Implementation of evapotranspiration data assimilation with catchment scale distributed hydrological model via an ensemble Kalman Filter

ABSTRACT In this work, the data assimilation method namely ensemble Kalman filter (EnKF) is applied to the Tocantins River basin. This method assimilates streamflow results by using a distributed hydrological model. The performance of the EnKF is also compared with an empirical assimilation method for hourly time intervals, in which two applications based on information transfer from gauged to ungauged sites and real time streamflow forecasting are assessed. In the first application, both assimilation methods are able to transfer streamflow to ungauged sites, obtaining better results when more than one station located upstream or downstream of the basin are gauged. In the second application, integration of a real time forecast model with EnKF is able to absorb errors at the beginning of the forecast. Therefore, a greater efficiency in the Nash-Sutcliffe index for the first 144 hours in advance in relation to its counterpart without assimilation is obtained. Finally, a comparison between both data assimilation methods shows a greater advantage for the EnKF in long lead times.

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Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model

Advances in Water Resources ◽

10.1016/j.advwatres.2008.06.005 ◽

2008 ◽

Vol 31 (10) ◽

pp. 1309-1324 ◽

Cited By ~ 298

Author(s):

Martyn P. Clark ◽

David E. Rupp ◽

Ross A. Woods ◽

Xiaogu Zheng ◽

Richard P. Ibbitt ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Hydrological Model ◽

Distributed Hydrological Model ◽

Hydrological Data

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Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow

Water Resources Research ◽

10.1029/2008wr007031 ◽

2009 ◽

Vol 45 (10) ◽

Cited By ~ 75

Author(s):

Matteo Camporese ◽

Claudio Paniconi ◽

Mario Putti ◽

Paolo Salandin

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Subsurface Flow ◽

Scale Model ◽

Catchment Scale ◽

Surface And Subsurface Flow

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Improving the Distributed Hydrological Model Performance in Upper Huai River Basin: Using Streamflow Observations to Update the Basin States via the Ensemble Kalman Filter

Advances in Meteorology ◽

10.1155/2016/4921616 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 6

Author(s):

Yongwei Liu ◽

Wen Wang ◽

Yiming Hu ◽

Wei Cui

Keyword(s):

Kalman Filter ◽

River Basin ◽

Ensemble Kalman Filter ◽

Hydrological Model ◽

Model Performance ◽

Real Data ◽

Distributed Hydrological Model ◽

Huai River Basin ◽

Huai River

This study investigates the capability of improving the distributed hydrological model performance by assimilating the streamflow observations. Incorrectly estimated model states will lead to discrepancies between the observed and estimated streamflow. Consequently, streamflow observations can be used to update the model states, and the improved model states will eventually benefit the streamflow predictions. This study tests this concept in upper Huai River basin. We assimilate the streamflow observations sequentially into the Soil and Water Assessment Tool (SWAT) using the ensemble Kalman filter (EnKF) to update the model states. Both synthetic experiments and real data application are used to demonstrate the benefit of this data assimilation scheme. The experiment shows that assimilating the streamflow observations at interior sites significantly improves the streamflow predictions for the whole basin. Assimilating the catchment outlet streamflow improves the streamflow predictions near the catchment outlet. In real data case, the estimated streamflow at the catchment outlet is significantly improved by assimilating the in situ streamflow measurements at interior gauges. Assimilating the in situ catchment outlet streamflow also improves the streamflow prediction of one interior location on the main reach. This may demonstrate that updating model states using streamflow observations can constrain the flux estimates in distributed hydrological modeling.

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