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

Abstract

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).

Improving the Applicability of Hydrologic Models for Food–Energy–Water Nexus Studies Using Remote Sensing Data

“…However, increased accuracy in ET comes at the cost of degrading SM and SF estimates. In the absence of SF estimates, calibration with observed ET offers the best alternative for reliably simulating SF [42,43]. ET-SM-SF: Incorporating all the water balance components (ET, SM, SF) for calibration provides the best compromise solution to preserve the accuracies in simulating each of the three components.…”

Impact of Hydropower Dam Operation and Management on Downstream Hydrogeomorphology in Semi-Arid Environments (Tekeze, Northern Ethiopia)

“…Either way, climate change will amplify the challenges to provide a reliable source of energy and will impact the returns generated from it [20]. In this regard, the implementation of a forecast-informed reservoir operation system would strongly improve the decision-making process, and would be especially beneficial in years with extreme (low or high) precipitation [19,56,57], although some uncertainty will always remain [58]. Forecasting, moreover, would reduce the risk induced by the current qualitative decision making, which strongly relies on judgments of the dam operator, a type of decision making that appears to be common in the entire Ethiopian hydropower sector [59].…”

Performance of the Global Forecast System's Medium-Range Precipitation Forecasts in the Niger River Basin

“…Optimal management of water resources is key to maximizing benefits, such as hydropower generation, and minimize disasters, such as flooding. Climate forecast information has the potential to improve water resources management, energy, and agriculture (e.g., Patt et al 2007;Breuer et al 2010;Mase and Prokopy 2014;Pandya et al 2015;Koppa et al 2019;Alexander et al 2021). For example, in a recent study, Koppa et al (2019) showed that the use of seasonal precipitation forecasts in reservoir planning of Omo Gibe dam in Ethiopia can increase annual hydropower generation by around 40%.…”