modelling of cascade dams & reservoir operation for ...€¦ · modelling of cascade dams &...

Modelling of Cascade Dams & Reservoir Operation for Optimal Water Use:

Application to the Omo Gibe River Basin, EthiopiaSupervisor

Prof. Dr. rer. nat Manfred Koch (Uni-Kassel)

SEAN-DEE Seminar: November 08, 2013

Kassel, Germany

Outline1. Background

2. Study Area

3. Objectives

4. Part-One (SDSM application)

5. Part-Two (SWAT Model)

6. Part-Three (HEC-ResSim Model)

Teshome 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 (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

Teshome Seyoum Modeling of Cascade Dams & Reservoir Operation

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.


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/susers & 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 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.


.

Modeling of Cascade Dams & Reservoir Operation

Lake Turkana

Teshome Seyoum

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.


Specific objectivesThe 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.


PART-ONE

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

Teshome Seyoum SDSM –Statistical Downscaling Model

Main objective

• To in-filling of missed & predicting values of daily precipitation & temperature data for Omo-Gibe metrological stations

SDSM –Statistical Downscaling ModelTeshome Seyoum

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


3. Result

Daily unfilled & filled RF (Asendabo at top) & Tmax(Hosana at bottom) time series from 1970-2000 data


Comparison of Observed & NCEP downscaled result using mean, variance & sum of monthly PCP, Tmax & Tmin for the base period of 1970-2000

Teshome Seyoum

..

SDSM –Statistical Downscaling Model

Comparison Cont...

Teshome Seyoum

.


1970-2000 monthly time series of RF for station Gibe & of Tmax of station Jima, together with

linear & polynomial trend lines.


Mann-Kendall trend test results of RF, Tmax & Tmin for observed data (1970-2000) of the

gage stations.

Teshome Seyoum

S.N StationsMann-Kendall trend

RF TMax Tmin

1 Asedabo NO NO Sign(+)

2 Bele NO3 Bonga NO Sign(+) NO

4 Chekorsa NO5 Cheleleki NO6 Dedo NO Sign(+) Sign(+)

7 Durame NO8 Gedo Sign(-) Sign(+) Sign(-)

9 Gibe NO10 Hosana NO NO Sign(+)11 Jima NO Sign(+) Sign(+)12 Jinka NO Sign(+) NO13 Kumbi NO14 Limu NO15 Meteso NO16 Morka Sign(-) NO Sign(+)17 Sawula NO Sign(+) Sign(+)18 Shebe NO Sign(+) NO19 Wolita NO Sign(+) Sign(+)20 Wolkite NO Sign(+) Sign(+)21 Yaya NO Sign(+) Sign(+)


Comparison of Observed & GCM (HadCM3) predictors’result using mean monthly PCP (mm/day), Tmax & Tmin(°C) for the base period of 1970-2000

Teshome Seyoum

..


2000-2040 HadCM3-downscaled monthly time series of RF for station Gibe & of Tmax of station Jima, together with linear &

polynomial trend lines.


Mann-Kendall trend test results of RF, Tmax & Tmin for observed data (2001-2040) of the

gage stations.

S.N StationsMann-Kendall trend

RF Tmax Tmin

1 Asedabo NO NO NO2 Bele NO3 Bonga NO NO NO4 Chekorsa NO5 Cheleleki NO6 Dedo NO7 Durame NO8 Gedo NO NO Sign(+)9 Gibe NO10 Hosana NO NO NO11 Jima NO NO NO12 Jinka NO NO Sign(+)13 Kumbi NO14 Limu NO15 Meteso NO16 Morka NO NO Sign(+)17 Sawula NO NO NO18 Shebe NO NO Sign(+)19 Wolita NO Sign(+) Sign(+)20 Wolkite NO Sign(+) Sign(+)21 Yaya NO Sign(+) Sign(+)22 Ambo NO NO NO


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


5. References

• Berryman, D., B. Bobee, D. Cluis, and J. Haemmerli. 1988. Nonparametric tests for trend detection in water quality time series. Water Resources Bulletin 24:545-556.

• Edward Parson. et al,2007.Global-Change Scenarios: Their Development and Use.

• Girvetz EH, Zganjar C, Raber GT, Maurer EP, Kareiva P, Lawler JJ.2009. Applied climate-change 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:http://www.iop.org/activity/policy/Publications/file_4147 .pdf

• 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), 229-249.


PART-TWO

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

Main objective

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

SWAT-Hydrologic Modeling & Simulation of InflowTeshome Seyoum

II. 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

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

3. 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


4. Results

I. Modeling of Abelti sub-watershed

• 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


.

Teshome Seyoum

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

.


Most sensitive parameters identified•SOL_K.sol, SURLAG.bsn, SOL_BD.sol, 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 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


.

Teshome Seyoum

Calibration & Validation

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

.


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

river basin

Simulation cont…

Stream Inflow to Cascade reservoirs in three decades

Inflow (cms) 2001-2010 2011-2020 2021-2030

Gibe I 68.6 63 60.8

Gibe II 74.3 72 68.4

Gibe III 521 552 530

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 & NSparameter 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.


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.


6. References

• Abbaspour, K.C., Johnson, A., Van Genuchten, M.Th, (2004). Estimating uncertain flow and transport parameters using a sequentialuncertainty fitting procedure. Vadose Zone Journal 3(4), 1340-1352.

• 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, 333:413-430.

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

• 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): 411-424.

• 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 227:21-40.

• 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: 279–298.

• 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): 1015-1031.

• Eckhardt, K., J. G. Arnold, (2001). Automatic calibration of a distributed catchment model. Journal of Hydrology 251, 103-109.

• 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. 111. 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 140:25-47.


PART-THREE

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

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.


Introduction cont…

• The Model has three modules

• Each module has a unique purposes & an associated set of functions accessible through menus, toolbars and schematic.

.


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


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


• 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

Methodology Cont…

Teshome Seyoum

I. Watershed Setup & Stream Alignment.


II. Reservoir Network.


III. Simulation Module


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): 1081-1079.

• 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): 73-89.

• Arunkumar, S., & Yeh, W. W. G. (1973). Probabilistic models in the design and operation of a multi-purpose reservoir system.


modelling of cascade dams & reservoir operation for ...€¦ · modelling of cascade dams &...

Documents