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Assessing land cover map accuracy and performance of hydrological models for small stream catchments using GIS.

机译:使用GIS评估小流域的土地覆盖图准确性和水文模型的性能。

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摘要

Geographic Information Systems (GIS) continue to be used more frequently and for a broader variety of applications. Careful consideration of the characteristics of underlying datasets that are incorporated in GIS models, particularly with respect to their accuracy for specific applications, is increasingly important. In this study, we evaluated the accuracy of two land cover datasets, the National Land Cover Dataset (2006) and the Gap Analysis Program (GAP) dataset at scales typical for Midwestern forest land ownership. We also evaluated the applicability of the Soil and Water Assessment Tool (SWAT 2005) for headwater streams in forested areas in central Iowa, including streams in urban, grazed, and preserved forests. For the landcover datasets, overall accuracy for Level I classification ranged from 59% for NLCD 2006 to 71% for the GAP dataset. Accuracy was relatively high for row crops (83% for both NLCD 2006 and for GAP) and developed areas (70% for NLCD 2006; 100% for GAP). Neither dataset generated optimal results for overall classification. Overall, the GAP dataset produced fewer errors for the areas we studied. For our evaluation of the SWAT 2005 model, we used the R2 coefficient and Nash-Sutcliffe Efficiency (NSE) statistic to characterize model performance using a multi-site approach for the set of nine streams. For calibration of discharge, R2 values ranged from 0.45 to 0.85, and NSE ranged from 0.41 to 0.84. Values of these statistics were lower for validation (R 2 of 0.07 to 0.72, NSE from -3.63 to 0.13). Model performance was variable for total suspended solids (calibration R2 from 0.01 to 0.80, and NSE from -0.55 to -0.04; validation R2 from 0.004 to 0.90, and NSE from -1.45 to 0.27). Overall, the SWAT model showed potential for prediction of discharge from small streams in forested areas, however, it did not perform as well for prediction of suspended solid concentration under our study conditions.
机译:地理信息系统(GIS)继续被更频繁地使用,并用于更广泛的应用中。仔细考虑合并到GIS模型中的基础数据集的特性,尤其是在特定应用程序的准确性方面,变得越来越重要。在这项研究中,我们评估了两个土地覆盖数据集的准确性,即国家土地覆盖数据集(2006)和差距分析程序(GAP)数据集在中西部林地土地所有权的典型尺度下的准确性。我们还评估了土壤和水评估工具(SWAT 2005)在爱荷华州中部森林地区的上游水流(包括城市,牧场和保护林中的水流)的适用性。对于土地覆盖物数据集,I级分类的总体准确度从NLCD 2006的59%到GAP数据集的71%不等。大田作物(NLCD 2006和GAP均为83%)和发达地区(NLCD 2006为70%; GAP为100%)的准确性相对较高。这两个数据集都没有为整体分类生成最佳结果。总体而言,GAP数据集对我们研究的区域产生的错误更少。为了评估SWAT 2005模型,我们使用R 2 系数和Nash-Sutcliffe效率(NSE)统计量,针对九个流的集合使用多站点方法来表征模型性能。对于放电的校准,R 2 值的范围是0.45至0.85,NSE的范围是0.41至0.84。这些统计数据的值较低(R 2 从0.07到0.72,NSE从-3.63到0.13)。总悬浮固体的模型性能是可变的(校准R 2 从0.01到0.80,NSE从-0.55到-0.04;验证R 2 从0.004到0.90,NSE从-1.45到0.27)。总体而言,SWAT模型显示了预测森林地区小溪流排放的潜力,但是,在我们的研究条件下,其预测悬浮固体浓度的效果不佳。

著录项

  • 作者

    Keninger, Zachary Aaron.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Agriculture Forestry and Wildlife.
  • 学位 M.S.
  • 年度 2012
  • 页码 68 p.
  • 总页数 68
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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