Development of Multivariable Dynamic System Response Curve Method for Real‐Time Flood Forecasting Correction

Sun, Yimeng; Bao, Weimin; Jiang, Peng; Ji, X.; Gao, Shanjun; Xu, Youpeng; Zhang, Q.; Si, Wei

doi:10.1029/2018wr022555

Cited by 28 publications

(17 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that uncertainty may stem from several sources, such as model structure, model parameters, model states, and initial conditions (Clark & Vrugt, 2006; Kuczera et al, 2006; Sun, Bao, Jiang, Ji, et al, 2018). However, in this study, we only update the model state, indicating all the uncertainties are attributed to state uncertainty, which may result in model states being twisted but a satisfactory performance from the perspective of the model residual (Sun, Bao, Jiang, Si, et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter

Sun

Bao

Valk

et al. 2020

Water Resources Research

Self Cite

View full text Add to dashboard Cite

For operational water management in lowlands and polders (for instance, in the Netherlands), lowland hydrological models are used for flow prediction, often as an input for a real‐time control system to steer water with pumps and weirs to keep water levels within acceptable bounds. Therefore, proper initialization of these models is essential. The ensemble Kalman filter (EnKF) has been widely used due to its relative simplicity and robustness, while the unscented Kalman filter (UKF) has received little attention in the operational context. Here, we test both UKF and EnKF using a lowland lumped hydrological model. The results of a reforecast experiment in an operational context using an hourly time step show that when using nine ensemble members, both filters can improve the accuracy of the forecast by updating the state of a lumped hydrological model (Wageningen Lowland Runoff Simulator, WALRUS) based on the observed discharge, while UKF has achieved better performance than EnKF. Additionally, we show that an increase in the ensemble members does not necessarily mean a significant increase in performance. WALRUS model with either UKF or EnKF could be considered for hydrological forecasting for supporting water management of polders and lowlands, with UKF being the computationally leaner option.

show abstract

Section: Discussionmentioning

confidence: 99%

Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter

Sun

Bao

Valk

et al. 2020

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…It takes precipitation and pan-evaporation data as inputs and estimates the actual evapotranspiration, soil moisture storage, surface runoff, interflow, and groundwater runoff from the watershed. The simulated streamflow is calculated by summing the routing results of the surface, interflow, and groundwater runoff (Sun et al, 2018). In this study, the surface runoff is routed by the instantaneous unit hydrograph (Lin et al, 2014), while the interflow and groundwater runoff are routed by the linear reservoir method (Jayawardena and Zhou, 2000).…”

Section: Xinanjiang Modelmentioning

confidence: 99%

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Zhang¹,

Liu²

2019

Preprint

View full text Add to dashboard Cite

Abstract. Although the parameters of hydrological models are usually regarded as constant, temporal variations can occur in a changing environment. Thus, effectively estimating time-varying parameters becomes a significant challenge. Following a survey of existing estimation methodologies, this paper describes a new method that combines (1) the basic concept of split-sample calibration (SSC), whereby parameters are assumed to be stable for one sub-period, and (2) the parameter continuity assumption, i.e., the differences between parameters in consecutive time steps are small. Dynamic programming is then used to determine the optimal parameter trajectory by considering two objective functions: maximization of simulation accuracy and maximization of parameter continuity. The efficiency of the proposed method is evaluated by two synthetic experiments, one with a simple two-parameter monthly model and the second using a more complex 15-parameter daily model. The results show that the proposed method is superior to SSC alone, and outperforms the ensemble Kalman filter if the proper sub-period length is used. An application to the Wuding River basin indicates that the soil water capacity parameter varies before and after 1972, which can be interpreted according to land use and land cover changes. Further application to the Xun River basin shows that parameters are generally stationary on an annual scale, but exhibit significant changes over seasonal scales. These results demonstrate that the proposed method is an effective tool for identifying time-varying parameters in a changing environment.

show abstract

“…The DSRC method was initially applied to correct single hydrological elements, including runoff [15], rainfall [16] and model state variables [17]. Then, it was used to correct several hydrological elements comprehensively [18]. However, some studies [17][18][19][20] found that the correction results are not always stable, reflected in the excessive correction of hydrological element series and none-smooth simulated flow hydrographs.…”

Section: Introductionmentioning

confidence: 99%

Improvement in Ridge Coefficient Optimization Criterion for Ridge Estimation-Based Dynamic System Response Curve Method in Flood Forecasting

Liu

Bao

Sun

et al. 2021

Water

View full text Add to dashboard Cite

The ridge estimation-based dynamic system response curve (DSRC-R) method, which is an improvement of the dynamic system response curve (DSRC) method via the ridge estimation method, has illustrated its good robustness. However, the optimization criterion for the ridge coefficient in the DSRC-R method still needs further study. In view of this, a new optimization criterion called the balance and random degree criterion considering the sum of squares of flow errors (BSR) is proposed in this paper according to the properties of model-simulated residuals. In this criterion, two indexes, namely, the random degree of simulated residuals and the balance degree of simulated residuals, are introduced to describe the independence and the zero mean property of simulated residuals, respectively. Therefore, the BSR criterion is constructed by combining the sum of squares of flow errors with the two indexes. The BSR criterion, L-curve criterion and the minimum sum of squares of flow errors (MSSFE) criterion are tested on both synthetic cases and real-data cases. The results show that the BSR criterion is better than the L-curve criterion in minimizing the sum of squares of flow residuals and increasing the ridge coefficient optimization speed. Moreover, the BSR criterion has an advantage over the MSSFE criterion in making the estimated rainfall error more stable.

show abstract

Development of Multivariable Dynamic System Response Curve Method for Real‐Time Flood Forecasting Correction

Cited by 28 publications

References 65 publications

Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter

Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Improvement in Ridge Coefficient Optimization Criterion for Ridge Estimation-Based Dynamic System Response Curve Method in Flood Forecasting

Contact Info

Product

Resources

About