SWAT-Hydrologic Modelling and Simulation of … · Obseved Vs Modelled Max Temp ... Application of...

SWAT-Hydrologic Modelling and Simulation of Inflow to Cascade Reservoirs of the semi-ungaged Omo-Gibe River Basin, Ethiopia

Teshome Seyoumand

Manfred Koch

Department of Geohydraulics and Engineering HydrologyUniversity of Kassel

June 04, 2013

Koblenz, Germany

Outline1. Background

2. Study Area

3. Objectives

4. Materials & Methods

5. SDSM application

5. SWAT Model

6. Result & Discussion

7. References

SWAT-Hydrologic Modeling & Simulation of InflowTeshome Seyoum

1. BackgroundEthiopia has abundant water resources, but they have yet to contribute more than a fraction of their potential to achieving the national economic & social dev’t goals

The primary water resource management challenges:

its extreme hydrological variability & seasonality &

the international nature of its most significant surface water resources

Runoff patterns in the Omo Gibe river basin have changed over the last twenty years


Background cont...forests & vegetation have been cleared

hydraulically developed

Hence, not be enough to sustain a healthy ecological env’t in the d/n sections of the Omoriver

to alleviate some of the conflicts of interest b/nmaximum power prodn & sufficient water availability for the local popn

all aspects of the water resources of the Basin need to be measured, estimated or simulatedto make effective & economically viable plans for sustainable future developments.

Modeling of Cascade Dams & Reservoir OperationTeshome Seyoum

2. Study Area• The Omo-Gibe River Basin is almost 79,000 km2 in area

• The basin lies:

– Longitude 4°30'N - 9°30'N,

– Latitude 35°0'E - 38°0'E,

– Altitude of 2800masl.

• The general direction of flow of the river is southwards towards the Lake Turkana.


Location of Dam sites & Lake TurkanaOmo-Gibe River System & dam sites Lake Turkana


3. Objective of Research

Main objective

• To simulate runoff & inflow to cascade reservoirs of the semi-ungaged Omo-Gibe river basin


I. Collection of Input data:

1.DEM data

• 30m*30m Resolution

(ASTGTM)

2. Climate Data

•Tmax, Tmin & RF

• 1970-2000 (31Yrs)

3. Hydrological Data

• 22 gage stations (@u/s)

4. Soil & Land use/cover

SWAT-Hydrologic Modeling & Simulation of Inflow

4. Methodology

Teshome Seyoum

Methodology cont...II. Filling climate & hydrological flow data:

1. Tmax, Tmin & RF (daily & monthly)

�WXGEN

�SDSM

2. Hydrological flow data were filled

�Multiple regression of R program

III. Simulation of SWAT Model

IV. Calibration, Validation & Uncertainity

V. Sensitivity analysis


5. SDSM-application

1 Introduction

• SDSM- produces high resolution climate change scenarios,

• enables the production of climate change time series at sites for which there are sufficient daily data for model calibration,

• as well as archived General Circulation Model (GCM) output to generate scenarios,

• used as a stochastic weather generator or to infill gaps in meteorological data.

2. Methodology


• 7-steps

1. Quality control & data transformation;

2. Screening of potential downscaling predictor variables;

3. Model calibration;

4. Generation of ensembles of current weather data using observed predictor variables;

5. Statistical analysis of observed data & climate change scenarios;

6. Graphing model output;

7. Generation of ensembles of future weather data

Teshome Seyoum

Asendabo Station-RF Unfilled & filled data chart


Statistical analysis using mean, variance,sum& pdf plot of unfilled & filled Precn data


0

13

0

13

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

PREC_unfilled

PREC_Filled

Asendabo Precipitation Bar Chart

0

205

0

205


PREC_unfilled

PREC_Filled


0

418

0

418


PREC_Unfilled

PREC_Filled


0

10000

0

10000

x axis label

AsendaboObsPCPunfilled.dat

Asendabo PDF Chart

Teshome Seyoum

Generated Precipitation data from 2001-2040.

-4

5

-4

5

Year

PCPNCEP_1970-2000.dat

PCPGCM_2001_2040.dat

Standardised Precipitation Index

Teshome Seyoum

Statistical analysis using mean, varience, sum & pdf of Observed & Modelled data

. .


0

12

0

12


Observed Prec

Model Prec

Observed V Model Mean Precipiritation

0

239

0

239


Observed Variance

Modelled Variance

Observed Vs Simulated Prec Varience

0

347

0

347


Observed Prec Sum

Modelled Prec Sum

Observed Vs Modelled Monthlly Prec Sum

0

10000

0

10000

x axis label

AssendaboObsPrec.dat

Mean

Asendabo Prec PDF Chart

Teshome Seyoum

Asendabo unfilled and filled Tmax datachart.

Teshome Seyoum


Statistical analysis cont...

0

58

0

58


Maximum Temperature_Unfilled

Mimum Temperature_Filled

Asendabo Maximum Temperature

0

46

0

46


Max. Temperature_Unfilled

Max. Temperature_Filled

Asendabo Maximum Temperature

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1810

0

1810


Max. Temperature_Unfilled

Max. Temperature_Filled

Asendabo Maximum Temperature Varience

0

6906

0

6906

0 38.5

x axis label

AsendaboObsTMAXunfilled.dat

Mean

Asendabo Max. Temperature PDF Chart

Teshome Seyoum

Generated Tmax from 2001-2040.

0

68

0

68

Data points

TEMPNCEP_1970-2000.dat

TEMPGCM_2001-2040.dat

Generated Temperature Maximum

Teshome Seyoum

Statistical analysis using mean, varience, sum & pdf of generated data

..


0

57

0

57


Observed Max Temp Mean

Modelled Max Temp Mean

Observed Vs Modelled Max Temp Mean

0

18

0

18


Observed Max Temp Variance

Modelled Max Temp Variance

Obseved Vs Modelled Max Temp Varience

0

1000

0

1000


Observed Max Temp Sum

Modelled Max Temp Sum

Observed Vs Modelled Max Temp Sum

0

4000

0

4000

x axis label

AssendaboObsTmax.dat

Mean

Asendabo Max Temp PDF Chart

Teshome Seyoum

Asendabo unfilled and filled Tmin datafrom 1970-2000.

Teshome Seyoum

Statistical analysis using mean, variance,sum& pdf plot of Tmin


0

16

0

16


Min.Temp_Unfilled

Min. Temp_Filled

Asendabo Min. Temp Mean

0

16

0

16


Min Temp_Unfilled Varience

Min. Temp._Filled Variance

Asendabo Min. Temperature Varience

0

500

0

500


Min. Temp Sum_Unfilled

Min. Temp Sum_Filled

Asendabo Min. Temp Sum

0

5382

0

5382

-5 22

x axis label

AsendaboObsTMINunfilled.dat

SDSM PDF Chart

Teshome Seyoum

.

-10

33

-10

33

Data points

TMINNCEP_1970-2000.dat

TMINGCM_2001-2040.dat

Mean Temperature Series

Generated Tmin from 2001-2040

Teshome Seyoum

. .


Statistical analysis using mean, varience, sum & pdf of generated data

0

16

0

16


Observed Min Temp Mean

Modelled Min Temp Mean

Observed Vs Modelled Min Temp Mean

0

16

0

16


Observed Min Temp Variance

Modelled Min Temp Variance

Observed Vs Modelled Min Temp Varience

0

500

0

500


Observed Min Temp Sum

Modelled Min Temp Sum

Observed Vs Modelled Min Temp Sum

0

5662

0

5662

x axis label

AssendaboObsTmin.dat

Asendabo Min Temp PDF Chart

Teshome Seyoum

Summary SDSM

By the same procedure

18 Rainfall stations were filled & future datawere generated

13 Maximum & Minimum temperature stationsdata were filled and also future data weregenerated

Teshome Seyoum

6. Hydrological Model SWAT1. Introduction

• SWAT is a hydrological model that attempt to describe the physical processes controlling the transformation of RF to runoff.

• SWAT was used to assess& predict the impact of land management practices on water with varying

– soils,

– land use &

– management conditions over long periods of time.

2. Water Balance

SWAT-Hydrologic Modeling & Simulation of Infloweration

Teshome Seyoum

3. ResultsI. Modeling of Abelti sub-watershed

• Watershed Area (WA) =15,495 km²,

• 30% of the tot WA delineated at Omorate,

• Land use was reclassified into 5 broad categories,

• delineated into 8 sub WS,

• No of HRUs=83

Abelti Watershed


.

Teshome Seyoum

Sensitivity, Calibration & ValidationSensitivity Analysis Calibration (1973-1991)

.


Most sensitive parameters identified•SOL_Z.sol, SURLAG.bsn, GW_REVAP.gw, GW_DELAY.gw, GWQMN.gw & SOL_AWC.sol

Teshome Seyoum

Sensitivity, Calibration & Validation cont...Validation (1991-2000) Uncertainity Analysis


Results cont...

II. Modeling of Gibe III sub-watershed

• Watershed area of 15,495 km²

• 49 % of the tot watershed

• Land use was reclassified into 5 broad categories

• delineated into 14 sub watersheds,

• No of HRUs= 182

Gibe III Watershed


.

Teshome Seyoum

Calibration & Validation

Calibration (1973-1991) Validation (1991-2000)

.


Average annual water balance for the calibrated and validated period

.

SWAT model slightly overestimates the annual streamflow/runoff


Comparison of SWAT result with EEPCO-Salini

ConclusionThe estimation of the inflow at Gibe I, Gibe II & Gibe III were carried out using SWAT model & compared with the hydrology result of EEPCO-Salini studied at Gibe I on 1995, Gibe II on 2004 & Gibe III on 2006 for the construction of dams at the respective places.

Hence, the comparison results show that the average annual streamflows are nearly the same.

Therefore, it is adequately reasonable to adopt the swat model for estimation of existing & predict runoff at the cascade reservoirs in the semi-ungaged Omo-Gibe river basin.


7. References• Abbaspour, K.C, Johnson, C.A., van Genuchten, M.T. 2004. Estimating uncertain flow and transport parameters using a sequential

uncertainty fitting procedure. Vadose Zone Journal 3(4): 1340-1352.

• Abbaspour, K.C., Faramarzi, M., Ghasemi, S.S., Yang, H. 2009. Assessing the impact of climate change on water resources in Iran, Water Resources. Research 45, W10434, doi:10.1029/2008WR007615.

• Alamrew, D., Tischbein, B., Eggers, H. and Vlek, P. (2007). Application of SWAT for Assessment of Spatial Distribution of Water Resources and Analyzing Impact of Different Land Management Practices on Soil Erosion in Upper Awash River Basin Watershed, FWU Water Resources Publications. Volume No: 06/2007, ISSN No. 1613-1045. pp 110-117.

• Arnold, J.G., Srinivasan, R., Muttiah, R.R., Williams, J.R. 1998. Large Area Hydrologic Modeling and Assessment Part I: Model Development. Journal of the American Water Resources Associa-tion 34(1): 73-89.

• Arnold, J.G. and P.M. Allen.(1999). Automated methods for estimating baseflow and ground water recharge from streamflowrecords.Journal of the American Water Resources Association 35(2): 411-424.

• Arnold, J.G., R.S. Muttiah, R. Srinivasan, and P.M. Allen.(2000). Regional estimation of base flow and groundwater recharge in the Upper Mississippi river basin. J. Hydrology 227:21-40.

• ARWG (Africa Resources Working Group), January 2009. A Commentary on the Environmental, Socioeconomic and Human RightsImpacts of the Proposed Gibe III.

• Beven, K., and Binley, A. 1992. The future of distributed models: model calibration and uncer-tainty prediction. Hydrological Processes 6: 279–298.

• Beven, K.J., (1999), Rainfall-runoff modeling. John Wiley & sons, Ltd.

• Mays L.W and Yung Y.K. (1992). Omo Gibe River Basin Master Plan Study: Final Water Availability, Supply and Potential Use Study. Hydrosystem Engineering and Management, McGraw-Hill.

• McKinney,D.C., Cai,X., Rosegrant, M.W., Ringler,C., Scott,C.A., (1990). Modeling water resources management at the basin level: Review and future directions. SWIM Paper 6, International Water Management Institute, Colombo.

• Nash, J. and Sutcliffe, J. (1970) River flow forecasting through conceptual models part 1 – a discussion of principles. Journal of Hydrology, 10, 282- 290.

• Neitsch, S.L., J.G. Arnold, J.R. Kiniry, R. Srinivasan, and J.R. Williams. (2002). Soil and Water Assessment Tool User's Manual, Version 2000.

• Smedema, L.K. and D.W. Rycroft. (1983). Land drainage—planning and design of agricultural drainage systems, Cornell University Press, Ithica, N.Y.

• Troch P.A., Paniconi,C., McLaughlin,D.,(2003). Catchment-scale hydrological modeling and data assimilation, Preface / Advances in Water Resources 26 :131–135.

• Van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., Srinivasan, R.. 2006. A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydroly 324(1-4): 10-23.

• Vandenberghe V., van Griensven A. and Bauwens W. (2002). Detection of the most optimal measuring points for water qualityvariables: Application to the river water quality model of the river Dender in ESWAT. Wat.Sci.Tech., 46(3). In press.


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