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首页> 外文期刊>Hydrology and Earth System Sciences Discussions >Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy
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Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy

机译：通过实时更新来改善未加注流量的流量预测：基于EnKF的状态参数估计策略的应用

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pstrongAbstract./strong The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remote-sensing observations./p.
机译：> >摘要。在未启用位置的流量预测面临的挑战主要归因于水文建模中的各种不确定性。许多研究致力于解决这个问题。相似性区域化方法是一种常用策略，通常受主观选择相似性度量的限制。本文提出了一种基于集合卡尔曼滤波器（EnKF）的分区更新方案，以减少预测的不确定性。该方案通过吸收测量的水流，对分布式水文模型的状态和参数进行实时更新。流量预测受分布式水文模型中定义的物理降雨径流过程以及从规范流域到未灌流盆地的相关信息的约束。该方案已在具有真实世界水文数据的嵌套盆地中成功演示，该盆地的子流域紧邻上游和下游。结果表明，来自下游邻国的同化观测数据在减少未开挖位置的水流预测误差方面具有更重要的作用。短期更新后，实时更新的模型参数保持稳定并具有合理的扩展，而其估计轨迹变化缓慢，这可能归因于气候和陆地表面变化。尽管此实时更新方案旨在用于嵌套盆地中的流量预测，但它可以通过吸收包括地面和遥感观测在内的多源数据集，在单独的盆地中提高水文预测价值。。

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