Storm Water Management Model

Résumé

The United States Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) is a dynamic rainfall–runoff–subsurface runoff simulation model used for single-event to long-term (continuous) simulation of the surface/subsurface hydrology quantity and quality from primarily urban/suburban areas. It can simulate the Rainfall- runoff, runoff, evaporation, infiltration and groundwater connection for roots, streets, grassed areas, rain gardens and ditches and pipes, for example. The hydrology component of SWMM operates on a collection of subcatchment areas divided into impervious and pervious areas with and without depression storage to predict runoff and pollutant loads from precipitation, evaporation and infiltration losses from each of the subcatchment. Besides, low impact development (LID) and best management practice areas on the subcatchment can be modeled to reduce the impervious and pervious runoff. The routing or hydraulics section of SWMM transports this water and po

Source officielle

https://en.wikipedia.org/wiki/Storm_Water_Management_Model

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Assessing the potential of using telecommunication microwave links in urban drainage modelling

Marc Schleiss

The ability to predict the runoff response of an urban catchment to rainfall is crucial for managing drainage systems effectively and controlling discharges from urban areas. In this paper we assess the potential of commercial microwave links (MWL) to capture the spatio-temporal rainfall dynamics and thus improve urban rainfall-runoff modelling. Specifically, we perform numerical experiments with virtual rainfall fields and compare the results of MWL rainfall reconstructions to those of rain gauge (RG) observations. In a case study, we are able to show that MWL networks in urban areas are sufficiently dense to provide good information on spatio-temporal rainfall variability and can thus considerably improve pipe flow prediction, even in small subcatchments. In addition, the better spatial coverage also improves the control of discharges from urban areas. This is especially beneficial for heavy rainfall, which usually has a high spatial variability that cannot be accurately captured by RG point measurements.

IWA Publishing2013

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, ,

A probabilistic assessment of climate change and related impacts should consider a large range of potential future climate scenarios. State-of-the-art climate models, especially coupled atmosphere-ocean general circulation models and Regional Climate Models (RCMs) can however not be used to simulate such a large number of scenarios. This paper presents a methodology to obtain future climate scenarios through a simple scaling methodology: The projections of several key meteorological variables obtained from a few regional climate model runs are scaled based on different global-mean warming projections drawn in a probability distribution of future global-mean warming. The resulting climate change scenarios are used to drive a hydrological and a water management model to analyse the potential climate change impacts on a water resources system. This methodology enables a joint quantification of the climate change impact uncertainty induced by the global-mean warming scenarios and the regional climate response. It is applied to a case study in Switzerland, a water resources system formed by three interconnected lakes located in the Jura Mountains. The system behaviour is simulated for a control period (1961 - 1990) and a future period (2070-2099). The potential climate change impacts are assessed through a set of impact indices related to different fields of interest (hydrology, agriculture and ecology). The obtained results show that future climate conditions have a significant influence on the system performance and that the uncertainty induced by the inter-RCM variability contributes to a large part to the total impact prediction uncertainty.

2007

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Incorporating Mental Maps in Route Choice Modeling: Preliminary Ideas

Michel Bierlaire, Jingmin Chen, Evanthia Kazagli

2013

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