List of Articles Flood warning

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Uncertainty Evaluation due to TIGGE Global System Precipitation Data for Flood Forecasting
  Soudabeh Behiyan Motlagh Afshin Honarbakhsh Asghar Azizian
  10.30495/wsrcj.2023.71323.11345
  Background and Aim: The occurrence of frequent floods in Iran necessitates a flood forecasting and warning system with a suitable lead time. The use of numerical rainfall forecasting models in flood forecasting and warning is one of the important measures taken by resea More

  Background and Aim: The occurrence of frequent floods in Iran necessitates a flood forecasting and warning system with a suitable lead time. The use of numerical rainfall forecasting models in flood forecasting and warning is one of the important measures taken by researchers in most parts of the world. The TIGGE database includes mid-term precipitation forecasts simulated by global forecast centers. The purpose of this research is to evaluate the efficiency and the degree of uncertainty caused by the rainfall forecasts of four numerical models of the TIGGE database (including CPTEC, ECCC, ECMWF, and KMA) for simulating floods with the HEC-HMS hydrological model.Methods: In this research, the precipitation data of seven meteorological stations were used to evaluate the uncertainty of discharge from TIGGE database precipitation prediction models in the Poldokhtar watershed. Also, three flood events on March 24, 2017, April 6, 2018, and April 15, 2018, were studied. At first, precipitation forecasts were extracted from four centers CPTEC, ECCC, ECMWF, and KMA. Due to the existence of systematic error in the forecasts, a bias correction was done on them, and to correct the bias, the Delta method was used. Processed and raw forecasts of four rainfall forecasting models were entered into the HEC-HMS model for flood forecasting, and in the next step, the flow uncertainty assessment of the HEC-HMS model was performed in all members of the four rainfall forecasting models. In this research, 5 factors P, R, S, T, and RD were used for uncertainty analysis.Results: The results indicate the significant superiority of the ECMWF model in predicting precipitation events. The use of all 4 rainfall sources led to an acceptable simulation of the flood peak flow in three different events. Also, the predicted peak discharge time had little difference from the observed data. According to the results of the uncertainty analysis, the ECMWF model was considered the best model based on P, R, S, T, and RD factors. The KMA model performed well in severe and very severe floods. The group prediction system of TIGGE models also had an acceptable performance in all events. Also, the hydrological-meteorological prediction model predicted the time of flood occurrence and the probability of occurrence well.Conclusion: The intended research investigates flood forecasting and warning in the Poldokhtar watershed using the meteorological-hydrological system, based on meteorological forecasts of the TIGGE database and flood simulation using the HEC-HMS hydrological model. The final product of this system is probable discharge and flood forecast. The results reveal the success of the TIGGE database in flood forecasting. The ECMWF model excelled in predicting peak discharge. The upper and lower band calculation method was used to determine the uncertainty, which showed the uncertainty well. This system displayed the time of peak discharge well and with a small time delay, which indicates its good performance. The predicted rainfall from the four centers used in this study (ECMWF, ECCC, CPTEC, and KMA) have significant differences. To reduce these differences, we used a multi-model group forecasting system that had encouraging results. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Enhancement of flood warning system performance using stochastic rainfall threshold curve
  Ahmad Sharafati
  Flood warning systems are the important and effective approaches to prevention or mitigation life and property loss in any flood event. All flood warning systems use one or more flood event indicator such as climatological signals. Rainfall threshold curve is one the co More

  Flood warning systems are the important and effective approaches to prevention or mitigation life and property loss in any flood event. All flood warning systems use one or more flood event indicator such as climatological signals. Rainfall threshold curve is one the conventional flood events indicator in many flood warning systems. In this respect, observed or forecasted rainfall was compared with rainfall threshold to notify flood event. Application of some assumptions like constant rainfall patterns and rainfall-runoff model parameters such as loss and base flow is known as the main drawback of using conventional rainfall threshold curves. This study has considered uncertainties of rainfall-runoff model parameters and variables for extracting rainfall threshold curve, while solving shortcomings of the previous works. Results of this study demonstrated that extraction of the rainfall threshold curves by considering uncertainties of the mentioned variables and parameters represents a very higher accuracy respect to conventional rainfall threshold curves. Manuscript profile