中小流域时空变源混合产流模型及参数区域化方法研究

针对山丘区中小流域洪水预报面临的产流机制混合多变和模型参数难以获取的问题,提出了适用于缺资料地区的中小流域时空变源混合产流模型和基于机器学习CART的参数区域化方法。在小流域地貌水文响应单元划分基础上,利用GARTO非饱和下渗计算模型,从超渗/蓄满机制的平面混合、垂向混合和时段混合三个方面构建时空变源混合产流模型,并采用机器学习CART方法进行模型参数区域化研究。选取不同地貌类型区的15个流域和河南省19个小流域实测降雨径流资料分别对模型适用性和参数区域化方法进行了验证。结果表明,通过与国内外8个水文模型的对比验证,时空变源混合产流模型模拟平均纳什系数为0.78,比其他模型提高约20%;利用本模型和CART参数区域化方法在河南省19个流域计算的平均纳什系数为0.70,比参数随机移植结果提高了35%,本模型和参数区域化方法在山丘区中小流域洪水模拟中应用效果较好。

Research on spatio temporally-mixed runoff model and parameter regionalization for small and medium-sized catchments

According to the problems faced in the flood prediction in poor-gauged small-sized catchment, such as the complicated runoff generation mechanism and the difficulty of obtaining model parameters, a spatiotemporally-mixed runoff model and a CART machine learning algorithm are proposed in this paper. Based on the division of geomorphologic and hydrological response units in small watershed and the unsaturated infiltration GARTO calculation model, a spatiotemporally-mixed runoff model is constructed from three aspects of horizontal mixing, vertical mixing and temporal mixing of infiltration excess and saturation excess runoff mechanism, and the model parameter regionalization is studied by using machine learning CART method. The applicability of the model and the regionalization method of parameters were verified by selecting 15 catchments with different morphological properties and 19 small basins in Henan Province. The results show that the average Nash coefficient of spatiotemporally-mixed runoff model is 0.78, 20% higher than that of other models. The average Nash coefficient machine-learning regionalization simulation of Henan 19 basins is 0.70, 35% higher than random transplantation. The model and parameter regionalization method presented in this paper show higher applicability in flood simulation of middle and small-sized mountainous catchments.