基于SWAT模型和多源DEM数据的流域水系提取精度分析

流域水系是研究水文水资源、地貌演化和生态环境及水土治理等的基础数据,高精度的水系提取对流域研究十分重要。本文以空间分辨率均为30 m的 AW3D30 DSM、SRTM1 DEM和ASTER GDEM2数字高程模型作为基本的地形数据,基于SWAT模型提取犟河流域水系,通过河网“套合差”、水系相对误差、Google Map水文数据及蓝线河网对提取结果进行误差分析与综合评价,探讨河道剖面和地形特征对水系提取精度的影响。结果表明：① 集水面积阈值是决定河网水系提取精度的关键参数,阈值越大,提取的河网密度越小,反之提取的河网密度越大;② 基于河网密度与集水阈值二阶导数的幂函数与直线相切的数学求值方法确定流域最佳集水面积阈值,能避免最佳集水阈值取值的主观性,提取的河网水系与实际河道相符;③ AW3D30 DSM数据提取的流域河网水系与Google Map高分辨率影像的水系偏差最小,且AW3D30 DSM数据提取的水系与蓝线河网的河网“套合差”和水系相对误差值均最低,能真实反映中低山丘陵山区流域水系发育的疏密程度,吻合度最好;④ 多源DEM数据提取结果均显示为河床比降大和横剖面曲线为窄深式的“V”形河谷提取的水系精度高于河床比降小和横剖面曲线为 “碟”形河谷的提取精度;⑤ AW3D30 DSM数据的地形起伏和坡度标准差最大,有利于山区河网水系的提取。因此,基于SWAT模型和AW3D30 DSM数据提取的山区流域水系可最大限度反映流域水系的真实情况,精度最高,此方法和数据源可应用于中低山丘陵山区流域的水系提取研究。

Accuracy Analysis of Watershed System Extraction based on the SWAT Model and Multi-Source DEM Data

Distributions of river systems are the basic data for studying hydrology and water resources, landform evolution, ecological environment, and water and soil management in a basin; so, high-precision extraction of river systems is very important for watershed research. Different digital elevation models (DEMs) are the basic data to extract river systems and delineate watershed. Taking Jiang River Basin in western Hubei Province of China as the study area, this paper extracted the river systems and discussed the extraction precision using different elevation model data including AW3D30 DSM, SRTM1 DEM and ASTER GDEM2 with a spatial resolution of 30 m. The slope flow simulation algorithm and ArcSWAT model were used to extract the river systems, and the relationship between catchment area threshold and stream network density were examined. Meanwhile, the match error and relative error of river system extraction, Google map hydrological data, and the blue line of stream network were used to evaluate the extraction accuracy. Results show that: (1) The threshold of catchment area is the key parameter to determine the accuracy of extracting stream network. The relationship shows that the larger the threshold, the smaller the density of the extracted stream network. The change rate of stream network density is equal to the change rate of catchment area threshold. The inflexion point which corresponds to the optimal catchment area threshold is determined by the power function of the second derivative tangent to the line, hence the optimal threshold of catchment area for AW3D30 DSM, SRTM1 DEM, and ASTER GDEM2 is 70.2 ha, 60.2 ha, and 50.8 ha respectively. (2) The matching error and relative error of the extracted stream network system from AW3D30 DSM data are the lowest and can accord best with the actual river system. (3) The narrower and deeper riverbed profile with a “V” shape has a high extracted precision for the DEMs. (4) The extraction of stream network density, drainage area and river length from AW3D30 DSM is very closer to real value, and can truly reflect the development degree of the river system in the study basin. (5) The topographic fluctuation and slope standard deviation of AW3D30 DSM data are the largest which is helpful to extract the river system in study area. Our findings indicate that, in general, the AW3D30 DSM data is more suitable for extracting river systems in montane basins.