Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

Wang, Yu; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

doi:10.1016/j.jhydrol.2016.05.047

Cited by 12 publications

(3 citation statements)

References 38 publications

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“…In the past few years, many researchers have made efforts to address this problem in ungauged or poorly gauged river basins (e.g., Bardossy, 2007;Hundecha et al, 2008;Bulygina et al, 2012;Woldemeskel et al, 2013;Shi et al, 2015;Yu et al, 2016;Gao et al, 2017;Garambois et al, 2017;Yoo et al, 2017), mainly focusing on the following two types of methods. The first type is the methods based on regional information representing river basin characteristics (e.g., Bardossy, 2007;Hundecha et al, 2008;Bulygina et al, 2012;Yoo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the global-scale satellite-based meteorological datasets have been developed rapidly with the development of science and technology, which are regarded to be an effective supplement for the gauge-station measurements (e.g., Pappenberger et al, 2008;Li et al, 2012;Woldemeskel et al, 2013). Hence, the second type is the methods based on the utilization of the satellite-based meteorological datasets (e.g., Shi et al, 2015;Yu et al, 2016;Gao et al, 2017). For the river basins with poor historical observations, especially for the ungauged river basins, the satellite-based meteorological datasets may provide the necessary input data to overcome the problem of lacking data when estimating the hydrological parameters through calibration and validation of models (e.g., Sun et al, 2012;Maswood and Hossain, 2016;Garambois et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Estimating hydrological parameters based on rainfall patterns in river basins with no long-term historical observations

Shi

2017

Journal of Hydrology

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Small and medium river basins may frequently suffer from the destructive hydrological extremes (e.g., floods). However, the common problem in such regions is a lack of longterm historical observations. Meteorological and hydrological station networks in some river basins in China were newly-built only a few years ago, and it is infeasible to estimate hydrological parameters from calibration and validation with a long time period directly. This paper aims to develop a method to estimate the feasible hydrological parameters based on rainfall patterns in such regions. Digital Yellow River Integrated Model (DYRIM) is adopted as the hydrological model, and the feasible hydrological parameters can be estimated based on limited rainfall-runoff events. First, for each rainfall-runoff event, the parameters are independently calibrated with the observed rainfall and hydrological data using a double-layer parallel system. Then, the performances of the simulation results are comprehensively evaluated, and the value ranges of the parameters can be obtained. Finally, the statistical relationships between hydrological parameters and rainfall patterns (i.e., amount and intensity) are established, which are expressed by the statistical equations and the distribution of hydrological parameters with the rainfall patterns. From a sample demonstration, it is concluded that this parameter estimation method will be useful to estimate the feasible hydrological parameters for future rainfall-runoff events in river basins with no long-term historical observations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating hydrological parameters based on rainfall patterns in river basins with no long-term historical observations

Shi

2017

Journal of Hydrology

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“…The ensemble of NWP and hydrological modelling is increasing fastly in operational hydrological forecast due to mainly its capacity of using available data and models in an objective approach. These methodologies also allow an enhance in accuracy and lead time of flood forecast (Seo et al, 2014;Yu et al, 2016). Even though uncertainties still remain and arises mainly from NWP rainfall estimates (Siddique et al, 2015), hydrometeorological data, and the hydrological models structure (Kauffeldt et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Coupling Numerical Weather Prediction and Hydrological Modelling for Runoff Forecast in Southern Brazil

Bertoncini¹,

Oliveira²,

Chaffe³

et al. 2016

Rev. Bras. Geog. Fis.

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Hydrological modelling is largely applied to forecast the effects of extreme rainfall on runoff dynamics. Little research has been done on coupling numerical weather prediction (NWP) and hydrological modelling to forecast runoff in southern Brazil given the limited hydrometeorological and NWP data available for the region. In this study, we evaluate a coupling method of NWP and a semi-distributed hydrological model to forecast runoff for a humid subtropical catchment (286 km²) in southern Brazil, state of Santa Catarina, municipality of Vidal Ramos. The HEC-HMS hydrological model was manually calibrated and validated using data of 2013. Four post-processing techniques of rainfall estimates were applied on rainfall products of two NWP models (BRAMS and GFS), providing rainfall input data for forecasting runoff. Then, a scenario of runoff forecast of an event in June 2014 was analyzed. We found the tested coupling method was able to forecast runoff with a lead time of 24 hours in which the best post-processing technique of rainfall is the "poor man ensemble" between the 90 th quantile sampling of BRAMS and the mean of the GFS product. Hence, the validation of NWP products must be done carefully to avoid the propagation of uncertainties through a flood forecasting system. Acoplamento de previsão numérica da atmosfera e modelagem hidrológica para a previsão do escoamento superficial no sul do Brasil R E S U M OA modelagem hidrológica é amplamente aplicada para a previsão dos efeitos de chuvas extremas na dinâmica do escoamento superficial. Entretanto, pouca pesquisa tem sido feita no acoplamento da previsão numérica da atmosfera (NWP) e modelagem hidrológica no Sul do Brasil, dada a limitação imposta pela falta de dados hidrometeorológicos e oriundos de NWPs na região. Neste estudo, são avaliados métodos de acoplamento entre NWPs e modelagem hidrológica semi-distribuída para a previsão do escoamento superficial de uma bacia hidrográfica subtropical úmida (286 km 2 ) no Sul do Brasil, estado de Santa Catarina, município de Vidal Ramos. O modelo hidrológico HEC-HMS foi calibrado e validado manualmente com dados do ano de 2013. Quatro técnicas de pós-processamento de estimativas de chuva foram aplicadas em dois produtos de modelos de NWPs (BRAMS e GFS), que foram utilizados como dados de entrada para a previsão do escoamento superficial. Por fim, um cenário de previsão do escoamento superficial foi analisado em Junho de 2014. O método de acoplamento utilizado neste estudo foi capaz de prever o escoamento superficial com antecedência de 24 horas, onde o melhor método de pós-processamento da chuva foi o "poor man ensemble" entre a amostragem do 90 o quartil do produto BRAMS e a média do GFS. Dessa forma, é importante ressaltar que o procedimento de validação dos produtos de NWPs deve ser realizado cuidadosamente para evitar a propagação de incertezas em um sistema de previsão do escoamento superficial. Palavras-chave: Modelagem Hidrológica, Previsão Numérica da Atmosfera, Previsão do Escoamento Superficial, Técnicas de Pó...

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Ensemble flood forecasting: Current status and future opportunities

et al. 2020

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Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication.

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Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

Cited by 12 publications

References 38 publications

Estimating hydrological parameters based on rainfall patterns in river basins with no long-term historical observations

Estimating hydrological parameters based on rainfall patterns in river basins with no long-term historical observations

Coupling Numerical Weather Prediction and Hydrological Modelling for Runoff Forecast in Southern Brazil

Ensemble flood forecasting: Current status and future opportunities

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