Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil

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

Journal of Hydrology ◽

10.1016/j.jhydrol.2017.04.036 ◽

2017 ◽

Vol 549 ◽

pp. 685-702 ◽

Cited By ~ 15

Author(s):

Lei Zou ◽

Chesheng Zhan ◽

Jun Xia ◽

Tiejun Wang ◽

Christopher J. Gippel

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Hydrological Model ◽

Distributed Hydrological Model ◽

Catchment Scale

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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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Assimilation of soil moisture data for improving streamflow prediction: Is there a role for the hydrological model structure?

10.5194/egusphere-egu2020-19109 ◽

2020 ◽

Author(s):

Aruna Kumar Nayak ◽

Basudev Biswal ◽

Kulamulla Parambath Sudheer

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Hydrological Model ◽

Assimilation Efficiency ◽

Hydrological Models ◽

Streamflow Forecasting ◽

Streamflow Prediction ◽

The Past ◽

Soil Moisture Data

<p>Soil moisture data assimilation has found increased applicability in hydrology due to easily available remotely sensed soil moisture data. Numerous studies in the past have explored the possibility of assimilating soil moisture information for improving streamflow forecasting. The general understanding is that if better soil moisture data can provide better streamflow forecast. However, to our knowledge no study has so far focused on understanding if the hydrological model itself has a role in assimilation of soil moisture data. In this regard, here we use three different conceptual hydrological models for soil moisture assimilation: (1) Dynamic Budyko (DB), (2) GR4J, and (3) PDM model. Assimilation of GLDAS observed soil moisture is carried out for four MOPEX basins using Ensemble Kalman Filter. DB model&#8217;s performance improved after soil moisture data assimilation for all the study basins. However, deterioration in performance was observed for GR4J and PDM for all the basins after the assimilation exercise. The performance of the assimilated models is evaluated in terms of Assimilation Efficiency (AE), which was found to be varying from 17.11 to 22.56%, from -20.98 to -41.29%, and from -8.4 to -38.23%, respectively, for DB, GR4J, and PDM. Overall, our results highlight the importance of the hydrological models structure in a soil moisture data assimilation exercise.</p>

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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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REAL-TIME PREDICTION ALGORITHM WITH A DISTRIBUTED HYDROLOGICAL MODEL USING KALMAN FILTER

PROCEEDINGS OF HYDRAULIC ENGINEERING ◽

10.2208/prohe.49.163 ◽

2005 ◽

Vol 49 ◽

pp. 163-168 ◽

Cited By ~ 1

Author(s):

Sunmin KIM ◽

Yasuto TACHIKAWA ◽

Kaoru TAKARA

Keyword(s):

Kalman Filter ◽

Real Time ◽

Hydrological Model ◽

Prediction Algorithm ◽

Distributed Hydrological Model ◽

Time Prediction

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Integration of an evolutionary algorithm into the ensemble Kalman filter and the particle filter for hydrologic data assimilation

Journal of Hydroinformatics ◽

10.2166/hydro.2013.088 ◽

2013 ◽

Vol 16 (1) ◽

pp. 74-94 ◽

Cited By ~ 6

Author(s):

Gift Dumedah ◽

Paulin Coulibaly

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Particle Filter ◽

Pareto Optimality ◽

Ensemble Kalman Filter ◽

Forecast Accuracy ◽

Streamflow Forecasting ◽

Daily Streamflow ◽

Pareto Optimal Set ◽

Creek Watershed

Data assimilation (DA) methods continue to evolve in the design of streamflow forecasting procedures. Critical components for efficient DA include accurate description of states, improved model parameterizations, and estimation of the measurement error. Information about these components are usually assumed or rarely incorporated into streamflow forecasting procedures. Knowledge of these components could be gained through the generation of a Pareto-optimal set – a set of competitive members that are not dominated by other members when compared using evaluation objectives. This study integrates Pareto-optimality into the ensemble Kalman filter (EnKF) and the particle filter (PF). Comparisons are made between three methods: evolutionary data assimilation (EDA) and methods based on the integration of Pareto-optimality into the EnKF (ParetoEnKF) and into the PF (ParetoPF). The methods are applied to assimilate daily streamflow into the Sacramento Soil Moisture Accounting model in the Spencer Creek watershed in Canada. The updated members are applied to forecast streamflows for up to 10 days ahead, where forecasts for 1 day, 5 day and 10 day lead times are compared to observations. The results show that updated estimates are similar for all three methods. An evaluation of updated members for multi-step forecasting revealed that EDA had the highest forecast accuracy compared to ParetoEnKF and ParetoPF, which have similar accuracies.

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