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Assessment of initial soil moisture conditions for event-based rainfall-runoff modelling

机译:基于事件的降雨径流模型的初始土壤水分条件评估

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摘要

Flash floods are the most destructive natural hazards that occur in the Mediterranean region. Rainfall-runoff models can be very useful for flash flood forecasting and prediction. Event-based models are very popular for operational purposes, but there is a need to reduce the uncertainties related to the initial moisture conditions estimation prior to a flood event. This paper aims to compare several soil moisture indicators: local Time Domain Reflectometry (TDR) measurements of soil moisture, modelled soil moisture through the Interaction-Sol-Biosphère-Atmosphère (ISBA) component of the SIM model (Météo-France), antecedent precipitation and base flow. A modelling approach based on the Soil Conservation Service-Curve Number method (SCS-CN) is used to simulate the flood events in a small headwater catchment in the Cevennes region (France). The model involves two parameters: one for the runoff production, S, and one for the routing component, K. The S parameter can be interpreted as the maximal water retention capacity, and acts as the initial condition of the model, depending on the antecedent moisture conditions. The model was calibrated from a 20-flood sample, and led to a median Nash value of 0.9. The local TDR measurements in the deepest layers of soil (80-140 cm) were found to be the best predictors for the S parameter. TDR measurements averaged over the whole soil profile, outputs of the SIM model, and the logarithm of base flow also proved to be good predictors, whereas antecedent precipitations were found to be less efficient. The good correlations observed between the TDR predictors and the S calibrated values indicate that monitoring soil moisture could help setting the initial conditions for simplified event-based models in small basins.
机译:暴洪是在地中海地区发生的最具破坏力的自然灾害。降雨径流模型对于山洪预报和预测非常有用。基于事件的模型对于操作目的非常受欢迎,但是需要减少与洪水事件发生之前的初始湿度条件估计有关的不确定性。本文旨在比较几种土壤水分指标:土壤水分的时域反射法(TDR)测量,通过SIM模型(法国巴黎)的相互作用-Sol-Biosphère-Atmosphère(ISBA)组件模拟的土壤水分,前期降水和基本流程。一种基于土壤保护服务曲线数方法(SCS-CN)的建模方法用于模拟塞文山脉地区(法国)的小水源流域的洪水事件。该模型涉及两个参数:一个用于径流产生,S,一个用于径流成分,K。S参数可以解释为最大保水量,并作为模型的初始条件,具体取决于先决条件。潮湿条件。该模型是从20个注水样品中校准的,得出的Nash中值是0.9。发现在土壤最深层(80-140 cm)中的局部TDR测量是S参数的最佳预测指标。 TDR测量值在整个土壤剖面,SIM模型的输出以及基流的对数上取平均值,也被证明是很好的预测指标,而先前的降水则效率较低。在TDR预测值和S校准值之间观察到的良好相关性表明,监测土壤湿度可以帮助为小盆地中基于事件的简化模型设置初始条件。

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