基于概率分布模型的流量预报及参数动态识别

概率分布模型(PDM)在国内流域的适用性及参数在不同径流阶段的变化研究较少。本文以浙江省两典型流域为例,建立PDM流量预报对比模型,根据参数动态识别分析确定不同时间窗口下参数变化,从而推断关键影响因子。研究结果表明:(1)PDM模型在两流域模拟结果较为满意,纳什效率系数均可达到0.7以上,且低流量为主的龙泉溪流域模拟效果明显优于高流量主导的金华江流域;(2)在不同洪水事件下PDM参数(经验参数α、b和最大蓄水能力S_max)均与前期土壤湿度显著负相关,与退水斜率有关的参数b在金华江流域与蒸发量显著正相关,而在龙泉溪流域与平均降雨负相关性显著;(3)受地表以下28 cm土壤湿度变化控制的参数α在洪峰及退水阶段识别度最大。

Runoff forecasting and dynamic parameter identification using probability distributed model

Studies on applicability of the probability distributed model (PDM) to the river basins in China and investigation of its parameter change across different flood stages are limited. This study compares the performance of PDM-based runoff forecasting for two typical basins in Zhejiang Province, and examines model parameter changes through dynamic identifiability analysis (DYNIA) to infer dominant controlling factors in rainfall-runoff process under different time windows. The results show that forecasting performance for the two basins is generally satisfactory with the Nash-Sutcliffe efficiency coefficient both over 0.7, and the model performs better in the Longquan basin dominated by low flows than the Jinhua basin dominated by high flows. Three parameters (empirical coefficientss α, b and maximum storage capacity Smax) of various flood events are negatively correlated to antecedent soil moisture. The recession slope-related b is positively correlated to average evaporation in the Jinhua basin but negatively to mean rainfall in the Longquan basin. Identifiability of α, a coefficient controlled by soil moisture at a depth of 28 cm, is generally higher for flood peak and recession periods.