Multistage stochastic linear programming model for daily coordinated multi-reservoir operation

Operation planning for a coordinated multi-reservoir is a complex and challenging task due to the inherent uncertainty in inflow. In this study, we suggest the use of a new, multi-stage and scenario-based stochastic linear program with a recourse model incorporating the meteorological weather prediction information for daily, coordinated, multi-reservoir operation planning. Stages are defined as prediction lead-time spans of the weather prediction system. The multi-stage scenarios of the stochastic model are formed considering the reliability of rainfall prediction for each lead-time span. Future inflow scenarios are generated by a rainfall–runoff model based on the rainfall forecast. For short-term stage (2 days) scenarios, the regional data assimilation and prediction system (RDAPS) information is employed, and for mid-term stage (more than 2 days) scenarios, precipitation from the global data assimilation and prediction system (GDAPS) is used as an input for the rainfall–runoff model. After the 10th day (third stage), the daily historical rainfall data are used following the ensemble streamflow prediction (ESP) procedure. The model is applied to simulate the daily reservoir operation of the Nakdong River basin in Korea in a real-time operational environment. The expected benefit of the stochastic model is markedly superior to that of the deterministic model with average rainfall information. Our study results confirm the effectiveness of the stochastic model in real-time operation with meteorological forecasts and the presence of inflow uncertainty.

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Uncertainty Evaluation in a Flood Forecasting Model Using JMA Numerical Weather Prediction

Journal of Disaster Research ◽

10.20965/jdr.2009.p0272 ◽

2009 ◽

Vol 4 (4) ◽

pp. 600-605 ◽

Cited By ~ 5

Author(s):

Hadi Kardhana ◽

◽

Akira Mano ◽

Keyword(s):

Numerical Weather Prediction ◽

Lead Time ◽

Weather Prediction ◽

Japan Meteorological Agency ◽

Rainfall Runoff ◽

Flood Prediction ◽

Numerical Weather ◽

Forecast Lead Time ◽

Rainfall Runoff Model ◽

Runoff Model

Numerical weather prediction (NWP) is useful in flood prediction using a rainfall-runoff model. Uncertainty occurring in the forecast, however, adversely affects flood prediction accuracy, in addition to uncertainty inherent in the rainfall-runoff model. Clarifying this uncertainty and its magnitude is expected to lead to wider forecast applications. Taking the case of Japan’s Shichikashuku Dam, 6 flood events between 2002 and 2007 were analyzed. NWP was based on short-range forecasts by the Japan Meteorological Agency (JMA). The rainfall-runoff model is based on a distributed tank model. This research calculates uncertainty by identifying and quantifying the relative error of forecasts by a) NWP and b) the runoff model. Results showed that NAP is the main cause of flood forecast uncertainty. They also showed the correlation between forecast lead time and uncertainty. Uncertainty rises with longer lead time, corresponding to the magnitude of observed discharge and precipitation.

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DEVELOPMENT OF DAM INFLOW PREDICTION SYSTEM BASED ON DISTRIBUTED RAINFALL-RUNOFF MODEL

PROCEEDINGS OF HYDRAULIC ENGINEERING ◽

10.2208/prohe.50.289 ◽

2006 ◽

Vol 50 ◽

pp. 289-294 ◽

Cited By ~ 1

Author(s):

Noriaki HASHIMOTO ◽

Akira FUJITA ◽

Michiharu SHIIBA ◽

Yasuto TACHIKAWA ◽

Yutaka ICHIKAWA

Keyword(s):

Prediction System ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Dam Inflow ◽

Inflow Prediction ◽

Runoff Model

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Ensemble Flash Flood Predictions Using a High-Resolution Nationwide Distributed Rainfall-Runoff Model: Case Study of the Heavy Rain Event of July 2018 and Typhoon Hagibis in 2019

10.21203/rs.3.rs-40714/v2 ◽

2020 ◽

Author(s):

Takahiro Sayama ◽

Masafumi Yamada ◽

Yoshito Sugawara ◽

Dai Yamazaki

Keyword(s):

Lead Time ◽

Flash Flood ◽

Heavy Rain ◽

Flash Floods ◽

Rain Event ◽

Rainfall Runoff ◽

Heavy Rain Event ◽

Peak Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events are investigated by using a high-resolution (~150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39-h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.

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Assessment of Satellite and Reanalysis Precipitation Products for Rainfall–Runoff Modelling in a Mountainous Basin

Environmental Sciences Proceedings ◽

10.3390/ecas2021-10345 ◽

2021 ◽

Vol 8 (1) ◽

pp. 25

Author(s):

Hamed Hafizi ◽

Ali Arda Sorman

Keyword(s):

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Rainfall Runoff ◽

Precipitation Measurement ◽

Gauge Data ◽

Low Performance ◽

Rainfall Runoff Model ◽

Mountainous Basin ◽

Runoff Model ◽

Precipitation Events

Precipitation measurement over a complex topography and highly elevated regions has always been a great challenge in recent decades. On the other hand, satellite-based and numerical weather prediction model outputs can be an alternative to fill this gap. Hence, the goal of this study is to evaluate the spatiotemporal stability and hydrologic utility of four precipitation products (TMPA-3B42v7, IMERGHHFv06, ERA5, and PERSIANN) over a mountainous basin (Karasu basin) located in the eastern part of Turkey. Moreover, the Kling–Gupta efficiency (KGE), including its correlation, bias, and variability ratio components, are used for a direct comparison of precipitation products (PPs) with observed gauge data, and the Hansen–Kuiper (HK) score is utilized to assess the detectability strength of PPs for different precipitation events. In the same way, the hydrologic utility of PPs is tested by exploiting a conceptual rainfall–runoff model under Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) metrics. Generally, all PPs show low performance for a direct comparison with observed data while their performance considerably increases for streamflow simulation. TMPA-3B42v7 has high reproducibility in streamflow (KGE = 0.84), followed by IMERGHHFv06 (KGE = 0.76), ERA5 (KGE = 0.75), and PERSIANN (KGE = 0.70), for the entire period (2015–2019) of this study.

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The real-time stochastic flow forecast

Soil and Water Research ◽

10.17221/13/2009-swr ◽

2010 ◽

Vol 5 (No. 2) ◽

pp. 49-57 ◽

Cited By ~ 4

Author(s):

L. Březková ◽

M. Starý ◽

P. Doležal

Keyword(s):

Lead Time ◽

Meteorological Data ◽

Rainfall Runoff ◽

Hydrological Simulation ◽

The Czech Republic ◽

Precipitation Prediction ◽

The Monte Carlo Method ◽

Rainfall Runoff Model ◽

River Profiles ◽

Runoff Model

In the Czech Republic, deterministic flow forecasts with the lead time of 48 hours, calculated by rainfall-runoff models for basins of a size of several hundreds to thousands square kilometers, are nowadays a common part of the operational hydrological service. The Czech Hydrometeorological Institute (CHMI) issues daily the discharge forecast for more than one hundred river profiles. However, the causal rainfall is a random process more than a deterministic one, therefore the deterministic discharge forecast based on one precipitation prediction is a significant simplification of the reality. Since important decisions must be done during the floods, it is necessary to take into account the indeterminity of the input meteorological data and to express the uncertainty of the resulting discharge forecast. In the paper, a solution of this problem is proposed. The time series of the input precipitation prediction data have been generated repeatedly (by the Monte Carlo method) and, subsequently, the set of discharge forecasts based on the repeated hydrological model simulations has been obtained and statistically evaluated. The resulting output can be, for example, the range of predicted peak discharges, the peak discharge exceeding curve or the outflow volume exceeding curve. The properties of the proposed generator have been tested with acceptable results on several flood events which occurred over the last years in the upper part of the Dyje catchment (Podhradí closing profile). The rainfall-runoff model HYDROG, which has been in operation in CHMI since 2003, was used for hydrological simulation.

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Ensemble Flash Flood Predictions Using a High-Resolution Nationwide Distributed Rainfall-Runoff Model: Case Study of the Heavy Rain Event of July 2018 and Typhoon Hagibis in 2019

10.21203/rs.3.rs-40714/v1 ◽

2020 ◽

Author(s):

Takahiro Sayama ◽

Masafumi Yamada ◽

Yoshito Sugawara ◽

Dai Yamazaki

Keyword(s):

Lead Time ◽

Flash Flood ◽

Heavy Rain ◽

Flash Floods ◽

Rain Event ◽

Rainfall Runoff ◽

Heavy Rain Event ◽

Peak Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events are investigated by using a high-resolution (~ 150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39-h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto region. The performance of the ensemble forecast was evaluated based on the bias scores and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.

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A REAL TIME DAM INFLOW PREDICTION SYSTEM BASED ON DISTRIBUTED RAINFALL-RUNOFF MODEL

PROCEEDINGS OF HYDRAULIC ENGINEERING ◽

10.2208/prohe.45.115 ◽

2001 ◽

Vol 45 ◽

pp. 115-120 ◽

Cited By ~ 1

Author(s):

Akira FUJITA ◽

Hidemitsu DAITOU ◽

Kaoru KAMISAKA ◽

Michiharu SHIIBA ◽

Yasuto TACHIKAWA ◽

...

Keyword(s):

Real Time ◽

Prediction System ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Dam Inflow ◽

Inflow Prediction ◽

Runoff Model

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Ensemble flash flood predictions using a high-resolution nationwide distributed rainfall-runoff model: case study of the heavy rain event of July 2018 and Typhoon Hagibis in 2019

Progress in Earth and Planetary Science ◽

10.1186/s40645-020-00391-7 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Takahiro Sayama ◽

Masafumi Yamada ◽

Yoshito Sugawara ◽

Dai Yamazaki

Keyword(s):

Lead Time ◽

Flash Flood ◽

Heavy Rain ◽

Flash Floods ◽

Rain Event ◽

Rainfall Runoff ◽

Heavy Rain Event ◽

Peak Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

AbstractThe heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events is investigated by using a high-resolution (~ 150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39 h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto Region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.

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