Subsurface topography to enhance the prediction of the spatial distribution of soil wetness
Subsurface topography to enhance the prediction of the spatial distribution of soil wetness
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DOI：
10.1002/hyp.1273
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发表时间：
2003-09-01
影响因子：
3.2
通讯作者：
Walter, C
中科院分区：
文献类型：
--
作者：
Chaplot, V;Walter, C
流域内土壤湿度空间分布的估计是水文和侵蚀模拟中最重要的问题之一。到目前为止，这些模型仅基于土壤表面地形信息。然而，土壤水文也控制地下水流的途径，可能无法解释的表面地形特征。本研究探讨了土壤覆盖下限的地形如何改善土壤湿度空间预测的定量建模。该研究是在阿莫里肯地块（法国西部）的一个农业集水区进行的，该集水区的特征是不透水的花岗岩腐泥土。两个数字高程模型（DEM）与10米的网格和0.3米的垂直分辨率从整个集水区的实地调查。一个DEM是土壤表面的数字表示，另一个描述了土壤覆盖层和底层不透水腐泥土之间的边界地形。1996 ~ 1997年沿着对一个坡地的土壤湿度进行了系统的观测。采样方案包括149个节点的10米网格，其中θ在0-10厘米估计使用时域反射计。使用重量分析法对112个数据点的子集估计了20-30、50-60和110-120 cm深度处的θ值。对于地表和地下DEM，土壤湿度在所有深度显着相关的地形属性，即到河岸的距离，海拔以上的河岸（E），下坡坡度，修订后的复合地形指数（CTI），和特定的单向和多向集水区。1996年冬季的theta（10）和基于物理的属性E和CTI估计使用地下DEM（r = 0.83和0.86，分别）之间的相关性最好。两个多重非线性回归模型θ（10）空间预测产生的非自相关的地形属性估计从表面和地下地形。使用一组新的41个数据点的模型验证显示，均方根误差（RMSE）低于θ（10）范围的10%。基于地下地形的模型使RMSE降低了43%。预测误差没有空间分布。最后，对这些结果进行了讨论，在坡面水文过程中所涉及的。版权所有（C）2003约翰威利父子有限公司。
The estimation of the spatial distribution of soil wetness within a catchment is one of the most important issues in hydrological and erosion modelling. So far, such models have been based on soil surface topographic information only. However, soil hydrology is also controlled by subsurface flow pathways that may not be explained only by surface terrain features. This study examined how the topography of the lower limit of the soil cover could improve quantitative modelling for the spatial prediction of soil wetness. The study was conducted in an agricultural catchment of the Armorican Massif (western France) characterized by impermeable granitic saprolites. Two digital elevation models (DEMs) with a 10-m grid mesh and with a 0.3 m vertical resolution were generated from field investigations throughout the catchment. One DEM was a numerical representation of the soil surface and the other described the topography of the boundary between the soil cover and the underlying impermeable saprolite. Soil wetness (theta) was surveyed systematically from 1996 to 1997 along a hillslope. The sampling scheme consisted of 149 nodes of a 10-m grid where theta at 0-10 cm was estimated using time-domain reflectometry. The value of theta at depths of 20-30, 50-60 and 110-120 cm was estimated for a subset of 112 data points using a gravimetric method. For both surface and subsurface DEMs, soil wetness at all depths significantly correlated with the topographic attributes, namely the distance to the stream bank, the elevation above the stream bank (E), the downslope gradient, the revised compound topographic index (CTI), and the specific monodirectional and multidirectional catchment areas. The best correlations were observed between theta(10) of winter 1996 and the physically based attributes E and CTI estimated by using the subsurface DEM (r = 0.83 and 0.86, respectively). Two multiple non-linear regression models for theta(10) spatial prediction were generated using non-autocorrelated topographic attributes estimated from both surface and subsurface topography. Model validation using a new set of 41 data points showed root mean square errors (RMSE) lower than 10% of the theta(10) range. The model based on subsurface topography decreased RMSE by 43%. Prediction errors were not spatially distributed. Finally, theses results are discussed in respect of processes involved in hillslope hydrology. Copyright (C) 2003 John Wiley Sons, Ltd.