Flood Prediction in Ungauged Basins by Physical-Based TOPKAPI Model

Kong, Xiangming; Li, Zhijia; Liu, Zhiyu

doi:10.1155/2019/4795853

Cited by 10 publications

(6 citation statements)

References 45 publications

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“…The values of peak error and peak time error also increase considerably after 7 days lead time. Although the results confirm that the satellite-based ECMWF precipitation can be appropriately employed to run MIKE 11 NAM-HD model for flood prediction purpose in Jhelum basin, complete accomplishment of the potential of smooth satellite-based precipitation estimates calls for better analysis of optimal calibration procedure for incorporating remote sensing data into a real-time hydrological modelling system for vast ungauged or scarcely gauged basins of the world(Kong et al 2019;Jiang and Wang 2019).…”

mentioning

confidence: 81%

Flood Forecasting in the Sparsely Gauged Jhelum River Basin of Greater Himalayas Using Integrated Hydrological and Hydraulic Modelling Approach

Parvaze

Khan

Kumar

et al. 2021

Preprint

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Flood forecasting using hydrological and hydraulic models is an efficient non-structural flood management option. For real-time flood forecasting in the data scarce Jhelum basin, the present study evaluated the capability of MIKE 11 Nedbor-Afstromings Model (NAM), hydrodynamic (HD) and flood forecasting (FF) models to forecast streamflow of four principal gauging stations of river Jhelum (Sangam, Ram Munshi Bagh, Asham and Baramulla). European Centre for Medium-Range Weather Forecast (ECMWF) model-forecasted precipitation and temperature data was used to increase the forecast lead time to 7 days. The forecast results were assessed by calculating coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and various error indices. Integration of the MIKE 11 NAM and HD modules improved the results of MIKE 11 NAM significantly. Values of R2 improved by 8–15% while that of NSE improved by 8–14% at various stations by using integrated model. MIKE 11 FF with ECMWF input was used to simulate the streamflow with lead times of 1to 10-days. The forecast results were efficient for lead times up to 7-days with R2 and NSE values 0.8 and 0.7, respectively. Peak error for 7-days forecast was 19.1% and the peak time error was 1.2 h. The developed model can be used for short to medium range flood forecasting for the data scarce, snow dominated Jhelum basin and can apply to similar basins around the globe as a component of the early flood warning system.

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confidence: 81%

Flood Forecasting in the Sparsely Gauged Jhelum River Basin of Greater Himalayas Using Integrated Hydrological and Hydraulic Modelling Approach

Parvaze

Khan

Kumar

et al. 2021

Preprint

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“…Specifically, most hydrological models consider the variables associated with real rivers as the influential variables, and the parameters of the hydrological model are estimated using empirical formulas or strategic values. To estimate the values of the actual discharge and water level, the water‐level or flow data must be acquired to adjust the model parameters (Kong et al, 2019; Kushwaha & Jain, 2013). In this study, the actual discharge and water level were used as the objective function for the model optimization, and the roughness coefficient of the channel, roughness coefficient of the conduit, impermeable area ratio, curve number (CN), and loss coefficient were considered.…”

Section: Methodsmentioning

confidence: 99%

Flooding time nomograph for urban river flood prediction: Case study of Dorim stream basin, Seoul

Yoon

Lee

Choi

et al. 2023

J Flood Risk Management

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Global climate change is intensifying flood damage in urban rivers. Notably, most small and medium-sized urban rivers have a brief concentration period and are highly vulnerable to sudden heavy rains that lead to a rapid increase in water levels. Therefore, rapid flood forecasting must be performed through accurate flood occurrence and timing prediction. In this study, a flooding time nomograph (FTN) was proposed to predict the flood occurrence time according to rainfall conditions, such as intensity, time distribution, and duration. In addition, rainfall-runoff simulations were performed by establishing different virtual rainfall scenarios using Huff's quartile rainfall time distribution. The simulation results were used to formulate the relationship between the rainfall intensity and flood occurrence time to generate the FTN. The applicability of this tool was verified through a comparison with the observed flood occurrence time for an actual rainfall event, which was highly accurate in the target watershed, with a correlation coefficient >0.8 and Nash-Sutcliffe efficiency >0.6. Therefore, the proposed FTN can be used to reasonably predict the occurrence of floods and the time of flood occurrence using only predicted rainfall information. K E Y W O R D S flood, flood forecasting and warning, flooding time nomograph, rainfall-runoff model, urban stream 1 | INTRODUCTION Extreme weather events caused by global climate change are frequent worldwide. According to the 6th evaluation report published by the Intergovernmental Panel on Climate Change (IPCC), the surface temperature has sharply increased since the fifth evaluation report, with the highest temperatures observed since 1850, and extreme climate change, unprecedented in modern human history, is occurring (IPCC, 2021). To examine the heavy rainfall characteristics of major urban areas in Korea, Yoon et al. (2016) performed quantile regression analyses by applying various classification criteria for extreme precipitation to eight major cities in Korea.According to the Mann-Kendall test and Sen test results, the annual top 10th maximum precipitation has increased by 3.1%-15.0% on average over the past 30 years . At present, the temperature is

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“…The GLUE method includes several steps: (1) select a hydrological model and determine the model parameter space; (2) select a likelihood function and determine the threshold value and the priori probability distribution; (3) randomly extract parameter sets from the parameter space; (4) run the model with the extracted parameter sets, calculate the likelihood value according to the model results and save the behavioral parameter sets if the likelihood value exceeds the threshold; (5) rescale the threshold values to formulate a cumulative distribution. In this study, the Nash-Sutcliffe efficiency (NSE) [53] was selected as the likelihood function, as in many other studies [23,28,29,52]:…”

Section: The Glue Methodsmentioning

confidence: 99%

“…To assess the parameter uncertainty, different methods have been used, for example, the Generalized Likelihood Uncertainty Estimation (GLUE) [19], Markov Chain Monte Carlo (MCMC) [20], Parameter Solution (ParaSol) [21], Sequential Uncertainty Fitting (SUFI-2) [22], etc. Among them, the GLUE attracts many users due to its simpler concept and convenience in implementation [13,23,24]. As a typical Monte Carlo method, the GLUE method requires sampling from the model parameter space and performing a run of the model with each set of parameters.…”

Section: Introductionmentioning

confidence: 99%

Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface

Yin

Liao

Lei

et al. 2020

Water

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Parameter uncertainty analysis is one of the hot issues in hydrology studies, and the Generalized Likelihood Uncertainty Estimation (GLUE) is one of the most widely used methods. However, the scale of the existing research is relatively small, which results from computational complexity and limited computing resources. In this study, a parallel GLUE method based on a Message-Passing Interface (MPI) was proposed and implemented on a supercomputer system. The research focused on the computational efficiency of the parallel algorithm and the parameter uncertainty of the Xinanjiang model affected by different threshold likelihood function values and sampling sizes. The results demonstrated that the parallel GLUE method showed high computational efficiency and scalability. Through the large-scale parameter uncertainty analysis, it was found that within an interval of less than 0.1%, the proportion of behavioral parameter sets and the threshold value had an exponential relationship. A large sampling scale is more likely than a small sampling scale to obtain behavioral parameter sets at high threshold values. High threshold values may derive more concentrated posterior distributions of the sensitivity parameters than low threshold values.

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Flood Prediction in Ungauged Basins by Physical-Based TOPKAPI Model

Cited by 10 publications

References 45 publications

Flood Forecasting in the Sparsely Gauged Jhelum River Basin of Greater Himalayas Using Integrated Hydrological and Hydraulic Modelling Approach

Flood Forecasting in the Sparsely Gauged Jhelum River Basin of Greater Himalayas Using Integrated Hydrological and Hydraulic Modelling Approach

Flooding time nomograph for urban river flood prediction: Case study of Dorim stream basin, Seoul

Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface

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