The Relationship between Surface Soil Moisture with Real ...

International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.2.56

Vol-2, Issue-2, Mar-Apr- 2017 ISSN: 2456-1878

The Relationship between Surface Soil Moisture with Real Evaporation and Potential Evaporation in Iraq Dr. Osama T. Al-Taai1, Safa H. Hadi2 1, 2

Department of Atmospheric Sciences, College of Science, Al-Mustansiriyah University, Ministry of Higher Education and Scientific Research, Baghdad - Iraq

Abstract— The aim of this research is to determine the relationship between surface Soil Moisture (SSM) of both Real Evaporation (E) and surface Potential Evaporation (SPE) for thirty years during the period of (1985-2014) for the eight stations (Sulaymaniya, Mosul, Tikrit, Baghdad, Rutba, Kut, Nukhayib, Basrah) in Iraq, from (NOAA) and taking advantage of some statistics such as the Simple Linear Regression (SLR) and the Spearman Rho test. Calculated the monthly average for Soil Moisture, Real Evaporation and Potential Evaporation, and found to increase the values of SPE in hot months and decreased in cold months while opposite to SM There was a strong inverse relationship between them, where the correlation coefficient was in Sulaymaniya -0.91, in Mosul -0.89, in the Rutba -0.92, in Tikrit -0.89, in Baghdad -0.89, in Nukhayib -0.89, in Kut -0.87, and in Basrah -0.83, and there is a high correlation in stations (Basrah, Kut, Nukhayib, and Rutba), while there is an average correlation in the stations (Baghdad and Tikrit), and there is low correlation in the stations (Sulaymaniya, Mosul), we also note an inverse correlation between RE and PE, where there is a low correlation in Sulaymaniya and medium correlation in the Mosul and Rutba stations, and there is a high correlation in the stations (Tikrit, Baghdad, Nukhayib, Kut, and Basrah). Keywords— Soil moisture, Potential evaporation, Real evaporation, Spearman rho test, Iraq. I. INTRODUCTION The evaporation of the main processes in the water cycle is a link between energy and water budget balance as the water cycle include the release of energy and water vapor transmission and for leading to condensation for precipitation on the surface of the globe. Water flow on the surface of the earth is through surface runoff, groundwater, eyes and others. Then the water cycle ends to the starting point, which is the water surfaces such as lakes, rivers, oceans, seas and the surface of the soil. Unfortunately, this process is one of the most incomprehensible processes in the water cycle. Water is from the liquid to the gaseous www.ijeab.com

state. There are three main reasons for evaporation from the surface of the soil. First, sufficient energy must be available to convert the water from the liquid phase to the gas phase. Second, there is a vapor pressure gradient in the atmosphere sufficient to transform the water from Liquid to vapor. Third, there is sufficient water level in the surface of the soil, which affects the moisture content [1]. Potential Evaporation (PE) can be defined as the amount of evaporation that can be obtained if sufficient water source is available. Real Evaporation (E) is a sum of both Potential Evaporation (PE) and Actual Evaporation (AE) in the soil. Several factors affect the potential evaporation (solar radiation as the most important atmospheric influences, temperature, relative humidity, and wind speed), other factors indirectly affecting (water level, atmospheric stability, soil temperature, latent heat and sensible heat emissions). The task in the biological processes as it contributes to the process of formation of clouds and then precipitation, which is one of the most important determinants of the cycle of water in nature and therefore measure the amount of potential evaporation to assess the water requirements as well as the need to calculate when planning irrigation projects president widely in many environmental studies, including meteorology, hydrology, agriculture, climate change and affect the surface of the soil, especially at a depth of one to two meters, a key interaction between the earth and the atmosphere is one of the key variables that control the exchange of and the thermal energy between the surface of the Earth and the atmosphere through evaporation and plant transpiration [2]. This variable has multiple links with other anaerobic variables, which makes it very effective predictively although it constitutes a very small layer compared to the global total water but is very important in many of the basic processes of many hydrologists, chemists and biologists are important variables used in many applications (numerical weather predictions, global climate change monitoring, flow forecasting and evaporation modeling) [3]. Spatial and temporal differences of soil water content [4]. Page | 991

International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.2.56 Agriculture is the sector most economically affected by extreme weather events such as drought. Many other economic sectors of society rely on agro-ecosystems, which are a specific form of human-adapted ecosystems for food production. Can lead to many negative economic and social impacts such as loss of income in agriculture and food industries and high costs for water and production technologies such as irrigation systems [5] [6]. II. METHODOLOGY A. Methods of Analysis There were several statistical tests available. Spearman rho was selected. The regression analysis was also selected, particularly the simple linear regression and the use of the P-value for the relationship [7]: 1. Simple Linear Regression (SLR) Is the study of the relationship between two variables only accessible to a linear relationship (i.e. a straight line equation) between these two variables, a parametric test as it is assumed that the data are distributed normally distributed and to find out the value of the regression slope of the regression is calculated by the following linear equation: 𝑌̅ = 𝑎 + 𝑏𝑋̅ 𝑏=

∑𝑛𝑖=1(𝑋𝑖 − 𝑋̅) − (𝑌𝑖 − ∑𝑛𝑖=1(𝑋𝑖 − 𝑋̅)2

𝑟𝑠 = 1 −

6 ∑𝑛𝑖=1 𝑑𝑖2 𝑛(𝑛2 ˗1)

(3)

If the value of n large can choose the value of r s to their importance by calculating the value of ts which is given by equation: 𝑡𝑠 = 𝑟𝑠 √

𝑛−2 1 − 𝑟𝑠 2

(4)

If the value of ts false calculated within the trusted boundary for the selection of a dual party from this we conclude that there is no trend in the data series and through the “Table 1”. Can determine the value of the degree of correlation and interpretation of test transactions. Table.1: The degree of correlation and interpretation of test transactions [8]. Value

Correlation

Interpretation of relation

Less 0.2 0.2-0.4 0.4-0.7 0.7-0.9 0.9-1

Few Low Medium High Very high

No relation Small relation Acceptable relation Special relation Strong relation

(1) 𝑌̅)

(2)

Where b: slope of the regression and found a mile straight line equation (1), a: a constant gradient and demonstrate the value of the lump of axis Ῡ for Straight equation (1). 2. Probability-Value It is purely a statistical term, which is a number or the number of measurements used to evaluate the statistical value of a show that was a contrast factor it is an influential factor or not really? If the P-Value is less than 0.05, the contrast factor it is an influential factor in the variable that we are trying to study the change may consider factor affecting even the value of P-Value equal to 0.1, but that exceeds 0.1, this factor should be removed from the form it is ineffective. 3. Spearman Rho Test It is a test of a set of observed data (xi = 1,2,……,n) is based on the null hypothesis that is, all xi values are independent and have the same distribution and to calculate the Spearman Rho coefficient statistical ranks (rs) must convert the original model to the ranks mediated arranged in descending order in terms of amount and then the value of the account through di (d i = ki -i) where the (i = 1,2,…..,n) and rs is given by the following [8]:

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B. The Data and Study Stations Was used the monthly average surface soil moisture, surface potential evaporation and real evaporation data for eight different stations in Iraq (Mosul, Sulaymaniya, Tikrit, Baghdad, Rutba, Kut Nukhayib, and Basrah) were used for thirty years (1985-2014) from The National Oceanic and Atmospheric Administration (NOAA) [9], (see “Fig. 1” and “Table 2”). Table.2: The latitude, longitude and altitude of the study stations in Iraq [10]. Stations Mosul Sulaymaniya Tikrit Baghdad Rutba Kut Nukhayib Basrah

Latitude (oN) 36.19 35.33 34.56 33.14 33.02 32.48 32.02 30.34

Longitude (oE) 43.09 45.27 43.70 44.14 40.17 45.73 42.15 47.47

Altitude (meter) 223 853 103 34 615 91 305 2

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III. RESULTS AND DISCUSSION 1. The Relationship between SPE and SSM The “Fig. 2”, shows the relationship between the surface PE with the surface SM, where there is strong inverse relationship where we note there is a high correlation in the eight stations where the highest value of the correlation coefficient was in Sulaimaniya and Rutba stations and the lowest value of the correlation coefficient in the Basra station and the reason for this relationship reverse that connects SPE and SSM, potential evaporative occurs only in the presence of moisture and when evaporation reaches the latent limit less moisture in the atmosphere where the high temperature and solar radiation directly affects the SPE and SSM, note that the southern stations characterized by high temperatures and this leads to the high in potential evaporation values and low values in SSM values, the opposite is happening in the northern stations (see “Table 3”). Fig.1: Iraq map, explaining the study stations [10].

0.30

0.16 0.14 0.12 0.10 0.08 0.06

-3

0.26

3

0.22

3

Surface Soil Moisture (m m )

-3

0.24

0.24


-3 3

0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06

0.04

0.04

0.02

0.02

0.00 100

200

300

400

500

600

700

800

0

100

200

Surface Potential Evaporation (W/m2)

300

400

600

700

800

900

0

0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.00

0.28

3

0.22

500

600

700

800

0.21 0.20 0.19 0.18 0.17

900

300

800

SSM = 0.198 - (0.0000392 * SPE)

0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06

400

500

600

0

700

100

200

300

400

500

600

700

800

900

1000


BASRAH 0.30

SSM = 0.194 - (0.0000433 * SPE)

0.28

0.21 0.20 0.19 0.18 0.17 0.16

-3

0.26

3


-3

0.24


SSM = 0.190 - (0.0000419 * SPE)

0.24

3

700

0.00 200

NUKHAYIB


600

0.02 100

0.25

0.22

500

0.04

Surfacs Potential Evaporation (W/m2)

0.23

400

0.26 -3

-3

0.23

0.15

400

300

0.30

0.16

0.02 300

200

KUT


0.18

200

100


SSM = 0.197 - (0.0000496 * SPE)

0.20

100

0.14

0.10

1000

0.24


-3 3


500

0.25

SSM = 0.202 - (0.0000473 * SPE)

0

0.16

RUTBA

0.30 0.26

0.18


BAGHDAD

0.28

0.20

0.12

0.00 0

0.22

3


0.18

0.22

0.28

0.26

0.20

0.24

SSM = 0.213 - (0.0000745 * SPE)

SSM = 0.262 - (0.000115 * SPE)

0.28

0.26 0.22

0.30

0.30

SSM= 0.255 - (0.000135 * SPE)

0.28 0.24

TIKRIT

SULAIMANIYA

MOSUL

0.24 0.22 0.20 0.18 0.16 0.14 0.12

0.15

0.10

0

100

200

300

400

500 2

Surface Potential Evaporation (W/m )

600

100

200

300

400

500

600

700

800


Fig.2: The relationship between the surface potential evaporation (SPE) and surface soil moisture (SSM) of eight different stations in Iraq for thirty years (1985-2014) www.ijeab.com

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Table.3: Spearman rho test results and Simple Linear Regression (SLR) to find the strength of the relationship between the SPE and SSM. Simple Linear Regression P-value Interpretation 0.0001 Linear relation 0.0001 Linear relation 0.0001 Linear relation 0.0001 Linear relation 0.0001 Linear relation 0.0002 Linear relation 0.0001 Linear relation 0.0009 Linear relation

Station Mosul Sulaymaniya Tikrit Baghdad Rutba Kut Nukhayib Basrah

rs -0.89 -0.91 -0.89 -0.89 -0.92 -0.87 -0.89 -0.83

2. The Relationship between E and SSM The “Fig. 3”, shows that there is a strong positive relationship between E and SSM, where there is a high correlation in the stations (Basrah, Kut, Nukhayib, and Rutba), while there is an medium correlation in stations (Baghdad and Tikrit), There is a low correlation in the stations (Sulaymaniya and Mosul), and also note through the P-Value, there is a nonlinear relationship in stations (Sulaymaniya and Mosul), while there is a linear relationship in the stations (Rutba, Tikrit, Baghdad, MOSUL

Spearman Rho Test Correlation High-inverse correlation Very high-inverse correlation High-inverse correlation High-inverse correlation Very high-inverse correlation High-inverse correlation High-inverse correlation High-inverse correlation

Nukhayib, Kut, and Basrah) for reasons the following is because evaporation occurs on the surface of the earth when the water moves into an atmosphere shaped like water vapor from the various confiscations as well as the evaporation plays a significant role in the occurrence of moisture and consequent upon the occurrence of condensation or fog or include rain dew, as well as the evaporation occurs only the existence and availability of sources of moisture (see “Table 4”).

SULAIMANIYA

0.30

TIKRIT

0.30

SSM = 0.178 + (0.0370 * E)

0.30

SSM = 0.158 + (0.0564 * E) SSM = 0.176 + (0.0388 * E)

0.24

0.22

0.20

-3

-3

0.26

0.24

0.22

0.20

0.18

0.18

0.16

0.16

0.4

0.6

0.8

1.0

1.2

1.4

1.6

3

0.26

3

3

0.28



0.28

-3


0.28

1.8

0.6

0.8

1.0

1.6

1.8

0.2

0.4

0.5

0.20

0.18

-3 3

0.26

0.24

0.22

0.20

0.7

0.8

0.26

0.24

0.22

0.20

0.18

0.16

0.16

0.6

0.9

SSM = 0.162 + (0.0487 * E)

0.18

0.16

0.8

0.28

-3 3

0.22

0.7

0.30



0.24

0.6

KUT

SSM = 0.167 + (0.0481 * E)

0.26

0.0

0.1

Evaporation (mm/month)

0.2

0.3

0.4

0.1

0.5

0.2

0.3

0.4

0.5

0.6

0.7



BASRAH

NUKHAYIB 0.30

0.30

SSM = 0.161 + (0.0509 * E)

SSM = 0.163 + (0.0595 * E) 0.28

-3

-3


0.28


0.3


0.28

-3 3

1.4

0.30

SSM = 0.158 + (0.0527 * E)


1.2

RUTBA

0.28

0.5

0.20


0.30

0.4

0.22

0.16 0.4

BAGHDAD

0.3

0.24

0.18


0.2

0.26

0.26

3

3

0.26 0.24 0.22 0.20 0.18

0.22 0.20 0.18 0.16

0.16 0.14 0.00

0.24

0.14 0.05

0.10

0.15

0.20

0.25


0.30

0.35

0.0

0.1

0.2

0.3

0.4

0.5

0.6


Fig.3: The relationship between the real evaporation (E) and SSM of eight different stations in Iraq for thirty years (1985-2014) www.ijeab.com

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Table.4: Spearman rho test results and Simple Linear Regression (SLR) to find the strength of the relationship between the E and SSM. Simple Linear Regression P-value Interpretation 0.2528 Non-Linear relation 0.2364 Non-Linear relation 0.0121 Linear relation 0.0139 Linear relation 0.0039 Linear relation 0.0013 Linear relation 0.0001 Linear relation 0.0001 Linear relation

Stations Mosul Sulaymaniya Tikrit Baghdad Rutba Kut Nukhayib Basrah

rs 63.0 6337 6301 6301 63.0 63.9 0.89 63..

we note there is a low correlation in the Sulaimaniya station and the medium correlation in the (Mosul and Rutba) stations and high correlation in the stations (Tikrit, Baghdad, Nukhayib, Kut and Basrah) (see “Table 5”).

3. The Relationship between E and SPE The “Fig. 4”, shows the relationship between surface real evaporation (E) and potential evaporation (SPE) where there is a strong inverse relationship between them, where MOSUL

SULAIMANIY

TIKRIT

1000

800 700 600 500 400 300 200 100 0

1000

SPE = 714.438 - (295.521 * E)

900 800 700 600 500 400 300 200 100

800 700 600 500 400 300 200 100

0 0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0 0.4

0.6

0.8

1.0

1.2

1.4



BAGHDAD

RUTBA

1000

1.6

1.8

0.2

600 500 400 300 200 100 0 0.3

0.4

0.5

0.6

0.7

0.8

0.7

0.8

0.9

SPE = 853.698 - (1039.201 * E)

800 700 600 500 400 300 200 100

800 700 600 500 400 300 200 100 0

0.0

0.1

0.2


0.3

0.4

0.5

0.1

0.2

0.3


0.4

0.5

0.6

0.7


NUKHAYIB

BASRAH

1000

1000

SPE = 673.351 - (861.832 * E)

900

2


SPE = 583.835 - (1156.003 * E)

900

2


0.6

900

0

0.2

0.5

1000

SPE = 587.213 - (846.153 * E)

900

2

700

0.4

KUT

Surface Potential Evaporation (W/m 2)


2

800

0.3

Evaporation(mm/month)

1000

SPE = 936.924 - (1128.300 * E)

900

SPE = 746.441 - (760.857 * E)

900

2

SPE = 605.068 - (319.365 * E)


900



1000


Spearman Rho Test Correlation Low-positive correlation Low-positive correlation Medium-positive correlation Medium-positive correlation High-positive correlation High-positive correlation High-positive correlation High-positive correlation

800 700 600 500 400 300 200 100 0 0.00

800 700 600 500 400 300 200 100 0

0.05

0.10

0.15

0.20

0.25


0.30

0.35

0.0

0.1

0.2

0.3

0.4

0.5

0.6


Fig.4: The relationship between the E and SPE of eight different stations in Iraq for thirty years (1985-2014)

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Table 5: Spearman rho test results and Simple Linear Regression (SLR) to find the strength of the relationship between the E and SPE. Stations Mosul Sulaymaniya Tikrit Baghdad Rutba Kut Nukhayib Basrah

Simple Linear Regression P-value Interpretation 0.1239 Non-Linear relation 0.2527 Non-Linear relation 0.0024 Linear relation 0.0033 Linear relation 0.0074 Linear relation 0.0026 Linear relation 0.0016 Linear relation 0.0026 Linear relation

IV. CONCLUSIONS The results showed a strong inverse relationship between SPE and surface SSM. SPE was found to increase in the southern stations and increase in hot months, in contrast to SSM. It also showed an inverse relationship between E and SPE. Results there is a strong direct relationship between E and SSM where E occurs only with the presence of water (Soil Moisture). The results show that SPE is an evaporative energy in the atmosphere E of surface soil moisture because real evaporation is a process of transformation from the liquid phase to the gas phase. E occurs only with soil moisture. E is the sum of SPE and Actual Evaporation (AE) in the soil. ACKNOWLEDGEMENTS An acknowledgment to The National Oceanic and Atmospheric Administration (NOAA) on the data used in this research.

rs -0.47 -6336 -0.79 -0.77 -0.73 -0.78 -0.80 -0.78

Spearman Rho Test Correlation Medium-inverse correlation Low-inverse correlation High-inverse correlation High-inverse correlation High-inverse correlation High-inverse correlation High-inverse correlation High-inverse correlation

http://www.ghcc.msfc.nasa.gov/landprocess/lp_home .html. [7] S. Mohammed, ''Data Analysis,'' Site Management and Industrial Engineering, 2009. http://samehar.wordpress.com/2009/08/13/0120809/ [8] Mathematics in Education and Industry (MEI), “Spearman’s rank correlation,'' December 2007. [9] A. Seidenglanz, and U. Bremen, ''Panoply a Tool for Visualizing Net CDF-Formatted Model Output,'' NASA, February 2016. http://www.noaa.gov/ [10] General Authority for The Rough Waters of The Iraqi Air and Seismic Monitoring, ''Atlas Climate of Iraq for The Period (1961-1990),'' Baghdad, Iraq, pp. 8-7, 1994.

REFERENCES [1] E. M. Shaw, ''Hydrology in practice 3rd ed.''. Stanly Thomas pub. Ltd, U. K, pp. 569, 1999. [2] C. A. Appelo, and D. Postma,''Geochemistry ground water and Pollution Rotterdam,'' Balkama Ltd, pp. 536, 1999. [3] J. Walker, ''Estimating Soil Moisture Profile Dynamics from Near-Surface Soil Moisture Measurements and Standard Meteorological Data,'' Ph.D. dissertation, The University of Newcastle, Australia, 1999. [4] W. Lingli, and J. Q. John, ''Satellite remote sensing applications for surface soil Moisture monitoring a review,'' Journal of Front, Earth Sci. Chine, vol. 3, issue2, pp. 237- 247, 2009. [5] J. D. Fast, and M. D. McCorcle, ''The Effect of Heterogonous Soil Moisture on a Summer Bar-clinic Circulation in the Central United States,'' Journal of Mon. Wee. Rev., vol.119, pp. 2140-2167, 1991. [6] E. A. James, "Soil moisture," Ltd, U. K, 1999. www.ijeab.com

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