资源环境科技发展态势分析平台

Predicting the proportion of the water year a given stream will remain at or above various flow thresholds is critically important for making sound water management decisions. Flow duration curves (FDCs) succinctly capture this information using all data available over some historical period, while annual flow duration curves (AFDCs) instead use data from each individual water year. Analyzing the population of AFDCs, and in particular the tails of this distribution, can allow water managers to better prepare for years with extreme streamflow conditions. However, long time series of observations are necessary to capture interannual streamflow variations and are problematic to obtain in rapidly changing and poorly gauged catchments. By incorporating a process-based model to construct AFDCs based on daily rainfall statistics and flow recession characteristics, the proposed approach is a first step toward addressing this challenge. Results indicate that prediction performance varies substantially across flow quantiles and that the current model fails to properly capture the interannual variability of low flows. Numerical analyses attributed these errors to nonlinearity in storage-discharge relation, rather than cross-scale streamflow correlations and non-Poissonian rainfall, explaining the origin of commonly observed heavy-tailed behavior in low flow quantiles. We present a case study on hydroelectric power generation, showing that faithfully capturing both interannual streamflow variability and recession nonlinearity has important implications for installation profitability.