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Assessment of Sediment Yields for a Mixed-landuse Great Lakes Watershed: Lessons from Field Measurements and Modeling

机译:混合土地利用大湖流域的产沙量评估:实地测量和建模的经验教训

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摘要

The Soil Water Assessment Tool (SWAT) was implemented to determine annual sediment yields and critical source areas of erosion for the Buffalo River Watershed. Model calibrations were performed by comparing simulated streamflow discharge and sediment concentrations against measured values. Monte-Carlo simulations were performed to identify the most sensitive parameters and the "best-fit" parameter ranges. This study especially highlighted the importance of snow parameters, which, previously had not been identified as sensitive for model simulations. The cover (C) and practice (P) values for croplands had to be reduced considerably from default model values to constrain simulated sediment yields within the observed data range. The model did not simulate an ice-scour event which generated a substantial amount of sediment. The average annual sediment yield simulated by SWAT for the Buffalo River watershed (108,593 ha) amounted to 0.8 tons/ha/yr. The Cazenovia Creek subwatershed contributed the largest portion (45%) of the total sediment yield from the Buffalo River watershed. We attribute the higher sediment yields from Cazenovia Creek to the greater proportion of steep slopes in this subwatershed. The accuracy and reliability of SWAT sediment predictions at the small watershed (second order or less) and storm-event scales will depend on the accuracy of input information, especially the resolution of the landuse-landcover (LULC) layer, the number of rainfall stations used in simulations, and the number of internal sites against which the model has been calibrated.
机译:实施了土壤水评估工具(SWAT),以确定布法罗河流域的年度沉积物产量和侵蚀的关键源区域。通过将模拟流量和沉积物浓度与测量值进行比较,进行模型校准。进行了蒙特卡洛模拟,以识别最敏感的参数和“最佳拟合”参数范围。这项研究特别强调了积雪参数的重要性,而积雪参数以前并未被确定为对模型模拟敏感。必须将农田的覆盖率(C)和实践(P)值从默认模型值大幅降低,以将模拟的沉积物产量限制在观测数据范围内。该模型没有模拟会产生大量沉积物的冰暴事件。通过SWAT模拟得出的布法罗河流域(108,593公顷)的年平均沉积物产量为0.8吨/公顷/年。卡泽诺维亚河小流域贡献了布法罗河小流域总沉积物产量的最大部分(45%)。我们将Cazenovia Creek较高的沉积物产量归因于该子流域中较大的陡坡比例。在小流域(二阶以下)和暴雨事件尺度上,SWAT沉积物预测的准确性和可靠性将取决于输入信息的准确性,尤其是土地利用-土地覆被(LULC)层的分辨率,降雨站的数量仿真中使用的数量,以及已针对其校准模型的内部站点的数量。

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