State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation

Gong, Junfu; Weerts, Albrecht; Yao, Cheng; Li, Zhijia; Huang, Yin; Chen, Yuanfang; Chang, Yifei; Huang, Ping

doi:10.1016/j.jhydrol.2023.129450

Cited by 10 publications

(4 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the perturbed ensembles x f [r] are collated into matrix X f of dimension VT × R. A subset of this matrix of dimension OT × R, referred to as HX f , contains the model outputs. This perturbation scheme has been the subject of numerous studies (Gong et al, 2023;Lei et al, 2014) with potentially complex parameterization such as the data driven approach presented by Pathiraja et al (2018c). A pragmatic approach is adopted here by using perturbations with mean 0 and fixed covariance defined similarly for the three vectors ẽd t [r] , ẽu t [r] , and ẽx t [r] as follows:…”

Section: Step 1: Data Assimilationmentioning

confidence: 99%

See 1 more Smart Citation

Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

Lerat,

Chiew,

Robertson

et al. 2024

Water Resources Research

View full text Add to dashboard Cite

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called “Data Assimilation Informed model Structure Improvement” (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes.

show abstract

Section: Step 1: Data Assimilationmentioning

confidence: 99%

“…A subset of this matrix of dimension

OT\times R

, referred to as

H{X}^{f}

, contains the model outputs. This perturbation scheme has been the subject of numerous studies (Gong et al., 2023; Lei et al., 2014) with potentially complex parameterization such as the data driven approach presented by Pathiraja et al. (2018c).…”

Section: Theorymentioning

confidence: 99%

Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

Lerat,

Chiew,

Robertson

et al. 2024

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…In recent years, the risk of extreme floods in the large basins of China has been substantially reduced due to improvements in flood forecasting [4]. However, the floods that occur in small and medium catchments (SMCs) are characterized by rapidity and suddenness, and usually have a short flood duration and cause a rapid rise in the flow peak, which makes flood prediction difficult [5]. Moreover, flood forecasting in both humid SMCs and semi-humid SMCs faces additional challenges due to the fact that flood forecasting in SMCs is more sensitive to the spatial and temporal distribution of data.…”

Section: Introductionmentioning

confidence: 99%

“…Hydrological models serve as important tools in flood forecasting and have been widely used in various regions [5,6]. Hydrological models can be classified into lumped, semi-distributed, and distributed models on the basis of the hydrological processes and watershed attributes [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China

Gong,

Hu,

Yao

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PEPM. The results in scenario Wm + PEPM showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PEPM performed better flood hygrographs than scenario Wr + PEPM. The flood volumes and flow peaks in scenario Wm + PEPM had an obvious improvement compare to scenario Wr + PEPM. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments.

show abstract

Generalized divergences for statistical evaluation of uncertainty in long-memory processes

Yoshioka,

Yoshioka

2024

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation

Cited by 10 publications

References 103 publications

Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China

Generalized divergences for statistical evaluation of uncertainty in long-memory processes

Contact Info

Product

Resources

About