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Unpacking some of the linkages between uncertainties in observational data and the simulation of different hydrological processes using the Pitman model in the data scarce Zambezi River basin

机译:在数据稀缺在数据稀缺的Zambezi河流域中解开了观察数据中的不确定性与不同水文过程的模拟之间的一些联系

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The main objective of this study was to use an uncertainty version of a widely used monthly time step, semi-distributed model (the Pitman model) to explore the equifinalities in the way in which the main hydrological processes are simulated and any identifiable linkages with uncertainties in the available observational data. The study area is the Zambezi River basin and 17 gauged sub-basins have been included in the analyses. Unfortunately, it is not generally possible to quantify some of the observational uncertainties in such a data scarce area and mostly we are limited to identifying where these data are clearly deficient (i.e., erroneous or non-representative). The overall conclusion is that the equifinalities in the model are hugely dominant in terms of the uncertainties in the relative occurrence of different runoff generating processes, although water use uncertainties in the semi-arid parts of the basin can contribute to these uncertainties. The identification of landscape features that suggest the occurrence of saturation excess surface runoff provides some information to constrain the model. Improved independent estimates of groundwater recharge is also identified as a key source of observational data that would help a great deal in constraining the model parameter space and therefore reducing some of the model equifinality.
机译:本研究的主要目的是利用广泛使用的月度时间步长,半分布式模型(PITMAS模型)的不确定性版本,以探讨模拟主要水文过程的等同性和任何与不确定性的可识别联系在可用的观察数据中。研究区是赞同河流域,并在分析中包含17个测量的子盆地。遗憾的是,通常不可能量化这样的数据稀缺区域中的一些观察不确定性,并且主要是我们仅限于识别这些数据显然缺乏的位置(即,错误或非代表性)。整体结论是,模型中的等同性在不同径流发电过程的相对发生的不确定性方面是非常主导的,尽管水在盆地的半干旱部位中的水分不确定性可能导致这些不确定性。识别横向特征,表明饱和过量径流的发生提供了一些限制模型的信息。改进的地下水补给的独立估计也被确定为观察数据的关键来源,这将有助于约束模型参数空间,从而减少一些模型的平等性。

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