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首页> 外文会议>World environmental and water resources congress >Effects of Soil Data Resolution on the Simulated Stream Flow and Water Quality: Application of Watershed-Based SWAT Model
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Effects of Soil Data Resolution on the Simulated Stream Flow and Water Quality: Application of Watershed-Based SWAT Model

机译：土壤数据分辨率对模拟水流和水质的影响：基于分水岭的SWAT模型的应用

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The hydrologic response of a watershed mostly depends on factors such as land use, soil type, and climatic inputs. Watershed modeling requires information on several soil properties, such as, texture, taxonomy, soil moisture, number of layers, and hydraulic conductivity. Several models are available that allow users to simulate the streamflow for a particular watershed using land use, topography, soil properties, and weather data. Better understanding of the future streamflows and water quality is essential for the long-term planning and management of water resources. The prediction of future flows and other hydrological parameters can be performed using different available modeling tools. Over the years, modeling of a watershed has significantly developed with the introduction of different sets of soil data such as Soil Survey Geographic (SSURGO) and State Soil Geographic (STATSGO). Both SSURGO and STATSGO data are compatible with GIS-enabled interfaces and can be used in both distributed and lumped hydrologic models. Soil and water assessment tool (SWAT) is one such tool that allow the use of soil properties data to estimate the runoff process of a watershed. The predictability of hydrological models vary significantly with the variation in input data resolution. The primary objective of this study was to compare the SWAT-simulated outputs: stream flow; and water quality parameters, for SSURGO and STASTGO data. The study was performed in the watershed of Lower Cumberland-Sycamore in Tennessee. The simulation results has demonstrated that the effectiveness of the model prediction depends mostly on the type and quality of input soil data, and suggested that the higher resolution data yields better model results when compared with observed data.
机译：流域的水文响应主要取决于土地利用，土壤类型和气候输入等因素。流域建模需要有关几种土壤属性的信息，例如质地，分类学，土壤湿度，层数和水力传导率。有几种模型可供使用，这些模型允许用户使用土地使用，地形，土壤特性和天气数据来模拟特定流域的径流。更好地了解未来的流量和水质对于水资源的长期规划和管理至关重要。可以使用其他可用的建模工具来执行对未来流量和其他水文参数的预测。多年来，随着引入不同的土壤数据集，例如土壤调查地理（SSURGO）和国家土壤地理（STATSGO），流域建模得到了显着发展。 SSURGO和STATSGO数据都与启用GIS的界面兼容，并且可以在分布式和集总水文模型中使用。土壤和水评估工具（SWAT）是这样一种工具，它允许使用土壤特性数据来估算流域的径流过程。水文模型的可预测性随着​​输入数据分辨率的变化而显着变化。这项研究的主要目的是比较SWAT模拟的输出：河流量;河水流量;河水流量。和水质参数，用于SSURGO和STASTGO数据。这项研究是在田纳西州下坎伯兰-梧桐的分水岭上进行的。仿真结果表明，模型预测的有效性主要取决于输入的土壤数据的类型和质量，并表明与观测数据相比，分辨率更高的数据可获得更好的模型结果。

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