干旱区地理2011，Vol.34Issue(1)：143-149,7.
基于DEM的分布式融雪汇流模型关键算法和实现
Key algorithm and its realization about distributed Snowmelt concentrating-flow model based on DEM
摘要
Abstract
The difficulty of Distributed Snowmelt Runoff Model (DSRM) is to simulate the concentrating process of snowmelt, which plays an important role in discharging of watershed outlet, especially upon its peak value. On researching DSIM, a time-spatial featured and DEM-based Distributed Snowmelt Concentrating-flow Model (DSCFM) according to the characteristics of high-resolution DEM data of small watershed is constructed after obtaining the corresponding traits information including slope degree, slope direction, water flow direction and average flow speed etc. and building up concentrating net of watersheds. This model has features as follows: ( 1 ) a new concept “Unit - Time period” is put forward. It is set to be an adjustable parameter to give facilities for the practical application of DSCFM, which enable the model to calculate out the runoff concentrating quantity of any grid during different period and visualize the dynamic simulation of snowrnelt concentrating quantity through combining GIS technique. (2) The key algorithm and detailed process of implementing method as well as its source code in Java for time length of concentrating-flow to its outlet and concentrating-flow volume are provided. The recursion programming are adopted for the key algorithm, the calculation velocity is thus fast. (3) The concentrating-flow simulation calculation and validation of flooding process during spring-snowmelt-flood period at representative researching area indicate that the simulating result of DSCFM is better. The model and the corresponding algorithm are, therefore, much more practically meaningful for prediction of snowmelt volume and springtime snowmelt flood.关键词
DEM/GeoTIFF/Java/融雪/算法Key words
DEM/ GeoTIFF/ Java/ snowmelt/ algorithm分类
天文与地球科学引用本文复制引用
刘永强,戴维,刘志辉..基于DEM的分布式融雪汇流模型关键算法和实现[J].干旱区地理,2011,34(1):143-149,7.基金项目
国家自然科学基金项目资助(70361001),新疆大学博士毕业生科研启动基金资助(BS080128) （70361001）
