期刊名称：International Journal of Geospatial and Environmental Research
印刷版ISSN：2332-2047
出版年度：2014
卷号：1
期号：2
页码：6
出版社：University of Wisconsin Milwaukee
摘要：Soil parameters for hydrology modeling in cropland dominated areas, from the regional to local scale, are part of critical biophysical information whose deficiency may increase the uncertainty of simulated conservation effects and predicting potential. Despite this importance, soil physical and hydraulic parameters lack common, wide-coverage repositories combined to digital maps as required by various hydrology-based agricultural water quality models. This paper describes the construction of a geoprocessing workflow and the resultant hydrology-structured soil hydraulic, physical, and chemical parameters geographic database for the entire United States, named US-SOILM-CEAP. This database is designed to store a-priori values for a suit of models, such as SWAT (Soil and Water Assessment Tool), APEX (Agricultural Policy Environmental EXtender) and ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria), which are commonly used for the across scale assessment of agricultural hydrology and conservation practice scenarios. The Soil Survey Geographic (SSURGO) database developed by the U.S. Department of Agriculture provided the main source data for this development. Additional spatial information, a geographic information system platform and Python computer programming language code were used to create hydrology-based tile coverage of the areal soil units linked to the specific and detailed attributes required by each model. The created repository adds value to the source soil survey data, while maintaining and extending the detailed information necessary for the across scale and combined application of the models. Ultimately, our multi-model database provides a comprehensive product achieving joined informational-mapping-geoprocessing functionality with the explicit maintenance of the original conceptual links between soil series and composing soil layers, allowing for efficient data retrieval, analysis and service as input for modeling conservation effects.
