The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method

Lü, Haishen; Hou, Ting Ting; Horton, Robert; Zhu, Yonghua; Chen, Xi; Jia, Yangwen; Wu, Wen; Fu, Xiaolei

doi:10.1016/j.jhydrol.2012.12.011

Cited by 96 publications

(41 citation statements)

References 48 publications

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“…The ensemble size, uncertainties in input and output have significant impacts on the assimilation performance of the EnKF, and they are specified following the previous studies (Moradkhani et al, 2005;Wang et al, 2009;Xie and Zhang, 2010;Nie et al, 2011;Lü et al, 2013;Samuel et al, 2014). The ensemble size is set to 1000 for the synthetic experiment and the two case studies.…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

“…In this study, the parameter errors are determined empirically; i.e., the standard deviation of C is set to 0.01 for all the cases, while that of SC is set to 5.0, 1.0 and 0.5 in the synthetic experiment, Wudinghe basin and Tongtianhe basin, respectively. The standard deviations of both model state and observation errors are assumed to be proportional to the magnitude of true values (Wang et al, 2009;Lü et al, 2013). The proportional factors of the model state are set to 0.05 for all the cases.…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

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Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

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Section: Ensemble Kalman Filtermentioning

confidence: 99%

Section: Ensemble Kalman Filtermentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…This potential is likely to receive greater attention in the coming decade as additional remotely sensed soil moisture data products reach maturity. Numerous attempts have been made to merge current and next-generation satellite retrievals (of SM or/and SD) with hydrological models and improve hydrological simulations or forecasts [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it also provides details concerning the design of synthetic DA experiments (used to initially evaluate Remote Sens. 2016, 8,503 3 of 20 various data assimilation) and the ground and remote sensing data sets applied during real DA cases. Section 3 presents HBV calibration and validation results, synthetic and real data assimilation experimental results with an emphasis on the cross-comparison of streamflow results obtained via the application of various techniques for integrating RS SD and SM retrievals.…”

Section: Introductionmentioning

confidence: 99%

Improving Streamflow Prediction Using Remotely-Sensed Soil Moisture and Snow Depth

et al. 2016

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Abstract:The monitoring of both cold and warm season hydrologic processes in headwater watersheds is critical for accurate water resource monitoring in many alpine regions. This work presents a new method that explores the simultaneous use of remotely sensed surface soil moisture (SM) and snow depth (SD) retrievals to improve hydrological modeling in such areas. In particular, remotely sensed SM and SD retrievals are applied to filter errors present in both solid and liquid phase precipitation accumulation products acquired from satellite remote sensing. Simultaneously, SM and SD retrievals are also used to correct antecedent SM and SD states within a hydrological model. In synthetic data assimilation experiments, results suggest that the simultaneous correction of both precipitation forcing and SM/SD antecedent conditions is more efficient at improving streamflow simulation than data assimilation techniques which focus solely on the constraint of antecedent SM or SD conditions. In a real assimilation case, results demonstrate the potential benefits of remotely sensed SM and SD retrievals for improving the representation of hydrological processes in a headwater basin. In particular, it is demonstrated that dual precipitation/state correction represents an efficient strategy for improving the simulation of cold-region hydrological processes.

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“…Therefore, recalibrating the model at every time step has no real advantages, other than perhaps some computational attraction and that only when applied to simple forecasting models of the system analysis type". For these reasons, updating of model parameters is less common than the other three types of updating in flood forecasting (Young, 1984;Xie and Zhang, 2010;Lü et al, 2013). Examples of combined updating of model states and parameters are reported in Moradkhani et al (2005a).…”

Section: Introductionmentioning

confidence: 99%

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

Mazzoleni¹

2017

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The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method

Cited by 96 publications

References 48 publications

Identification of hydrological model parameter variation using ensemble Kalman filter

Identification of hydrological model parameter variation using ensemble Kalman filter

Improving Streamflow Prediction Using Remotely-Sensed Soil Moisture and Snow Depth

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

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