Assessment of Climate Change Impacts on the Hydrology of ... - ITC

Assessment of Climate Change Impacts on the Hydrology of Gilgel Abbay Catchment in Lake Tana Basin, Ethiopia

Abdo Kedir Shaka March, 2008

Assessment of Climate Change Impacts on the Hydrology of Gilgel Abbay Catchment in Lake Tana Basin, Ethiopia by Abdo Kedir

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Integrated Watershed Modelling and Management

Thesis Assessment Board Prof. Dr. Z. Bob Su Dr. M. McCartney Dr.Ing. T.H.M. Rientjes Dr. A.S.M. Gieske

(chairman) (external examiner) (first supervisor) (second supervisor)

Head WRS dept, ITC Enschede IWMI, East Africa office WRS dept, ITC, Enschede WRS dept., ITC, Enschede

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

Dedicated Dedicated to my mother mother Lubaba Aman Mom, Mom, you are the main reason for being what I am

Abstract Climate scenarios differ substantially due to uncertainties with regard to climate forcing caused by greenhouse emissions, uncertainties caused by imperfect representation of the process in the models and uncertainties with regard to initial condition. The General Circulation Models (GCMs) which are considered as the most advance tools for estimating future climate change scenarios operate on a coarse scale. However, the climate impact studies in hydrology often require climate change information at fine spatial scale. Therefore the output from a GCM has to be downscaled to obtain information relevant to hydrological studies. This report presents the results of a study on downscaling large scale atmospheric variables simulated with General Circulation Models (GCMs) to meteorological variables at local scale in order to investigate the hydrological impact of possible future climate change in Gilgel Abbay catchment (Ethiopia). Statistical DownScaling Model (SDSM) was employed to convert the GCM output into daily meteorological variables appropriate for hydrological impact studies. The meteorological variables (minimum temperature, maximum temperature and precipitation) downscaled from SDSM were used as input to the HBV hydrological model which was calibrated (R2=0.86) and validated (R2=0.76) with historical data to investigate the possible impact of climate change in the catchment. The results obtained from this investigation indicate that there is significant variation in the seasonal and monthly flow. In the main rainy season (June-September) the runoff will be reduced by 12% in the 2080s. The result from synthetic (incremental) scenario also indicates that the catchment is sensitive to climate change. As much as 33% of the seasonal and annual runoff will be reduced if an increment of 2oC in temperature and reduction of 20% rainfall occur simultaneously in the catchment.

Key words Climate change, GCM, HBV, SDSM

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Acknowledgements First of all I thank the almighty ALLAH for His endless Grace and Blessing on me during all these months here at ITC and in all my life. I would like to express my thanks and gratitude to the Directorate of ITC for granting me this opportunity to study for a Master of Science degree. I am also grateful to my employer, South Water Resources Development bureau for providing me leave during my study. Very special thanks to my first supervisor, Dr.Ing.Tom Rientjes and second supervisor, Dr. Ambro Gieske for their support and encouragement during all the course of my study. Their critical comments and valuable advices helped me to take this research in the right directions. I would like to thanks Dr. M. McCartney and Dr.Yasir Mohamed from IWMI for their valuable comment in my work. I would like to thanks also Ethiopian Ministry of Water Resources and National Meteorological Agency for providing hydrological and meteorological data for my study. I would like to express my appreciation to all my course mates for their support and wonderful social atmosphere. Back home, I wish to express my deep gratitude to all friends and family members. Their prayers for me were the main source of inspiration, motivation and encouragement to continue this work. Special thanks to my sister, Siti Shifa. Last of all, thanks to everyone who helped me and I wish all of you wonderful happy days.

Abdo Kedir March 2007

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Table of contents Abstract....................................................................................................................................i Acknowledgements.................................................................................................................ii List of figures.......................................................................................................................... v List of tables .........................................................................................................................vii 1. INTRODUCTION............................................................................................................. 1 1.1. Background ................................................................................................................ 1 1.2. Research objective and question ................................................................................ 2 1.3. Thesis layout .............................................................................................................. 3 2. STUDY AREA AND DATA AVAILABILITY ............................................................... 5 2.1. Study area................................................................................................................... 5 2.1.1. 2.1.2. 2.1.3. 2.1.4. 2.1.5.

Location .......................................................................................................................5 Topography ..................................................................................................................6 Climate.........................................................................................................................6 Drainage Network........................................................................................................9 Soil, geology and land cover......................................................................................10

2.2. Data availability ....................................................................................................... 10 2.2.1. 2.2.2. 2.2.3. 2.2.4.

Meteorological data ...................................................................................................10 Hydrological data.......................................................................................................12 Remote sensing data ..................................................................................................12 Climate scenario data.................................................................................................12

3. LITERATURE REVIEW................................................................................................ 13 3.1. Climate model .......................................................................................................... 13 3.1.1. The Climatological baseline ......................................................................................14 3.1.2. Climate Scenario........................................................................................................15 3.1.3. Source of GCM and Emission Scenario ....................................................................18

3.2. Downscaling methods and tools............................................................................... 20 3.2.1. Dynamic downscaling................................................................................................21 3.2.2. Empirical (statistical) downscaling............................................................................21

3.3. Hydrological model.................................................................................................. 23 3.3.1. Use of hydrological modelling in climate change impact studies .............................23 3.3.2. Short description of the HBV model .........................................................................24

4. METHODOLOGY.......................................................................................................... 29 4.1. General Circulation Model (GCM).......................................................................... 29 4.2. Statistical DownScaling Model................................................................................ 30 4.2.1. General description of the model...............................................................................30 4.2.2. Model setup................................................................................................................33

4.3. HBV-96 .................................................................................................................... 37 4.3.1. General description ....................................................................................................37 4.3.2. Model input................................................................................................................37 iii

4.3.3. Calibration, validation and evaluation ...................................................................... 41

4.4. Hydrological impact of climate change....................................................................44 5. RESULTS AND DISCUSSION......................................................................................47 5.1. Downscaling the GCM output..................................................................................47 5.1.1. Downscaling the GCM for the baseline period......................................................... 47 5.1.2. Downscaling the GCM for future scenario............................................................... 51

5.2. Hydrological model calibration and validation results.............................................56 5.3. Hydrological impact of future climate change scenario ...........................................59 5.4. Uncertainties and sensitivity analysis.......................................................................61 6. CONCLUSION AND RECOMMENDATION...............................................................65 6.1. Conclusion................................................................................................................65 6.2. Recommendation......................................................................................................66 REFERENCE........................................................................................................................69 ANNEX.................................................................................................................................71 Annex A Definition of common terms and acronymys ...................................................71 Annex B Elevation and vegetation zone of the subbasins...............................................72

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List of figures Figure 2.1: Location of Gilgel Abbay catchment.....................................................................................5 Figure 2.2: Slope map of Gilgel Abbay catchment ..................................................................................6 Figure 2.3: Mean annual rainfall from 1996-2004...................................................................................7 Figure 2.4: Mean monthly rainfall distribution (1996-2004) for various stations in study area .............8 Figure 2.5: Mean monthly temperature from 1996-2004.........................................................................9 Figure 2.6: Flow record of Gilgel Abbay at Merawi (1997-2005) ........................................................10 Figure 2.7: Meteorological and gauging station ....................................................................................11 Figure 3.1: Schematic view of the processes and interaction in the global climate system (based on Hadley Centre for Climate Prediction and Research) ..........................................................13 Figure 3.2: Some alternative data sources and procedures for constructing climate scenarios for use in impact assessment. Highlighted boxes indicate the baseline climate and common types of scenario. Grey shading encloses the typical component of climate scenario generators (Houghton, 2001) .................................................................................................................17 Figure 3.3: The four IPCC SRES scenario storylines (Carter, 2007) ....................................................20 Figure 3.4: Schematic structure of HBV-96 model................................................................................25 Figure 4.1: A schematic illustrating the general approach to downscaling (Dawson & Wilby, 2007) .30 Figure 4.2: SDSM Version 4.1 climate scenario generation (Dawson & Wilby, 2007)........................32 Figure 4.3: The Africa Continent window with 2.5° latitude x 3.75 longitude grid size and location of the study area in the grid box. ..............................................................................................33 Figure 4.4: Steps in DEM hydroprocessing ...........................................................................................38 Figure 4.5: DEM of the Gilgel Abbay....................................................................................................39 Figure 4.6: Relationship between parameter in soil routine (SMHI, 2006)...........................................42 Figure 4.7: The response routine............................................................................................................43 Figure 4.8: Conceptual framework.........................................................................................................45 Figure 5.1: Observed and downscaled monthly mean minimum temperature for the baseline period (1961- 1990).........................................................................................................................47 Figure 5.2: Absolute model error in estimate of monthly minimum temperature .................................48 Figure 5.3: Variance of observed and downscaled monthly minimum temperature .............................48 Figure 5.4: Observed and downscaled monthly mean maximum temperature for the baseline period (1961-1990)..........................................................................................................................49 Figure 5.5: Absolute model error in estimate of monthly maximum temperature.................................49 Figure 5.6: Variance of observed and downscaled monthly maximum temperature.............................50 Figure 5.7: Mean daily observed and downscaled precipitation for the baseline period (1960-1990)..50 Figure 5.8: Absolute model error in estimates of the mean daily precipitation.....................................51 Figure 5.9: Variance of observed and downscaled monthly mean precipitation ...................................51 Figure 5.10: Change of downscaled monthly minimum temperature from the baseline period for HadCM3A2a ........................................................................................................................52 Figure 5.11: Change of downscaled monthly minimum temperature from the baseline period for HadCM3B2a ......................................................................................................................52 Figure 5.12: Change in monthly maximum temperature between the baseline period and future for HadCM3A2a......................................................................................................................53 v

Figure 5.13: Change in monthly maximum temperature between the baseline period and future for.. 53 Figure 5.14: Mean daily precipitation downscaled from HadCM3A2a................................................ 54 Figure 5.15: Mean daily precipitation downscaled from HadCM3B2a ................................................ 54 Figure 5.16: Mean daily precipitation for Ethiopian highland between the present and future ........... 55 Figure 5.17: Daily observed and simulated hydrograph during calibration period .............................. 57 Figure 5.18: Observed and simulated mean monthly hydrograph during calibration period................ 58 Figure 5.19: Daily observed and simulated hydrograph during the model development period (19962005) ................................................................................................................................. 58 Figure 5.20: Mean monthly flow for A2 scenario................................................................................. 60 Figure 5.21: Mean monthly flow for B2 scenario................................................................................. 60 Figure 5.22: Comparison of change in monthly runoff between the baseline period and 2080s.......... 61

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List of tables Table 2.1: List of station name, location and meteorological variables ................................................11 Table 3.1: Coupled Atmospheric General Circulation Models for which climate change simulations held by the IPCC Data Distribution Centre (Carter, 2007).................................................18 Table 3.2: The SRES Emission Scenarios (Houghton, 2001)................................................................19 Table 4.1: Types of predictor variables .................................................................................................34 Table 4.2: Correlation matrix.................................................................................................................35 Table 4.3: Large scale predictor variables selected for SDSM..............................................................36 Table 4.4: Weight of meteorological station by the inverse distance method .......................................39 Table 4.5: Weight of meteorological station by the Thiessen polygon method ....................................40 Table 4.6: Long term potential evapotranspiration for BahirDar meteorological station (1996-2005).41 Table 4.7: Long term potential evapotranspiration for Dangila meteorological station (1996-2005)...41 Table 5.1: List of optimum parameter set in calibration........................................................................56 Table 5.2: List of objective function and its value obtained during calibration ....................................57 Table 5.3: Average increase/decrease of runoff (%) from the present condition ..................................59 Table 5.4: Annual and seasonal runoff change in percentage from incremental scenario ....................62

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ASSESSMENT OF CLIMATE CHANGE IMPACTS ON THE HYDROLOGY OF GILGEL ABBAY CATCHMENT IN LAKE TANA BASIN, ETHIOPIA

1. INTRODUCTION 1.1.

Background

Climate changes refers to a change in the state of the climate that can be identified by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decade or more. Climate change may be due to internal processes and /or external forcings. Some external influences, such as changes in solar radiation and volcanism, occur naturally and contribute to the natural variability of the climate system. Other external changes, such as the change in the composition of the atmosphere that began with the industrial revolution, are the result of human activity. The temperature of the Earth is determined by the balance between the incoming solar radiation and the outgoing terrestrial radiation energy. The energy coming in from the sun can pass through atmosphere and therefore heats the surface of the Earth. But the radiation emitted from the surface of the Earth is partly absorbed by some gases in the atmosphere, and some of it re-emitted downwards. The effect of this is to warm the surface of the Earth and the lower part of the atmosphere. Without this natural greenhouse effect, the temperature of the Earth would be about 30ºC cooler than it is, and it would not be habitable. However, this important function of the atmosphere is being threatened by the rapidly increasing concentrations of greenhouse gases well above the natural level while also new greenhouse gases such as CFCs and the CFC replacement is added to the atmosphere as a result of human activities (for example, CO2 from fossil-fuel burning). This will add further warming which could threaten sustainability of the Earth (Jenkins, 2005). Nowadays there is strong scientific evidence that indicates the average temperature of the Earth’s surface is increasing due to greenhouse gas emissions. For instance, the average global temperature has increased by about 0.6ºC since the late 19th century. Also the latest IPCC (Intergovernmental Panel on Climate Change) scenarios project temperature rises of 1.4-5.8ºC, and sea level rises of 9-99 cm by 2100 (Houghton, 2001). Warming and precipitation are expected to vary considerably from region to region. Changes in climate average and the changes in frequency and intensity of extreme weather events are likely to have major impact on natural and human systems (Aerts & Droogers, 2004). With respect to hydrology, climate change can cause significant impacts on water resources by resulting changes in the hydrological cycle. For instance, the changes on temperature and precipitation can have a direct consequence on the quantity of evapotranspiration and on both quality and quantity of the runoff component. Consequently, the spatial and temporal availability of water resource, or in general the water balance, can be significantly affected which in turn affects agriculture, industry and urban development.

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Climate change is expected to have adverse impacts on socioeconomic development in all nations although the degree of the impact will differ. The IPCC findings indicate that developing countries such as Ethiopia will be more vulnerable to climate change. Climate Change may have far reaching implications for Ethiopia due to various reasons. The economy of the country mainly depends on agriculture, which is very sensitive to climate variations. A large part of the country is arid and semiarid and is highly prone to desertification and drought. The country has also a fragile highland ecosystem which is currently under stress due to population pressure. Forest, water and biodiversity resources of the country are also climate sensitive. Climate change is therefore a case for concern (NMSA, 2001). Despite the fact that the impact of climate change is forecasted at the global scale, the type and magnitude of the impact at a catchment scale is not investigated in most part of the world. Therefore it is necessary to study the effect of climate change at this scale in order to take the effect into account by the policy and decision makers when planning water resources management.

1.2.

Research objective and question

The general objective of this study is to assess the impact of climate change on the hydrology of the Gilgel Abbay catchment. The specific objectives of this study are: • •

• •

to develop a better understanding of hydrological impact of climate change on the Gilgel Abbay catchment; to develop and evaluate climate scenario data for maximum temperature, minimum temperature and precipitation based on a General Circulation Model and a Statistical DownScaling Model for Gilgel Abbay catchment; to develop incremental scenarios to assess the sensitivity of the catchment to climate; to quantify possible effects of climate change on the hydrology of Gilgel Abbay catchment based on the downscaled climate scenario data using selected hydrological model.

In order to meet the above objectives, the research questions for this study are: • •

2

what are the possibilities and limitations of a selected General Circulation Model and Statistical Downscaling Model for the hydrological assessment at the catchment scale; what are general trends of maximum temperature, minimum temperature and precipitation scenario in the future compared to the present condition and how this is reflected on the hydrology of the Gilgel Abbay River.


1.3.

Thesis layout

This thesis comprises six chapters and is organized as follows: Chapter one is an introduction to the study. Chapter two describes the study area and availability of data. Chapter three reports on a literature review about the subject matter. Chapter four describes the methodology applied in this research. In chapter five the results are shown and discussed. Chapter six finalizes the thesis by conclusions and recommendations.

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2.

STUDY AREA AND DATA AVAILABILITY

2.1.

Study area

2.1.1.

Location

Gilgel Abbay catchment is situated in the Northwest part of Ethiopia between 10º56' to11º51' N latitude and 36º44' to 37º23'E longitudes. The Gilgel Abbay River originates from small spring located near Gish Abbay at elevation of 2900 m a.m.s.l. (as extracted from SRTM) and drains to the Southern part of the Lake Tana. The catchment area of Gilgel Abbay River at the outlet to Lake Tana is around 4100 km2 (from SRTM) and it is the largest tributary of the Lake Tana basin which account around 30% of the total area of the basin. The catchment contributes the largest inflow into the Lake.

Figure 2.1: Location of Gilgel Abbay catchment

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2.1.2.

Topography

The elevation of Gilgel Abbay catchment varies from 1787 to 3518m a.m.s.l. (SRTM). The higher elevation ranges are located at the Southeast corner while the remaining area is relatively uniform. From the slope map of the catchment area, around 70% of the catchment area falls in the slope range from 0-8% and 25% of the area falls in the slope range of 8-30%. The remaining 5% of the area has slope greater than 30%. The longest flow path of the river towards the outlet is 163.2 km.

Figure 2.2: Slope map of Gilgel Abbay catchment

2.1.3.

Climate

The climate of Ethiopia is mainly controlled by seasonal migration of Intertropical Convergence Zone (ITCZ) and its associated atmospheric circulation but the topography has also an effect on the local climate. The traditional climate classification of the country is based on altitude and temperature shows the presence of five climatic zones namely: Wurch (cold climate at more than 3000 m altitude), Dega (temperate like climate-highland with 2500-3000 m altitude), Woina Dega ( warm-1500-2500 m altitude), Kola (hot and arid type, less than 1500 m in altitude), and Berha (hot and hyper-arid type) climate (NMSA, 2001). According to this classification, the majority part of the study area falls in Woina Dega climate however, small part of study area that is mainly at the South tips of the catchment falls in Dega Zone. There is high spatial and temporal variation of rainfall in the study area.

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The main rainfall season which accounts around 70-90% of the annual rainfall occurs from June to September. Small rains also occur sporadically during February/March to May. The mean annual rainfall (1996-2004) of the study area as shown in Figure 2.3 varies from around 1200 mm (Abbay Shelko) up to 2400 mm for Enjebara which is just outside the catchment boundary. The mean annual rainfall (1996-2004) of Sekela station which is located around the source of Gilgel Abbay River is around 1900 mm. 3000

Rainfall (mm)

2500 2000 1500 1000 500

Ze ge

ke la Se

aj a Ki da m

l G un di

je ba ra En

gi la D an

ir D ar

t Ad e

Ba h

Ab ba y

sh

el ko

0

Station name

Figure 2.3: Mean annual rainfall from 1996-2004

The monthly rainfall distributions of the study area indicate that July and August are the wettest month of the year which gets monthly rainfall amounts larger than 350 mm. The mean monthly rainfall for Abbay shelko, BahirDar, Dangila and Sekela for the period of 1996-2004 is shown in Figure 2.4. In the study area there is high diurnal change in temperature i.e. there is high variation between the daily maximum and minimum temperature. However, the seasonal variation of temperature is less compared to diurnal change. Generally the temperature of the area is highly affected by altitude where the temperature decreases with increases in altitude. The mean monthly temperature of Dangila, BahirDar and Adet meteorological stations from 1996-2004 is plotted in Figure 2.5 and this figure indicates that the mean monthly temperature at BahirDar station which is situated at low elevation is high compared to the other stations throughout the whole period. The mean monthly maximum and minimum temperature of Dangila (1996-2004) at elevation of 2226 m a.m.s.l (from SRTM) varies from 20.6ºC to 29.8ºC and 3.4ºC to 13.2ºC respectively. The mean monthly maximum and minimum temperature of BahirDar (1996-2004) at elevation of 1821 m a.m.s.l (from SRTM) varies from 23ºC to 33.5ºC and 6.3ºC to 16.7ºC respectively. Generally speaking the months of March through May are the hottest month whereas the lowest temperatures occur during December and January.

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BahirDar

Dangila

Time (month)

N ov

D ec

150.0

Time (month)

D ec

N ov

Oct

Sep

Aug

Jul

Jun

May

Apr

0.0

Figure 2.4: Mean monthly rainfall distribution (1996-2004) for various stations in study area

8

Oct

Se p

300.0

M ar

D ec

Oct

N ov

Sep

Aug

Jul

Jun

May

Apr

M ar

0.0

450.0

Feb

150.0

600.0

Jan

R ainfall (m m )

300.0

Feb

Aug

Sekela

450.0

Jan

Ju l

Time (month)

Time (month)

R ain fall (m m )

Ju n

M ay

0.0

D ec

Oct

N ov

Sep

Aug

Jul

Jun

May

Apr

M ar

Feb

0.0

150.0 Ap r

150.0

300.0

Mar

300.0

Fe b

450.0

450.0

Ja n

R ain fall (m m )

600.0

Jan

R a infall (m m )

Abbay Shelko


26

o

Temperature ( C)

24 22 20 18 16

Ja

n9

6 Ju l-9 6 Ja n97 Ju l-9 7 Ja n98 Ju l-9 8 Ja n99 Ju l-9 9 Ja n00 Ju l-0 0 Ja n01 Ju l-0 1 Ja n02 Ju l-0 2 Ja n03 Ju l-0 3 Ja n04 Ju l-0 4

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Time (month, year) Dangila (Elev 2126 m)

BahirDar ( Elev 1821 m)

Adet ( Elev 2230 m)

Figure 2.5: Mean monthly temperature from 1996-2004

2.1.4.

Drainage Network

The source of the Gilgel Abbay River is a spring that emerges from Gish Abbay Mountain near Sekela town. Locally the name of the stream is Abbay which is considered as the source of the Blue Nile River. On its way downstream, the river receives inflow from several rivers and streams and is named as Gilgel Abbay near Wetet Abbay town. Many perennials river such as Koga, Kilti, Bered, Areb drain in the river downstream of Wetet Abbay town till it reaches to the outlet at Lake Tana. The total drainage area of the river is around 4100 km2 and the longest flow path of river from source to the outlet of the catchment is around 163.2 km. There are two main gauging stations in the catchment which have continuous records for a long period. The first one is Gilgel Abbay at Merawi which is found close to Wetet Abbay town near the bridge of Gilgel Abbay River on the road from Addis Ababa to BahirDar. The area upstream of this gauging station is 1656 km2 (from SRTM). The other gauging station is Koga at Merawi which is found in Koga River before it joins the Gilgel Abbay River. The area upstream of this gauging station is 298 km2 (from SRTM). The longest flow path of Gilgel Abbay River from the source to the gauging station of Gilgel Abbay at Merawi is 84 km which is approximately half of the longest flow path of the river.

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01/07/2005

01/01/2005

01/07/2004

01/01/2004

01/07/2003

01/01/2003

01/07/2002

01/01/2002

01/07/2001

01/01/2001

01/07/2000

01/01/2000

01/07/1999

01/01/1999

01/07/1998

01/01/1998

01/07/1997

450 400 350 300 250 200 150 100 50 0

01/01/1997

3

Discharge (m /s)


Time (day,month,year)

Figure 2.6: Flow record of Gilgel Abbay at Merawi (1997-2005)

2.1.5.

Soil, geology and land cover

Quaternary volcanic rocks overlay the older Tertiary volcanics over much of the Gilgel Abbay catchment boundary. The Quaternary volcanic sequence comprises blocky and fractured vesicular basalt, some basaltic breccias and tuffs perhaps as much as 200-300 m thick (SMEC, 2007). The soil of Gilgel Abbay catchment is mostly covered by Haplic Luvisols with area coverage of around 2583 km2. Luvisol are generally fertile soils because of their mixed mineralogy, relatively high nutrient content and presence of weatherable minerals. The cultivated areas are mostly located on this type of the soil throughout the catchment (Tessema, 2006). The main land covers in the Gilgel Abbay catchment are grassland, marshland, cultivated land, forest and grassland with frequent patches of shrubs, woods, trees and cultivated lands.

2.2.

Data availability

2.2.1.

Meteorological data

Meteorological data was required for two purposes in this study. First the data was used as input to the HBV model in hydrological model development. Second the data was used for downscaling the GCM data using Statistical DownScaling Model (SDSM). Based on these objectives, meteorological data was collected during the field survey from the Ethiopian National Meteorological Agency in Addis Ababa and BahirDar office. Since there are few meteorological stations which have relatively long period of record inside the catchment, data was also collected from the station surrounding the catchment as shown in Figure 2.7.

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• Meteorological station ▪ Gauging station

Figure 2.7: Meteorological and gauging station

The number of meteorological variables collected varies from station to station depending on the class of the stations that are grouped into three. The first group of stations contain only rainfall data. The second group include maximum and minimum temperature in addition to rainfall data. There are also stations which contain variables like humidity, sunshine hours, and wind speed in addition to rainfall, maximum temperature and minimum temperature. Table 2.1: List of station name, location and meteorological variables

No

Station Name

Latitude Longitude Rainfall (degree) (degree)

Max Temp

Min Temp

1

Sekela

11

37.22

√

2

Gundil

10.95

37.07

√

√

√

3

Dangila

11.12

36.83

√

√

√

4

Abay Shelko

11.38

36.87

√

√

√

5

Kidamaja

11

36.8

√

√

√

6

Zege

11.68

37.32

√

√

√

7

BahirDar

11.6

37.42

√

√

8

Adet

11.27

37.47

√

√

9

Enjebara

10.97

36.9

√

Relative Wind Sunshine humidity speed hours

√

√

√

√

√

√

√

√

√

√

√

11


The length of data collected also varies greatly from station to station. There are few stations which have long continuous records. The only station which has data for more than 25 years in the study area is BahirDar meteorological station for rainfall, maximum temperature and minimum temperature. Dangila and Enjebara station have relatively long period of records. All stations listed above contain daily rainfall data for at least ten years. Therefore all stations were used for hydrological model development. However, for deriving statistical relationships between the predictand and predictor long period of records are required. Hence downscaling experiments have been executed based on BahirDar meteorological data which fulfil all input requirements for SDSM.

2.2.2.

Hydrological data

The streamflow of Gilgel Abbay River was required for calibrating and validating the model. There are two main gauging stations (Gilgel Abbay at Merawi and Koga at Merawi) inside the catchment which have continuous record for a relatively long period and therefore daily streamflow data (19732005) for these stations were collected during the field work from the Hydrology Department of Ministry of Water resources.

2.2.3.

Remote sensing data

The HBV-96 model can be applied as a semi distributed model, hence the catchment was divided into different subbasins where the subbasin further divided in elevation and vegetation zones. Based on this, a digital elevation model of the catchment was prepared using Shuttle Radar Topography Mission (SRTM) with resolution of 90 m and the DEM was processed using DEM hydroprocessing to extract drainage area, drainage network and to divide the area into different subbasins and elevation zones. Moreover, an ASTER image together with ground truth collected during field survey was used to classify the catchment in different vegetation zones (forest and field) to use as input in the HBV model.

2.2.4.

Climate scenario data

Climate scenario data is required to quantify the relative change of climatic variables between the current and future time horizon which in turn is used as input to hydrological model for assessment of hydrological impacts. The climate scenario data used for statistical downscaling model (SDSM) was obtained from the Canadian Institute for climate studies website for model output of HadCM3 (http://www.cics.uvic.ca/scenarios/sdsm/select.cgi). The predictor variables (see Table 4.1) are supplied on a grid by grid basis so that the data was downloaded from the nearest grid box to the study area.

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3. 3.1.

LITERATURE REVIEW Climate model

The Earth’s climate is governed by the interaction between many processes in the atmosphere, ocean, land surface and cryosphere. The interactions are complex and extensive so that quantitative predictions of the impact on the climate of greenhouse gas increase cannot be made through simple intuitive reasoning. For this reason, computer models have been developed which try to mathematically simulate the climate system, including the interaction between the system component (Dibike & Coulibaly, 2004). For climate simulation, the major components of the climate system that must be represented in submodels are atmosphere, ocean, land surface, cryosphere and biosphere, along with the processes that go on within and between them. The mathematical models generally used to simulate the present climate and project future climate with forcing by greenhouse gases and aerosols are generally referred to as GCMs (General Circulation Models). General Circulation Models in which the atmosphere and ocean components have been coupled are also known as Atmosphere-Ocean General Circulation Models (AOGCMs). Currently, the resolution of the atmospheric part of a typical model is about 250 km in the horizontal and about 1 km in the vertical above the boundary layer. The resolution of a typical ocean model is about 200 to 400 m in the vertical, with a horizontal resolution of about 125 to 250 km. Many physical processes, such as those related to clouds or ocean convection, take place at much smaller spatial scales than the model grid and therefore cannot be modelled and resolved explicitly. Their average effects are approximately included in a simple way by taking advantage of physically based relationships with the larger-scale variables through the techniques of parameterizations (Houghton, 2001).

Figure 3.1: Schematic view of the processes and interaction in the global climate system (based on Hadley Centre for Climate Prediction and Research)

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3.1.1.

The Climatological baseline

In order to have a basis for assessing future impacts of climate change, it is necessary to obtain a quantitative description of the changes to be expected. However, before considering future climate it is important to characterize the present-day or recent climate in a region-often referred to as the climatological baseline. This baseline period is needed to define the observed climate with which climate change information is usually combined to create a climatic scenario. When using climate model results for scenario construction, the baseline serves as the reference period from which the modelled future change in climate is calculated (Houghton, 2001). The choice of baseline period has often been governed by availability of the required climate data. The baseline period is usually selected according to the following criteria (Carter, 2007): • representative of the present-day or recent average climate in the study region; • of a sufficient duration to encompass a range of climatic variations, including number of significant weather anomalies (e.g. severe droughts or cool seasons); • covering a period for which data on all major climatological variables are abundant, adequately distributed over space and readily available; • including data of sufficiently high quality for use in evaluating impacts. A popular climatological baseline period is the non-overlapping 30-year “normal” period as defined by the World Meteorological Organization (WMO). The current WMO normal period is 1961-1990. There are a number of alternative source of baseline climatological data that can be applied in impact assessments (Carter, 2007). These are not mutually exclusive, and include: I. National meteorological agencies and archives II. Supranational and global data sets III. Climate model outputs IV. Weather generators I National meteorological agencies and archives The most common source of observed climatological data applied in impact assessments are the national meteorological agencies. These agencies usually have the responsibility for the day-to-day operation and maintenance of the national meteorological observation networks for the purposes of weather forecasting and other public services. II Supranational and global data sets As well as serving national needs, climatological data from different countries have also been combined into various supranational and global data sets. The data sets include observations of surface variables at a monthly time step over land and ocean, surface and upper air observations at a daily time step from sites across certain regions and, for recent decades, satellite observations. III Climate model outputs There are two types of information from global climate models that may be also useful in describing 14


the climatological baselines: reanalysis data and output from GCM and RCM. Reanalysis data: These are fine resolution gridded data which combine observations with simulated data from numerical models. Through a process known as data assimilation, the observations (available only sparsely and irregularly over the globe), along with data from the satellites and information from a previous model forecast, are input to a short–range weather forecast model. This is integrated by one time step (typically 6 hours) and combined with observation data for the corresponding period. The result is a comprehensive and dynamically consistent three-dimensional gridded data set (the “analysis”) which represents the best estimate of the state of the atmosphere at that time. Large quantities of past observational data that were used operationally as input to earlier versions of weather forecasting models have subsequently been “reanalysed” using the current generation of numerical models to produce high resolution data set. These data sets are primarily used by the atmospheric scientists for model development and testing. However, impact analysts and scenario developers are continuously finding uses for such data, for instance, by examining observed relationships between reanalysed upper air fields and surface variables to produce regional climate scenarios downscaled from GCM. Output from GCM and RCM simulations: Another model-based source of information on the present day climate are multi-century simulations from AOGCMs. These simulations attempt to represent the dynamics of the global climate system unforced by anthropogenic changes in atmospheric composition. IV Weather generators A fourth method of characterizing the baseline climate is to apply stochastic weather generators. These are computer models that generate synthetic series of daily or sub-daily resolution weather at a site conditional on the statistical features of the historically observed climate.

3.1.2.

Climate Scenario

Climate scenario refers to a plausible future climate that has been constructed for explicit use in investigating the potential consequence of anthropogenic climate change (Houghton, 2001). It is important to emphasise that, unlike weather forecast, climate scenarios are not predictions. Weather forecasts make use of enormous quantities of information on the observed state of the atmosphere and calculate, using the laws of physics, how this state will evolve during the next few days, producing a prediction of the future – a forecast. In contrast, a climate scenario is a plausible indication of what the future could be like over the decades or centuries, given a specific set of assumptions. These assumptions include future trends in energy demand, emissions of greenhouse gases, land use change as well as assumptions about the behaviour of the climate system over long time scales. It is largely the uncertainty surrounding these assumption which determine the range of possible scenarios (Carter, 2007). Various types of climate scenarios are used in impact assessment. The most common scenario type applied is based on the outputs from the climate models. The other types have been applied with 15


reference to, or in conjunction, with model-based scenarios, namely: incremental scenario for sensitivity studies and analogue scenarios. The suitability of each type of scenario for use in policy relevant impact assessment can be assessed according to five criteria (Houghton, 2001): 1. Consistency at regional level with global projections: Scenario changes in the regional climate may lie outside the range of global mean changes but should be consistent with theory and model based results. 2. Physical plausibility and realism: Changes in climate should be physically plausible, such that changes in different climatic variables are mutually consistent and credible. 3. Appropriateness of information for impact assessments: Scenarios should present climate changes at an appropriate temporal and spatial scale, for a sufficient number of variables, and over an adequate time horizon to allow for impact assessment. 4. Representativeness of the potential range of future regional climate change. 5. Accessibility: The information required for developing climate scenarios should be readily available and easily accessible for use in impact assessment. I Incremental scenario: Incremental scenarios describe techniques where a particular climate (or related) elements are changed by arbitrary amounts. For example, adjustments of baseline temperature by +1, +2, +3, +4ºC and baseline precipitation by ± 5, 10, 15 and 20 percent could represent various magnitude of future change (Carter, 2007). Incremental scenarios provide information on an ordered range of climate changes and can readily be applied in a consistent and replicable way in different studies and regions, allowing for direct intercomparison of results. However, such scenarios do not necessarily present a realistic set of changes that are physically plausible. They are usually adopted for exploring system sensitivity prior to the application of more credible, model based scenario (Houghton, 2001). II Analogue scenario: Analogue scenarios are constructed by identifying recorded climate regimes which may resemble the future climate in a given region. Both temporal and spatial analogues have been used in constructing climate scenarios. Temporal analogues make use of climatic information from the past as analogue of possible future climate. Spatial analogues are regions which today have a climate analogues to that anticipated in the study region in the future. Since the causes of the analogue climate are most likely due to changes in the atmospheric circulation, rather than to greenhouse gas induced climate change, these types of scenarios are not ordinarily recommended to represent the future climate in quantitative impact assessments. III Scenarios based on outputs from climate models: Climate models at different spatial scales and levels of complexity provide the major source of information for constructing scenarios. The most common method of developing climate scenarios for quantitative impact assessment is to use results from General Circulation Models (GCMs). These are numerical models representing physical processes in the atmosphere, ocean, cryosphere and land surface and considered as the most advanced tools currently available for simulating the response of the global climate system to increasing greenhouse gas concentrations. GCMs depict the climate using a three dimensional grid over the

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globe, typically having a horizontal resolution of between 250 and 600 km, 10 to 20 vertical layers in the atmosphere and sometimes as many as 30 layers. The evolving (transient) pattern of climate response to gradual changes in atmospheric composition was introduced into climate scenarios using the outputs from Coupled Atmosphere-Ocean Models from the early 1990s onwards. Recent AOGCM simulations begin by modelling historical forcing by greenhouse gases and aerosols from the 19th or early 20th century onwards. Climate scenarios based on these simulations are being increasingly adopted in impact studies. However, there are several limitations that restrict the usefulness of AOGCM outputs for impact assessment (Houghton, 2001): I. the large resources required to undertake GCM simulations and store their outputs, which have restricted the range of experiments that can be conducted; II. their coarse spatial resolution compared to the scale of many impact assessments; III. the difficulty of distinguishing an anthropogenic signal from the noise of natural internal model variability; IV. the difference in climate sensitivity between the models. The source of the various scenario and their mutual linkages discussed above is shown in Figure 3.2.

Figure 3.2: Some alternative data sources and procedures for constructing climate scenarios for use in impact assessment. Highlighted boxes indicate the baseline climate and common types of scenario. Grey shading encloses the typical component of climate scenario generators (Houghton, 2001)

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3.1.3.

Source of GCM and Emission Scenario

Source of GCM The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre (DDC) was established in 1998 by the World Meteorological Organization and the United Nations Environmental Programme, following a recommendation by the Task group on Data and Scenario Support for Impact and Climate Assessment, to facilitate the timely distribution of a consistent set of up-to-date scenarios of changes in climate and related environmental and socio-economic factor for use in impact and adaptation assessment. Therefore the result from the following modelling centre can be found through IPCC DDC. Table 3.1: Coupled Atmospheric General Circulation Models for which climate change simulations held by the IPCC Data Distribution Centre (Carter, 2007)

Modelling centre Commonwealth Scientific and Industrial Research Organization (CSIRO) Max Planck Institut fur Meteorologie (formerly Deutsches Klimarechenzentrum, DKRZ) Hadley Centre for Climate Prediction and Research Canadian Centre for Climate modelling and Analysis (CCCMA) Geophysical Fluid Dynamics Laboratory (GFDL)

country Australia

Model(s) CSIRO-Mk2

Germany ECHAM4/OPYC and ECHAM3/LSG UK HadCM2 and HadCM3 Canada CGCM1 and CGCM2 USA

National Centre for Atmospheric Research (NCAR) USA Centre for Climate Research Studies (CCSR) and National JAPAN Institute for Environmental Studies (NIES)

GFDL-R15 and GFDLR30 NCAR DOE-PCM CCSR-NIES

The full sets of monthly results from the experiment (and more detailed technical information) can be obtained from the DDC GCM Archive (http://www.ipcc-data.org/index.html, 2007). However, daily fields are only available directly from the respective modelling centres. For example the UK Hadley Centre archived over 20 daily variables from their HadCM3SRES A2 and B2 experiments (including temperature, humidity, energy and dynamic variables at several levels in the atmosphere). Some groups, such as the Canadian Climate Impacts Scenarios (CCIS) project have begun supplying gridded predictor variables on-line (http://www.cics.uvic.ca/scenarios/sdsm/select.cgi) for the specific needs of the downscaling community. In addition, a large suite of secondary variables such as atmospheric stability, vorticity, divergence, zonal and meridional airflows may be derived from standard daily variables such as mean sea level pressure or geopotential height (Wilby et al., 2004). Emission scenario In 1996, the IPCC began the development of a new set of emission scenarios, effectively to update and replace the well-known IS92 scenarios. The approved new set of scenarios is described in the

18


IPCC special report on emission Scenarios (SRES). Four different narrative storylines were developed to describe consistently the relationships between the forces driving emission and their evolution and to add context for the scenario quantification. The resulting set of 40 scenarios cover a wide range of the main demographic, economic and technological driving forces of the future greenhouse gas and sulphur emission. Each scenario represents a specific quantification of one of the four storylines. All the scenarios based on the same storyline constitute a scenario “family” which briefly describe the main characteristics of the four SRES storylines and scenario family as shown in Table 3.2 and Figure 3.3. Table 3.2: The SRES Emission Scenarios (Houghton, 2001)

A1. The A1 storylines and scenario family describes a future world of very rapid economic growth, global population that peaks in the mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per Capital income. The A1 scenario family develops into three groups that describe alternative directions of technological change in energy system. The three A1 groups are distinguished by their technological emphasis: fossil intensive (A1FI), nonfossil energy source (A1T), or a balanced across all sources (A1B) (where balanced is defined as not relying too heavily on one particular energy sources, on the assumption that similar improvement rates apply to all energy supply and end-use technologies). A2. The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing population. Economic development is primarily regionally oriented and per Capital economic growth and technological change more fragmented and slower than other storylines. B1. The B1 storyline and scenario family describes a convergent world with the same global population, that peaks in the mid-century and declines thereafter, as in the A1 storyline, but with rapid change in economic structures towards a service and information economy, with reductions in material intensity and the introduction of clean and resource efficient technologies. The emphasis is on global solutions to economic, social and environmental sustainability, including improved equity, but without additional climate initiatives. B2. The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social and environmental sustainability. It is a world with continuously increasing global population, at a rate lower than A2, intermediate levels of economic development, and less rapid and more diverse technological change than in the A1 and B1 storylines. While the scenario is also oriented towards environmental protection and social equity, it focuses on local and regional levels.

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Figure 3.3: The four IPCC SRES scenario storylines (Carter, 2007)

3.2.

Downscaling methods and tools

The General Circulation Models (GCMs) used to simulate the present and project future climate with forcing by greenhouse gases and aerosols, typically divide the atmosphere and ocean into a horizontal grid with a resolution of 2 to 4º latitude and longitude, with 10 to 20 layers in the vertical. In general, most GCMs simulate global and continental scale processes in detail and provide a reasonably accurate representation of the average planetary climate. Over the past decade, the sophistication of such models has increased and their ability to simulate present and past global and continental scale climates has substantially improved. Nevertheless, while GCMs demonstrated significant skill at the continental and hemispherical scale and incorporate a large proportion of the complexity of the global system, they are inherently unable to represent local sub-grid scale features and dynamics, such as local topographical features and convective cloud process (Dibike & Coulibaly, 2005). Moreover, GCMs were not designed for climate change impact studies and do not provide a direct estimation of the hydrological responses to climate change. For example, assessment of future river flows may require (sub-) daily precipitation scenarios at catchment, or even station scales. Therefore, there is a need to convert GCM outputs into at least a reliable daily rainfall series at the scale of the watershed to which the hydrological impact is going to be investigated. The methods used to convert GCM outputs into local meteorological variables required for reliable hydrological modelling are usually referred to as “downscaling” techniques. There are two categories of climatic downscaling, namely dynamic downscaling and statistical downscaling.

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3.2.1.

Dynamic downscaling

Dynamic downscaling is a method of extracting local-scale information by developing and using limited-area models (LAMs) or regional climate models (RCMs) with the coarse GCM data used as boundary condition. The basic steps are then to use the GCMs to simulate the response of the global circulation to large-scale forcing and RCM to account for sub-GCM grid scale forcing such as complex topographical features and land cover heterogeneity in a physically-based way, and thus enhance the simulation of atmospheric circulations and climate variables at fine spatial scales. RCMs have recently been developed that can attain horizontal resolution in the order of tens of kilometres or less over selected areas of interest. Despite the fact that the resolution of RCM is finer than the GCM, there are several acknowledged limitations. RCMs still require considerable computing resources and are as expensive to run as a global GCM. Moreover these models cannot meet the needs of spatially explicit models of hydrological systems. Hence there remains the need to downscale the results from such models to individual sites or localities for impact studies (Xu, 1999).

3.2.2.

Empirical (statistical) downscaling

Empirical (statistical) downscaling involves developing a quantitative relationship between largescale atmospheric variables (predictors) and local surface variables (predictands). From this perspective, regional or local climate information is derived by first determining a statistical model which relates large-scale climate variables (or “predictor”) to regional and local variables (or “predictands”). Then the large-scale output of a GCM simulation is fed into the statistical model to estimate the corresponding local and regional climate characteristics. The most common form has the predictand as a function of the predictor(s). The concept of regional climate being conditioned by the large-scale state may be written as: R = F(L) (1) Where, R represents the predictand (a regional or local climate variables), L is the predictor (a set of large-scale climate variables), and F is a deterministic/stochastic function conditioned by L and has to be found empirically from observation or modelled data sets. Most statistical downscaling work has focussed on single-site (i.e. point scale) daily precipitation as the predictand because it is the most important input variable for many natural systems models and cannot be obtained directly from climate model output. Predictor sets are typically derived from sea level pressure, geopotential height, wind fields, absolute or relative humidity, and temperature variables (Wilby et al., 2004). One of the primary advantages of the statistical downscaling method is that they are computationally inexpensive and thus can be easily applied to output from different GCM experiments. Another advantage is that they can be used to provide site-specific information, which can be critical for many climate change studies. The major theoretical weakness of statistical downscaling is that their basic assumption is not verifiable, i.e. the statistical relationships developed for the present day climate also hold under the different forcing conditions of possible future climates (Wilby et al., 2004). Despite this limitation, statistical downscaling is applied in this study because this method require less 21


computational resource and less knowledge of atmospheric chemistry compared to dynamic downscaling method. A diverse range of empirical/statistical downscaling techniques have been developed over the past few years and each method lies in one of the three major categories, namely, regression (transfer function) methods, stochastic weather generators and weather typing schemes. I Regression model Regression-based downscaling methods rely on direct quantitative relationship between the local scale climate variables (predictand) and the variables containing the large scale climate information (predictors) through some form of regression functions. Individual downscaling schemes differ according to the choice of mathematical transfer function, predictor variables or statistical fitting procedure. One of the well recognized statistical downscaling tools that implements a regression based method is the Statistical Down-Scaling Models (SDSM). SDSM 4.1 facilitates the rapid development of multiple, low cost, single-site scenarios of daily surface weather variables under the present and future climate forcing. Additionally, the software performs ancillary tasks of data quality control and transformation, predictor variables pre-screening, automatic model calibration, basic diagnostic testing, statistical analyses and graphing of climate data (Dawson & Wilby, 2007). II Stochastic weather generators Weather generators (WGs) are models that replicate the statistical attribute of local climate variables (such as the mean and variance) but not observed sequence of events. These models are based on the representations of precipitation occurrence via Markov processes for wet-/dry or spell transitions. Secondary variables such as wet-day amounts, temperatures and solar radiation are often modelled conditional on precipitation occurrence. WGs are adapted for statistical downscaling by conditioning their parameters on large-scale atmospheric predictors, weather states or rainfall properties (Wilby et al., 2004). One well known stochastic downscaling tool for use in climate impact studies is the Long Ashton Research Station Weather Generator (LARS-WG). LARS-WG is a stochastic weather generator which can be used for the simulation of weather data at a single site under both current and future climate condition. These data are in the form of daily time-series for a suite of climate variables, namely precipitation (mm), maximum and minimum temperature (ºC) and solar radiation(MJm-2day-1) (Semenov, 2002). III Weather typing scheme Weather classification methods group days into a finite number of discrete weather types or “states” according to their synoptic similarity. Typically, weather states are defined by applying cluster analysis to atmospheric fields or using subjective circulation classification schemes (Wilby et al., 2004). 22


3.3.

Hydrological model

3.3.1.

Use of hydrological modelling in climate change impact studies

Hydrological models are mathematical formulations which determine the runoff signal which leaves a watershed basin from the rainfall signal received by this basin. They provide a means of quantitative prediction of catchment runoff that may be required for efficient management of water resources. Such hydrological models are also used as means of extrapolating from those available measurements in both space and time into the future to assess the likely impact of future hydrological change. Changes in global climate are believed to have significant impacts on local hydrological regimes, such as in streamflows which support aquatic ecosystem, navigation, hydropower, irrigation system, etc. In addition to the possible changes in total volume of flow, there may also be significant changes in frequency and severity of floods and droughts. Hence hydrological models provide a framework to conceptualize and investigate the relationship between climate and water resource. Chong-vu Xu mention the advantages of hydrological models in climate change impact studies as follows (Xu, 1999): 1. Models tested for different climatic/physiographic conditions, as well as models structured for use at various spatial scales and dominant process representations, are readily available. 2. GCM-derived climate perturbations (at different level of downscaling) can be used as model input. 3. A variety of response to climate change scenarios can be modelled. 4. The models can convert climate change output to relevant water resource variables related, for example, to reservoir operation, irrigation demand, drinking and water supply. An investigation of climate-change effects on regional water resources consists of the following three steps: (1) using climate models to simulate climatic effect of increased atmospheric concentration of greenhouse gases, (2) using downscaling techniques to link climate models and catchment-scale hydrological models or to provide catchment-scale climate scenarios as input to hydrological models, and (3) using hydrological models to simulate hydrological impacts of climate change. Many investigations were done in the past two decades on the application of hydrological model for assessment of the potential effect of climate change on variety of water resource issues. These investigations can range from the evaluation of annual and seasonal streamflow variation using simple water-balance models to the evaluation of variations in surface and groundwater quantity, quality and timing using complex distributed-parameter model that simulate a wide range of water, energy and biochemical processes. Based on the level of complexity, these models can be grouped into four categories: (1) Empirical models (annual base), (2) Water-balance models (monthly), (3) Conceptual lumped-parameter models (daily), and (4) Process-based distributed models (hourly or finer base). The choices of a model for a particular case study depend on many factors, the purpose of the study and model availability being the dominant ones. For detailed assessment of surface flow, conceptual models were applied in many parts of the world. Booij,(2005) discusses the advantages of conceptual 23


models for climate change study as a nice compromise between the need for simplicity on one hand and the need for a firm physical basis on the other hand. One of the more frequently used conceptual model for climate change impact study is the HBV model. The HBV model is widely used in Nordic countries as a tool to assess the climate change effects. Climate change impact on runoff and hydropower in the Nordic countries have been studied by using the HBV models (Xu, 1999). Booij,(2005) applied the HBV model to assess the impact of climate change on river flooding on Meuse River in the Netherlands. Dibike and Coulibaly,(2005) applied the HBV model to study the hydrological impact of climate change in the Saguenay watershed in Canada. In conclusion, the HBV model is selected for this study because of the following reason: 1. the input data requirement is moderate; 2. the model simulate the major hydrological process in the catchments; 3. the model was tested for the impact of climate change on hydrological study in different parts of the world and 4. the availability of the model

3.3.2.

Short description of the HBV model

The HBV model is a conceptual hydrological model for continuous calculation of runoff. It was originally developed at the Swedish Meteorological and Hydrological Institute (SMHI) in the early 1970s. Since then the model has found application in more than 40 countries. Originally the HBV model was developed for runoff simulation and hydrological forecasting, but the scope of the application has increased steadily. Today the HBV model can be used (Seibert, 2002): • for water balance studies; • for runoff forecasting (flood warning and reservoir operation); • to compute design floods for dam safety; • to investigate the effects of changes with in the catchmnent; • to simulate climate change effects. In 1993 the Swedish Association of River Regulation Enterprises (VASO) and SMHI initiated a major revision of the structure of the HBV model with the same philosophy of simplicity as the original HBV model to make the model more physically reasonable and up-to-date with the current hydrological and meteorological knowledge. HBV-96 is the final result of this model revision. The HBV-96 is best described as a semi-distributed conceptual model. The model simulates daily discharge using daily rainfall, temperature and estimates of potential evapotranspiration as input together with geographic information about the river catchment. The evapotranspiration values used are long-term monthly averages. Discharge observations are used to calibrate the model, and to verify and correct the model before a runoff forecast. The model consists of subroutines for snow accumulation and melt, soil moisture accounting procedure, routines for runoff generation and finally, a simple routing procedure. It is possible to run the model separately for several subbasins and then add the contributions from all subbasins. Calibration as well as forecasts can be made for each subbasin. For basin of considerable 24


elevation range a subdivision into elevation zones can be made. Each elevation zone can further be divided into different vegetation zones (forested and non-forested areas). A schematic sketch of the HBV-96 model is shown in Figure 3.4.

Figure 3.4: Schematic structure of HBV-96 model

Precipitation and snowmelt Precipitation calculations are made separately for each elevation/ vegetation zone within a subbasin. To separate between snow and rainfall a threshold temperature is used: RF = Pcorr·rfcf·P if T>tt SF = Pcorr·sfcf·P if T