Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

Chen, Xinchi; Zhang, Liping; Gippel, Christopher J.; Shan, Lijie; Chen, Shaodan; Yang, Wei

doi:10.1155/2016/2710457

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…Firstly, most data available are not real-time [14]- [18]. Secondly, these data were not of sufficiently high resolution [11], [28]. The most notable example of the latter is rainfalls being recorded at specific monitoring stations, normally located sparingly.…”

Section: A Meteorological and Hydrological Datamentioning

confidence: 99%

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Puttinaovarat

Horkaew

2020

IEEE Access

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Flood is one of the most disruptive natural hazards, responsible for loss of lives and damage to properties. A number of cities are subject to monsoons influences and hence face the disaster almost every year. Early notification of flood incident could benefit the authorities and public to devise both short and long terms preventive measures, to prepare evacuation and rescue mission, and to relieve the flood victims. Geographical locations of affected areas and respective severities, for instances, are among the key determinants in most flood administration. Thus far, an effective means of anticipating flood in advance remains lacking. Existing tools were typically based on manually input and prepared data. The processes were tedious and thus prohibitive for real-time and early forecasts. Furthermore, these tools did not fully exploit more comprehensive information available in current big data platforms. Therefore, this paper proposes a novel flood forecasting system based on fusing meteorological, hydrological, geospatial, and crowdsource big data in an adaptive machine learning framework. Data intelligence was driven by stateof-the-art learning strategies. Subjective and objective evaluations indicated that the developed system was able to forecast flood incidents, happening in specific areas and time frames. It was also later revealed by benchmarking experiments that the system configured with an MLP ANN gave the most effective prediction, with correct percentage, Kappa, MAE and RMSE of 97.93, 0.89, 0.01 and 0.10, respectively. INDEX TERMS Flood forecasting system, big data, machine learning, crowdsource, deep learning. PARAMATE HORKAEW received the B.Eng. degree (Hons.

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Section: A Meteorological and Hydrological Datamentioning

confidence: 99%

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Puttinaovarat

Horkaew

2020

IEEE Access

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“…Flood is a hazard with potentially serious consequences of loss of life and economic costs which occur more frequently with the impact of climate change and human activities [1,2]. Hydrological forecasting has been proven to be an effective non-engineering measure to resist flood disasters and reduce losses; its accuracy depends on the input precipitation data [3].…”

Section: Introductionmentioning

confidence: 99%

Post-Processing and Evaluation of Precipitation Ensemble Forecast under Multiple Schemes in Beijiang River Basin

Chen

Huang

et al. 2020

Water

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Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales.

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“…Floods are mostly localized. Besides the need for high spatial resolution to focus on floods, there is also need for temporal resolution such as sub daily time scales to capture the actual flood processes (Chen et al, 2016). By their improved spatial coverage and ability to provide rainfall estimates at recurrent time steps.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of sub daily satellite rainfall estimates through flash flood modelling in the Lower Middle Zambezi Basin

2018

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Abstract. Flash floods are experienced almost annually in the ungauged Mbire District of the Middle Zambezi Basin. Studies related to hydrological modelling (rainfall-runoff) and flood forecasting require major inputs such as precipitation which, due to shortage of observed data, are increasingly using indirect methods for estimating precipitation. This study therefore evaluated performance of CMORPH and TRMM satellite rainfall estimates (SREs) for 30 min, 1 h, 3 h and daily intensities through hydrologic and flash flood modelling in the Lower Middle Zambezi Basin for the period 2013-2016. On a daily timestep, uncorrected CMORPH and TRMM show Probability of Detection (POD) of 61 and 59 %, respectively, when compared to rain gauge observations. The best performance using Correlation Coefficient (CC) was 70 and 60 % on daily timesteps for CMORPH and TRMM, respectively. The best RMSE for CMORPH was 0.81 % for 30 min timestep and for TRMM was 2, 11 % on 3 h timestep. For the year 2014 to 2015, the HEC-HMS (Hydrological Engineering Centre-Hydrological Modelling System) daily model calibration Nash Sutcliffe efficiency (NSE) for Musengezi sub catchment was 59 % whilst for Angwa it was 55 %. Angwa sub-catchment daily NSE results for the period 2015-2016 was 61 %. HEC-RAS flash flood modeling at 100, 50 and 25 year return periods for Angwa sub catchment, inundated 811 and 867 ha for TRMM rainfall simulated discharge at 3 h and daily timesteps, respectively. For CMORPH generated rainfall, the inundation was 818, 876, 890 and 891 ha at daily, 3 h, 1 h and 30 min timesteps. The 30 min time step for CMORPH effectively captures flash floods with the measure of agreement between simulated flood extent and ground control points of 69 %. For TRMM, the 3 h timestep effectively captures flash floods with coefficient of 67 %. The study therefore concludes that satellite products are most effective in capturing localized hydrological processes such as flash floods for sub-daily rainfall, because of improved spatial and temporal resolution.

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Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

Cited by 14 publications

References 38 publications

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Post-Processing and Evaluation of Precipitation Ensemble Forecast under Multiple Schemes in Beijiang River Basin

Evaluation of sub daily satellite rainfall estimates through flash flood modelling in the Lower Middle Zambezi Basin

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