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Trevor Quinn A.-Xing Zhu James E. Burt 《International Journal of Applied Earth Observation and Geoinformation》2005,7(4):324-338
Hydro-ecological modelers often use spatial variation of soil information derived from conventional soil surveys in simulation of hydro-ecological processes over watersheds at mesoscale (10–100 km2). Conventional soil surveys are not designed to provide the same level of spatial detail as terrain and vegetation inputs derived from digital terrain analysis and remote sensing techniques. Soil property layers derived from conventional soil surveys are often incompatible with detailed terrain and remotely sensed data due to their difference in scales. The objective of this research is to examine the effect of scale incompatibility between soil information and the detailed digital terrain data and remotely sensed information by comparing simulations of watershed processes based on the conventional soil map and those simulations based on detailed soil information across different simulation scales. The detailed soil spatial information was derived using a GIS (geographical information system), expert knowledge, and fuzzy logic based predictive mapping approach (Soil Land Inference Model, SoLIM). The Regional Hydro-Ecological Simulation System (RHESSys) is used to simulate two watershed processes: net photosynthesis and stream flow. The difference between simulation based on the conventional soil map and that based on the detailed predictive soil map at a given simulation scale is perceived to be the effect of scale incompatibility between conventional soil data and the rest of the (more detailed) data layers at that scale. Two modeling approaches were taken in this study: the lumped parameter approach and the distributed parameter approach. The results over two small watersheds indicate that the effect does not necessarily always increase or decrease as the simulation scale becomes finer or coarser. For a given watershed there seems to be a fixed scale at which the effect is consistently low for the simulated processes with both the lumped parameter approach and the distributed parameter approach. 相似文献
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Spatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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SWAT模型对高精度土壤信息的敏感性研究 总被引：2，自引：0，他引：2
土壤信息是SWAT模型的重要输入数据,通常认为,土壤信息的精度直接影响着模拟结果的准确性。本文以美国Brewery Creek流域(19.5km2)为例,在其他输入不变的情况下,通过比较不同精度土壤数据(美国农业部SSURGO土壤图与SoLIM方法获得的土壤图)的模拟径流,分析SWAT模型对高精度土壤信息的敏感性。应用结果显示,在模型的校正前后,两种土壤数据的径流模拟结果均近似,差别并不显著。这表明在小流域水文模拟中,SWAT模型的径流模拟对高精度土壤信息的敏感性较弱,模拟径流不能很好的体现一定精度基础上土壤信息的差别。本文将此现象主要归因于:SWAT模型所采用的SCS-CN径流计算方法,在计算CN值(Curve Number)时将不同土壤类型综合到四个土壤水文组的做法,概括了土壤信息,模糊了土壤之间的属性差别,损失了土壤精度信息。本研究发现了SCS-CN径流计算方法在利用高精度土壤数据时存在的问题,并进行了分析,为水文模拟中参数的确定和数据的准备提供了参考。 相似文献
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The present study tests the usefulness of SoLIM software and GIS techniques for the reconstruction of large vegetation formations from the mid-Holocene period in Romania. We used current reference climate data (temperature, precipitation) and the current extent of ecological regions in Romania to derive climate optimality functions for each vegetation formation. The optimality functions were used to simulate current and past distributions of vegetation. The results showed that the current and mid-Holocene simulated spatial distributions of vegetation are quite similar. Changes were found for about 14% of the Romanian territory (33 946 km2), the most important being the retreat of coniferous forests in favor of mixed forests and of mixed forests in favor of forest steppe. The former is validated by previous pollen-based studies showing the transition from coniferous forests to mixed forests that occurred in several areas of the country after the mid-Holocene. The study demonstrates the potential of this methodological approach to reconstruct past vegetation formations, and at the same time that it is a straightforward and expert knowledge-based method. Although our application uses only climate factors, the results can be further refined by incorporating additional drivers (soil and landform information, site-specific pollen and fossil data, wildfire data) for a more accurate inference of paleovegetation. 相似文献
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