Seasonal Forecasts in Ethiopia: Hydropower, Ag-Econ & Flood Modeling NASA GHA 1 st Participatory Research Workshop and Project Meeting Addis Ababa 12 August 2014 Paul Block, Ying Zhang University of Wisconsin - Madison

UNDERSTANDING and PREDICTABILITY Seasonal-to -Interannual Decadal Climate Change Timescale “Good” “Some Info” “Frontier” From a WRM perspective, this provides prospects for predicting and managing water system risks (design, operation, allocation…) Credit: L. Goddard, IRI Prediction: Where Are We? 2

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, UT Courtesy of Dorling Kindersley 3 Upper Blue Nile Basin - Hydropower

4 Linked Model System

How is moisture transported to the basin? Winds: June – September 5 Diagnostics and Attribution

Large-scale climate Predictors What are the drivers of JJAS Precipitation? Average March – May Sea-surface Temperatures 6 Diagnostics and Attribution

Statistical model prediction technique: Nonparametric local polynomial approach, one season lead forecast R 2 = 0.7 RPSS = 0.4 Cross-validated forecast ensembles 7 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, High (+) mean precipitation bias Courtesy of Brooks/Cole – Thomson Learning 8 Linked Model System

Hydrology model: Simple, lumped parameter model Simulates changes in soil moisture and runoff Produces Streamflow and Net PET Courtesy of NWS 9 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) 10 Linked Model System

Median = marginal improvement; reduction in probability of low decades 11 Hydropower Benefits

12 Manager’s Risk Preference

13 Forecast Value, Reliability, Threshold Trade-off between reliability and benefits

Source: World Bank 2005 Climate Variability & Agriculture 14

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

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

VC approach allows for probabilistic interpretation SC overestimates potential outcomes Unexpected economic declines typically much more difficult to address than a surprise upswing Agronomic – Economic Modeling

20 Research under the NASA Project Regional Forecasts Identify homogenous regions Compare with Existing Seasonal forecast per region

21 Research under the NASA Project Ag-Econ model Add regional forecasts to the model through climate-yield factor. (Conditioned on the seasonal forecast) Farmers make decisions based on the forecast crop type, inputs, etc. Vary farmer adoption levels Run in an implicit stochastic mode

22 Research in parallel to NASA Project Global Flood Forecasting (PCR-GLOBWB) Define Peak Season (2 nd season important)

23 Research in parallel to NASA Project Global Flood Forecasting El Nino for 2014 Peak SF and Nino in ENSO years

24 Research in parallel to NASA Project Global Flood Forecasting Analogues 1994 DFO

25 Research in parallel to NASA Project GERD operations ethiopian-renaissance-dam/ Optimal storage/release timing Hydropower & downstream flows