Ensemble forecasting applied to the field of hydrology is currently an established area of
research embracing a broad spectrum of operational situations. This work catalogs the …

Skillful streamflow forecasts provide key support to several water‐related applications.
Because of the critical impact of initial conditions (ICs) on forecast accuracy, ever‐growing …

Data assimilation techniques have received growing attention due to their capability to
improve prediction. Among various data assimilation techniques, sequential Monte Carlo …

We investigate the effects of noise specification on the quality of hydrological forecasts via
an advanced data assimilation (DA) procedure using a distributed hydrological model driven …

The performance of the ensemble Kalman filter (EnKF) and the particle filter (PF) is
assessed for short-term streamflow forecasting with a distributed hydrologic model, namely …

This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF)
and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward …

This paper presents an investigation on the effect of dam operation on the ensemble Kalman
filter (EnKF) performance in a distributed hydrological model based on kinematic wave …

通过数据同化方法合理地将实时水文观测数据融入到洪水预报模型中, 可提高洪水预报模型的
实时性和精确度. 选取沿程断面流量, 水位和糙率系数作为代表水流状态的基本粒子 …

Accurate streamflow predictions are crucial for mitigating flood damage and addressing
operational flood scenarios. In recent years, sequential data assimilation methods have …

This study aims to evaluate applicability of both online and offline parameter calibration
techniques on rainfall-runoff modeling using a conceptual lumped hydrologic model. To …