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Performances of Rainfall-Runoff Models Calibrated over Single and Continuous Storm Flow Events

机译:在单次和连续暴雨流量事件中校准的降雨径流模型的性能

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Accurate parameter estimation is important for reliable rainfall-runoff modeling. Previous studies emphasize that a sufficient length of continuous events is required for model calibration to overcome the effect of initial conditions. This paper investigates the feasibility of calibrating rainfall-runoff models over a number of limited storm flow events. For a subcatchment having a moderate influence from initial soil moisture conditions, this study shows that rainfall-runoff models could still be calibrated reliably over a set of representative events provided that the events cover a wide range of peak flow, total runoff volume, and initial soil moisture conditions. This approach could provide an alternative calibration strategy for a small watershed that has a limited data length but consists of runoff events with a wide range of magnitudes. Compared to continuous-event calibration, event-based calibration appears to perform better in simulating the overall shape of hydrograph, peak flow and time to peak. However, continuous-event calibration was found to be more reliable in providing runoff volume, suggesting that continuous-event calibration should still be used when runoff volume is the main concern of a study.
机译:准确的参数估算对于可靠的降雨径流建模非常重要。先前的研究强调,模型校准需要足够长的连续事件来克服初始条件的影响。本文研究了在一些有限的暴风雨事件中校准降雨径流模型的可行性。对于受初始土壤湿度条件影响中等的子汇水面积,本研究表明,如果一系列降雨事件涵盖大范围的峰值流量,总径流量和初始降雨,则仍可以可靠地对降雨径流模型进行校准。土壤湿度条件。该方法可以为数据长度有限但由范围较大的径流事件组成的小流域提供替代性的校准策略。与连续事件标定相比,基于事件的标定在模拟水文图的总体形状,峰流量和到达峰的时间方面表现更好。但是,发现连续事件校准在提供径流量方面更可靠,这表明当径流量是一项研究的主要内容时,仍应使用连续事件校准。

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