将内部气候变率从气候变量转化为水电生产
Translating the internal climate variability from climate variables to hydropower production
摘要：
在估计未来水电产量时量化不确定性直接或间接影响印度的能源安全、规划和管理。大气过程的混沌和非线性性质导致了气候变量未来预测中相当大的内部气候变率（ICV）。多初始条件集合（MICE）和多模式集合（MME）常用于分析ICV和模式不确定性在降水和温度中的作用。然而，有有限的研究侧重于量化内部可变性对影响变量，包括水电生产的作用。在这项研究中，我们分析了ICV和模型的不确定性的作用，印度三个突出的水电厂使用MICE的EC-Earth 3和MME从CMIP 6。我们估计径流预测的所有合奏使用可变入渗能力水文模型的四个时间段，历史，近，中期和远期。我们估计最大的水力发电量产生的每月释放和水力水头在水库。分析了偏差校正在水电生产中的作用。结果表明，ICV分别在季风和全年的径流和水电估算中起着重要的作用。在径流量和潜在水电量估算中，模型不确定性对总不确定性的贡献大于ICV。然而，ICV正在向长远发展。我们还表明，偏差校正不保留内部的变化估计流量。虽然有一个增加的不确定性估计径流，平均水电显示减少对长期的2月至5月，更突出的MICE比MME。结果表明，需要将不确定性，由于内部的变化，以解决电力安全在不断变化的气候情景。
英文摘要：
Quantifying uncertainties in estimating future hydropower production directly or indirectly affects India's energy security, planning, and management. The chaotic and nonlinear nature of atmospheric processes results in considerable Internal Climate Variability (ICV) for future projections of climate variables. Multiple Initial Condition Ensembles (MICE) and Multi-Model Ensembles (MME) are often used to analyze the role of ICV and model uncertainty in precipitation and temperature. However, there are limited studies focusing on quantifying the role of internal variability on impact variables, including hydropower production. In this study, we analyze the role of ICV and model uncertainty on three prominent hydropower plants of India using MICE of EC-Earth3 and MME from CMIP6. We estimate the streamflow projections for all ensembles using the Variable Infiltration Capacity hydrological model for four time periods, historical, near, mid and far-term. We estimate maximum hydropower production generated using monthly release and hydraulic head available at the reservoir. We also analyzed the role of bias correction in hydropower production. The results show that ICV plays a significant role in estimating streamflow and hydropower estimation for monsoon and throughout the year, respectively. Model uncertainty contributes more to total uncertainty than ICV in estimating the streamflow and potential hydropower. However, ICV is increasing towards the far-term. We also show that bias correction does not preserve the internal variability in estimating the streamflow. Although there is an increase in uncertainty for estimated streamflow, mean hydropower shows the decrease towards the far-term for February to May, more prominent for MICE than MME. The results suggest a need to incorporate uncertainty due to internal variability for addressing power security in changing climate scenarios.
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