The real-time stochastic flow forecast

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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Using hydrological simulation to detect human-disturbed epoch in runoff series

Water Science & Technology ◽

10.2166/wst.2014.526 ◽

2014 ◽

Vol 71 (5) ◽

pp. 691-699 ◽

Cited By ~ 5

Author(s):

G. Q. Wang ◽

J. Y. Zhang ◽

T. C. Pagano ◽

Y. L. Liu ◽

C. S. Liu ◽

...

Keyword(s):

Water Conservation ◽

Rainfall Runoff ◽

The Loess Plateau ◽

Human Influence ◽

Hydrological Simulation ◽

River Catchment ◽

Runoff Series ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Critical Interest

Runoff in major rivers in China has been decreasing in recent decades, mainly due to climate change and human activity. River basin managers have a critical interest in detecting and diagnosing non-stationaries in runoff time series. Here we use a rainfall runoff model-based approach to identify the human-disturbed periods of the record. The method is applied to the Kuye River catchment, located in the Loess Plateau, China. The SimHyd model performs well for simulation of monthly natural discharges, and the method suggests that discernable human influence began in 1980. Anthropogenic effects were detectable several years earlier at the downstream stations than the upstream stations, consistent with pace and timing of soil and water conservation measures implemented across the Kuye River catchment.

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

Journal of Hydroinformatics ◽

10.2166/hydro.2008.007 ◽

2008 ◽

Vol 10 (1) ◽

pp. 23-41 ◽

Cited By ~ 9

Author(s):

Yongdae Lee ◽

Sheung-Kown Kim ◽

Ick Hwan Ko

Keyword(s):

Stochastic Model ◽

Reservoir Operation ◽

Lead Time ◽

Weather Prediction ◽

Prediction System ◽

Operation Planning ◽

Rainfall Runoff ◽

Multi Stage ◽

Rainfall Runoff Model ◽

Runoff Model

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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Model analysis of forest thinning impacts on the water resources during hydrological drought periods

10.22541/au.161760827.71851449/v1 ◽

2021 ◽

Author(s):

Hiroki Momiyama ◽

Tomo'omi Kumagai ◽

Tomohiro Egusa

Keyword(s):

Water Resources ◽

Meteorological Data ◽

Rainfall Runoff ◽

Forest Thinning ◽

Flow Duration ◽

Mountain Catchment ◽

Data Generator ◽

Flow Duration Curves ◽

Rainfall Runoff Model ◽

Runoff Model

In Japan, there has recently been an increasing call for forest thinning to conserve water resources from forested mountain catchments in terms of runoff during prolonged drought periods of the year. How their water balance and the resultant runoff are altered by forest thinning is examined using a combination of 8-year hydrological observations, 100-year meteorological data generator output, and a semi-process-based rainfall-runoff model. The rainfall-runoff model is developed based on TOPMODEL assuming that forest thinning has an impact on runoff primarily through an alteration in canopy interception. The main novelty in this analysis is that the availability of the generated 100-year meteorological data allows the investigations of the forest thinning impacts on mountain catchment water resources under the most severer drought conditions. The model is validated against runoff observations conducted at a forested mountain catchment in the Kanto region of Japan for the period 2010–2017. It is demonstrated that the model reproduces temporal variations in runoff and evapotranspiration at inter- and intra-annual time scales, resulting in well reproducing the observed flow duration curves. On the basis of projected flow duration curves for the 100-year, despite the large increase in an annual total runoff with ordinary intensifying thinning, low flow rates, i.e., water resources from the catchment in the drought period in the year, in both normal and drought years were impacted by the forest thinning to a lesser extent. Higher catchment water retention capacity appreciably enhanced the forest thinning effect on increasing available water resources.

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Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations

Water Resources Management ◽

10.1007/s11269-019-02368-8 ◽

2019 ◽

Vol 33 (13) ◽

pp. 4509-4524 ◽

Cited By ~ 1

Author(s):

Adam P. Piotrowski ◽

Marzena Osuch ◽

Jarosław J. Napiorkowski

Keyword(s):

Error Correction ◽

Meteorological Data ◽

Optimization Procedure ◽

Model Error ◽

Joint Optimization ◽

Data Series ◽

Model Parameters ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract Conceptual lumped rainfall-runoff models are frequently used for various environmental problems. To put them into practice, both the model calibration method and data series of the area-averaged precipitation and air temperature are needed. In the case when data from more than one measurement station are available, first the catchment-averaged meteorological data series are usually obtained by some method, and then they are used for calibration of a lumped rainfall-runoff model. However, various optimization methods could easily be applied to simultaneously calibrate both the aggregation weights attributed to various meteorological stations to obtain a lumped meteorological data series and the rainfall-runoff model parameters. This increases the problem dimensionality but allows the optimization procedure to choose the data that are most important for the rainfall-runoff process in a particular catchment, without a priori assumptions. We test the idea using two conceptual models, HBV and GR4J, and three mutually different, relatively recently proposed Evolutionary Computation and Swarm Intelligence optimization algorithms, that are applied to three catchments located in Poland and northwestern USA. We consider two cases: with and without the model error correction applied to the rainfall-runoff models. It is shown that for the calibration period, joint optimization of the weights used to aggregate the meteorological data and the parameters of the rainfall-runoff model improves the results. However, the results for the validation period are inconclusive and depend on the model, error correction, optimization algorithm, and catchment.

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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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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.

Download Full-text