论文标题
将内部气候变化从气候变量转换为水电生产
Translating the internal climate variability from climate variables to hydropower production
论文作者
论文摘要
量化不确定性直接或间接影响印度的能源安全，计划和管理时，量化不确定性。大气过程的混乱和非线性性质为气候变量的未来预测带来了相当大的内部气候变异性（ICV）。多次初始条件集合（小鼠）和多模型集合（MME）通常用于分析ICV和模型不确定性在降水和温度中的作用。但是，有限的研究重点是量化内部变异性对影响变量（包括水力发电生产）的作用。在这项研究中，我们使用CMIP6的Ec-Earth3和MME小鼠分析了ICV和模型不确定性对印度三种突出的水电植物的作用。我们使用可变的浸润能力水文模型在四个时期（历史，近，中期和远期）估算所有合奏的流量预测。我们估计使用每月释放和液压头产生的最大水电生产。我们还分析了偏差校正在水力发电生产中的作用。结果表明，ICV分别在季风和全年分别估计季风的水流和水力发电估计中起着重要作用。模型不确定性在估计流量和潜在水力发电时对总不确定性的影响更大。但是，ICV朝着遥远的时间越来越多。我们还表明，偏差校正并不能保留估计流流的内部变异性。尽管估计流量的不确定性有所增加，但平均水电表明，截至5月至5月的远期下降，小鼠比MME更为突出。结果表明，由于内部变异性在改变气候方案中解决功率安全性，需要纳入不确定性。
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.
