基于长短记忆模型的鄱阳湖流域径流模拟及其演变的归因分析

气候变化和人类活动直接或间接的影响着全球和区域水文循环过程,是导致水文水资源时空分布的主要因素,同时也是流域-湖泊水文情势变化的根本原因.本文基于长短记忆模型构建了鄱阳湖气象—径流模型,同时引入了基准期的概念,定量区分了导致鄱阳湖流域径流变化的主要影响因素.研究结果表明:在同时考虑计算效率和模拟效果的前提下,采用10 ...

Simulation and attribution analysis based on the long-short-term-memory network for detecting the dominant cause of runoff variation in the Lake Poyang Basin

Climate change and human activities directly or indirectly affect the global and regional hydrologic cycle, which is the main factor leading to the temporal and spatial distribution of water resources. Based on the long-short-term-memory (LSTM) model, this paper attributes the contribution of climate change and human activities on runoff variation in the Lake Poyang Basin. Results show that the runoff process was simulated well by LSTM framework with a window size of 10 days for both accuracy and computational efficiency. The overall performance of the model was good with Nash-Sutcliffe Efficiency varied from 0.94 to 0.95 in the training period and 0.90 to 0.98 in the test period, respectively. Based on the simulation results, the contribution of human activities and climate change on runoff change in Lake Poyang Basin was then quantified. Results show that the runoff increased by 139.47 m³/s due to human activities in spring, accounting for 77.26% of runoff change. Whereas climate change was the dominant factor in changing runoff in the other seasons, which led the runoff to decrease by 34.37 m³/s, accounting for about 75.84% of runoff changes. The results can provide scientific basis and theoretical guidance for water resources management in Lake Poyang Basin.