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首页> 外文期刊>Hydrology and Earth System Sciences >The effect of input data resolution and complexity on the uncertainty of hydrological predictions in a humid vegetated watershed
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The effect of input data resolution and complexity on the uncertainty of hydrological predictions in a humid vegetated watershed

机译:输入数据分辨率和复杂性对潮湿植被流域中水文预测不确定性的影响

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Uncertainty in hydrological modeling is of significant concern due to its effects on prediction and subsequent application in watershed management. Similar to other distributed hydrological models, model uncertainty is an issue in applying the Soil and Water Assessment Tool (SWAT). Previous research has shown how SWAT predictions are affected by uncertainty in parameter estimation and input data resolution. Nevertheless, little information is available on how parameter uncertainty and output uncertainty are affected by input data of varying complexity. In this study, SWAT-Hillslope (SWAT-HS), a modified version of SWAT capable of predicting saturation-excess runoff, was applied to assess the effects of input data with varying degrees of complexity on parameter uncertainty and output uncertainty. Four digital elevation model (DEM) resolutions (1, 3, 10 and 30 m) were tested for their ability to predict streamflow and saturated areas. In a second analysis, three soil maps and three land use maps were used to build nine SWAT-HS setups from simple to complex (fewer to more soil types/land use classes), which were then compared to study the effect of input data complexity on model prediction/output uncertainty. The case study was the Town Brook watershed in the upper reaches of the West Branch Delaware River in the Catskill region, New York, USA. Results show that DEM resolution did not impact parameter uncertainty or affect the simulation of streamflow at the watershed outlet but significantly affected the spatial pattern of saturated areas, with 10m being the most appropriate grid size to use for our application. The comparison of nine model setups revealed that input data complexity did not affect parameter uncertainty. Model setups using intermediate soil/land use specifications were slightly better than the ones using simple information, while the most complex setup did not show any improvement from the intermediate ones. We conclude that improving input resolution
机译:水文建模的不确定性由于其对流域管理中的预测和随后的应用而产生了重大关注。类似于其他分布式水文模型,模型不确定性是应用土壤和水评估工具(SWAT)的问题。以前的研究表明,在参数估计和输入数据分辨率中,如何受到不确定性的影响。尽管如此,少数信息如何在参数不确定性和输出不确定性受到不同复杂性的输入数据的影响。在本研究中,Swat-Hillslope(SWAT-HS)是一种能够预测饱和过量径流的改进版本的SWAT,以评估输入数据在参数不确定度和输出不确定性上具有不同程度的复杂性的影响。测试四种数字高度模型(DEM)分辨率(1,3,10和30米)以预测流流和饱和区域的能力。在第二个分析中,三个土壤图和三种土地使用地图用于构建九个SWAT-HS设置,从简单到复杂(更少的土壤类型/土地使用类),然后比较输入数据复杂性的效果关于模型预测/输出不确定性。案例研究是纽约Catskill Region的西部分支河的上游城镇布鲁克流域。结果表明,DEM分辨率不会影响参数不确定性或影响流域出口的流流量的模拟,但显着影响了饱和区域的空间模式,10米是我们应用的最合适的网格尺寸。九个模型设置的比较显示输入数据复杂性不会影响参数不确定性。使用中间土壤/土地使用规范的模型设置比使用简单信息的略好稍微好转,而最复杂的设置没有显示出中间体的任何改进。我们得出结论,提高输入分辨率

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