scholarly journals Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

2016 ◽  
Vol 16 (8) ◽  
pp. 1821-1839 ◽  
Author(s):  
Kenichiro Kobayashi ◽  
Shigenori Otsuka ◽  
Kazuo Saito ◽  
Keyword(s):  
Flood Control ◽  
Threshold Value ◽  
Ensemble Member ◽  
Japan Meteorological Agency ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Nonhydrostatic Model ◽  
Discharge Simulation ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.

scholarly journals Ensemble flood forecasting to support dam water release operation using 10 and 2 km-resolution JMA Nonhydrostatic Model ensemble rainfalls

2015 ◽  
Vol 3 (12) ◽  
pp. 7411-7456
Author(s):  
K. Kobayashi ◽  
S. Otsuka ◽  
K. Saito ◽  
Keyword(s):  
Flood Control ◽  
Flood Forecasting ◽  
Ensemble Member ◽  
Japan Meteorological Agency ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Water Release ◽  
Nonhydrostatic Model ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency Nonhydrostatic Model are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolution. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km-resolution JMA-NHM ensemble simulation are more appropriate than the 10 km-resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1; a threshold value for flood control. The inflows with the 10 km-resolution ensemble rainfall are all considerably smaller than the observations, while, at least one simulated discharge out of 11 ensemble members with the 2 km-resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls show much better results than the original ensemble discharge simulations.

scholarly journals Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls. Part 2: Flood forecasting using 1600 member 4D-EnVAR predicted rainfalls

2019 ◽  
Author(s):  
Kenichiro Kobayashi ◽  
Le Duc ◽  
Tsutao Oizumi ◽  
Kazuo Saito ◽  
Keyword(s):  
Flood Control ◽  
Optimization Technique ◽  
Flood Forecasting ◽  
Rainfall Runoff ◽  
Flood Simulation ◽  
Nonhydrostatic Model ◽  
Rainfall Runoff Model ◽  
Inflow Discharge ◽  
Runoff Model ◽  
Assimilation System

Abstract. This paper elaborated the feasibility of flood forecasting using a distributed rainfall-runoff model and huge number of ensemble rainfalls with an advanced data assimilation system. Specifically, 1600 ensemble rainfalls simulated by a four-dimensional ensemble variational assimilation system with the JMA nonhydrostatic model (4D-EnVAR-NHM) were given to the rainfall-runoff model to simulate the inflow discharge to a small dam catchment (Kasahori dam; approx. 70 km2) in Niigata, Japan. The results exhibited that the ensemble flood forecasting can indicate the necessity of flood control operation and emergency flood operation with the occurrence probability and a lead time (e.g. 12 hours). Thus, the ensemble flood forecasting may be able to inform us the necessity of the early evacuation of the inhabitant living downstream of the dam e.g. half day before the occurrence. On the other hand, the results also showed that the exact forecasting to reproduce the discharge hydrograph several hours before the occurrence is yet difficult, and some optimization technique is necessary such as the selection of the good ensemble members.

scholarly journals Sensitivity Analysis to Investigate the Reliability of the Grid-Based Rainfall-Runoff Model

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1839 ◽  
Author(s):  
Mun-Ju Shin ◽  
Yun Choi
Keyword(s):  
Water Movement ◽  
Parameter Sensitivity ◽  
Low Flow ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rainfall Events ◽  
Flow Calculation ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Grid Based

This study aimed to assess the suitability of the parameters of a physically based, distributed, grid-based rainfall-runoff model. We analyzed parameter sensitivity with a dataset of eight rainfall events that occurred in two catchments of South Korea, using the Sobol’ method. Parameters identified as sensitive responded adequately to the scale of the rainfall events and the objective functions employed. Parameter sensitivity varied depending on rainfall scale, even in the same catchment. Interestingly, for a rainfall event causing considerable runoff, parameters related to initial soil saturation and soil water movement played a significant role in low flow calculation and high flow calculation, respectively. The larger and steeper catchment exhibited a greater difference in parameter sensitivity between rainfall events. Finally, we found that setting an incorrect parameter range that is physically impossible can have a large impact on runoff simulation, leading to substantial uncertainty in the simulation results. The proposed analysis method and the results from our study can help researchers using a distributed rainfall-runoff model produce more reliable analysis results.

scholarly journals Preliminary Study of Computational Time Steps in a Physically Based Distributed Rainfall–Runoff Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1269 ◽  
Author(s):  
Yun Choi ◽  
Mun-Ju Shin ◽  
Kyung Kim
Keyword(s):  
Optimal Parameter ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Stream Network ◽  
Time Step ◽  
Simulation Results ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results.

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

2009 ◽  
Vol 4 (4) ◽  
pp. 600-605 ◽  
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.

scholarly journals Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls – Part 2: Flood forecasting using 1600-member 4D-EnVar-predicted rainfalls

2020 ◽  
Vol 20 (3) ◽  
pp. 755-770
Author(s):  
Kenichiro Kobayashi ◽  
Le Duc ◽  
Tsutao Oizumi ◽  
Kazuo Saito ◽  
Keyword(s):  
Lead Time ◽  
Extreme Event ◽  
Flood Forecasting ◽  
Ensemble Member ◽  
Japan Meteorological Agency ◽  
Ensemble Forecasts ◽  
Nonhydrostatic Model ◽  
Noteworthy Feature ◽  
Rainfall Runoff Model ◽  
Near Future

Abstract. This paper is a continuation of the authors' previous paper (Part 1) on the feasibility of ensemble flood forecasting for a small dam catchment (Kasahori dam; approx. 70 km2) in Niigata, Japan, using a distributed rainfall–runoff model and rainfall ensemble forecasts. The ensemble forecasts were given by an advanced four-dimensional, variational-ensemble assimilation system using the Japan Meteorological Agency nonhydrostatic model (4D-EnVar-NHM). A noteworthy feature of this system was the use of a very large number of ensemble members (1600), which yielded a significant improvement in the rainfall forecast compared to Part 1. The ensemble flood forecasting using the 1600 rainfalls succeeded in indicating the necessity of emergency flood operation with the occurrence probability and enough lead time (e.g., 12 h) with regard to an extreme event. A new method for dynamical selection of the best ensemble member based on the Bayesian reasoning with different evaluation periods is proposed. As the result, it is recognized that the selection based on Nash–Sutcliffe efficiency (NSE) does not provide an exact discharge forecast with several hours lead time, but it can provide some trend in the near future.

Rainfall-Discharge Simulation in Bah Bolon Catchment Area by Mock Method, NRECA Method, and GR2M Method

2016 ◽  
Vol 845 ◽  
pp. 24-29 ◽  
Author(s):  
Hadiani Rintis ◽  
Suyanto ◽  
Yosephina Puspa Setyoasri
Keyword(s):  
Catchment Area ◽  
The Other ◽  
Series Data ◽  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Flow Duration ◽  
Discharge Simulation ◽  
Rainfall Runoff Model ◽  
North Sumatra ◽  
Runoff Model

Rainfall-discharge simulation is a process transformation from rainfall to discharge in a catchment area by modelling. The most popular models are Mock method and NRECA method. It is according to the handbook of irrigation that is written by government (Indonesia). GR2M (Global Rainfall-Runoff Model) is a new model that is not usual to be used in Indonesia. GR2M is a simulation model that needs less parameter than Mock and NRECA methods. This research was conducted in the Bah Bolon catchment area, Simalungun, North Sumatra. It will analyze the simulation of rainfall-discharge by three methods, Mock, NRECA, and GR2M without considering whether the watershed was wet or dry watershed. The analysis was computed the dependable discharge by flow duration curve (fdc) in a series data on each method. The parameter that compared was the dependable discharge, i.e. the discharge with probability 70% (Q70), probability 80% (Q80), and probability 90% (Q90). GR2M will compared with Mock, then compared with NRECA. The results show that the discharge simulation by GR2M methods and the discharge simulation by Mock method has correlation 0.968. The discharge simulation by GR2M method and the discharge simulation by NRECA method has correlation 0,955. It means that GR2M close to the both of them, but GR2M can used easily because it has less parameter than the other. Based on the graphic, GR2M close to the Mock method for probability more than 50%. So, if the probability is 70%, 80%, and 90%, then GR2M method close to Mock method.

Investigation of marginal distribution and dependence structure of simulated streamflow by a rainfall-runoff model

2020 ◽  
Author(s):  
Vasileios Kourakos ◽  
Theano Iliopoulou ◽  
Panayiotis Dimitriadis ◽  
Demetris Koutsoyiannis ◽  
Vassilios Kaleris ◽  
...  
Keyword(s):  
Marginal Distribution ◽  
Dependence Structure ◽  
Rainfall Time Series ◽  
State Variables ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rainfall Runoff Model ◽  
Stochastic Properties ◽  
Runoff Model

<p>Runoff simulation using hydrological models has a key role in water resources<br>management. Thus, there is a need to investigate how rainfall-runoff models preserve<br>the stochastic characteristics of real-world streamflow data. It is also useful to compare<br>the stochastic properties of output with those of input processes (rainfall) and internal<br>state variables (soil moisture), with focus on marginal distribution tails and long-term<br>persistence. To this aim, we perform a case study using the ENNS rainfall-runoff model<br>with real and synthetic rainfall time series, and for all processes we study the marginal<br>distributions and the dependence structures. In the analyses we use recently developed<br>stochastic tools such as K-moments and climacograms.</p>

scholarly journals Hydrologic comparison between a lowland catchment (Kielstau, Germany) and a mountainous catchment (XitaoXi, China) using KIDS model in PCRaster

2009 ◽  
Vol 21 ◽  
pp. 125-130 ◽  
Author(s):  
X. Zhang ◽  
G. Hörmann ◽  
N. Fohrer
Keyword(s):  
Weather Conditions ◽  
Rainfall Runoff ◽  
Northern Germany ◽  
Wetland Area ◽  
Mountainous Catchment ◽  
Discharge Simulation ◽  
Physical Background ◽  
Rainfall Runoff Model ◽  
Mountainous Basin ◽  
Runoff Model

Abstract. The KIDS model (Kielstau Discharge Simulation model) is a simple rainfall-runoff model developed originally for the Kielstau catchment. To extend its range of application we applied it to a completely different catchment, the XitaoXi catchment in China. Kielstau is a small (51 km2) lowland basin in Northern Germany, with large proportion of wetland area. And XitaoXi is a mesoscale (2271 km2) mountainous basin in the south of China. Both catchments differ greatly in size, topography, landuse, soil properties, and weather conditions. We compared two catchments in these features and stress on the analysis how the specific catchment characteristics could guide the adaptation of KIDS model and the parameter estimation for streamflow simulation. The Nash and Sutcliffe coefficient was 0.73 for Kielstau and 0.65 for XitaoXi. The results suggest that the application of KIDS model may require adjustments according to the specific physical background of the study basin.

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