A Study on Parameters of Rainfall Runoff Model and The Prediction Method of Landslide Disaster in Mountainous Area

2020 ◽  
Author(s):  
Huimyeong yoo ◽  
Naoki koyama ◽  
Tadashi yamada
Keyword(s):  
Prediction Method ◽  
Heavy Rain ◽  
Mountainous Area ◽  
Rainfall Runoff ◽  
Mountainous Areas ◽  
Average Amount ◽  
Tank Model ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Landslide Disaster

<p>This study is to analyze the evacuation behavior of residents living in the mountainous area and predict landslide disasters during heavy rain. 70% of Japan has are mountainous areas, and landslide disasters have occurred due to heavy rains caused by typhoons and heavy rainfall, etc. the annual average amount of damage caused by landslide disasters is 1000 in recent years. Also, landslide disaster warning information and evacuation information are important, it is difficult to predict landslide disasters, however, if we issued the evacuation advisory when the disasters already happened, there will be not enough time for the evacuation. In order to protect residents from such disasters, it is important to clarify "what information is effective for evacuation" and "when should those information be released?" Therefore, we conducted a survey on the residents in the mountainous areas which suffered from the heavy rain disaster in 2017 and analyzed the answers.</p><p>As a result, some residents evacuated before the evacuation information was issued. Because some landslide disasters occurred even before the first evacuation information was transmitted, and they felt danger. This result shows that the early information based on the prediction of the disasters is important in mountainous areas.</p><p>Therefore, we suggested a method for predicting landslide disasters, the method uses a rainfall and runoff tank model with high reproducibility and robustness of geological characteristics and uses the cumulative rainfall at the time of disaster occurrence as an index. As a result, this model predicted the occurrence of the landslide disaster 3 hours earlier by using forecasted rainfall. it is an effective method.</p>

scholarly journals 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

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.

scholarly journals Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2257 ◽  
Author(s):  
Jung-Hun Song ◽  
Younggu Her ◽  
Kyo Suh ◽  
Moon-Seong Kang ◽  
Hakkwan Kim
Keyword(s):  
Hydrological Modeling ◽  
Rainfall Runoff ◽  
Tank Model ◽  
Statistical Relationships ◽  
Function Selection ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model ◽  
Selection Of

Regionalized lumped rainfall-runoff (RR) models have been widely employed as a means of predicting the streamflow of an ungauged watershed because of their simple yet effective simulation strategies. Parameter regionalization techniques relate the parameter values of a model calibrated to the observations of gauged watersheds to the geohydrological characteristics of the watersheds. Thus, the accuracy of regionalized models is dependent on the calibration processes, as well as the structure of the model used and the quality of the measurements. In this study, we have discussed the potentials and limitations of hydrological model parameter regionalization to provide practical guidance for hydrological modeling of ungauged watersheds. This study used a Tank model as an example model and calibrated its parameters to streamflow observed at the outlets of 39 gauged watersheds. Multiple regression analysis identified the statistical relationships between calibrated parameter values and nine watershed characteristics. The newly developed regional models provided acceptable accuracy in predicting streamflow, demonstrating the potential of the parameter regionalization method. However, uncertainty associated with parameter calibration processes was found to be large enough to affect the accuracy of regionalization. This study demonstrated the importance of objective function selection of the RR model regionalization.

scholarly journals Coupling Tank Model and Lars-Weather Generator in Assessments of the Impacts of Climate Change on Water Resources

2019 ◽  
Vol 27 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Naser Mohammadzadeh ◽  
Bahman Jabbarian Amiri ◽  
Leila Eslami Endergoli ◽  
Shirin Karimi
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Weather Generator ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
The Caspian Sea ◽  
Tank Model ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract With the aim of assessing the impact of climate change on surface water resources, a conceptual rainfall-runoff model (the tank model) was coupled with LARS-WG as a weather generator model. The downscaled daily rainfall, temperature, and evaporation from LARS-WG under various IPCC climate change scenarios were used to simulate the runoff through the calibrated Tank model. A catchment (4648 ha) located in the southern basin of the Caspian Sea was chosen for this research study. The results showed that this model has a reasonable predictive capability in simulating minimum and maximum temperatures at a level of 99%, rainfall at a level of 93%, and radiation at a level of 97% under various scenarios in agreement with the observed data. Moreover, the results of the rainfall-runoff model indicated an increase in the flow rate of about 108% under the A1B scenario, 101% under the A2 scenario, and 93% under the B1 scenario over the 30-year time period of the discharge prediction.

scholarly journals 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

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.

scholarly journals 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

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.

scholarly journals Improving Efficiency of Hydrological Prediction Based on Meteorological Classification: A Case Study of GR4J Model

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2546
Author(s):  
Xiaojing Wei ◽  
Shenglian Guo ◽  
Lihua Xiong
Keyword(s):  
Prediction Method ◽  
Meteorological Condition ◽  
Meteorological Conditions ◽  
Rainfall Runoff ◽  
Glue Method ◽  
Hydrological Prediction ◽  
Rainfall Runoff Model ◽  
One Year ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Runoff Model

Distribution of hydrological parameters is varied under contrasting meteorological conditions. However, how to determine the most suitable parameters on a predefined meteorological condition is challenging. To address this issue, a hydrological prediction method based on meteorological classification is established, which is conducted by using the standardized runoff index (SRI) value to identify three categories, i.e., the dry, normal and wet years. Three different simulation schemes are then adopted for these categories. In each category, two years hydrological data with similar SRI values are divided into a set; then, one-year data are used as the calibration period while the other year is for testing. The Génie Rural à 4 paramètres Journalier (GR4J) rainfall-runoff model, with four parameters x1, x2, x3 and x4, was selected as an experimental model. The generalized likelihood uncertainty estimation (GLUE) method is used to avoid parameter equifinality. Three basins in Australia were used as case studies. As expected, the results show that the distribution of the four parameters of GR4J model is significantly different under varied meteorological conditions. The prediction efficiency in the testing period based on meteorological classification is greater than that of the traditional model under all meteorological conditions. It is indicated that the rainfall-runoff model should be calibrated with a similar SRI year rather than all years. This study provides a new method to improve efficiency of hydrological prediction for the basin.

scholarly journals Effects of streamflow isotope sampling strategies on the calibration of a tracer‐aided rainfall‐runoff model

2021 ◽  
Author(s):  
Jamie Lee Stevenson ◽  
Christian Birkel ◽  
Aaron J. Neill ◽  
Doerthe Tetzlaff ◽  
Chris Soulsby
Keyword(s):  
Sampling Strategies ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1226
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Rainfall Runoff ◽  
Management Planning ◽  
Runoff Variation ◽  
Groundwater Exchange ◽  
Monthly Runoff ◽  
Rainfall Runoff Model ◽  
Two Parameters ◽  
Runoff Model

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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