Hydropower is a key part of the increasing shift in power production from nonrenewables to renewable energy. In regions such as Africa, hydropower reservoirs are vital for achieving several sustainable development goals, including clean water, energy, and poverty elimination. However, the operations of hydropower reservoirs are often suboptimal due to the lack of hydrologic data for generating reliable inflow forecasts. Here, we present a decision support system (DSS) framework for hydropower planning at daily to seasonal time scales by combining data from earth observation satellites (EOS) with ensemble climate forecasts from dynamical models and hydrologic modeling. The large uncertainty inherent in satellite-based datasets is overcome by using a data validation framework which does not require ground-based measurements. In addition, an EOS evapotranspiration product is used as a proxy for streamflow in calibrating hydrologic models. Compared to a DSS forced with a climatological forecast (zero-skill), the hydropower production with the new DSS increased by 20%. The study highlights the advantage of using data from EOS in overcoming the issue of data scarcity in water resources applications, particularly in developing regions of the world such as Africa.

中文翻译：

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基于可扩展地球观测的非洲水电规划决策支持系统

水电是电力生产日益从不可再生能源转向可再生能源的关键部分。在非洲等地区，水电水库对于实现清洁水、能源和消除贫困等多项可持续发展目标至关重要。然而，由于缺乏用于生成可靠入流预测的水文数据，水电水库的运行往往不是最理想的。在这里，我们通过将来自地球观测卫星 (EOS) 的数据与来自动力学模型和水文建模的集合气候预测相结合，提出了一个决策支持系统 (DSS) 框架，用于在每日到季节性时间尺度上进行水电规划。通过使用不需要地面测量的数据验证框架，克服了卫星数据集固有的巨大不确定性。此外，在校准水文模型中，EOS 蒸发蒸腾产物用作流量的代理。与被迫采用气候预测（零技能）的 DSS 相比，采用新 DSS 的水电产量增加了 20%。该研究强调了使用 EOS 数据在克服水资源应用中数据稀缺问题方面的优势，特别是在非洲等世界发展中地区。