title: modelling of cascade dams & reservoir operation for optimal water use: application to the...

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

Application to the Omo River Basin, EthiopiaSupervisor

Prof. Dr. rer. nat Manfred Koch (Uni-Kassel)Dr. Yilma Sileshi (AAU, Ethiopia)

November 09, 2012

Kassel, Germany

Outline1. Background2. Study Area3. Objectives4. SDSM application5. SWAT Model6. HEC-ResSim Model7. References

Modeling of Cascade Dams & Reservoir Operation

1. Background• Over 45 000 times in the last century, people took

the decision to build a dam. • Dams were built to provide water for irrigated

agriculture, domestic or industrial use, to generate hydropower or help control floods (WCD, 2000).

• Hydroelectric power dams currently provide 19% of the world’s electricity supply.

• Worldwide, water demands have roughly tripled since 1950, & dams have helped satisfy that demand.

• Contribute directly to 12–16% of the global food production (WCD, 2000).


Background cont...• In relation to constructed dams, Africa contains

some of the world’s largest dams (e.g., Owen Falls (Uganda), Kariba (Zimbabwe) & Aswan High (Egypt). (ICOLD, 2003).

• Furthermore, Ethiopia has 13 hydropower dams and out of these ten were completed & three are under construction.

• According to 2005 plan of EEPCo, when the 3 dams complete, the capacity of the hydropower will increase to the capacity of 3,125 MW which will satisfy the electricity demand of the country.


Background cont...• On the contrary, dams have considerable

influence on d/s river ecosystems, in many cases extending for hundreds of kilometers below a dam.

• One of the problem encountered for the Ethiopian Gov’t has been opposition from International River (IR)-People-Water-Life in the construction of Gibe dam III in Omo river which will expect catastrophic effect on the d/s users & ecosystem.


Background cont...• To take account of this problem the WCD called for

a more equitable distribution of the benefits to be gained from large dams & proposed the inclusion of all identified stakeholders in the planning & management of water resources stored in a reservoir (WCD, 2000).

• To achieve this, dams & reservoirs operation must take into account the availability of the water resource in the basin, water uses u/s & d/s of the dam & must give consideration to political, organizational, social & environmental factors, as well as economic factor (McCartney & Acreman, 2001).


Background cont...• Hence, 1. New strategies for effective use of the water in the

basin particularly in the Omo River basin will be needed for water development & management to avert water scarcities that could depress d/s users & damage the environment.

2. A large share of water to meet new demands must come from water saved from existing uses through a comprehensive reform of water policy.

3. Integrated management must be the primary approach to addressing sustainable water resources, both for subsystem & river basin level.


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 Omo River/Lake Turkana Trough, a fault feature.


3. Objective of Research

Main objective

• The purpose of this study is to model cascade dams & reservoirs operation in the Omo 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.


Hydrological & Hydraulic Situation in the Omo River Basin


GIBE-I


GIBE-II


GIBE-III


GIBE-IV


GIBE-V


.


Lake Turkana

4. The Statistical DownScaling Model: application to filling and forcasting

Metrological data 4.1 Introduction• The Statistical DownScaling Model (SDSM) is a

freely available tool that produces high resolution climate change scenarios.

• Downscaling is a technique by which properties of the free atmosphere are used to predict local meteorological conditions.

• The large-scale information may originate from systematic weather observations or from climate model outputs.


SDSM cont....• This freely available software 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.

• SDSM can also be used as a stochastic weather generator or to infill gaps in meteorological data.


Objective

• To filling and forecasting Rainfall and Temperature data for Omo Metrological Stations


Methdology• The structure and operation of SDSM has seven

tasks to infill and generate data. These are: 1. Quality control and 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 and climate

change scenarios; 6. Graphing model output; 7. Generation of ensembles of future weather data.



RESULT

Asendabo Station-Precipitation Unfilled & filled data chart


Statistical Analysis using Mean, Variance,Sum & pdf plot of unfilled & Filled Precipition 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 Mean

Asendabo PDF Chart

Generated Precipitation data from 2001-2040.

-4

5

-4

5

Year

PCPNCEP_1970-2000.dat

PCPGCM_2001_2040.dat

Standardised Precipitation Index

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

Asendabo Unfilled and Filled Maximum Temperature data chart.


Statistical Analysis of using Mean, Variance,Sum & pdf plot of Max Unfilled & Filled Temp data

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

0

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

Generated Maximum Temperature from 2001-2040

.

0

68

0

68

Data points

TEMPNCEP_1970-2000.dat

TEMPGCM_2001-2040.dat

Generated Temperature Maximum

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

Asendabo Unfilled and Filled Minimum Temperature data from 1970-2000.

Statistical Analysis using Mean, Variance,Sum & pdf plot of Min. Temp


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 Mean

SDSM PDF Chart

.

-10

33

-10

33

Data points

TMINNCEP_1970-2000.dat

TMINGCM_2001-2040.dat

Mean Temperature Series

Generated Minimum Temperature from 2001-2040

. .


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 Mean

Asendabo Min Temp PDF Chart

Summary

By the same procedure• 18 Precipitation station were filled and generated• 13 Maximum and Minimum temperature station data were filled and generated

5. Hydrological Model SWAT5.1. Introduction• SWAT is a hydrological model that attempt to

describe the physical processes controlling the transformation of precipitation to runoff.

• The major hydrologic processes described by this model include: Canopy interception, Evaporation, Transpiration, Snowmelt, Interflow, Overland flow, Channel flow, unsaturated subsurface flow and saturated subsurface flow.


Introduction cont...

• SWAT was used to assess and predict the impact of land management practices on water in Omo river basin with varying soils, land use and management conditions over long periods of time.


5.2. Objective

• To simulate runoff & inflow to the reservoirs in the Omo river basin.


• Input files needed for daily stream flow computation were :

• the digital elevation model (DEM),

• land cover, • soil layers, • daily values of

precipitation, max.& min. air temp, solar radiation, RH, & WS,

• Hydrological flow data.


5.3. Methodology

Methodology cont...• Missing metrological data were filled using WXGEN weather generator model of SWAT, SDSM, and Hydrological flow data were filled by Multiple

regression of R program• The Digital Elevation Model (DEM) was used to

create stream network, subbasin & delineate the watershed boundary & also calculate the sub basin parameters.

• Threshold value of 2%, 5% & 5% were taken for land use, soil & slope in order to keep the number of HRUs to a reasonable number for modeling the water assessment of the basin.


Methodology cont...• Runoff was predicted separately for each HRU

& routed to obtain the total runoff for the watershed, and

• Calibration, Validation & Uncertainity of the model using SWAT_CUP 4.3.7.


5.4. Results1. Model Calibration and Validation of

Abelti Sub watershed• Abelti sub-watershed has an area of 15,495 km²

and 30% of the total watershed delineated at Omorate.


SWAT Land Use Area (ha)% Watershed Area

AGRC 92603.24 1.37

AGRL 1122014.08 16.62

FRSD 115774.54 1.72

RNGW 124815.46 1.85

WATR 85006.00 1.26

Soil Area (ha)

% Watershed Area

Chromic Luvisols (LVx) 173730.74 2.57

Dystric Vertisol (VRd) 228955.24 3.39

Eutric Vertisols (VRe) 304403.79 4.51

Humic Alisol (Ntu) 385865.94 5.72

Humic Nitisols (NTu) 406320.23 6.02

Lithic Leptosols (LPq) 40937.38 0.61

Model Calibration and Validation of Abelti Sub watershed

• Using SWAT model the area was delineated into 9 sub watersheds, which were further, divided into 122 HRUs.

• Simulated flow at the outlet was compared with the observed flow.


Flow Calibration At Abelti


Sensitivity Analysis


2. Model Calibration and Validation of Karodus Sub watershed


• Karo Duse sub-watershed covers 64,518 km² and 95.6% of the total watershed delineated at the Omorate.

• Land use and land cover was reclassified into 7 broad categories

SWAT Land Use

Area (ha)

% Watershed Area

% sub basin

AGRC 92603.2 1.4 1.4

AGRL272001

0.3 40.3 42.2

FRSD661280.

5 9.8 10.3FRST 69943.8 1.0 1.1

RNGR292470.

2 4.3 4.5

RNGW242747

1 18.8 37.6

WATR186396.

5 2.8 2.9

Soil Area (ha)

% Watershed Area

% sub basin

Chromic Luvisol 351598.7 5.2 5.5Dystric leptosol 14272.9 0.2 0.2

Dystric Vertisol 228955.2 1.7 3.5

Eutric Cambisol 146060.0 1.6 2.3

Eutric Fluvisol 438059.3 6.5 6.8Eutric Leptosol 11503.9 0.2 0.2

Eutric Vertisols 827509.7 12.3 12.8

Humic Alisol1381854.

1 20.5 21.4

Humic Nitisol2130999.

1 31.6 33.0

Lithic Leptosol 919362.5 13.6 14.3

Model Calibration and Validation of Karodus Sub watershed

• Using SWAT model the area was delineated into 24 sub watersheds, which were further, divided into 311 HRUs.

• Simulated flow at the outlet was compared with the observed flow.


Flow Calibration At Karodus


Sensitivity Analysis


6. HEC-ResSim (Reservoir System Simulation) Model6.1. Introduction• HEC-ResSim (USACE) is a modeling software

program used to assist in planning studies for evaluating existing & proposed reservoirs, reservoir operations, & to assist in sizing the flood risk management and conservation storage requirements for each project.

• It is intended to meet the needs of real-time reservoir regulators for a decision support tool, as well as the needs of modelers doing reservoir projects studies.


Introduction cont...• There are three modules that make up HEC-

ResSim to simulate the dam & reservoir operations.

• These are watershed set up, reservoir network and simulation. Each module has a unique purposes & an associated set of functions accessible through menus, toolbars and schematic.


ResSim Module Concepts

.


6.2. 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


• Insertion of the map layers in to the HEC-ResSim model

• Schematization & configuration of stream alignment & configurations of the projects

• Developing network schematic • Describing the physical & operational

elements of the reservoir model & analyze the alternatives

• Configuration of simulation to isolate the output analysis,

• Simulation of the dams & reservoirs network,


6.3. Methodology

Watershed Setup & Stream Alignment


• Evaluation of the effects of various reservoir operating alternatives on flooding at locations d/s of the reservoirs,

• Calibration & Verification of the model,• Development of a model that represents the

cascade dams & reservoirs, • Delivery of optimal water use operational model

for Omo River Basin and• Interpretation of the results.


Methodology cont…

6. 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.


title: modelling of cascade dams & reservoir operation for optimal water use: application to the...

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