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

Transcription

1 SWAT-Hydrologic Modelling and Simulation of Inflow to Cascade Reservoirs of the semiungaged Omo-Gibe River Basin, Ethiopia and Manfred Koch Department of Geohydraulics and Engineering Hydrology University of Kassel June 4, 213 Koblenz, Germany

2 Outline 1. Background 2. Study Area 3. Objectives 4. Materials & Methods 5. SDSM application 5. SWAT Model 6. Result & Discussion 7. References

3 1. Background Ethiopia 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

4 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 Omo river to alleviate some of the conflicts of interest b/n maximum power prodn & sufficient water availability for the local popn all aspects of the water resources of the Basin need to be measured, estimated or simulated to make effective & economically viable plans for sustainable future developments. Modeling of Cascade Dams & Reservoir Operation

5 2. Study Area The Omo-Gibe River Basin is almost 79, km 2 in area The basin lies: Longitude 4 3'N - 9 3'N, Latitude 35 'E - 38 'E, Altitude of 28masl. The general direction of flow of the river is southwards towards the Lake Turkana. Modeling of Cascade Dams & Reservoir Operation

6 Location of Dam sites & Lake Turkana Omo-Gibe River System & dam sites Lake Turkana Modeling of Cascade Dams & Reservoir Operation

7

8 3. Objective of Research Main objective To simulate runoff & inflow to cascade reservoirs of the semi-ungaged Omo-Gibe river basin

9 4. Methodology I. Collection of Input data: 1.DEM data 3m*3m Resolution (ASTGTM) 2. Climate Data Tmax, Tmin & RF (31Yrs) 3. Hydrological Data 22 gage stations 4. Soil & Land use/cover

10 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

11 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

12 Asendabo Station-RF Unfilled & filled data chart

13 Statistical analysis using mean, variance,sum & pdf plot of unfilled & filled Precn data Asendabo Precipitation Bar Chart Asendabo Precipitation Bar Chart PREC_Unfilled PREC_unfilled PREC_Filled PREC_Filled Asendabo Precipitation Bar Chart Asendabo PDF Chart PREC_unfilled PREC_Filled 1 AsendaboObsPCPunfilled.dat x axis label

14 Generated Precipitation data from Standardised Precipitation Index PCPNCEP_197-2.dat PCPGCM_21_24.dat Year -4

15 Statistical analysis using mean, varience, sum & pdf of Observed & Modelled data.. Observed V Model Mean Precipiritation Observed Prec Model Prec Observed Vs Modelled Monthlly Prec Sum Observed Prec Sum Modelled Prec Sum Asendabo Prec PDF Chart Observed Vs Simulated Prec Varience AssendaboObsPrec.da Mean Observed Variance 1 1 Modelled Variance x axis label

16 Asendabo unfilled and filled Tmax data chart.

17 Statistical analysis cont... Asendabo Maximum Temperature Maximum Temperature_Unfilled Asendabo Maximum Temperature Varience Mimum Temperature_Filled Max. Temperature_Unfilled Max. Temperature_Filled Asendabo Max. Temperature PDF Chart Asendabo Maximum Temperature AsendaboObsTMAXunfilled.dat Mean Max. Temperature_Unfilled Max. Temperature_Filled x axis label

18 Generated Tmax from Generated Temperature Maximum TEMPNCEP_197-2.dat TEMPGCM_21-24.dat Data points

19 . Statistical analysis using mean, varience, sum & pdf of generated data 57 Observed Vs Modelled Max Temp Mean Observed Max Temp Mean Modelled Max Temp Mean Observed Vs Modelled Max Temp Sum Observed Max Temp Sum Modelled Max Temp Sum 1 Asendabo Max Temp PDF Chart Obseved Vs Modelled Max Temp Varience AssendaboObsTmax.dat Mean Observed Max Temp Varianc Modelled Max Temp Varianc x axis label

20 Asendabo unfilled and filled Tmin data from

21 Statistical analysis using mean, variance,sum & pdf plot of Tmin Asendabo Min. Temp Mean Asendabo Min. Temp Sum Min.Temp_Unfilled Min. Temp_Filled Min. Temp Sum_Unfilled Min. Temp Sum_Filled SDSM PDF Chart Asendabo Min. Temperature Varience Min Temp_Unfilled Varienc Min. Temp._Filled Varianc AsendaboObsTMINunfilled.dat x axis label

22 Generated Tmin from Mean Temperature Series TMINNCEP_197-2.dat TMINGCM_21-24.dat Data points -1

23 Statistical analysis using mean, varience, sum & pdf of generated data. Observed Vs Modelled Min Temp Mean. Observed Min Temp Mea Observed Vs Modelled Min Temp Sum Modelled Min Temp Mea Observed Min Temp Sum Modelled Min Temp Sum 5 5 Observed Vs Modelled Min Temp Varience Asendabo Min Temp PDF Chart AssendaboObsTmin.dat Observed Min Temp Variance Modelled Min Temp Variance x axis label

24 Summary SDSM By the same procedure 18 Rainfall stations were filled & future data were generated 13 Maximum & Minimum temperature stations data were filled and also future data were generated

25 6. Hydrological Model SWAT 1. 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 eration

26 3. Results I. Modeling of Abelti subwatershed Watershed Area (WA) =15,495 km², 3% 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.

27 Sensitivity, Calibration & Validation Sensitivity Analysis Calibration ( ). Most sensitive parameters identified SOL_Z.sol, SURLAG.bsn, GW_REVAP.gw, GW_DELAY.gw, GWQMN.gw & SOL_AWC.sol

28 Sensitivity, Calibration & Validation cont... Validation (1991-2) Uncertainity Analysis

29 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.

30 Calibration & Validation Calibration ( ) Validation (1991-2).

31 Average annual water balance for the calibrated and validated period. SWAT model slightly overestimates the annual streamflow/runoff

32 Comparison of SWAT result with EEPCO- Salini

33 Conclusion The 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 24 & Gibe III on 26 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.

34 7. References Abbaspour, K.C, Johnson, C.A., van Genuchten, M.T. 24. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal 3(4): Abbaspour, K.C., Faramarzi, M., Ghasemi, S.S., Yang, H. 29. Assessing the impact of climate change on water resources in Iran, Water Resources. Research 45, W1434, doi:1.129/28wr7615. Alamrew, D., Tischbein, B., Eggers, H. and Vlek, P. (27). 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: 6/27, ISSN No pp Arnold, J.G., Srinivasan, R., Muttiah, R.R., Williams, J.R Large Area Hydrologic Modeling and Assessment Part I: Model Development. Journal of the American Water Resources Associa-tion 34(1): Arnold, J.G. and P.M. Allen.(1999). Automated methods for estimating baseflow and ground water recharge from streamflow records.journal of the American Water Resources Association 35(2): Arnold, J.G., R.S. Muttiah, R. Srinivasan, and P.M. Allen.(2). Regional estimation of base flow and groundwater recharge in the Upper Mississippi river basin. J. Hydrology 227:21-4. ARWG (Africa Resources Working Group), January 29. A Commentary on the Environmental, Socioeconomic and Human Rights Impacts of the Proposed Gibe III. Beven, K., and Binley, A The future of distributed models: model calibration and uncer-tainty prediction. Hydrological Processes 6: 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., (199). 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. (197) River flow forecasting through conceptual models part 1 a discussion of principles. Journal of Hydrology, 1, Neitsch, S.L., J.G. Arnold, J.R. Kiniry, R. Srinivasan, and J.R. Williams. (22). Soil and Water Assessment Tool User's Manual, Version 2. 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.,(23). Catchment-scale hydrological modeling and data assimilation, Preface / Advances in Water Resources 26 : Van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., Srinivasan, R A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydroly 324(1-4): Vandenberghe V., van Griensven A. and Bauwens W. (22). Detection of the most optimal measuring points for water quality variables: Application to the river water quality model of the river Dender in ESWAT. Wat.Sci.Tech., 46(3). In press.

35