Seasonal Forecasts in Ethiopia:Hydropower, Ag-Econ & Flood Modeling

NASA GHA 1st Participatory Research Workshop and Project Meeting

Addis Ababa12 August 2014

Paul Block, Ying ZhangUniversity of Wisconsin - Madison

UNDERSTANDING

andPREDICTABILITY

Seasonal-to-Interannual

Decadal ClimateChange

TimescaleTimescale

“Good”

“Some Info”

“Frontier”

From a WRM perspective, this provides prospects for predicting and From a WRM perspective, this provides prospects for predicting and managing water system risks (design, operation, allocation…)managing water system risks (design, operation, allocation…)

Credit: L. Goddard, IRI

Prediction: Where Are We?

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Four large-scale dams proposed (one started)Could a seasonal forecast improve benefits?Does the prediction technique have any influence?Does increased prediction skill translate to greater benefits?

Base Map Courtesy of PLC Map Collection, UTCourtesy of Dorling Kindersley

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Upper Blue Nile Basin - Hydropower

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Linked Model System

How is moisture transported to the basin?

Winds: June – September

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Diagnostics and Attribution

Large-scale climate PredictorsWhat are the drivers of JJAS Precipitation?

Average March – May Sea-surface Temperatures

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Diagnostics and Attribution

Statistical model prediction technique:Nonparametric local polynomial approach, one season lead forecast

R2 = 0.7RPSS = 0.4 Cross-validated forecast

ensembles7

Linked Model System

Dynamical model prediction technique:

• CFS model from NOAA’s NCEP/EMC (Saha et al 2006)

• Fully coupled ocean-atmosphere model

• 15 ensemble members, 1981-2004

• High (+) mean precipitation bias

Courtesy of Brooks/Cole – Thomson Learning

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Linked Model System

Hydrology model:

• Simple, lumped parameter model

• Simulates changes in soil moisture and runoff

• Produces Streamflow and Net PET

Courtesy of NWS

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Linked Model System

IMPEND (Investment Model for Planning Ethiopian Nile Development)

• Planning/systems model with operational level detail

• Decision variable is reservoir head level

• Objective value: maximize net present value (benefits)

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Linked Model System

Median = marginal improvement; reduction in probability of low decades

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Hydropower Benefits

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Manager’s Risk Preference

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Forecast Value, Reliability, ThresholdTrade-off between reliability and benefits

-80

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8019

82

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rainfall variation around the mean

GDP growth

Ag GDP growth

Ethiopia: Rainfall, GDP and Agric. GDP

World Bank

-80

-60

-40

-20

0

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60

8019

82

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year

per

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-30

-25

-20

-15

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0

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25

rainfall variation around the mean

GDP growth

Ag GDP growth

Ethiopia: Rainfall, GDP and Agric. GDP

-80

-60

-40

-20

0

20

40

60

8019

82

1983

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1985

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year

per

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-30

-25

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rainfall variation around the mean

GDP growth

Ag GDP growth

Ethiopia: Rainfall, GDP and Agric. GDP

World Bank

Source: World Bank 2005

Climate Variability & Agriculture

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Agro-economic model of Ethiopia (IFPRI); 12 year simulations

Analyzes agricultural supply and demand and market opportunities; tied to world market

56 independent zones; 36 commodities (ag and non-ag); rainfed

Climate tied to Yield function through climate-yield factor (CYF)yield reduction due to water constraints; 0 < CYF < 1

Output: economic indicators (gdp, poverty rates, calories, etc.)

Based on FAO Pubs 33 & 56

Agronomic – Economic Modeling

Agronomic – Economic Modeling

Agronomic – Economic Modeling

Typical CYF and FF Relationship During the Harvest Stage

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0.25

0.5

0.75

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-1 -0.5 0 0.5 1 1.5 2 2.5

FF - flood factor

CY

F

Transform model from static climate SC (climatology) to variable climate VC (monthly variability and multiple runs) Base case (no infrastructure investment)

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2003 2006 2009 2012 2015

Simulation Year

GD

P (

bill

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US

$)

VC ensemble member SC VC mean

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2003 2006 2009 2012 2015

Simulation Year

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VC ensemble member SC VC mean

Agronomic – Economic Modeling

VC approach allows for probabilistic interpretationSC overestimates potential outcomesUnexpected economic declines typically much more difficult to address than a surprise upswing

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GDP (billion US$)

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Poverty Rate (%)

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VC SC

Agronomic – Economic Modeling

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Research under the NASA ProjectRegional Forecasts

Identify homogenous regions

Compare with Existing

Seasonal forecast per region

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Research under the NASA ProjectAg-Econ model

Add regional forecasts to the model through climate-yield factor. (Conditioned on the seasonal forecast)

Farmers make decisions based on the forecastcrop type, inputs, etc.

Vary farmer adoption levels

Run in an implicit stochastic mode

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Research in parallel to NASA ProjectGlobal Flood Forecasting (PCR-GLOBWB)

Define Peak Season(2nd season important)

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Research in parallel to NASA ProjectGlobal Flood Forecasting

El Nino for 2014

Peak SF and Ninoin ENSO years

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Research in parallel to NASA ProjectGlobal Flood Forecasting

Analogues1994

DFO

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Research in parallel to NASA ProjectGERD operations

http://seeker401.wordpress.com/2011/10/01/the-grand-ethiopian-renaissance-dam/

Optimal storage/release timing

Hydropower & downstream flows