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This Presentation

Downscaling and Modeling the Climate of Blue Nile River Basin-Ethiopia

By: Netsanet Zelalem

Supervisors: 1. Prof. Dr. rer.nat.Manfred Koch, Kassel University2. Dr. Solomon Seyoum, IWMI, Ethiopia

Nov9/2012Kassel University, Germany

Statement of the Problem

• High population pressure, poor water and land management and climate change are inducing declining agricultural productivity and vulnerability to climate impact [Haileslassie et al., 2008].

• In order to alleviate poverty and food insecurity, it is widely recognized to utilize water resources such as Blue Nile.

• So, assessment of the impact of climate change on future water resource may provide substantial information to the area where more than 85% of the basin depends entirely on rain-fed agriculture.

Objective

• Evaluate the possible relationships between large scale variables with local meteorological variables.

• Evaluate the most common statistical downscaling methods, SDSM and LARSWG, for the assessment of the hydrological conditions of the basin.

• Generate climate change scenarios for the basin using different emission scenarios and AOGCMs (Atm.and Ocean).

• Investigate the possiblity of climate change on hydrology in UBRB based on the downscaled meteorological scenario data.

• Provide streamflow predictions of the basin for current and downscaled future climate conditions.

Contents

• Background on Climate System• Study Area• Data collection, analysis and results• Climate Modeling• Results of Climate Modeling• Conclusions

Background (Climate system)Climate is a statistical description of weatherincluding averages and variability.The earth climate system is an interaction of various

components of climate system: Ocean Land surface Atmosphere Cryospher Biosphere Anthropogenic

---Background (Climate system)• Climate Change: refers to a statistical significant variations

that persist for an extended period, typically decades or longer.

• The mea annual global temperature has increased by about 0.3-0.60C since the late 19 century.

---Background (Climate change Impact )• Today, the impact of climate change become the

biggest concern of mankind.

---Background (Climate Change Impact)• This will impact the hydrology of the watershed systems

and hence it exhibits long-term changes.

---Background (Climate Change Impact)• This impact needs integrated modeling to evaluate

alternate future watershed scenarios.• IPCC findings indicate that developing countries, such as

Ethiopia, will be more vulnerable to climate change

Higher Relative Risks

Lower Relative Risks

---Background (Climate Model)• Climate Models try to simulate the likely responses of

climate system to a change in any of the parameter interactions between them mathematically.

• Generally refers as GCMs (Global Circulation Models)• The 3-D model formulation is based on the fundamental

laws of physics:Conservation of energyConservation of momentumConservation of mass andThe “Ideal Gas Law”

---Background (Emission Scenarios)• Emission scenarios are

important components and tools for the modeling of climate change (Werner and Gerstengarbe, 1997)

Emissions 2011-2030 2046-2065 2080-2099

A2 0.64 1.65 3.13

A1B 0.69 1.75 2.65

B1 0.66 1.29 1.79

---Background (Downscaling GCM)• In climate change impact

studies, hydrological modeling:Are usually required to

simulate sub-grid scale phenomenon.

Require input data (such as pcp, temp) at similar sub-grid scale.

• Downscaling is a means of relating the large scale atmospheric predictor variables to local scale so as to use for hydrological model inputs.

---Background (Downscaling Methods)1. Dynamic downscaling

Extract local-scale information by developing and using regional climate models (RCMs) with the coarse GCM data used as boundary conditions.

2. Statistical downscalingDrive the local scale information from the larger scale

through inference from the cross-scale relationship.

It Can be categorized in to three typesRegression downscalingStochastic weather generatorsWeather typing schemes

---Background (Statistical downscaling)

1. Regression downscaling techniques: Predicted=f(Predictors). The function f could be. Linear or non-linear regression.2.Stochastic weather generators: The relationships between daily weather generator

parameters and climatic average can be used to characterize the nature of future daily statistics (wilby, 1999).

---Background (Statistical downscaling)3. Weather typing schemes Involve grouping local, meteorological variables in

relation to different classes of atmospheric circulation. Future regional climate scenarios are constructed by:

Resembling from observed variable distribution Climate change is then estimated by determining the

change of the frequency of weather classes.

Study area

---Study AreaFeatures of Upper Blue Nile watershed

The total area=176,000 km2

Latitude: 7° 45’ and 12° 45’N and longitude: 34° 05’ and 39° 45’E

Altitude: Min. 485m to Max. 4,257m aslUBNB has 14 sub-basinsIt contributes 40% of Ethiopia surface water resources

[World Bank 2006]87% of the Nile flow at Aswan dam is from Ethiopia

from this UBNB contributes 60% and the Atbara (13%) and the Sobat (14%)

Data sources

Data Name Sources

PrecipitationMaximum TemperatureMinimum Temperature

NMA

NCEP www.ncep.noaa.gov

GCMs

WCRP CMIP3 Multi-Modal data sethttp://esg.llnl.gov:8080/index.jsp

World Climate Data Center http://www.mad.zmaw.de/wdc-for-climate/cera-data-model/index.html

Data Collection and Quality Checking• After collection of precipitation data from 53 stations and

temperature from 33 stations for 1970-2000 period at daily time scale, data quality( Such as, filling missing data and consistency check) control has been conducted.

• Areal precipitation and temperature based on ThiessenPolygon method: Stn.Results:

Sub-Basin Results of Observed Data

Large Scale Data

Criterion to chose GCMs

1. Based on outputs of

MAGICC-SCENGEN

2. Based on data availability

3. Based on their participation

IPCC-AR4

4. Allowable number of GCMs

ECHAM-5, GFDLCM21 and

SCIRO-MK3

Data of selected GCMs

• A1b and A2 emission scenarios are considered to account the worst (A2) and the middle(A1B).

• Re-griding has been done using Xconv package.

GCM EmissionScenario of A1B and A2

Current ConditionScenario

65 yearsInto Future Scenario

100 years Into FutureScenario

AtmosphericResolutions(Deg)

Echam5 1970-2000 2046-2065 2081-2100 1.9x1.9

GFDLCM2.1 1970-2000 2046-2065 2081-2100 2.0x2.5

CSIRO-MK3 1970-2000 2046-2065 2081-2100 1.9x1.9

NCEP 1970-2000 2.5X2.5

Large-scale Predictor VariablesS

No Predictor variablesDesign

ation

S

No Predictor variablesDesignat

ion

1 Air pressure at sea level mslp 11 Northward wind @850mpa p8_v

2 Precipitation flux prat 12 Northward wind @500mpa p5_v

3 Minimum air temperature tmin 13 Meridional surface wind speed p_v

4 Maximum air temperature tmax 14 Specific humidity @850mpa s850

5 Surface air tempratur@2m temp 15 Specific humidity @500mpa s500

6 Air temperature @850mpa t850 16 Geopotential height @850mpa p850

7 Air temperature@500mpa t500 17 Geopotential height @500mpa p500

8 Eastward wind@850mpa p8_u 18 Relative humidity @500mpa r500

9 Eastward wind@500mpa p5_u 19 Relative humidity @850mpa r850

10 Zonal surface wind speed p_u

Large Scale Data

Re-analysis grid lines covering the study areaName of

Subbasin

Grid box

considered

Name of

sub basin

Grid box

considered

Tana 22 and 23 Anger 12and 22

Belles 12,13,

22 and 23

Wonbera 12 and 22

Dabus 12 Muger 22 and 32

D idessa 11,12,

21and 22

Beshilo 22,23,

32 and 33

Guder 22 Wolaka 22 and 32

Fincha 22 N/Gojam 22 and 23

S/Gojam 22 Jimma 22 and 32

Statistical Downscaling Tools

• Two statistical downscaling tools:• *SDSM: A regression based statistical downscaling

model (wilby, et al., 2002)

• *LARS-WG: Long Ashton Research Station Stochastic Weather Generators (Semenov et al, 1998).

SDSM: A regression based Statistical Downscaling models

• Identify predictand relationships using multiple linear regression techniques.

• The predictor variables provide daily information concerning the large-scale state of the atmosphere,

• The predictand describes condition at the site scale.

LARS-WG• Generate precipitation, min and

max temperature.• Semi-empirical distributions are

used to state a day as wet/dry series.

• Semi-empirical distributions are used for precipitation amounts, dry/wet series.

• Semi-empirical distributions are used for Temperature. It is conditioned on wet/dry status of a day.

Cases considered• Three cases are employed in climate modeling

• All the cases are applied for each of 14 sub-basins in UBNB.

Type GCMs Emission Period ToolsCase-1 echam5 a1b, a2 2050s, 2090s SDSMCase-2 echam5 a1, a2 2050s, 2090s LARS-WGCase-3 Echam5, gfdl21

& csiro-mk3a1b, a2 2050s, 2090 LARS-WG

Climate modeling-Case1 SDSM reduces the task into a number

of discrete processes as follows: 1. Quality control of data and

transformation. 2. Selection of appropriate predictor

variables for model calibration. 3. Calibrate Model. 4. Generate the daily data. 5. Analyze the outputs. 6. Scenario generation: Then analysis

of climate change scenarios

Selecting predictor variables• Predictor is selected based on correlation analysis off-line of

SDSM and using SDSM screening methods in the software.

SDSM Calibration ApproachModel calibration is performed in two approaches:Unconditional: It assumes a direct link between the

regional-scale predictors and the local predictand. • Maximum and minimum temperatureConditional: depend on an intermediate variable such as

the probability of wet-day occurrence, intensity, amount etc.

• PrecipitationThe performance of calibration result for each sub basin

Results-Case1

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

2050s_A1B

56.5398570682714

50.6178592203631

18.7517964941868

20.680995641808

17.6311267120

3

30.3049786971396

50.0999916256843

53.0836467398137

9.47728277495148

18.6656023946175

45.4398817586599

71.6198618835854

2050s_A2

58.037791609252

40.7018163799141

16.152452080642

18.3858223906661

16.4661748979571

28.9431768157827

42.7483752505986

47.8417676360748

8.90037634372148

21.4068305948191

38.3561599392011

59.4481031407495

2090s_A1B

108.789001475902

73.2560000113955

42.0655849483734

35.4505823281153

41.5313957283356

82.0454731115789

102.226095199723

100.220246311381

15.4098335664761

32.4556475754166

66.9899801297406

109.605317992617

2090s_A2

111.14322060747

84.0650531908927

45.861560978674

39.4375328118225

45.1181453291102

90.6020198906623

108.758896122186

107.088399085689

20.9833029139384

32.2608226714777

72.0939149812107

116.656888395815

1030507090

110130

Simulated RF change from observed at Muger

Re

la

tive

ch

an

ge

(%

)

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2047

2049

2051

2053

2055

2057

2059

2061

2063

2065

2082

2084

2086

2088

2090

2092

2094

2096

2098

2100

0500

10001500200025003000 Trend line of simulated RF at Muger

Observed Control 2050s_A1B2050s_A2 2090s_A1B 2090s_A2

RF

(m

m)

Results-case1Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2050s_A1B

-11.9930058267654

-32.0980301738977

-38.547057330613

-36.2256394665523

-48.2886452950688

-46.186351170232

-41.957561295554

-23.4202136313885

-11.0395047910016

-31.7250646563016

-21.1008447573487

-9.82121216341527

2050s_A2

-4.46968300268275

-34.1996522532874

-38.155983099162

-28.0665395772585

-42.176674085796

-42.6980184567346

-34.4911947966843

-25.1120590860266

-17.0114756425644

-24.3961467042082

-21.3427597564296

-9.97921040628668

2090s_A1B

-20.3668397911135

-52.6938176530704

-63.1316600966

6

-65.6534137933796

-66.1744957152001

-64.7676491513639

-58.1420110835669

-51.806218694887

-55.4310153262774

-38.0095288946405

-31.7302803329002

-23.5413602886664

2090s_A2

-31.3900435522635

-54.8524440900349

-65.7767566643183

-65.6271651253081

-68.4318014870236

-67.9810644647242

-61.1894460632419

-49.9965652795155

-49.0428098605348

-42.6868309053216

-44.7927641388414

-24.3913811212018

-70

-50

-30

-10

Simulated RF change from observed at Wonbera

Re

la

tiv

e ch

an

ge

(%

)19

7019

7319

7619

7919

8219

8519

8819

9119

9419

9720

0020

4820

5120

5420

5720

6020

6320

8120

8420

8720

9020

9320

9620

99

0

500

1000

1500

2000

2500Trend line of simulated RF at Wonbera

Observed Control2050s_A1B 2050s_A2

RF

(m

m)

Climate Modeling –Case2 The weather generator consists of three main sections: Model calibrationAnalysis of observed station data in order to calculate the weather

generators. Model validationQtest is used for determining how well the model is simulating

observed conditions. The statistical characteristics of the observed data are compared

with those of the synthetic data. Model useGenerating the synthetic weather based on the available data

parameter generated during model calibration or by combining scenario file with the generated parameter to account climate change.

Incorporating Climate Scenario• Climate changes derived from GCMs can be incorporated

in stochastic weather generator by applying climate change scenarios expressed on a monthly basis in the relevant climate variable.

  e5ab_2050 e5a2_2090

month m.rain wet dry min max tsd rad m.rain wet dry min max tsd radJan 1.66 1.04 0.97 2.31 1.85 1.31 1.00 2.94 1.01 0.98 2.54 1.51 1.06 1.00Feb 2.20 0.97 1.02 2.60 1.89 1.13 1.00 1.20 1.01 1.00 2.08 1.99 1.04 1.00Mar 0.91 0.98 1.01 2.39 2.60 1.53 1.00 1.05 0.99 0.99 2.00 2.36 1.26 1.00Apr 1.11 1.05 1.00 2.19 1.95 1.10 1.00 1.12 1.03 1.01 1.85 1.49 1.06 1.00May 0.85 1.30 1.17 2.73 3.06 1.27 1.00 0.87 0.74 1.03 2.33 2.43 1.17 1.00Jun 0.80 0.98 0.84 2.97 4.06 1.23 1.00 0.87 0.89 1.04 2.70 3.63 1.12 1.00Jul 1.00 1.44 1.18 2.96 3.59 1.18 1.00 1.02 1.48 1.06 2.56 2.91 1.24 1.00Aug 1.24 1.54 1.80 2.27 1.71 1.22 1.00 1.20 1.76 1.28 2.18 1.80 1.13 1.00Sep 1.17 1.62 1.98 2.15 1.63 1.52 1.00 1.10 1.52 1.34 1.90 1.56 1.19 1.00Oct 1.01 1.24 1.31 2.56 2.23 1.32 1.00 1.05 1.03 0.84 2.26 1.79 1.16 1.00Nov 1.33 0.98 0.98 2.92 2.00 1.33 1.00 1.16 1.02 1.03 2.49 1.85 1.07 1.00Dec 2.93 1.02 1.01 3.17 2.21 1.54 1.00 2.33 1.00 1.00 2.55 1.87 1.04 1.00

Results-Case2

Winter Spring Summer Autumn

pcpa1b_2050s

125.937342978295

0.222532507815593

1.47799219153169

-1.4035038016

4694

pcpa2_2050s

74.8228589525257

1.12651227796044

1.14450375511057

-4.3898727272

6738

pcpa1b_2090s

4.90951271560589

-11.463109518

4856

-0.4338644706

63076

10.0827704673863

pcpa2_2090s

-17.502366327

1532

-4.3769455586

3903

-1.5848989934

8576

10.886970651294

-25

25

75

125

UBNB Seasonal pcp

Re

lati

ve

ch

an

ge

(%

)Tm

xa1b_

2050s

Tmxa

2_2050

sTm

na1b_

2050

sTm

na2_2

050s

Tmxa

1b_209

0sTm

xa2_2

090s

Tmna

1b_20

90s

Tmna

2_209

0s

1.0

2.0

3.0

4.0

5.0 WinterSpringSummerAutumn

Tem

per

atu

re C

han

ge

(0C

)

UBNB Seasonal Temprature Change

Climate Modeling: Case-3• The methodology is same as case-2. • The climate change scenario is constructed from 3GCMs.

Winter Spring Summer Autumn

pc-pa1b_2050s

0.154857092426357

-3.116675556

97433

-5.199142559

43182

-6.146566987

79637

pcpa2_2050s

3.90832887314738

-2.063423865

45666

-4.239632875

90028

-6.815913772

98443

pc-pa1b_2090s

-14.62056405

03331

-8.542518929

40405

-5.739208881

47881

4.22672097466272

pcpa2_2090s

-11.46112809

87629

-8.079263240

83682

-6.188251033

69393

5.16059747666773

-18

-13

-8

-3

3

8

UBNB Seasonal pcp

Rela

tive

chan

ge (%

)

Comparison of Mono-Modal and Multi-Modal Approaches

• Multi-modal approach under estimated pcp prediction and this is more apparent in 2050s than 2090s.

• Annual relative % change in pcp increases due to relatively high increase in dry periods.

• Tmx and Tmn change has no significant difference between two approaches in 2050s.

• Multi-modal approach underestimates both Tmx and Tmn during 2090s

• Summer season in the case of mono-modal is warmer while spring season is warmer in multimodal approach.

Comparison of Mono-Modal and Multi-Modal Approaches

Mono/Multi-modal Comparisons

Comparisons of SDSM and LARS-WG outputs

• Generally, downscaled precipitation results from SDSM and LARS-WG show marked difference.

• Both downscaling tools illustrate an increase in maximum and minimum temperature in both 2050s and 2090s time compare with the base line period.

SDSM and LARS-WG Comparison

SDSM and LARS-WG Comparison

Conclusions

• LARS-WG performs better in precipitation prediction than SDSM.

• simulation of future precipitation using SDM has significant spatial variation than LARS-WG.

• LARS-WG illustrate similar trend across each sub-basins in the simulation of precipitation, maximum and minimum temperature.

• LARS-WG shows better performance over the study area than SDSM.

THANK YOU.