع ) .(https://earthexplorer.usgs.gov/اعقب ذلك إعـداد وبرمجـة النمـاذج الرياضية المستخدمة في إجراء العمليات الحسابية الالزمة لتقدير البخر نتح ,حيث تم حساب تدفقات الطاقة السطحية مثل اإلشعاع الصافي ) (Rnوالحرارة المحسوسة ) (Hوالتدفق الحراري للتربة ) (Gوقدرة سطح على عكس أشعة (α).وأظهرت التسعة خرائط للبخر نتح المنتجة من نموج )( (SEBALمن مايو إلى أكتوبر )2014تطورا ملحوظا في البخر نتح مع مرور الوقت خالل موسم النمو في عام 2014حيث تغيرت ظروف سطح األرض باستمرار .ويستخدم ) (SEBALبيانات الصور الرقمية التي تجمعها Landsatأو وسائل االستشعار عن بعد األخرى التي تسجل األشعة الحرارية تحت الحمراء ،واألشعة المرئية وشبه القريبة من األشعة تحت الحمراء. يتم حساب البخر على انه الكمية المتبقية من معادلة توازن الطاقة ،حيث البخر نتح = صافي اإلشعاع الشمسي -التدفق الحراري للتربة -الحرارة المحسوسة.
الباب األول (لمقدمة) يشتمل الباب على نبذه مختصره عن الموارد المائية العالمية والموارد المائية في مصر ونبذة مختصره عن البخر نتح ونظم المعلومات الجغرافية و االستشعار عن بعد وتم إيضاح المشكلة والهدف من الدراسة و يلي ذلك نظرة عامة علي أبواب الدراسة بشرح مختصر
3
الملخص العربي
الباب الثاني (الدراسات السابقة) يتطرق هذا الباب الى طرق تقدير البخر نتح باستخدام االستشعار عن بعد وخوارزميات توازن الطاقة السطحية المختلفة و التي تم عرضها في دراسات سابقة.
الباب الثالث (منهجية البحث) يتضمن الوصف التفصيلي لمنطقة الدراسة والتي تسمي "هندسة طنطا" طبقا لتقسيم وزارة الري والموارد المائية وهي جزء من محافظة الغربية من حيث الموقع والطقس ونظام الري والصرف بها ويوضح البيانات المستخدمة ومصدر الحصول عليها باإلضافة لتوضيح النهج المستخدم لحساب المكونات االزمة لحساب البخر نتح ( )ETوشرح بناء نموذج ( )SEBAL Modelباستخدام برنامج ( (ERDAS IMAGINE 2014وحساب البخر نتح الموسمي وطريقة حساب كمية المياه المفقودة في صوره البخر نتح .تناول هذا الباب نبذه عن النهج التحليلي المتبع لتقييم األداء لنظام الري في منطقة الدراسة.
الباب الرابع (النتائج والمناقشة) يتضمن عرض تفصيلي لنتائج جميع المكونات االزمة إلنتاج خريطة توزيع البخر نتح الموسمي لموسم الصيف عام 2014م باستخدام ( )SEBAL Modelوالتحقق من صحة عمله ونتائج حساب كميات المياه المفقودة في صورة بخر نتح ومن ثم تقييم األداء لنظام الري في منطقة الدراسة
الباب الخامس (االستنتاجات والدراسات المستقبلية) تم تطبيق هذه الدراسة على منطقة تجريبية في دلتا النيل وتهدف هذه الدراسة إلى تقييم التبخر الفعلي في "هندسة طنطا" من خالل استخدام االستشعار عن بعد واألرصاد الجوية ،ومن ثم تقييم أداء نظام الري في منطقة الدراسة لتقييم الفاقد في نظام الري على المستوى المحلي ،وكانت أهم االستنتاجات التى تم التوصل اليها مايلى: .1تم تطبيق تقنية ( )SEBALللحصول علي خريطة االختالف المكاني في البخر نتح الفعلي لدلتا النيل ،وذلك باستخدام صور Landsat 8خالل الفترة من 2014 / 5/17الي/ 7 .2014/10وتمت مقارنة البخر نتح المحسوب من نتائج بخر الوعاء المقاسة .واظهرت النتائج فاعلية كبيرة لتقدير البخر نتح الفعلي عن طريق ( )SEBALباستخدام صور األقمار
4
الملخص العربي
الصناعية وقياسات األرصاد الجوية لسرعة الرياح واإلشعاع الشمسي والرطوبة ودرجة حرارة الهواء. - .2وجرى تحليل التوزيع المكاني للبخر نتح باإلضافة الى خريطة الغطاء األرضي .تراوحت تقديرات البخر نتح الموسمية من صفر لتربة الجافة والمناطق السكنية .والي 1477مم للمناطق النباتية الكثيفة مع متوسط قيمة بخر نتح حوالي 651.77مم للمنطقة بأكملها. .3معامل االرتباط بين البخر نتح المحسوب من نتائج بخر الوعاء المقاسة والبخر نتح الفعلي المحسوب باستخدام ) (SEBALيساوي 0.8927مما يدل علي قابلية حساب البخر نتح الفعلي عن طريق ( )SEBALباستخدام صور األقمار الصناعية وبعض قياسات األرصاد الجوية. .4والمزايا الرئيسية ل ( )SEBALلحساب تدفقات سطح األرض من بيانات االستشعار عن بعد الحرارية هي ( )1الحد األدنى من استخدام البيانات األرضية المساعدة ( )2التصحيح الداخلي التلقائي ،ومن ثم عدم الحاجة الي اجراء تصحيحات دقيقة إلزالة اآلثار الجوية على درجة حرارة السطح. .5إلى جانب العديد من المزايا ،لهذه الطريقة اال انها لديها بعض السلبيات .والعيوب الرئيسية و هي: )1يمكن أن تتغير قيم الحرارة المحسوسة ) (Hوقيم البخر نتح ETالمحسوب من SEBAL مع اختالف البكسالت المتطرفة التي يختارها المستخدم والدقة المكانية لمستشعرات األقمار الصناعية. ( )2تتأثر قيم الحرارة المحسوسة ( )Hالمقدرة بشكل كبير باألخطاء في اختالفات درجة حرارة الهواء السطحي أو قياسات درجات الحرارة السطحية .6وتشير ميزانية المياه في منطقة الدراسة إلى أن إجمالي إمدادات المياه في موسم الصيف 2014يبلغ حوالي 216.1211مليون متر مكعب ،ويبلغ استهالك المحاصيل (حسب )SEBALحوالي 126.1043مليون متر مكعب ( )٪58.35والفواقد خالل نظام النقل حوالي 77.309مليون متر مكعب (.)٪35.87 .7وتبلغ كفاءة الري في منطقة الدراسة حوالي ٪61.07حيث تبلغ المياه التي دخلت الحقول حوالي 206.133مليون متر مكعب ،ويبلغ استهالك المياه للمحاصيل حوالي 126.1043
5
الملخص العربي
مليون متر مكعب ،مما يشير إلى أن حوالي 80مليون متر مكعب من إجمالي إمدادات المياه هي فواقد في الجريان السطحي و /أو الترشيح. .8وتبلغ كفاءة التوزيع في منطقة الدراسة حوالي ،٪59.61حيث تبلغ نسبة فقدان مياه النقل نحو ٪35.87من إجمالي إمدادات المياه في منطقة الدراسة. وفي نهاية الرسالة تم ترتيب المراجع المستخدمة في الرسالة وكذلك تم ترتيب الملحقات الخاصة بالرسالة ويلي ذلك ملخص الرسالة باللغة العربية
المالحق تم ارفاق مجموعة من المالحق بنهاية الرسالة وهي: ملحق (أ) يشمل علي شرح طريقة ادخال صور Landsat 8الي برنامج ERDAS IMAGINE ملحق (ب) يشمل علي شرح طريقة اعداد بيانات االرصاد الجوية و طريقة استخدام برنامج REF-ET ملحق (ج) يشمل علي بيانات الطقس و نتائج برنامج REF-ET
الملخص العربي وفي نهاية الرسالة تم وضع ملخص للرسالة باللغة العربية للقارئ العربي.
لجنة الحكم االسم
الوظيفة
أ.د / .أسامه خيري صالح
أستاذ الهيدروليكا بقسم هندسة المياه و المنشأت المائية -كلية الهندسة -جامعة الزقازيق
أ.د / .نهي سمير دنيا
أستاذ الهيدروليكا البيئية – معهد الدراسات البيئية جامعة عين شمسأستاذ الموارد المائية ورئيس قسم هندسة الري والهيدروليكا
أ.د /.باكيناز عبد العظيم زيدان
كلية الهندسة -جامعة طنطا أستاذ مساعد أ.د.م / .مسعد بيومي أحمد خضر
بقسم هندسة الري والهيدروليكا كلية الهندسة -جامعة طنطا
توقيع لجنة الحكم االسم أ .د /أسامه خيري صالح أ .د /نهي سمير دنيا أ .د /باكيناز عبد العظيم زيدان أ .م .د /مسعد بيومي أحمد خضر
التوقيع
لجنة االشراف الوظيفة
االسم
أستاذ الموارد المائية أ .د / .باكيناز عبد العظيم زيدان
ورئيس قسم هندسة الري والهيدروليكا كلية الهندسة -جامعة طنطا
أستاذ مساعد أ .م .د / .مسعد بيومي أحمد خضر
بقسم هندسة الرى والهيدروليكا كلية الهندسة -جامعة طنطا
توقيع لجنة االشراف م
االسم
1
أ .د / .باكيناز عبد العظيم زيدان
2
أ .م .د / .مسعد بيومي أحمد خضر
التوقيع
جامعة طنطا كلية الهندسة قسم هندسة الري والهيدروليكا
نمذجة موارد المياه في دلتا النيل باستخدام االستشعار عن بعد ونظم المعلومات الجغرافية رسالة علمية مقدمة لكلية الهندسة جامعة طنطا كجزء من متطلبات الحصول على درجة ماجستير العلوم في الهندسية (هندسة الرى والهيدروليكا) إعداد المهـــــندس
صبحي رزق صبحي عماره بكالوريوس الهندسة المدنية – كلـــية الهندسة -جامعة طنطا (عام )٢٠١٣ معيد بقسم هندسة الري والهيدروليكا – كلية الهندسة – جامعة طنطا
تحت اشراف
أ.د /.باكيناز عبد العظيم زيدان أستاذ الموارد المائية ورئيس قسم هندسة الري والهيدروليكا -كلية الهندسة -جامعة طنطا
&
أ.م.د /.مسعد بيومي أحمد خضر أستاذ مساعد بقسم هندسة الري والهيدروليكا -كلية الهندسة -جامعة طنطا
