Flood Forecasting

With continuing urbanization and climate change, it’s critical that the dynamics of urban flooding be better understood to improve prediction and mitigate water-related hazards under changing conditions. The large number of human-created structures and hydrologic, hydraulic and hydrometeorological processes involved makes real-time prediction of flood inundation in urban areas particularly challenging.

For the Dallas-Fort Worth metroplex (DFW), accurate prediction of the extent and depth of flooding and related hazards is equally imperative. In response, PCSWMM was utilized to assess the impact of variations in precipitation and impervious cover on simulated urban flood inundation maps.

The main study area was the 3.3 km² Forest Park–Berry catchment in North Central Texas, which has a high density of underground storm drainage. Storm drainage network performance between current conditions and altered conditions under continuing urbanization and climate change were studied.

PCSWMM, which combines a semi-distributed hydrologic routing model, a 1D hydraulic model for the storm drainage network and a 2D overland flow model, was chosen to create the 1D-2D integrated model based on its capacity to connect junctions, nodes, conduits and links to a 2D overland flow model for 1D-2D dual drainage modeling, and the need to account for both types of flow in the study basins.

Findings of this project are now being used as part of the flash flood warning system under implementation for the area. The sensitivity analyses undertaken are helping to assess the feasibility of real-time operation of a 1D-2D model, to identify potential alternatives for reduced complexity and computational requirements, and to increase lead time by the use of rainfall nowcasts.

Each year, the Brazilian City of São Paulo suffers from the impact of heavy rains. Flooding occurs in various parts of the city due to the large number of culverts, canals and bridges with insufficient sections for stormwater, silted streams and obstructions by debris.

To promote stormwater management efficiency and avoid the social and economic losses as well as land use issues associated with flooding, the São Paulo City Hall (PMSP) invested in a flood management system centered on evaluation, monitoring, forecasting and warning.

Hydrological modeling of the system’s subcatchments and hydraulic modeling of its main channels were performed using PCSWMM. The software’s Real-Time feature was utilized to disclose prediction results for flood inundation sites addressed to the Emergency Management Center (CGE). PCSWMM was chosen due to the software’s intuitive graphical interface, ease of allowing access to information and ability to analyze large, complex problems.

Rain forecast was taken every 10 minutes to a prediction horizon of 3 hours, and PCSWMM delivered hydraulic gradelines and hydrographs along the simulated channels, reservoir states, flood inundation extents and affected buildings of six priority watersheds. Rainfall and prediction data were obtained, processed and disclosed by the Flooding Alert System of São Paulo (SAISP).

Serving as an important assistance to emergency management during floods and while forecasting heavy rains, the model provides real-time, user-friendly information on affected areas. It is also able to identify inefficiencies and failures of hydraulic structures and operating rules and/or procedures - indicating the best improvement opportunities.

In addition, the developed system allows for fast access to subcatchment data and alerts CGE technicians of vulnerable areas in advance so that preventive and contingency measures can be taken.

Flooding in urban areas can cause significant property damage and human injury. Historical extreme precipitation events in the Toronto area have highlighted the need for enhanced warning systems to inform decision making prior to a storm’s arrival.

In response, Computational Hydraulics International (CHI) and the Toronto and Region Conservation Authority developed a high resolution, deterministic, physically-based, remote-sensing, real-time flood forecasting and web-based decision support system. This system has since been applied to the Don River watershed, which is home to 1.2 million people and covers 360 km² of drainage area – 80% of which is highly urbanized.

The project goal was to enable flood duty officers to make more informed decisions by providing them with predicted peak flows of a storm event at least two hours, and as much as 24 hours in advance. Integrating PCSWMM Real-Time, NEXRAD radar data, US EPA SWMM5, HTML5 and Google Maps, the system forecasts near-future water surface elevations and relays the information to a web-based platform for analysis and decision making.

Key features of the flood forecasting system include: real-time radar-rainfall acquisition, processing and forecasting, real-time flow and rain gauging, continuous hydrologic modelling, flood vulnerable asset analysis and predictive weather modelling.

RADAR acquisition and processing capabilities in PCSWMM allowed use of high spatial and temporal resolution rainfall and the ability to track storm speed and direction, as well as the ability to “ground-truth” or calibrate rainfall estimates using a network of ground-based rain gages in the watershed. CHI used the HEC-RAS model import tools of PCSWMM to generate the hydraulic components for the model.

The Don Valley flood forecasting model computed eight of the ten highest peak flows within a 10% margin of error and the remaining two within 20%. Performance of the PCSWMM model for historical events provided sufficient confidence to Toronto and Region Conservation Authority officials to use the methodology in real-time flood forecasting and warning.