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首页> 外文期刊>Advances in Water Resources >Assimilation of hydrologic and hydrometeorological data into distributed hydrologic model: Effect of adjusting mean field bias in radar-based precipitation estimates
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Assimilation of hydrologic and hydrometeorological data into distributed hydrologic model: Effect of adjusting mean field bias in radar-based precipitation estimates

机译：将水文和水文气象数据同化为分布式水文模型：调整平均场偏在基于雷达的降水估计中的作用

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摘要

This paper investigates the effect of adjusting the mean field bias (MFB) in radar-based precipitation data on analysis and prediction of streamflow and soil moisture in assimilating streamflow or streamflow and in situ soil moisture data into distributed hydrologic models. To evaluate the effect of adjusting the MFB under realistic as well as idealized conditions, both real-world and synthetic experiments are carried out for the Eldon Catchment on the border of Oklahoma and Arkansas in the US. In the synthetic experiment, the MFB is modeled as a stationary Markov chain process. The synthetic experiment showed that adjusting the MFB in the assimilation process significantly improves streamflow analysis when the initial conditions are known with reasonable certainty, and that assimilating soil moisture in addition to streamflow improves analysis of streamflow as well as soil moisture if the initial conditions are largely uncertain. Adjusting the MFB during the assimilation process noticeably improved streamflow analysis over ranges of the MFB and random noise in the precipitation data. On the other hand, increasing the MFB and random noise in the precipitation data tended to degrade soil moisture analysis due possibly to over-adjusting soil moisture to mitigate the precipitation error. The real-world experiment with one-year dataset showed that adjusting the MFB during the assimilation process helped capture the peak as well as volume of outlet flow analysis as well as prediction, and that additionally assimilating interior flow observations was necessary to improve analysis and prediction of peak flows at interior locations.
机译：本文研究了将基于雷达的降水数据中的平均场偏（MFB）调整对流量和土壤湿度的分析和预测在将流量或流量和原地土壤湿度数据吸收到分布式水文模型中的影响。为了评估在现实和理想条件下调整MFB的效果，针对美国俄克拉荷马州和阿肯色州边界的Eldon集水区进行了实际和综合实验。在合成实验中，MFB被建模为平稳的马尔可夫链过程。合成实验表明，在合理确定初始条件的情况下，在同化过程中调整MFB可以显着改善流量分析，并且如果初始条件较大，则除溪流以外还吸收土壤水分可以改善流量和土壤水分的分析。不确定。在同化过程中调整MFB可以显着改善MFB范围内的水流分析和降水数据中的随机噪声。另一方面，增加MFB和增加降水数据中的随机噪声，可能会由于过度调节土壤水分以减轻降水误差而使土壤水分分析质量下降。带有一年数据集的真实世界实验表明，在同化过程中调整MFB有助于捕获峰值，出口流量分析以及预测量，此外，还需要对内部流量观测值进行同化以改善分析和预测内部位置的峰值流量。

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