High-Resolution, Coupled Hydro-Meteorological Modelling for Operational Forecasting of Severe Flooding Events in Rio de Janeiro
The city of Rio de Janeiro often faces the consequences of intense rainfall, which include landslides and flooding. In early April 2010, the city endured one of the worst storms in decades with over 300mm of rain recorded in less than one day in several areas. There was significant loss of life and tens of thousands lost their homes. To assist in planning for such events in the future, the city's leaders have enabled sophisticated capabilities for the coordinated management of emergencies. As part of that effort, the integration of advances in hydro-meteorological research is required. Given the geography of the city, such capabilities have significant challenges. In addition to its near-tropical setting along the coast of the Atlantic Ocean, the western portion of Guanabara Bay and the eastern portion of Sepetiba Bay, there are regions where the terrain has a high aspect ratio, related to the Serra do Mar mountains. Although sea breezes moderate the temperatures along the coast, especially during the summer, cold fronts from the Antarctic can lead to rapid changes in local weather. Of particular concern is the rainy, summer season from December to March, during which O(100mm/day) precipitation events occur, when significant flooding becomes likely.
State-of-the-art numerical weather prediction models operating at the meso-gamma scale have been shown potential in predicting weather conditions at the scale that a city like Rio de Janeiro requires. Therefore, the WRF-ARW community model was adapted for use in this area, beginning in early 2011. An operational configuration was developed by retrospective analysis of significant precipitation events and compared against data from a network of 32 rain gauges operated by the City Government. Those results coupled with throughput and computational considerations led to a configuration of four two-way nests focused on the metropolitan area at one km horizontal resolution. To address the orographic influence of the complex terrain, 65 vertical levels were established with typically the lowest 15 being within the boundary layer. The configuration has parameterization and a selection of physics options appropriate for the range of geography in the region and the weather conditions of concern. This configuration was placed into operations in May 2011, producing 48-hour forecasts every 12 hours. The results of each model-based forecast are provided to the City Government via a web portal in their command center, which has been dubbed in Portuguese as Previsão Meteorológica de Alta Resolução (High-Resolution Weather Forecast), or PMAR. It includes HDTV-resolution animations of two- and three-dimensional visualizations of key weather variables, specialized meteograms at locations of key landmarks, weather stations, etc., and detailed tables of weather data at those locations. The web-based content contains information every 10 minutes of forecast time for each model run. The visualizations are customized to the model configuration and the requirements of the end users, and incorporate data from the city's geographic information system.
To comprehensively validate the precipitation forecasts, specific metrics were developed using hourly rain gauge data from the aforementioned sensor networks. The current methodology is based upon the analysis of multi-categorical contingency tables, whose categories are based upon thresholds of precipitation rates that relate to impacts on city operations (i.e., flooding). In parallel with the verification effort, reassessment of the model configuration and input data was conducted earlier this year. Tthe underlying surface and boundary layer physics have been improved through remotely-sensed land use, soil and vegetation data, including stable operation of an urban canopy model. This has led to further improvement in the forecasting skill for strong convective events during the summer rainy season.
A feasibility study was done to understand the flooding conditions and the quality of relevant data for the city including 1m resolution terrain derived from LiDAR data, and maps of soil type, land use and city structure. Little digital drainage data were available, which limits the creation of an accurate flooding model for the city. Very good historical flooding data were available, and the city has cataloged at least 232 recurrent points of flooding. These data only record the location and approximated peak time of flooding. At a few sites, the peak flooding polygon has been mapped for particularly severe events. Neither detailed streamflow measurements nor an urban system drainage model were available. Using the limited historical data, a simplified high resolution-analytical model was developed for flood prediction. It takes into account geomorphological data and historical data. A mathematical model was implemented simulating surface flow and water accumulation using a locally conservative approach by employing the shallow water equations. It employs precipitation estimates generated by the aforementioned WRF-ARW configuration, which are used to analyze if a site historically prone to flood, could receive a surface runoff flow that leads to flooding.
This coupled model approach to enable highly accurate and precise, operational forecasts of severe weather events has shown to be feasible. It has enabled the City Government to better anticipate and plan for the storm impacts on local infrastructure. However, challenges remain for forecast verification, visualization and interpretation techniques, specialized metrics for operational impact assessment, the quality of available data for the model's initial conditions, and optimal model configurations.
We will discuss the approach, and lessons that were learned through the development, deployment and operations to date. We will present how the forecast information is being used and discuss the overall effectiveness of our approach for these and related applications as well as recommendations for future work.