SEAN-DEE Seminar: November 08, 2013 Kassel, Germany

Transcription

1 Modelling of Cascade Dams & Reservoir Operation for Optimal Water Use: Application to the Omo Gibe River Basin, Ethiopia Supervisor Prof. Dr. rer. nat Manfred Koch (Uni-Kassel) SEAN-DEE Seminar: November 08, 2013 Kassel, Germany

2 Outline 1. Background 2. Study Area 3. Objectives 4. Part-One (SDSM application) 5. Part-Two (SWAT Model) 6. Part-Three (HEC-ResSim Model)

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 (MoWR, 2002). The primary water resource management challenges (World Bank, 2006): 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 Modeling of Cascade Dams & Reservoir Operation

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 Background cont... New strategies for effective use of the water in the basin are needed for water development & management to avert water scarcities that could depress d/s users & damage the environment A large share of water to meet new demands must come from water saved from existing uses through a comprehensive reform of water policy Integrated management is the primary approach to addressing sustainable water resources, both for subsystem & river basin level Modeling of Cascade Dams & Reservoir Operation

6 2. Study Area The Omo-Gibe River Basin is almost 79,000 km 2 in area The basin lies: Longitude 4 30'N 'N, Latitude 35 0'E 'E, Altitude of 2800masl. The general direction of flow of the river is southwards towards the Lake Turkana. Modeling of Cascade Dams & Reservoir Operation

7 Modeling of Cascade Dams & Reservoir Operation

8 . Lake Turkana Modeling of Cascade Dams & Reservoir Operation

9 3. Objective of Research Main objective The purpose of this study is to model cascade dams & reservoirs operation in the Omo Gibe river basin to satisfactorily simulate the operation of dams & reservoirs for optimal water use. Modeling of Cascade Dams & Reservoir Operation

10 Specific objectives The specific objectives of the proposed study are To simulate runoff & inflow to the reservoirs in the Omo river basin using the SWAT model. To develop & recommend optimal dam & reservoir operation rule curves for cascade dams & reservoirs, more soundly based on evaluating the feasibility of various reservoir operating alternatives. To evaluate the effects of various reservoir operating alternatives on either preventing flooding or avoiding precarious low flow at locations d/s of the reservoirs. Modeling of Cascade Dams & Reservoir Operation

11 PART-ONE SDSM Method for In-filling & Prediction Values Applied to Daily Precipitation & Temperature Data in the Omo Gibe River Basin (Ethiopia) SDSM Statistical Downscaling Model

12 Main objective To in-filling of missed & predicting values of daily precipitation & temperature data for Omo-Gibe metrological stations SDSM Statistical Downscaling Model

13 I. 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, 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 SDSM Statistical Downscaling Model

14 3. Result Daily unfilled & filled RF (Asendabo at top) & Tmax (Hosana at bottom) time series from data SDSM Statistical Downscaling Model

15 . Comparison of Observed & NCEP downscaled result using mean, variance & sum of monthly PCP, Tmax & Tmin for the base period of SDSM Statistical Downscaling Model

16 Comparison Cont.... SDSM Statistical Downscaling Model

17 monthly time series of RF for station Gibe & of Tmax of station Jima, together with linear & polynomial trend lines. SDSM Statistical Downscaling Model

18 Mann-Kendall trend test results of RF, Tmax & Tmin for observed data ( ) of the gage stations. S.N Stations Mann-Kendall trend RF TMax Tmin 1 Asedabo NO NO Sign(+) 2 Bele NO 3 Bonga NO Sign(+) NO 4 Chekorsa NO 5 Cheleleki NO 6 Dedo NO Sign(+) Sign(+) 7 Durame NO 8 Gedo Sign(-) Sign(+) Sign(-) 9 Gibe NO 10 Hosana NO NO Sign(+) 11 Jima NO Sign(+) Sign(+) 12 Jinka NO Sign(+) NO 13 Kumbi NO 14 Limu NO 15 Meteso NO 16 Morka Sign(-) NO Sign(+) 17 Sawula NO Sign(+) Sign(+) 18 Shebe NO Sign(+) NO 19 Wolita NO Sign(+) Sign(+) 20 Wolkite NO Sign(+) Sign(+) 21 Yaya NO Sign(+) Sign(+) SDSM Statistical Downscaling Model

19 . Comparison of Observed & GCM (HadCM3) predictors result using mean monthly PCP (mm/day), Tmax & Tmin( C) for the base period of SDSM Statistical Downscaling Model

20 HadCM3-downscaled monthly time series of RF for station Gibe & of Tmax of station Jima, together with linear & polynomial trend lines. SDSM Statistical Downscaling Model

21 Mann-Kendall trend test results of RF, Tmax & Tmin for observed data ( ) of the gage stations. S.N Stations Mann-Kendall trend RF Tmax Tmin 1 Asedabo NO NO NO 2 Bele NO 3 Bonga NO NO NO 4 Chekorsa NO 5 Cheleleki NO 6 Dedo NO 7 Durame NO 8 Gedo NO NO Sign(+) 9 Gibe NO 10 Hosana NO NO NO 11 Jima NO NO NO 12 Jinka NO NO Sign(+) 13 Kumbi NO 14 Limu NO 15 Meteso NO 16 Morka NO NO Sign(+) 17 Sawula NO NO NO 18 Shebe NO NO Sign(+) 19 Wolita NO Sign(+) Sign(+) 20 Wolkite NO Sign(+) Sign(+) 21 Yaya NO Sign(+) Sign(+) 22 Ambo NO NO NO SDSM Statistical Downscaling Model

22 4. Summary & Conclusion 21 RF stations were filled & future data were generated 13 Tmax & Tmin stations data were filled & also future data were generated The result of the climate projection show that SDSM is able to replicate the observed Tmax & Tmin SDSM couldn t able to replicate well the observed PCP with the simulated PCP due to its conditional nature & high variability in space Overall performance of SDSM was considered satisfactory SDSM Statistical Downscaling Model

23 5. References Berryman, D., B. Bobee, D. Cluis, and J. Haemmerli Nonparametric tests for trend detection in water quality time series. Water Resources Bulletin 24: Edward Parson. et al,2007.global-change Scenarios: Their Development and Use. Girvetz EH, Zganjar C, Raber GT, Maurer EP, Kareiva P, Lawler JJ Applied climatechange analysis: the climate wizard tool. Plos One 4: e8320. Helsel, Dennis R., and Hirsch, Robert M., 1992, Statistical Methods in Water Resources, Elsevier, 522 p. Thorpe A.J., 2005, Climate Change Predictions: A challenging scientific problem. Institute of physics,[online]4apr.,availableat: Wilby, R.L. and Fowler, H.J. (2010). Regional climate downscaling. In Modelling the Impact of Climate Change on Water Resources. Fung CF, Lopez A, New M (eds). Wiley-Blackwell Publishing:Chichester. Wilby, R.L. and Fowler, H.J. (2010). Regional climate downscaling. In Modelling the Impact of Climate Change on Water Resources. Fung CF, Lopez A, New M (eds). Wiley-Blackwell Publishing:Chichester. Wilby, R.L. and Dawson, C. W. (2007). SDSM 4.2 A decision support tool for the assessment of regional climate change impacts:.department of Geography, Lancaster University, UK Science Department, Environment Agency of England and Wales, UK 3 Department of Computer Science, Loughborough University, UK Xu, C. Y. (1999). From GCMs to river flow: a review of downscaling methods and hydrologic modeling approaches. Progress in Physical Geography, 23(2), SDSM Statistical Downscaling Model

24 PART-TWO SWAT-Hydrologic Modelling and Simulation of Inflow to Cascade Reservoirs of Semi-Ungaged Omo- Gibe River Basin, Ethiopia

25 Main objective To simulate runoff & inflow to cascade reservoirs of the semi-ungaged Omo-Gibe river basin SWAT-Hydrologic Modeling & Simulation of Inflow

26 II. 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 SWAT-Hydrologic Modeling & Simulation of Inflow eration

27 3. Methodology I. Collection of Input data: 1.DEM data 30m*30m Resolution (ASTGTM) 2. Climate Data Tmax, Tmin & RF (31Yrs) 3. Hydrological Data 22 gage stations 4. Soil & Land use/cover SWAT-Hydrologic Modeling & Simulation of Inflow

28 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 SWAT-Hydrologic Modeling & Simulation of Inflow

29 4. Results I. Modeling of Abelti subwatershed Watershed Area (WA) =15,495 km², 30% of the tot WA delineated at Omorate, Land use was reclassified into 3 broad categories, delineated into 8 sub basin, No of HRUs=122 Abelti Sub-watershed. SWAT-Hydrologic Modeling & Simulation of Inflow

30 Sensitivity, Calibration & Validation Sensitivity Analysis Calibration ( ). Most sensitive parameters identified SOL_K.sol, SURLAG.bsn, SOL_BD.sol, GW_REVAP.gw, GW_DELAY.gw, GWQMN.gw & SOL_AWC.sol SWAT-Hydrologic Modeling & Simulation of Inflow

31 Sensitivity, Calibration & Validation cont... Validation ( ) Uncertainity Analysis SWAT-Hydrologic Modeling & Simulation of Inflow

32 Results cont... II. Modeling of Gibe III sub-watershed Watershed area of 34,159 km² 49 % of the tot watershed Land use was reclassified into 4 broad categories delineated into 14 sub basins, No of HRUs= 182 Gibe III Sub-watershed. SWAT-Hydrologic Modeling & Simulation of Inflow

33 Calibration & Validation Calibration ( ) Validation ( ). SWAT-Hydrologic Modeling & Simulation of Inflow

34 Simulation of present & future inflows into the cascade reservoirs in the Omo Gibe river basin

35 Simulation cont Stream Inflow to Cascade reservoirs in three decades Inflow (cms) Gibe I Gibe II Gibe III

36 5. Summary & Conclusion The hydrologic model SWAT has been applied to the semi-ungaged Omo Gibe river basin for the purpose to estimate the present-day & future inflow to the three cascade reservoirs along the river The calibration & validation performance of the SWAT model were measured by R² & NS parameter of the fit of simulated daily & monthly stream flows to observed ones Overall good agreement of observed & simulated hydrographs The validation had some problems with the mimicking of the low-flow periods of the streamflow. SWAT-Hydrologic Modeling & Simulation of Inflow

37 Conclusion cont The stream flow hydrographs indicate a slightly decreasing trend of inflow into these reservoirs, A review of the future integrated water resource management of these reservoirs as well as of the overall water resources in the Omo river basin is required. SWAT-Hydrologic Modeling & Simulation of Inflow

38 6. References Abbaspour, K.C., Johnson, A., Van Genuchten, M.Th, (2004). Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal 3(4), Abbaspour, K.C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., Srinivasan, R. (2007). Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333: 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 pp Arnold, J.G., Srinivasan, R., Muttiah, R.R. and Williams, J.R. (1998). Large Area Hydrologic Modeling and Assessment Part I: Model Development. Journal of the American Water Resources Association 34(1): Arnold, J.G. and Allen, P.M. (1999). Automated methods for estimating base flow and ground water recharge from stream flow records. Journal of the American Water Resources Association 35(2): Arnold, J.G., Muttiah, R.S., Srinivasan, R. and Allen, P.M. (2000). Regional estimation of base flow and groundwater recharge in the Upper Mississippi river basin. J. Hydrology 227: ARWG (Africa Resources Working Group), (2009). A Commentary on the Environmental, Socio economic and Human Rights Impacts of the Proposed Gibe III. Beven, K. and Binley, A. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6: Beven, K.J. (1999). Rainfall-runoff modeling. John Wiley & sons, Ltd. Cherie, N.Z. (2013) Downscaling and modeling the effects of climate change on hydrology and water resources in the Upper Blue Nile River Basin, Ethiopia, PhD dissertation, University of Kassel, Germany Di Luzio, M., R. Srinivasan, J.G. Arnold, and S. Neitsch, (2001): Arcview Interface for SWAT 2000 User s Guide. Duan, Q., Sorooshian, S. and Gupta, V.K. (1992). Effective and efficient global optimisation for conceptual rainfall-runoff models, Water resour. Res. 28(4): Eckhardt, K., J. G. Arnold, (2001). Automatic calibration of a distributed catchment model. Journal of Hydrology 251, EEPCO (Ethiopian Electric Power Corporation). (1995). Gilgel Gibe Hydroelectric Project. Final Report on the Project Implementation April 30, 2004, Addis Ababa. EEPCO (Ethiopian Electric Power Corporation). (2004). Gilgel Gibe II Hydroelectric Project. Weir General Report, November 2004, Addis Ababa. EEPCO (Ethiopian Electric Power Corporation). (2006). Gibe III Hydroelectric Project. Hydrology Report Volume I, May 2006, Addis Ababa. Gan T. Y., (1988). Application of scientific modelling of hydrological response from hypothetical small catchments to assess a complex conceptual rainfall runoff model. Water Resources Series Technical reports no Department of Civil Engineering, University of Washington, Seattle, Washington. Jain,S.K.B., Storm,J.C., Bathurst,J.C., Refsgaard,J.C., Sing,R.D., (1992). Application of the SHE to catchmants in India. Part 2. Field experiments 206 and simulation studies with the SHE on the Kolar subcatchment of the Narmada river. Journal of hydrology 140: SWAT-Hydrologic Modeling & Simulation of Inflow

39 PART-THREE Modelling of Cascade Dams & Reservoir Operation of Omo Gibe River Basin for Optimal Water Use with HEC- ResSim (Ethiopia)

40 1. Introduction HEC-ResSim is a modeling of hydraulic software program used to assist in: planning studies for evaluating existing & proposed reservoirs, Planning reservoir operations, & sizing the flood risk management & conservation storage requirements for each project. This program simulates reservoir operations including all characteristics of a reservoir & channel routing downstream. The model also allows users to define alternatives and run simulations simultaneously to compare results. Modeling of Cascade Dams & Reservoir Operation

41 Introduction cont The Model has three modules Each module has a unique purposes & an associated set of functions accessible through menus, toolbars and schematic.. Modeling of Cascade Dams & Reservoir Operation

42 2. Main Objective To develop & recommend optimal dam & reservoir operation rule curves for cascade dams & reservoirs To evaluate the effects of various reservoir operating alternatives on either preventing flooding or avoiding precarious low flow at locations d/s of the reservoirs Modeling of Cascade Dams & Reservoir Operation

43 3. Methodology 1. Gather & analyze data required for flow-routing & reservoir modelling. This data includes: Time-series data (computed inflow & incremental local flow hydrographs from SWAT, observed flow hydrographs, & the associated computed reservoir inflows, etc) Physical & operational reservoir data including reservoir pool definition (elevation-area-storage tables), outlet capacity curves, hydro power plant data (outflow & generation capacities, efficiency, losses, etc), operational zones, minimum & maximum release requirements, etc Modeling of Cascade Dams & Reservoir Operation

44 Methodology Cont Rating curves at each stream gage location & routing reach parameters (i.e., HEC-HMS). 2.Develop a model schematic that identifies the key locations in the watershed. 3.Evaluate the use of several alternative approaches for flow routing in the main channel & major tributaries of the selective part of Omo Gibe River Basin. 4.Define the physical & operational data for each major reservoir in the basin 5.Calibration & verification of the model. Modeling of Cascade Dams & Reservoir Operation

45 I. Watershed Setup & Stream Alignment. Modeling of Cascade Dams & Reservoir Operation

46 . II. Reservoir Network Modeling of Cascade Dams & Reservoir Operation

47 III. Simulation Module Modeling of Cascade Dams & Reservoir Operation

48 4. References Akter, T. & Simonovic, S. P., (2004). Modelling uncertainties in short-term reservoir operation using fuzzy sets and a genetic algorithm. Hydrological Science Journal 49(6): Arnold, J.G., Srinivasan, R.S., Muttiah, & J.R. Williams. (1998). Large area hydrologic modeling and assessment part I : Model development. J. American Water Resource. Assoc. 34(1): Arunkumar, S., & Yeh, W. W. G. (1973). Probabilistic models in the design and operation of a multi-purpose reservoir system. Modeling of Cascade Dams & Reservoir Operation

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