Uncertainty on Hydrological Models in Climate Change Scenarios

1

Uncertainty on Hydrological Models in Climate Change Scenarios

S. Jamshid Mousavi

Associate Professor, Civil Engineering Department, Amirkabir

University of Technology (Polytechnic of Tehran), Tehran, Iran

jmosavi@aut.ac.ir

Regional Asian G-WADI Workshop,

June 2011, Tehran, Iran

Outline• Steps in climate change impact studies

• Uncertainty in climate change studies

• Hydrological models and uncertainty sources

• Uncertainty-based calibration/simulation of

hydrological models

• Successive uncertainty fitting (SUFI) approach

• Uncertainty-based calibration of HEC-HMS

model: Tamar Basin experience

• Uncertainty-based calibration of SWAT model: Karkheh River Basin experience

• Climate-change-driven runoff simulation and water allocation at basin scale: Karkheh River Basin experience

2

Downscaling

Calibrated hydrological model

Management and water allocation model

Emission scenarios

GCM outputs

Precipitation, temperature, etc

1-Select a few number of emission

scenarios

2-Take GCMs outputs of metrological

variables

3-Downscale the output of the GCMs

4-Build a calibrated hydrological model

of the basin

5-Simulate hydrologic variables of

interest (runoff) subject to downscaled

climate-change-driven inputs

6-Extend the study chain to water

management system

Typical steps of climate change impact studies on water resources

3

CC-driven simulated runoff scenarios

Downscaled precipitation, temperature, etc

4

Uncertainty on future emission of green gasses: Emission

scenarios

Uncertainty on nature of ocean-atmosphere physical processes

and so structure of GCMs

Uncertainty related to quantification of regional and local effects

reflected in downscaling models

Uncertainty on hydrological models

Uncertainty on behavior of complex socio-economic and

management systems

Uncertainty Issue

5

Approaches to deal with uncertaintyScenario generation-based techniques

Techniques based on probabilistic representation of processes involved (e.g. Reliability Ensemble Averaging [Giorgi and Mearns 2002], Bayesian-based Multi-model Ensembles of GCMs [Tebaldi et al. 2005], …)

6

Uncertainty on Hydrological ModelsSources of uncertainty:

1- Structural uncertainty

2- Data uncertainty

3- Parameter uncertainty Input processing Output

known Unknown

Precipitation Watershed Runoff

Hydrologic Modeling

77

CALIBRATIONModel parameters are determined through model calibration, because parameters may not have exact physical meaning or may not be easily measurable

Manual Calibration: Trial and error-based parameter estimation procedure Time consuming Depends on expertise and judgment of the modeler Does not account for uncertainty

Automatic Calibration: Systematic search procedures for finding parameter values The search procedure is guided based on an objective function

measuring how well any set of parameters perform

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8

CALIBRATION PROCEDURE

Rainfall-precipitation data

Rainfall-precipitation data

startstart Parameter EstimationParameter Estimation

Simulation in HEC-HMSSimulation in HEC-HMS

Compare HydrographCompare Hydrograph

Error?

EndEnd

Better Estimation of

parameters

Better Estimation of

parameters

Yes

NO

17

9

Difficulties with (Automatic) Calibration TechniquesModel uncertainty because of imperfect model structure due to either parameters

interdependence or conceptual simplifications of physical processes taken place in real natural systems

Input uncertainty due to erroneous or approximate input data (Extending point rain

data to areal data)

Parameter uncertainty and nonuniquness due to non-identifiability feature

a) different parameter sets may not give rise to different model outputs resulting in parameter nonuniquness in inverse-type problems

b) model response surface can be insensitive to a number of parameters in the region of optimum solution

Multiple convergence regions and local optima solutions

Nonconvex shape of the response surface with discontinuous long curved ridges 9

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Quality of Calibration Results Depend on:The conceptual base and structure of the CRR model

The modeling technique addressing different types of uncertainty depending on quality and quantity of information and data available

The power and robustness of the optimization algorithm

Performance criteria or objective function

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Uncertainty-based Calibration of Hydrological Models

Automatic calibration techniques:

1- Monte-Carlo2- Generalized likelihood uncertainty estimator (GLUE)3- Bayesian techniques4- Parasol5- Successive uncertainty fitting (SUFI)

12

SUFI TechniqueExtensively used in calibration of SWAT hydrological

model at continent, regional and basin scales

13

HMS-SUFI: Uncertainty-based Calibration Technique Step 1. Select an objective function (RMSE in the current application)

More importance is given to some desired discharges like the peak flow

Step 2. Define physically meaningful absolute minimum and maximum ranges for each parameter

Step 3. Carry out a sensitivity analysis with respect to parameters

Step 4. Assign an initial uncertainty range to each parameter used in the first round of Latin hypercube. These ranges are smaller than the absolute ranges

Step 5. Generate n different sets of parameter values using Latin Hypercube sampling technique

Step 6. Evaluate the objective function, g, for each of n generated sets (Run HEC-HMS n times)

Step 7: Calculate the sensitivity analysis matrix J, Hessian Matrix H, Covariance matrix C, and parameter correlations matrix r

Step 8: Calculate uncertainty indicators P-factor, R-factor,.., and new parameter ranges based on best parameter set and matrices C and …

2 2

2

1

*( )(1)i observed simulated

n

ii

C Q QRMSE

C

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Conceptual basis of SUFI Uncertainty AnalysisInput parameters:

(Uniform distribution)Output variables:

(95% PPU)

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95PPU:

Uncertainty Bound as 95% of probability distribution 

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Month

Riv

er

dis

ch

arg

e(m

3 s

-1)

Jelogir Station

R-factor : Measures total predictive uncertainty in terms of normalized sum of 95% PPU bounds

R-factor : Measures model predictive uncertainty

  P-factor: Percent of observed data (runoff) locating within 95PPU bounds of simulated ones

br2

r2 :Correlation coefficient between measured and simulated valuesb: Slope of regression line

Uncertainty measures

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350

Month

Riv

er

dis

ch

arg

e(m

3 s

-1

)

Ghurbaghestan StationP-factor: 0.78R-factor: 0.91

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Uncertainty Analysis using SUFI

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HMS-SUFI MODELCase Study:Tamar Subbasin

One of the basins of Gorganrood River located in the North west of Khorasan province in Iran.

Area of Gorganrood River 52826 square kilometers

Area of Tamar basin about 1530 square kilometers

• Considered important because of experiencing severe floods .

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Gorgan-Roud Basin

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Case Study-Tamar Subbasin Lack of sufficient data at hydrometric and rainfall stations makes modeling the basin’s

response to floods challenging

4 reliable flood events were available the first 3 of which were used for calibration and the last one for verification

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1919

Observed Flood Events

Event Date Peak Flow (m^3.s) Duration(hr)

1 19Sep2004 128 20

2 6May2005 299 30

3 9Aug2005 783 19

4 8Oct2005 120 13

Date, peak flow and duration of flood events

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2020

Hydrograph of first event0 5 10 15 20 25 30 35 40 450

5

10

15

20

25

30

35

40

Rai

n(m

m)

0 5 10 15 20 25 30 35 40 450

20

40

60

80

100

120

140

Event 1

Time(hr)

Q(M

3 /s)

Time 0 equals to (19Sep2004, 18:00)

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2121

Hydrograph of Second event

Time 0 equals to (06May2005, 01:00)

0 5 10 15 20 25 30 35 40 450

5

10

15

20

25

30

35

40

Rai

n(m

m)

0 5 10 15 20 25 30 35 40 450

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100

150

200

250

300

350

event 2

Time(hr)

Q(c

ms)

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2222

Hydrograph of Second event0 5 10 15 20 25 30 35 40 450

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10

15

20

25

30

35

40

Rai

n(m

m)

0 5 10 15 20 25 30 35 40 450

100

200

300

400

500

600

700

800

event3

Time(hr)

Q(c

ms)

Time 0 equals to (09Aug2005, 20:00)

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2323

Hydrograph of Second event0 5 10 15 20 25 30 35 40 450

5

10

15

20

25

30

Rai

n(m

m)

0 5 10 15 20 25 30 35 40 450

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40

60

80

100

120

140

event 4

Time(hr)

Q(c

ms)

Time 0 equals to (08Oct2005 ,

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HEC-HMS STRUCTURE

HEC-HMS Components

Basin module Meteorological module Control Specification

Loss module Transformation module

Base flow module

Routing module

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BASIN MODULE

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Loss Method Routing (for Reach ) Base Flow Method Transform Method

Deficient and Constant Wave kinematics Bounded Recession Clark Unit hydrograph

Exponential Lag Constant Monthly Mod Clark

Green-Ampt Muskingum Linear Reservoir Kinematic Wave

Gridded Deficient and Constant

Muskingum-Cunge Nonlinear Boussinesq SCS Unit Hydrograph

SCS Curve number Modified Puls recession Snyder Unit hydrograph

Gridded SCS Curve Number Straddle Stagger Specified Unit hydrograph

Initial and ConstantUnit hydrograph S

hydrograph

Soil moisture Accounting

Smith Parlange

Gridded Soil moisture Accounting

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BASIN MODULE

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Model of Tamar Basin in HEC-HMS

Divided into 7 sub-basin with 3 routing reaches

SCS-CN Method Estimating hydrologic losses

Clark Method Transforming rainfall to runoff

Flood routing in reaches Muskingum method.

No base flow

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Tamar Basin in HEC-HMS

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Calibration Parameters Loss method: SCS-CN Method

1- Curve number: 7 parameters (1 for each subbasin)2- Initial abstraction: 7 parameters (1 for each subbasin)

Transformation Method: Clark 1-Time of concentration: estimated by SCS synthetic unit hydrograph approach2- Storage Coefficient (R):

Routing Method: Muskingum (K,X)

Total No. of Calibration Parameters=2428

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Manually-calibrated parameter values for each event and their upper and lower limits (suggested by IWRI (2008)

Upper limit

Lower limit

Event-1 Event-2 Event-3 Event-4 Upper

limit

Lower

limit

Event-1

Event-2

Event-3 Event-4

Curve number

Subbasin1 91 60 66.1 86.3 80.6 91

Constant

Subbasin1 0.65 0.2 0.34 0.47 0.53 0.28

Subbasin2 91 61 63.2 85.5 76.3 91 Subbasin2 0.65 0.2 0.50 0.15 0.52 0.58

Subbasin3 87 58 80.1 82.1 70.2 87 Subbasin3 0.65 0.2 0.53 0.53 0.63 0.49

Subbasin4 85 60 78.1 64.85 60.6 85 Subbasin4 0.65 0.2 0.25 0.49 0.61 0.64

Subbasin5 84 50 51 84 84 82.9 Subbasin5 0.65 0.2 0.25 0.32 0.64 0.58

Subbasin6 91 70 86.3 80.2 79.9 91 Subbasin6 0.65 0.2 0.43 0.53 0.63 0.47

Subbasin7 91 70 77.2 72.1 80.0 91 Subbasin7 0.65 0.2 0.43 0.51 0.64 0.61

Initial abstraction

Subbasin1 0.25S 0.15S 0.24S 0.16S 0.22S 0.15S

Muskingum

X

Reach 1 0.5 0.2 0.47 0.48 0.49 0.42

Subbasin2 0.25S 0.15S 0.18S 0.2S 0.20S 0.24S

Subbasin3 0.25S 0.15S 0.17S 0.24S 0.22S 0.13S Reach 2 0.5 0.2 0.21 0.41 0.40 0.42

Subbasin4 0.25S 0.15S 0.25S 0.22S 0.21S 0.24S

Subbasin5 0.25S 0.15S 0.21S 0.23S 0.20S 0.21S Reach 3 0.5 0.2 0.25 0.46 0.47 0.48

Subbasin6 0.25S 0.15S 0.19S 0.24S 0.23S 0.16S

Subbasin7 0.25S 0.15S 0.22S 0.13S 0.19S 0.17S

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Results and AnalysisSingle-event Calibration Scenario-Event 1

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Results and AnalysisSingle-event Calibration Scenarios-Events 2 and 3

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Results and AnalysisJointly-calibrated events: Scenario 0

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Results and AnalysisJointly-calibrated events scenario with bigger weights assigned to Event

1 in the Obj. Function (Scenario 0W)

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Results and AnalysisJointly-calibrated events with 31 parameters and increased Ia values of event-1 to 0.45

(Scenario 1)

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Results and AnalysisJointly-calibrated events with 31 Parameters and decreased Ia values of event-3

to zero (Scenario 2)

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Jointly-calibrated events with 31 Parameters and adjusted Ia values of event-1 and event-3 (Scenario 3)

we were keen to see if there is another set of parameters with Ia values closer to initially-set bounds. The 31-parameter problem was run with Ia lower bounds of event-1 as 0.35 (instead of 0.45 in scenario 6) and those of other events (1 and 2) as 0.05 (instead of zero in scenario 7).

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Results and AnalysisNonuniqueness of Parameter Sets

The only difference between scenarios 1-3 is in their Ia lower and upper bounds

What about the other parameter values? Were they same?

Answer: NoNonuniqueness

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Verification Analysis Simulating Event 4 by 9 sets of Parameter Values Obtained in Calibration

Stage

Before recalibration of Ia After recalibration of Ia

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Simulated objective function and Ia values of all recalibrated parameters (Screening Step)

Parameter sets

Event-1 Event-2 Event-3 Mean Events

Scenario 0 Scenario 0W

Scenario 1 Scenario 2

Scenario 3

Simulated RMSE 21.6431 22.8237 21.4624 38.0544 21.4112 21.6312 21.5938 21.5938 24.6156

Sub-basin’s 1 Ia coefficient 0.4005 0.2989 0.5201 0.3008 0.4567 0.2938 0.4070 0.95 0.2205

Sub-basin’s 2 Ia coefficient 0.3579 0.2586 0.6050 0.3013 0.4545 0.2941 0.4227 0.95 0.3092

Sub-basin’s 3 Ia coefficient 0.3380 0.2894 0.4394 0.2631 0.4584 0.2100 0.4538 0.9500 0.2309

Sub-basin’s 4 Ia coefficient 0.4052 0.2747 0.5936 0.3141 0.3902 0.3200 0.4263 0.9500 0.3570

Sub-basin’s 5 Ia coefficient 0.3563 0.3628 0.4826 0.2964 0.3606 0.2710 0.3968 0.0054 0.3581

Sub-basin’s 6 Ia coefficient 0.3846 0.3113 0.4698 0.2875 0.3474 0.2312 0.2258 0.6586 0.2564

Sub-basin’s 7 Ia coefficient 0.3155 0.3906 0.4749 0.2913 0.4426 0.1979 0.1796 0.8876 0.1246

Min Ia 0.3155 0.2586 0.4749 0.2631 0.3474 0.1979 0.1796 0.0054 0.1246

Max Ia 0.4052 0.3906 0.6050 0.3141 0.4584 0.3200 0.4538 0.95 0.3581

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Comparison of parameter values, other than Ia s, of the 6 screened parameter sets (See nonuniquness)

Parameter values

Event-1 Event-2 Scenario-0 Scenario-0W Scenario 1 Scenario 3

CN1 65.97 78.69 82.57 77.70 83.60 78.10

CN2 84.34 72.56 83.59 82.72 78.76 76.31

CN3 76.03 67.82 67.28 60.91 68.66 58.30

CN4 60.05 60.84 62.11 60.01 61.09 60.96

CN5 58.39 79.92 77.11 63.42 72.94 71.47

CN6 88.88 76.43 74.35 76.37 73.76 70.19

CN7 83.61 75.07 72.34 70.32 79.52 71.04

SC1 0.5199 0.4171 0.2785 0.2232 0.2879 0.2355

SC2 0.2984 0.5614 0.4742 0.4091 0.3182 0.3575

SC3 0.3095 0.2776 0.5406 0.5968 0.4700 0.4476

SC4 0.5987 0.5660 0.5961 0.5988 0.2696 0.4446

SC5 0.4968 0.2005 0.2640 0.2302 0.3039 0.3102

SC6 0.5213 0.3188 0.5529 0.2593 0.5114 0.5380

SC7 0.4201 0.3144 0.3512 0.2559 0.3877 0.2502

X1 0.4464 0.3245 0.3226 0.2012 0.2381 0.4232

X2 0.2540 0.4333 0.4564 0.3781 0.3563 0.3831

X3 0.4063 0.3692 0.3345 0.3912 0.3186 0.4711

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Dealing with Uncertainty Uncertainty measures and the best obj. function obtained from another SUFI run with the new parameter bounds derived from 6 screened parameter sets : event 4

(values within parenthesis are for a smoothed hydrograph) Iteration 1 2 3 4 5 6 7 8 9 10

P-factor% 89.47 (94.7

4)

84.21 (78.9

5)

73.68 (84

.21)

68.42 (68.

42)

47.3 (68

.42)

31.58 (47.

37)

36.84 (42.1

1)

26.32 (36.8

4)

15.79 (26.

32)

15.78 (26.

32)

R-factor 1.6559 (1.65

38)

1.0795 (0.81

3)

0.8849 (0.

4887)

0.7457 (0.3

653)

0.5234 (0.

3430)

0.2096 (0.2

267)

0.1767 (0.16

76)

0.1296 (0.11

40)

0.1129 (0.1

110)

0.0704 (0.0

837)

Par-factor

0.2047 (0.20

47)

0.1285 (0.10

98)

0.0783 (0.

0741)

0.0571 (0.0

566)

0.0395 (0.

0433)

0.0289 (0.0

319)

0.0227 (0.02

25)

0.0149 (0.01

54)

0.0117 (0.0

125)

0.0085 (0.0

090)

Best sol’s RMSE

14.45 (9.17

06)

13.62 (9.20

93)

13.74 (8.

7700)

13.36 (7.6

496)

13.22 (7.

4637)

13.05 (7.2

927)

12.78 (7.13

73)

12.69 (7.09

89)

12.62 (7.0

065)

12.56 (6.9

803)

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Parameter Bounds and Simulated Discharges in iterations 1 and 3 of SUFI with

Newly-Selected Parameter Bounds:Event-4

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Measuring Input Parameter Uncertainty

)17(2/)minmax(

)minmax(

1

1

m

jjj

m

jjj

bb

bb

factorPar

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Iteration’s 4 simulated discharges of the events in SUFI for jointly –calibrated eventswith fixed parameter intervals except initial abstraction ranges to find de-calibrated Ia values

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ConclusionsThe proposed 3-stage procedure

1- Obtain different parameter values by calibrating events either separately or jointly to come up with candidate parameter sets entering in verification stage

2- Recalibrate the parameters reflecting initial basin conditions and then screen out the above candidate parameter sets based on how well they perform in verification and also how physically meaningful their recalibrated parameters are

3- Calculate the new parameter ranges from the screened parameter values and re-run the SUFI model to find narrower parameter ranges, as the final parameter ranges, at which uncertainty indicators are still acceptable. Also check if the final ranges perform well in simulating all calibration events (Backward step). This needs again re-calibration of initial-basin-condition parameters with respect to calibration events 46

Karkheh River Basin Study

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1- Climate Change Impact on Surface Runoff

2- Climate Impact on Water Allocation

Downscaling

Calibrated hydrological model

Management and water allocation model

Emission scenarios

GCM outputs

Precipitation, temperature, etc

1-Select a few number of emission

scenarios

2-Take GCMs outputs of metrological

variables

3-Downscale the output of the GCMs

4-Build a calibrated hydrological model

of the basin

5-Simulate hydrologic variables of

interest (runoff) subject to downscaled

climate-change-driven inputs

6-Extend the study chain to water

management system

Typical steps of climate change impact studies on water resources

48

CC-driven simulated runoff scenarios

Downscaled precipitation, temperature, etc

÷One of the most important basins in

Iran in terms of Surface and

groundwater resources, agriculture

potential, hydropower generation,…

16000 MCM of potential storage

capacity 40% percent of which has

been constructed

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Karkheh River Basin

Area: 4763592 Hectar

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Karkheh River Basin

Tangmashoureh

Garshaگ

Seymareh

Koranbuzan

Sazbon

ROR Karkeh

Karkheh

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Location of Dams in Karkheh

Basin

ScenarioA2

B1A1B

Emission Scenarios

A1

World’s Rapid but Uniform Economy Grow with

Tendency to Increasing Consumption

International Cooperation toward Clean Technologies

and Environment ProtectionBalanced Utilization of Fossil and No-fossil Fuels as Energy

Resources

Nonuniform Rapid Economy Grow with Variable Regional

Technologies along with Increasing Consumption

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Downscaling

CGCM 3.1 T & P outputs were related to basin’s

meteorological stations data from 1980-2002 to estimate

Near (2010-2040) and Far (2070-2100) future downscaled

outputs of the emission scenarios

Calibration Period: 13 yearsValidation Period: 10 Years

T:

: P

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1 2 3 4 5 6 7 8 9 10 11 120

2

4

6

8

10

12

14Hamedan. Station

A1B (2070-2100)

A1B (2010-2040)

A2 (2070-2100)

A2 (2010-2040)

B1 (2070-2100)

B1 (2010-2040)

Obs. (1982-2002)

Month

Ave

rage

Pre

cipi

tati

on (m

m)

1 2 3 4 5 6 7 8 9 10 11 120

1

2

3

4

5

6

7

8

9

10Ahwaz Station

A1B (2070-2100)

A1B (2010-2040)

A2 (2070-2100)

A2 (2010-2040)

B1 (2070-2100)

B1 (2010-2040)

Obs. (1982-2002)

Month

Ave

rage

Pre

cipi

tati

on (m

m)

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Downscaled/Historical Precipitation at Hamedan and Ahwaz Stations

1 2 3 4 5 6 7 8 9 10 11 120

10

20

30

40

50

60

Hamed fo. Station

A1B (2070-2100) A1B (2010-2040) A2 (2070-2100) A2 (2010-2040)

B1 (2070-2100) B1 (2010-2040) Obs. (1982-2002)

Month

Max

. Tem

pera

ture

(ْC)

1 2 3 4 5 6 7 8 9 10 11 120

10

20

30

40

50

60 Ahwaz Station

Month

Max

. Tem

pera

ture

(ْC

)

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Downscaled/Historical Temprature at Hamedan and Ahwaz Stations

SWATSoil & water Assessment Tool

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.

Calibration (1990-2002)

br2: 0.46P-factor: 71%R-factor: 0.83

Validation (1980-1990)

br2: 0.42P-factor: 65%R-factor: 0.74

Vlidation Calibration

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Series1

Observed

MonthR

ive

r d

isc

ha

rge

(m3

s-1

)

Watershed area: 2781 km 2

𝜙: 0.46P-factor: 0.75R-factor: 0...84

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95PPU

Observed

Month

Riv

er

dis

ch

arg

e(m

3 s

-1)

Watershed area: 2781 km 2

𝜙: 0.40P-factor: 0.77R-factor: 0.72

Polchehr Station 8 Hydrometric Stations used

Sample Results of the Model Calibration Using SUFI

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1980-2002

2070-2100 2010-2040

Max. Temperature (C)

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Precipitation (mm)

1980-2002

2070-2100 2010-2040

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Runoff (m3)1982-2002

2070-2100 2010-2040

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Garsha Seymare Tang-mashure Krkhe0

500

1000

1500

2000

2500

3000

3500

4000

4500

A1B (2073-2099)A1B (2013-2039)A2 (2073-2099)A2 (2013-2039)B1 (2073-2099)B1 (2013-2039)Historic (1985-2002)

Dam

Wat

er a

vail

able

(M

CM

)

Average Annual Runoff @ Grasha, Seymareh and Karkheh Dams Basins

30%9%B1 (2013-2039)A2 (2013-2039)

A2 (2073-2099)

B1 (2073-2099)46%

10%

64

4163 MCM

1590 MCM

Jan Feb Mar Apr May Jun Jul Aug Sep Nov Oct Dec0

100

200

300

400

500

600

700

800

A1B (2073-2099) A1B (2013-2039) A2 (2073-2099) A2 (2013-2039)

B1 (2073-2099) B1 (2013-2039) Historic (1985-2002)

Month

Ava

ilab

le W

ater

(M

CM

)

A1B (2073-2099)A1B (2013-2099)

Monthly Distribution of Runoff @Karheh Dam Site

65

66

Summary Results of CC-Driven Runoff Scenarios

67

Scenario

Region

B1

( 2013-2039)

B1

( 2073-2099)

A2

( 2013-2039)

A2

( 2073-2099)

A1B (2013-2039)

A1B

( 2073-2099)

13 10.4 31.8 45.7 22.3 35.42 North

Diff. Discharge (%)-30 -32 -18.5 -9.3 -25.18 -17 South

6.09 6.4 10.93 19.7 8.54 12.18 North

Diff. Precipitation (%)-97.91 -97.9 -97.84 -97.58 -97.84 -97 South

1.5 2.51 1.74 4.3 2.7 2.51 North Diff. Max. Temperature

(0C)0.98 1.00 0.99 1.047 1.03 0.98 South

Summary Results of CC-Driven Runoff ScenariosHistoric (1985-2002)

Region  

85.49 NorthDischarge (m3/s)

132 South

409 NorthPrecipitation (mm)549.3 South

22.2 NorthMax. Temperature (0C)31 South

6.16 NorthMin. Temperature (0C)14.47 South

2. Climate Change Impact Assement on water Allocation

68

MODSIM

River Basin Management Decision Support

69

S275762 ha

24.05

PS1

PS21645 ha

11270 ha21993 ha

6000 ha

24000 ha

14000 ha

11000 ha

20%

15%

35000 ha

15%

S1611787 ha

outlet18907 ha

68000 ha

45333 ha

AMC

2750 ha30%

30%

85 15%

510

100

70

30%

30%

30%

30%

100

5019 ha

0.12

10%

10%

93

20%

35000 ha

3950 ha

13000 ha

14660 ha

60000 ha

2000 ha

S1611787 ha

15%

AMC

5019 ha

1645 ha

)(

:

:

:

5

6000 ha

-

-

- -

-

20 Agricultural nodes

Type No. Demand Type

Prioاrity

1 Domestic 1

2 Environment 2

3 Agriculture 3

4 Hydropower 4

: Target Reservoir storage: 5

Schematic of Karkheh Water Resource System

7 Reservoirs

71

Dams and Powerplants Characteristics

73

A1B (2073-2099)

A2 (2073-2099)

B1 (2073-2099)

Historic (1985-2002)0

5

10

15

20

25

Potation

Scenario

Flo

w (

MC

M)

A1B (2073-2099)

A1B (2013-2039)

A2 (2073-2099)

A2 (2013-2039)

B1 (2073-2099)

B1 (2013-2039)

Historic (1985-2002)0

1000

2000

3000

4000

5000Enviromental

Scenario

Flo

w (

MC

M)

A1B (2073-2099)

A1B (2013-2039)

A2 (2073-2099)

A2 (2013-2039)

B1 (2073-2099)

B1 (2013-2039)

Historic (1985-2002)0

1000

2000

3000

4000

5000

Agriculture

Scenario

Flo

w (

MC

M)

Annual Allocated Water to Different Demand types ???

Environment:

A1B (2073-2099) 5%

B1 (2013-2039) 11%

Agriculture:

A1B (2073-2099) 22%

A2 (2073-2099) 76%

Domestic:

A1B (2073-2099) 3%

B1 (2013-2039) 7%

A2 (2073-2099) 7%

75

16.5

1836 2196

Agriculture Enviromental Potation0

10

20

30

40

50

60

70

80

A1B (2073-2099) A1B (2013-2039) A2 (2073-2099) A2 (2013-2039)

B1 (2073-2099) B1 (2013-2039) Historic (1985-2002)

Demand

Per

cen

tage

of

sup

ply

(%

)

Long-term (Annual) Percentage of Meeting Demands

76

1 2 3 4 5 6 7 8 9 10 11 120

102030405060708090

100

Agriculture

A1B (2073-2099) A1B (2013-2039) A2 (2073-2099) A2 (2013-2039)

B1 (2073-2099) B1 (2013-2039) Historic (1985-2002)

Month

Per

cen

tsge

of

sup

ply

(%

)

Significant decrease in meeting Agr. Demand except in Autumn Season

Monthly Percentage of Meeting Agricultural Demand

77

0 10 20 30 40 50 60 70 80 90 1000

50

100

150

200

250

Exceedance Probability (%)

Ene

rgy

(GW

H)

A1B (2073-2099)A1B (2013-2039)

A2 (2073-2099)

A2 (2013-2039)

B1 (2073-2099)

B1 (2013-2039)Historic (1985-2002)

Total MonthlyEnergy Duration Curve of the System

(GWh)

30Firm EnergyA1B (2073-2099)

78

1597

1278

958

639

319.5

Ene

rgy

(GW

H)

asd

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Scenario

En

ergy

(G

WH

)

Annual Total Energy Generated

(GWh)

A1B (2073-2099): 26%

B1 (2073-2099): 7%

79

3195

A1B (2073-2099)

A1B (2013-2039)

A2 (2073-2099)

A2 (2013-2039)

B1 (2073-2099)

B1 (2013-2039)

Historic (1985-2002)0

30

60

90

120

150

180Karkhe Jarayani

Scenario

Ene

rgy

(GW

H)

A1B (2073-2099)

A2 (2073-2099)

B1 (2073-2099)

Historic (1985-2002)0

200

400

600

800

1000

1200

Seymare

Scenario

Ene

rgy

(GW

H)

733

Annual Energy Generated at two Sites (GWh)

ROR Karkheh

Seymareh

80

531

1 2 3 4 5 6 7 8 9 10 11 120

100

200

300

400

500

600

700

A1B (2073-2099) A1B (2013-2039) A2 (2073-2099) A2 (2013-2039) B1 (2073-2099)

B1 (2013-2039) Historic (1985-2002)

Month

En

ergy

(G

WH

)Monthly Distribution of Total Energy Generated (GWh)

Increase in Fall and Winter Months

Decrease in Spring and Summer Months

81

82

Historic

(1985-2002)

B1

(2013-2039)

B1

(2073-2099)

A2

(2013-2039)

A2

(2073-2099)

A1B

(2013-2039)

A1B

(2073-2099)

3529.3

(GWh) 0 -3 4 14 1 14

Difference Percentage of Total Energy Generation

Compared to Historical Scenario

83

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