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On evaluating the validity of continuous, distributed hydrologic model predictions in spatially heterogeneous Hortonian watersheds.

机译：在评估空间非均质霍顿流域中连续分布式水文模型预测的有效性方面。

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To improve the usefulness of distributed hydrologic models as effective prediction tools, a detailed evaluation of the validity of distributed hydrologic model predictions in spatially heterogeneous watersheds is necessary. In this study, the distributed-parameter, two-dimensional, hydrologic model, CASCC2D is converted from an event-based model into a continuous one by adding a new parameterization for the simulation of the evolution of soil moisture. This model is calibrated using a 40-day flow record from the outlet of the Goodwin Creek watershed in Mississippi. Areas of the watershed with similar land-use and soil texture properties are changed uniformly during the calibration process. Four validation tests are conducted to test the applicability of continuous CASCC2D for the simulation of catchment dynamics at ungaged internal locations. Results of these tests reveal that the simulations of continuous CASC2D are statistically comparable to the runoff observations at Goodwin Creek. To compare the effects of parameter uncertainty of distributed hydrologic model predictions with those of a lumped model, two other single-event validation tests and a Monte Carlo uncertainty analysis are conducted by using HEC-1 and CASCC2D Results of this comparative study reveal that, in general, CASC2D model predictions are less uncertain than those of HEC-1. These results also show that Monte Carlo uncertainty analysis causes significant biases in expected runoff volume. A more detailed Monte Carlo uncertainty analysis is conducted using continuous CASC2D to investigate the effect of increased spatial heterogeneity of saturated hydraulic conductivity (Ks) on model predictions. Results of this investigation confirm that increased spatial heterogeneity of Ks leads to an over-prediction in expected runoff volume and peak discharge. A hypothetical catchment with an open-book type of configuration is used to investigate the effect of this bias on model predictions under different combinations of rainfall and initial soil-moisture conditions, and coefficients of variation of Ks. Results of this study show that intense, short-duration rainfall-events in watersheds with highly spatially-varied Ks-values lead to significant increases in sampling biases. These findings also show that the coefficient of variation of Ks has the most significant impact on sampling biases.
机译：为了提高分布式水文模型作为有效预测工具的有效性，有必要对空间非均质流域中分布式水文模型预测的有效性进行详细评估。在这项研究中，通过添加用于模拟土壤水分演变的新参数化，将分布式参数二维水文模型CASCC2D从基于事件的模型转换为连续模型。该模型使用密西西比州古德温溪流域出口处的40天流量记录进行了校准。在标定过程中，具有相似土地利用和土壤质地特性的流域面积会发生统一变化。进行了四个验证测试，以测试连续CASCC2D在模拟内部未集水区集水动力学方面的适用性。这些测试的结果表明，连续CASC2D的模拟在统计上与古德温溪的径流观测值具有可比性。为了比较分布式水文模型预测的参数不确定性与集总模型的参数不确定性的影响，使用HEC-1和CASCC2D进行了另外两个单事件验证测试和蒙特卡洛不确定性分析，该比较研究的结果表明，通常，CASC2D模型预测的不确定性要低于HEC-1。这些结果还表明，蒙特卡洛不确定性分析在预期径流量方面造成了明显的偏差。使用连续CASC2D进行更详细的蒙特卡洛不确定性分析，以研究饱和水力传导率（ K s ）的空间异质性增加对模型预测的影响。这项研究的结果证实， K s 的空间异质性增加会导致对预期径流量和峰值流量的过度预测。以假设的集水区为开本，研究了这种偏差对降雨和初始土壤水分条件不同组合以及 K s变异系数的模型预测的影响。 sub> 。这项研究的结果表明，在具有高度空间变化 K s 值的流域，强烈的短期降雨事件会导致采样偏差的显着增加。这些发现还表明， K s 的变异系数对采样偏差的影响最大。

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