摘要
构建大尺度分布式水文模型是当前大气水文模型耦合研究的一项重要内容。本文介绍一种根据1km DEM生成更大网格尺度DEM数据,同时可以保持流域河网信息并减缓高程、坡度等地貌参数信息量衰减速度的有效方法——ZB算法。利用该方法和常规的网格平均法生成黄河唐乃亥以上流域的5km、10km、15km和20km两套DEM数据,分别提取高程、坡度、地形指数、河网密度、主河道长度、流域面积等流域特征参数,并与1km DEM提取的上述参数进行比较,对两种方法作出评价。结果显示,随着网格尺度的增大,ZB算法获得的DEM数据可以保持河网的连续性,提取出合理的流域范围,减缓地形信息量的衰减速度。该方法满足构建大尺度分布式水文模型提取数字流域的需要。
Large-scale distributed hydrological model plays an important role in the coupling atmospheric and hydrological models research at present. In this paper, ZB algorithm is proposed to obtain base on 1 km grid scale DEM data, because it can maintain data information about drainage basin boundaries and river networks very well at coarser resolutions. We apply ZB algorithm and grid-averaged algorithm at 5km, 10km, 15km, and 20km scales in the upper Yellow river （above Tangnaihai station, drainage area 121,972 km^2）. Elevation, slope, wetness index, drainage density, length of main channel, watershed area and other parameters are extracted and compared with the parameters that are obtained based on 1 km DEM. Results show that DEM data obtained by ZB algorithm can hold stream network continuity and real watershed boundaries very well, and meanwhile it can decrease the amount of other geographical information falling off, compared to other algorithms. The method can supply satisfied digital watersheds for constructing large-scale distributed hydrological model.
出处
《地理科学进展》
CSCD
北大核心
2007年第1期68-76,共9页
Progress in Geography
基金
国家自然科学基金项目(50679018)
国家自然科学基金(40575040)资助
关键词
大尺度水文模型
DEM
ZB算法
数字流域
large-scale hydrological model
DEM
ZB algorithm
digital watershed
