Abstract
Urbanization and associated increase of imperviousness alters the hydrological
cycle of urbanizing catchments. Low Impact Development (LID) tools have been
developed and applied to mitigate these hydrological impacts. Hydrological
models are one way to evaluate the performance of LID tools before their
implementation. As these tools represent small-scale hydrological processes,
hydrological models used in assessment require a high spatial and temporal
resolution in their process descriptions. Both flow and rainfall data at high
recording frequencies (e.g. 1 min) are usually not available for large urban
catchments and detail in spatial data for surface description has to be
complemented through on-site observations. Thus, the assessment of LID
performance for large urban areas has to overcome these constraints. Previous
studies provide suggestions to overcome the lack of flow data for model
calibration through parameter regionalization. Recently presented methods for
reductions in spatial resolution while maintaining a detailed surface description
provide a feasible way to characterize large urban catchments for LID
performance assessment. However, rainfall data at high temporal and spatial
resolution remains a key element for hydrological model applications in urban
areas. We evaluated the impact of spatial and temporal rainfall variability on
model performance using the Stormwater Management Model (SWMM) and
high-resolution parameterizations of three urban catchments in combination with two rain gauges. While the distance between rain station and catchment did not affect model parameters we found a reduction in model efficiency with an
increasing rain station distance from the catchment.
cycle of urbanizing catchments. Low Impact Development (LID) tools have been
developed and applied to mitigate these hydrological impacts. Hydrological
models are one way to evaluate the performance of LID tools before their
implementation. As these tools represent small-scale hydrological processes,
hydrological models used in assessment require a high spatial and temporal
resolution in their process descriptions. Both flow and rainfall data at high
recording frequencies (e.g. 1 min) are usually not available for large urban
catchments and detail in spatial data for surface description has to be
complemented through on-site observations. Thus, the assessment of LID
performance for large urban areas has to overcome these constraints. Previous
studies provide suggestions to overcome the lack of flow data for model
calibration through parameter regionalization. Recently presented methods for
reductions in spatial resolution while maintaining a detailed surface description
provide a feasible way to characterize large urban catchments for LID
performance assessment. However, rainfall data at high temporal and spatial
resolution remains a key element for hydrological model applications in urban
areas. We evaluated the impact of spatial and temporal rainfall variability on
model performance using the Stormwater Management Model (SWMM) and
high-resolution parameterizations of three urban catchments in combination with two rain gauges. While the distance between rain station and catchment did not affect model parameters we found a reduction in model efficiency with an
increasing rain station distance from the catchment.
| Original language | English |
|---|---|
| Title of host publication | The Sustainable City IX |
| Publisher | WIT Press |
| Pages | 1593 |
| Number of pages | 1602 |
| Volume | 2 |
| ISBN (Print) | 978-1-78466-024-6 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 9th International Conference on Urban Regeneration and Sustainability - Siena, Italy Duration: 23 Sept 2014 → 25 Sept 2014 |
Publication series
| Name | WIT Transactions on Ecology and The Environment |
|---|---|
| Volume | 191,2 |
Conference
| Conference | 9th International Conference on Urban Regeneration and Sustainability |
|---|---|
| Country/Territory | Italy |
| City | Siena |
| Period | 23/09/14 → 25/09/14 |
Keywords
- SWMM
- high spatial resolution
- spatial rainfall variability
