Using ensemble precipitation forecasts and a rainfall-runoff model for hourly reservoir inflow forecasting during typhoon periods

Lee, Kwan Tun; Ho, Jui-Yi; Kao, Hong Ming; Lin, Gwo‐Fong; Yang, Tsun-Hua

doi:10.1016/j.jher.2018.05.002

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…Many recent studies worldwide and in Japan have focused on simulating river water or inundation levels during heavy rainfall events using various approaches and methodologies. One frequently applied methodology is using physically distributed hydrological modeling, which can be further classified into modeling with kinematic wave-based methods [47][48][49][50][51][52], dynamic wave momentum equation methods [14,22,53], diffusive wave transfer methods [13,44,45,[54][55][56][57] and multimodel comparisons from lumped to fully distributed [19]. Among them, refs.…”

Section: Recent Hydrological Studies On Heavy Rainfall Disastersmentioning

confidence: 99%

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Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations

Trošelj,

Lee,

Hobohm

2023

Sustainability

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This study marks a significant step toward the future development of river discharges forecasted in real time for flash flood early warning system (EWS) disaster prevention frameworks in the Chugoku region of Japan, and presumably worldwide. To reduce the disaster impacts with EWSs, accurate integrated hydrometeorological real-time models for predicting extreme river water levels and discharges are needed, but they are not satisfactorily accurate due to large uncertainties. This study evaluates two calibration methods with 7 and 5 parameters using the hydrological Cell Distributed Runoff Model version 3.1.1 (CDRM), calibrated by the University of Arizona’s Shuffled Complex Evolution optimization method (SCE-UA). We hypothesize that the proposed ensemble hydrological parameter calibration approach can forecast similar future events in real time. This approach was applied to seven major rivers in the region to obtain hindcasts of the river discharges during the Heavy Rainfall Event of July 2018 (HRE18). This study introduces a new historical extreme rainfall event classification selection methodology that enables ensemble-averaged validation results of all river discharges. The reproducibility metrics obtained for all rivers cumulatively are extremely high, with Nash–Sutcliffe efficiency values of 0.98. This shows that the proposed approach enables accurate predictions of the river discharges for the HRE18 and, similarly, real-time forecasts for future extreme rainfall-induced events in the Japanese region. Although our methodology can be directly reapplied only in regions where observed rainfall data are readily available, we suggest that our approach can analogously be applied worldwide, which indicates a broad scientific contribution and multidisciplinary applications.

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Section: Recent Hydrological Studies On Heavy Rainfall Disastersmentioning

confidence: 99%

“…Among them, refs. [14,19,22,48,51,52,56] recommended the possible application of their modeling to real-time forecasting flood events using various approaches and lead times. Refs.…”

Section: Recent Hydrological Studies On Heavy Rainfall Disastersmentioning

confidence: 99%

Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations

Trošelj,

Lee,

Hobohm

2023

Sustainability

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“…Taiwan's geographical position is between 21 O 45' 25" to 25 O 56' 31" north latitude and 119 O 18' 3" to 124 O 34' 30" east longitude [21]. The average annual rainfall in Taiwan is 2515 mm, around 2.6 times the global average, which frequently occurs in the period between May and October [22]. Tropical storms regularly occur in Taiwan during typhoon seasons and signi cantly increased sediments and nutrients in the reservoirs [23].…”

Section: Description Of the Study Areamentioning

confidence: 99%

Eutrophication Factor Analysis Using Carlson Trophic State Index (CTSI) Towards Non-Algal Impact Reservoirs in Taiwan

Lin

Karangan

Huang

et al. 2021

Preprint

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Carlson trophic state index (CTSI) has been commonly adopted to assess the eutrophication potential of reservoirs or lakes in water quality management. This study aims to analyze the influencing factors of CTSI-based eutrophication by using Pearson correlation analysis and principal component analysis (PCA) with long-term data from 2008 to 2019 on 21 drinking water reservoirs in Taiwan. The trophic state index (TSI) deviation indicates that most drinking water reservoirs in Taiwan, around 45.5% of statistical data fall into non-algal turbidity with surplus phosphorus, especially in the spring and winter season. Besides, there are about 78 % of total collected data show that TSI (Chl-a) is less than TSI (SD) due to the small particulate predominance. On the other hand, three TSI variables (Secchi depth (SD), total phosphorus (TP) and chlorophyll-a (Chl-a)) of CTSI exhibits insignificant correlation to each other in most cases. At such a condition, the probability of eutrophication (TSI>50) based on TSI (SD) is 63%, while it is only as low as 20% based on TSI (TP) and TSI (Chl-a). The influencing factors of eutrophication variables by suspended solids (SS) composition and turbidity have shown that the SD is strongly influenced by non-algal SS. The deviations of three TSI have shown that the highest algae-induced eutrophication potential occurs in the summer season. In addition, the TP is the most significant loading factor of algae-induced eutrophication for drinking water reservoirs. It is concluded that the CTSI has limited applicability to identify the trophic status of drinking water reservoirs in Taiwan in the presence of sustainable non-algal turbidity comparative Chl-a that completely represents algal growth potential (AGP).

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“…Several researches have demonstrated that probabilistic flood prediction can play a crucial role in ensuring a more conscious decision-making and considering the uncertainty of meteorological phenomena, both globally and locally (e.g. Moore et al 2005;Verbunt et al 2007;Pedrozo-Acuña et al 2013;Revilla-Romero et al 2015;Emerton et al 2016;Lee et al 2018). Among the new research methodologies and techniques for flood risk reduction and mitigation based on EPS are those used by the European Centre for Medium-Range Weather Forecasts (ECMWF, https://www.ecmwf.int/) and the Joint Research Centre (JRC), which consists of forecasting systems for climate variables at the global scale, in order to provide information in an emergency situation to grant an adequate response time and also an effective tool for forecasters and decision makers.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of ECMWF's forecasting system for probabilistic urban flood prediction: a case study in Mexico City

López

Acuña

Covarrubias

2021

Journal of Hydroinformatics

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As the world continues urbanizing, including efforts to forge a new framework of urban development is necessary. Recent studies related to flood prediction and mitigation have shown that Ensemble Prediction Systems (EPSs) constitute a valuable and essential tool for an Early Warning System. However, the use of EPS for flood forecasting in urban zones has yet to be understood. This work has the objective to investigate the potential use of the Operational EPS, issued by the European Centre for Medium-Range Weather Forecasts (ECMWF), for probabilistic urban flood prediction. In this research, a precipitation forecast verification was carried out in two study zones: (1) Mexico Valley Basin and (2) Mexico City, where for the latter, forecasts were compared against real-time observed data. The results showed good forecast reliability for a rain threshold of up to 20 mm in 24-hourly accumulations, with the first 36 h of the forecast horizon being the most reliable. The EPS has sufficient resolution and precision for flood prediction in Mexico City, which represents a further step toward developing a flood warning system at the local level based on ensemble forecasts.

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Using ensemble precipitation forecasts and a rainfall-runoff model for hourly reservoir inflow forecasting during typhoon periods

Cited by 10 publications

References 21 publications

Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations

Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations

Eutrophication Factor Analysis Using Carlson Trophic State Index (CTSI) Towards Non-Algal Impact Reservoirs in Taiwan

Evaluation of ECMWF's forecasting system for probabilistic urban flood prediction: a case study in Mexico City

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