Seasonal Hydropower Planning for Data‐Scarce Regions Using Multimodel Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming

In data‐scarce regions, seasonal hydropower planning is hindered by the unavailability of reliable long‐term streamflow observations, which are required for the construction of inflow scenario trees. In this study, we develop a methodological framework to overcome the problem of streamflow data scar...

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Bibliographic Details
Published in:Water resources research 2019-11, Vol.55 (11), p.8583-8607
Main Authors: Koppa, Akash, Gebremichael, Mekonnen, Zambon, Renato C., Yeh, William W.‐G., Hopson, Thomas M.
Format: Article
Language:English
Subjects:
Data
data‐scarce regions
Ensemble forecasting
Evapotranspiration
Frameworks
Hydroelectric power
Hydrologic data
Hydrologic models
Hydrology
hydropower planning
Inflow
Optimization
Parameter uncertainty
Precipitation
Precipitation forecasting
Prediction models
Regions
Remote sensing
Reservoirs
River basins
Rivers
Seasonal precipitation
Stochastic programming
Stochasticity
Stream discharge
Stream flow
Streamflow data
taxonomy
taxonomy numbers
Uncertainty
Weather forecasting
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Summary:In data‐scarce regions, seasonal hydropower planning is hindered by the unavailability of reliable long‐term streamflow observations, which are required for the construction of inflow scenario trees. In this study, we develop a methodological framework to overcome the problem of streamflow data scarcity by combining precipitation forecasts from ensemble numerical weather prediction models, spatially distributed hydrologic models, and stochastic programming. We use evapotranspiration as a proxy for streamflow in generating reliable reservoir inflow forecasts. Using the framework, we compare three different formulations of inflow scenario structures and their applicability to data‐scarce regions: (1) a single deterministic forecast, (2) a scenario fan with the first stage deterministic, and (3) a scenario fan with all stages stochastic. We apply the framework to a cascade of two reservoirs in the Omo‐Gibe River basin in Ethiopia. Future reservoir inflows are generated using a 3‐model 30‐member ensemble seasonal precipitation forecast from the North American Multimodel Ensemble and the Noah‐MP hydrologic model. We then perform deterministic and stochastic optimization for hydropower operation and planning. Comparing the results from the three different inflow scenario structures, we observe that the uncertainty in reservoir inflows is significant only for the dry stages of the planning horizon. In addition, we find that the impact of model parameter uncertainty on hydropower production is significant (0.14–0.18×106 MWh). Key Points This presents a methodology that uses remote sensing and distributed models to generate reservoir inflow forecasts in data‐scarce regions This combines ensemble inflow forecasts with stochastic programming with recourse for hydropower optimization This studies the impact of uncertainty in precipitation forecasts and model parameters on hydropower production
ISSN:0043-1397
1944-7973
DOI:10.1029/2019WR025228