北京师范大学学报（自然科学版）2016，Vol.52Issue(3)：393-401,9.DOI:10.16360/j.cnki.jbnuns.2016.03.023
基于神经网络与半分布式水文模型相结合的缺资料区径流估计模型 ——以莺落峡流域为例
Estimation of streamflow in ungauged basins using a combined model of black-box model and semi-distributed model:case study in the Yingluoxia watershed
摘要
Abstract
To estimate streamflow in ungauged basins,an integrated scheme of artificial neural network (ANN)and TOPMODEL is proposed.ANN is used to fuse rainfall data from different observation stations at lower altitude areas with a regional climate model for a higher region.Rainfall input to TOPMODEL was directly replaced by output of ANN model. Generation and routing of runoff were conformed to the TOPMODEL.Particle Swarm Optimization was adopted for global parameter calibration for the whole scheme. The Yingluoxia watershed was used to validate performance of the new scheme in ungauged catchments. Observed discharges from 19901995 and 19962000 were used for model calibration and validation, respectively.Natural discharges from 20012010 were then estimated and analyzed. Compared with TOPMODEL,R-NN-TOP and CLM4.5-RTM,the proposed scheme can better tradeoff the predictive accuracy and data availability in sparse rainfall sites so that its performance as indicated by several evaluation indexes was more satisfactory,especially during peak flood periods.Moreover,reconstructed discharge hydrograph from 20012010 showed reasonable increase from 20012009 and a slight decrease later.Hence integrated scheme of black-box model and semi-distributed model may be promising in similar watersheds,to provide reference for flood warning and water resources management.关键词
降水缺测区/神经网络/TOPMODEL/径流Key words
ungauged basin/ANN/TOPMODEL/discharge分类
天文与地球科学引用本文复制引用
刘双,谢正辉,曾毓金..基于神经网络与半分布式水文模型相结合的缺资料区径流估计模型 ——以莺落峡流域为例[J].北京师范大学学报（自然科学版）,2016,52(3):393-401,9.基金项目
国家自然科学基金资助项目(91125016,41575096) （91125016,41575096）
