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

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

RBRH ◽

10.1590/2318-0331.241920180031 ◽

2019 ◽

Vol 24 ◽

Cited By ~ 1

Author(s):

Karena Quiroz Jiménez ◽

Walter Collischonn ◽

Rodrigo Cauduro Dias de Paiva

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Real Time ◽

Ensemble Kalman Filter ◽

Information Transfer ◽

Hydrological Model ◽

Distributed Hydrological Model ◽

Streamflow Forecasting ◽

Ungauged Sites ◽

Tocantins River

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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Assessment of SMOS and SMAP soil moisture products against new estimates combining physical model, a statistical model, and in-situ observations: a case study over the Huai River Basin, China

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.126468 ◽

2021 ◽

pp. 126468

Author(s):

Xiaoyi Wang ◽

Haishen Lü ◽

Wade T. Crow ◽

Yonghua Zhu ◽

Qimeng Wang ◽

...

Keyword(s):

Soil Moisture ◽

Statistical Model ◽

Physical Model ◽

River Basin ◽

Huai River Basin ◽

Huai River ◽

In Situ Observations ◽

The Huai River Basin

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ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

Advances in Meteorology ◽

10.1155/2018/7301314 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Yongwei Liu ◽

Wen Wang ◽

Yuanbo Liu

Keyword(s):

Soil Moisture ◽

River Basin ◽

Hydrological Model ◽

European Space Agency ◽

Low Flow ◽

Observation Error ◽

Rainfall Runoff ◽

Huai River Basin ◽

Huai River ◽

Streamflow Simulation

The assimilation of satellite soil moisture (SM) products with coarse resolution is promising in improving rainfall-runoff modeling, but it is largely impacted by the data assimilation (DA) strategy. This study performs the assimilation of a satellite soil moisture product from the European Space Agency (ESA) Climate Change Initiative (CCI) in a physically based semidistributed hydrological model (SWAT) in the upper Huai River basin in China, with the objective to improve its rainfall-runoff simulation. In this assimilation, the ensemble Kalman filter (EnKF) is adopted with full consideration of the model and observation error, the rescaling technique for satellite SM, and the regional applicability of the hydrological model. The results show that the ESA CCI SM assimilation generally improves the streamflow simulation of the study catchment. It is more effective for low-flow simulation, while for very high-flow/large-flood modeling, the DA performance shows uncertainty. The less-effective performance on large-flood simulation lies in the relatively low dependence of rainfall-runoff generation on the antecedent SM as during which the SM is nearly saturated and the runoff is largely dominated by precipitation. Besides, the efficiency of DA is deteriorated by the dense forest coverage and the complex topography conditions of the basin. Overall, the ESA CCI SM assimilation improves the streamflow simulation of the SWAT model in particular for low flow. This study provides an encouragement for the application of the ESA CCI SM in water management, especially over low-flow periods.

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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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Projecting Hydrological Responses to Climate Change Using CMIP6 Climate Scenarios for the Upper Huai River Basin, China

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.759547 ◽

2021 ◽

Vol 9 ◽

Author(s):

Guodong Bian ◽

Jianyun Zhang ◽

Jie Chen ◽

Mingming Song ◽

Ruimin He ◽

...

Keyword(s):

Climate Change ◽

River Basin ◽

Return Period ◽

Hydrological Model ◽

Flood Frequency ◽

Precipitation Series ◽

Climate Scenarios ◽

Huai River Basin ◽

Huai River ◽

Increasing Trend

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

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Rainfall-Runoff Modelling of the West Rapti Basin, Nepal

Technical Journal ◽

10.3126/tj.v2i1.32846 ◽

2020 ◽

Vol 2 (1) ◽

pp. 99-107

Author(s):

Bibek Thapa ◽

Anusha Danegulu ◽

Naresh Suwal ◽

Surabhi Upadhyay ◽

Bikesh Manandhar ◽

...

Keyword(s):

River Basin ◽

Water Resource Management ◽

Hydrological Model ◽

Model Performance ◽

Rain Gauge ◽

Army Corps ◽

Distributed Hydrological Model ◽

Rainfall Runoff ◽

The West ◽

Us Army

A hydrological model helps in understanding, predicting, and managing water resources. The HEC-HMS (Centre for Hydrological Engineering - Hydrological Modelling Systems, US Army Corps of Engineers) is one of the hydrological models used to simulate rainfall-runoff and routing processes in diverse geographical areas. In this study, a semi-distributed hydrological model was developed using HEC-HMS for the West-Rapti river basin. The model was calibrated and validated at each outlet of sub-basins and used to simulate the outflow of each sub-basins of the West Rapti river basin. A total of eight rain gauge stations, five meteorological stations, and three hydrological stations, within the basin, were used. The simulated results closely matched the observed flows at the three gauging stations. The Nash-Sutcliffe Efficiency indicated the good model performance of the simulated streamflow with the observed flow at two stations and satisfactory model fit at one station. The performance based on percentage bias and root mean square error was good. This model provides a reference to study water balance, water resource management, and flooding control of the West Rapti basin and can be replicated in other basins.

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