Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

doi:10.5194/hess-21-1279-2017

Cited by 73 publications

(43 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the revised factor could be preserved as an empirical value for future flood prediction in the LKRB. The Liuxihe model proposed by Yangbo Chen (Chen, 2009) of Sun Yat-Sen University, China, is employed as the fully distributed hydrological model in this study, which is a physically based distributed hydrological model (PBDHM) mainly for catchment flood simulation and prediction (Chen et al, 2016Li et al, 2017). The Liuxihe model earned its name by being the first successful application in the Liuxihe catchment, Guangdong Province, China.…”

Section: The Post-processed Persiann-ccs Qpesmentioning

confidence: 99%

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

Yuan

Liu³

et al. 2019

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km 2 , in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the postprocessed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter reoptimization via the post-processed PERSIANN-CCS QPEs.The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of the Published by Copernicus Publications on behal...

show abstract

Section: The Post-processed Persiann-ccs Qpesmentioning

confidence: 99%

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

Yuan

Liu³

et al. 2019

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, the effect of forecasting lead-time on the accuracy of predicted values showed that the accuracy of the predictions and the forecast capabilities significantly improved by decreasing the forecasting lead-time [19]. The comparison of different lead-times for WRF model forecast showed that increasing the lead-time caused the overestimation of rainfall in the Liujiang River basin and decreased the forecast accuracy [20].…”

Section: Introductionmentioning

confidence: 98%

Precipitation Forecast Contribution Assessment in the Coupled Meteo-Hydrological Models

Jabbari

Bae

2019

Atmosphere

View full text Add to dashboard Cite

A numerical weather prediction and a rainfall-runoff model employed to evaluate precipitation and flood forecast for the Imjin River (South and North Korea). The real-time precipitation at point and catchment scales evaluated to select proper hydrological model to couple with atmospheric model. As a major limitation of previous studies, temporal and spatial resolutions of hydrological model are smaller than those of meteorological model. Here, through high resolution of temporal (10 min) and spatial (1 km × 1 km), the optimal resolution determined. The results showed Weather Research and Forecasting (WRF) model underestimated precipitation in point and catchment assessment and its skill was relatively higher for catchment than point scale, as illustrated by the lower Root Mean Square Error (RMSE) of 59.67, 160.48, 68.49 for the catchment and 84.49, 212.80 and 91.53 for the point scale in the events 2002, 2007 and 2011, respectively. The findings led to choose the semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated accuracy decrease; additionally, the optimal spatial resolution obtained at 8 km and temporal resolution did not affect the inherent inaccuracy of the results. Lead-time variation demonstrated that lead-time dependency was almost negligible below 36 h. With reference to this study, comparisons of model performance provided quantitative knowledge for understanding credibility and restrictions of meteo-hydrological models.

show abstract

“…Prediction of catchment response and the flooding processes induced by rainfall is another essential component in an effective fluvial flood forecasting system, which may involve the use of a wide variety of hydrological or hydraulic/hydrodynamic models (Campolo et al, 1999; Chau et al, 2005; Chiang et al, 2007; Nayak et al, 2005). In addition to those overly simplified statistical approaches, hydrological models are commonly used to predict flooding at a catchment scale (Blöschl et al, 2008; Garrote & Bras, 1995; J. Li et al, 2017; Liu et al, 2005). The output of a hydrological model is typically time series of flow rate in the river channels.…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Flood Forecasting Based on a High‐Performance 2‐D Hydrodynamic Model and Numerical Weather Predictions

Ming

Liang

Xia

et al. 2020

Water Resources Research

144

View full text Add to dashboard Cite

A flood forecasting system commonly consists of at least two essential components, that is, a numerical weather prediction (NWP) model to provide rainfall forecasts and a hydrological/hydraulic model to predict the hydrological response. While being widely used for flood forecasting, hydrological models only provide a simplified representation of the physical processes of flooding due to negligence of strict momentum conservation. They cannot reliably predict the highly transient flooding process from intense rainfall, in which case a fully 2‐D hydrodynamic model is required. Due to high computational demand, hydrodynamic models have not been exploited to support real‐time flood forecasting across a large catchment at sufficiently high resolution. To fill the current research and practical gaps, this work develops a new forecasting system by coupling a graphics processing unit (GPU) accelerated hydrodynamic model with NWP products to provide high‐resolution, catchment‐scale forecasting of rainfall‐runoff and flooding processes induced by intense rainfall. The performance of this new forecasting system is tested and confirmed by applying it to “forecast” an extreme flood event across a 2,500‐km2 catchment at 10‐m resolution. Quantitative comparisons are made between the numerical predictions and field measurements in terms of water level and flood extent. To produce simulation results comparing well with the observations, the new flood forecasting system provides 34 hr of lead time when the weather forecasts are available 36 hr beforehand. Numerical experiments further confirm that uncertainties from the rainfall inputs are not amplified by the hydrodynamic model toward the final flood forecasting outputs in this case.

show abstract

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

Cited by 73 publications

References 39 publications

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

Precipitation Forecast Contribution Assessment in the Coupled Meteo-Hydrological Models

Real‐Time Flood Forecasting Based on a High‐Performance 2‐D Hydrodynamic Model and Numerical Weather Predictions

Contact Info

Product

Resources

About