scholarly journals Evaluation of ECMWF's forecasting system for probabilistic urban flood prediction: a case study in Mexico City

2021 ◽  
Author(s):  
Marco R. López ◽  
Adrián Pedrozo-Acuña ◽  
Marcela L. Severiano Covarrubias
Keyword(s):  
Mexico City ◽  
Local Level ◽  
Warning System ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Forecast Horizon ◽  
Ensemble Forecasts ◽  
Flood Prediction ◽  
Urban Flood ◽  
Flood Warning

Abstract As the world continues urbanizing, including efforts to forge a new framework of urban development is necessary. Recent studies related to flood prediction and mitigation have shown that Ensemble Prediction Systems (EPSs) constitute a valuable and essential tool for an Early Warning System. However, the use of EPS for flood forecasting in urban zones has yet to be understood. This work has the objective to investigate the potential use of the Operational EPS, issued by the European Centre for Medium-Range Weather Forecasts (ECMWF), for probabilistic urban flood prediction. In this research, a precipitation forecast verification was carried out in two study zones: (1) Mexico Valley Basin and (2) Mexico City, where for the latter, forecasts were compared against real-time observed data. The results showed good forecast reliability for a rain threshold of up to 20 mm in 24-hourly accumulations, with the first 36 h of the forecast horizon being the most reliable. The EPS has sufficient resolution and precision for flood prediction in Mexico City, which represents a further step toward developing a flood warning system at the local level based on ensemble forecasts.

scholarly journals Towards an automatic early warning system of flood hazards based on precipitation forecast: the case of the Miño River (NW Spain)

2019 ◽  
Vol 19 (11) ◽  
pp. 2583-2595 ◽  
Author(s):  
José González-Cao ◽  
Orlando García-Feal ◽  
Diego Fernández-Nóvoa ◽  
José Manuel Domínguez-Alonso ◽  
Moncho Gómez-Gesteira
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Precipitation Forecast ◽  
Flood Hazards ◽  
Nw Spain ◽  
Forecast Horizon ◽  
Flood Prediction ◽  
Rainfall Forecast ◽  
Risk Areas

Abstract. An early warning system for flood prediction based on precipitation forecast is presented. The system uses rainfall forecast provided by MeteoGalicia in combination with a hydrologic (Hydrologic Modeling System, HEC-HMS) and a hydraulic (Iber+) model. The upper reach of the Miño River and the city of Lugo (NW Spain) are used as a study area. Starting from rainfall forecast, HEC-HMS calculates the streamflow and Iber+ is automatically executed for some previously defined risk areas when a certain threshold is exceeded. The analysis based on historical extreme events shows that the system can provide accurate results in less than 1 h for a forecast horizon of 3 d and report an alert situation to decision makers.

scholarly journals Towards an Automatic Early Warning System of Flood Hazards based on Precipitation Forecast: The case of the Miño River (NW Spain)

2019 ◽  
Author(s):  
José González-Cao ◽  
Orlando García-Feal ◽  
Diego Fernández-Nóvoa ◽  
José Manuel Domínguez-Alonso ◽  
Moncho Gómez-Gesteira
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Precipitation Forecast ◽  
Flood Hazards ◽  
Nw Spain ◽  
Forecast Horizon ◽  
Flood Prediction ◽  
Rainfall Forecast ◽  
Risk Areas

Abstract. An Early Warning System for flood prediction based on precipitation forecast is presented. The system uses rainfall forecast provided MeteoGalicia in combination with a hydrologic (HEC-HMS) and a hydraulic (Iber+) models. The upper reach of the Miño River and the city of Lugo (NW Spain) are used as a study area. Starting from rainfall forecast, HEC-HMS calculates the streamflow and Iber+ is automatically executed when a certain threshold is exceeded for some previously defined risk areas. The analysis based on historical extreme events shows that the system can provide accurate results in less than one hour for a forecast horizon of 3 days and report an alert situation to decision-makers.

Simulation of karst floods with a hydrological model improved by meteorological model coupling

2021 ◽  
Keyword(s):  
Flood Control ◽  
Underground Structure ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Karst Area ◽  
Peak Time ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Flood Peak ◽  
Flood Warning

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst area in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting model (WRF) and quantitative precipitation estimation (QPE) from remote sensing information by an artificial neural network cloud classification system (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst-Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 hours, respectively. Coupling the WRF model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 hours, which is important for flood warning and control.

scholarly journals An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

2009 ◽  
Vol 6 (4) ◽  
pp. 4891-4917
Author(s):  
J. A. Velázquez ◽  
T. Petit ◽  
A. Lavoie ◽  
M.-A. Boucher ◽  
R. Turcotte ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast Horizon ◽  
Time Step ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Hydrological Forecasting ◽  
Updating Procedure ◽  
First Time

Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

scholarly journals Ensembles of radar nowcasts and COSMO-DE-EPS for urban flood management

2018 ◽  
Vol 2017 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Alrun Jasper-Tönnies ◽  
Sandra Hellmers ◽  
Thomas Einfalt ◽  
Alexander Strehz ◽  
Peter Fröhle
Keyword(s):  
Urban Areas ◽  
Flood Management ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Urban Flood ◽  
Rainfall Events ◽  
Flood Warning ◽  
Urban Catchments ◽  
Heavy Rainfall Events

Abstract Sophisticated strategies are required for flood warning in urban areas regarding convective heavy rainfall events. An approach is presented to improve short-term precipitation forecasts by combining ensembles of radar nowcasts with the high-resolution numerical weather predictions COSMO-DE-EPS of the German Weather Service. The combined ensemble forecasts are evaluated and compared to deterministic precipitation forecasts of COSMO-DE. The results show a significantly improved quality of the short-term precipitation forecasts and great potential to improve flood warnings for urban catchments. The combined ensemble forecasts are produced operationally every 5 min. Applications involve the Flood Warning Service Hamburg (WaBiHa) and real-time hydrological simulations with the model KalypsoHydrology.

scholarly journals Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1752 ◽  
Author(s):  
Na ◽  
Yoo
Keyword(s):  
Flash Flood ◽  
Weighted Average ◽  
Warning System ◽  
Error Variance ◽  
Ensemble Forecast ◽  
Urban Flood ◽  
Target Time ◽  
Flood Warning ◽  
Flood Warning System ◽  
Better Than

The rainfall forecasts currently available in Korea are not sufficiently accurate to bedirectly applied to the flash flood warning system or urban flood warning system. As the lead timeincreases, the quality becomes even lower. In order to overcome this problem, this study proposesan ensemble forecasting method. The proposed method considers all available rainfall forecasts asensemble members at the target time. The ensemble members are combined based on the weightedaverage method, where the weights are determined by applying the two conditions of theunbiasedness and minimum error variance. The proposed method is tested with McGill Algorithmfor Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) rainfall forecasts for four stormevents that occurred during the summers of 2016 and 2017 in Korea. In Korea, rainfall forecasts aregenerated every 10 min up to six hours, i.e., there are always a total of 36 sets of rainfall forecasts.As a result, it is found that just six ensemble members is sufficient to make the ensemble forecast.Considering additional ensemble members beyond six does not significantly improve the quality ofthe ensemble forecast. The quality of the ensemble forecast is also found to be better than that of thesingle forecast, and the weighted average method is found to be better than the simple arithmeticaverage method.

scholarly journals Urban Storm Flooding: Characteristics and Management in Beijing

2018 ◽  
Vol 246 ◽  
pp. 01042 ◽  
Author(s):  
Chao-chen Fu ◽  
Jia-hong Liu ◽  
Hao Wang ◽  
Chen-yao Xiang ◽  
Xiao-ran Fu ◽  
...  
Keyword(s):  
Management System ◽  
Flood Control ◽  
Public Awareness ◽  
Warning System ◽  
Flood Events ◽  
Land Utilization ◽  
Urban Flood ◽  
Flood Warning ◽  
Extreme Storm ◽  
Urban Storm

Beijing is located on the North China Plain with five rivers, which belong to the Hai River Basin. Its continental monsoon climate with uneven precipitation distribution is extreme likely lead to serious urban flood disasters. According to the disaster results, urban storm flood in Beijing can be classified into four types. Here typical extreme storm flood events and their characteristics in Beijing were analyzed in detail. It showed that storm flood events in recently decades had a trend, which centered in a relatively small area with high intensity and short duration. The main reasons of urban storm flood disaster were urbanization and basic facilities with low flood and drainage standard. Urbanization means land utilization significantly altering hydraulic processes, and extreme storm can easily exceed those facilities capacity. In order to deal with urban storm flood, Beijing government have taken four measurements, which were upgrading and reconstruction of rainwater pumping stations, improving projects of small and medium rivers, building sponge city, and implementing the West Suburb Storm-water Regulation Project. In addition, the flood warning and emergency management system has been established. Furthermore, some measurements were pointed to be done in the future, including improvement of the flood control management system, improvement of flood control plans, strengthening flood warning system, and strengthening social management and public awareness of flood prevention. With these improvements of management and engineering measurements, it can be more secure under intensive storms in Beijing. These experiences of flood control in Beijing can provide references for other cities.

scholarly journals An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

2009 ◽  
Vol 13 (11) ◽  
pp. 2221-2231 ◽  
Author(s):  
J. A. Velázquez ◽  
T. Petit ◽  
A. Lavoie ◽  
M.-A. Boucher ◽  
R. Turcotte ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast Horizon ◽  
Time Step ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Hydrological Forecasting ◽  
Updating Procedure ◽  
First Time

Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

scholarly journals Review and Prospect on Development of Urban Flood Warning System

2018 ◽  
Vol 246 ◽  
pp. 01008
Author(s):  
Jianhua Li ◽  
Zhaosong Qu ◽  
Jun Zheng ◽  
Chong Jiang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Driving Forces ◽  
Warning System ◽  
Urban Flood ◽  
Flood Warning ◽  
The Future ◽  
Flood Warning System ◽  
Further Development

An overall research and analysis on past development track was of great significance for the further development of new theories, models, technologies and applications of urban flood warning system. Thus, the paper comprehensively discussed the development track of urban flood warning system, especially its structures and supporting technologies. From the analysis, it could be seen clearly that demands and technologies were two major driving forces for the development of urban flood warning system in the era of big data. Moreover, it was illustrated that new demands and problems had to be faced, and discussed how to meet them in the future by integrating technologies of big data and cloud computing.

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