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Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model
Urban surfaces are often covered by impermeable materials such as concrete and asphalt which intensify urban runoff and pollutant concentration during storm events, and lead to the deterioration of the quality of surrounding water bodies. Detention ponds are used in urban stormwater management, prov...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076266/ https://www.ncbi.nlm.nih.gov/pubmed/37019977 http://dx.doi.org/10.1038/s41598-023-32556-x |
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author | Abduljaleel, Yasir Salem, Ali ul Haq, Faraz Awad, Ahmed Amiri, Mustapha |
author_facet | Abduljaleel, Yasir Salem, Ali ul Haq, Faraz Awad, Ahmed Amiri, Mustapha |
author_sort | Abduljaleel, Yasir |
collection | PubMed |
description | Urban surfaces are often covered by impermeable materials such as concrete and asphalt which intensify urban runoff and pollutant concentration during storm events, and lead to the deterioration of the quality of surrounding water bodies. Detention ponds are used in urban stormwater management, providing two-fold benefits: flood risk reduction and pollution load minimization. This paper investigates the performance of nine proposed detention ponds (across the city of Renton, Washington, USA) under different climate change scenarios. First, a statistical model was developed to estimate the pollutant load for the current and future periods and to understand the effects of increased rainfall on stormwater runoff and pollutant loads. The Personal Computer Storm Water Management Model (PCSWMM) platform is employed to calibrate an urban drainage model for quantifying stormwater runoff and corresponding pollutant loads. The calibrated model was used to investigate the performance of the proposed nine (9) detention ponds under future climate scenarios of 100-year design storms, leading to identifying if they are likely to reduce stormwater discharge and pollutant loads. Results indicated significant increases in stormwater pollutants due to increases in rainfall from 2023 to 2050 compared to the historical period 2000–2014. We found that the performance of the proposed detention ponds in reducing stormwater pollutants varied depending on the size and location of the detention ponds. Simulations for the future indicated that the selected detention ponds are likely to reduce the concentrations (loads) of different water quality constituents such as ammonia (NH(3)), nitrogen dioxide (NO(2)), nitrate (NO(3)), total phosphate (TP), and suspended solids (SS) ranging from 18 to 86%, 35–70%, 36–65%, 26–91%, and 34–81%, respectively. The study concluded that detention ponds can be used as a reliable solution for reducing stormwater flows and pollutant loads under a warmer future climate and an effective adaptation option to combat climate change related challenges in urban stormwater management. |
format | Online Article Text |
id | pubmed-10076266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100762662023-04-07 Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model Abduljaleel, Yasir Salem, Ali ul Haq, Faraz Awad, Ahmed Amiri, Mustapha Sci Rep Article Urban surfaces are often covered by impermeable materials such as concrete and asphalt which intensify urban runoff and pollutant concentration during storm events, and lead to the deterioration of the quality of surrounding water bodies. Detention ponds are used in urban stormwater management, providing two-fold benefits: flood risk reduction and pollution load minimization. This paper investigates the performance of nine proposed detention ponds (across the city of Renton, Washington, USA) under different climate change scenarios. First, a statistical model was developed to estimate the pollutant load for the current and future periods and to understand the effects of increased rainfall on stormwater runoff and pollutant loads. The Personal Computer Storm Water Management Model (PCSWMM) platform is employed to calibrate an urban drainage model for quantifying stormwater runoff and corresponding pollutant loads. The calibrated model was used to investigate the performance of the proposed nine (9) detention ponds under future climate scenarios of 100-year design storms, leading to identifying if they are likely to reduce stormwater discharge and pollutant loads. Results indicated significant increases in stormwater pollutants due to increases in rainfall from 2023 to 2050 compared to the historical period 2000–2014. We found that the performance of the proposed detention ponds in reducing stormwater pollutants varied depending on the size and location of the detention ponds. Simulations for the future indicated that the selected detention ponds are likely to reduce the concentrations (loads) of different water quality constituents such as ammonia (NH(3)), nitrogen dioxide (NO(2)), nitrate (NO(3)), total phosphate (TP), and suspended solids (SS) ranging from 18 to 86%, 35–70%, 36–65%, 26–91%, and 34–81%, respectively. The study concluded that detention ponds can be used as a reliable solution for reducing stormwater flows and pollutant loads under a warmer future climate and an effective adaptation option to combat climate change related challenges in urban stormwater management. Nature Publishing Group UK 2023-04-05 /pmc/articles/PMC10076266/ /pubmed/37019977 http://dx.doi.org/10.1038/s41598-023-32556-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Abduljaleel, Yasir Salem, Ali ul Haq, Faraz Awad, Ahmed Amiri, Mustapha Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model |
title | Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model |
title_full | Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model |
title_fullStr | Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model |
title_full_unstemmed | Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model |
title_short | Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model |
title_sort | improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the pcswmm model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076266/ https://www.ncbi.nlm.nih.gov/pubmed/37019977 http://dx.doi.org/10.1038/s41598-023-32556-x |
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