NSP variation on SWAT with high-resolution data: A case study
As a typical lake in the early stage of eutrophication, the non-point source pollution (NSP) in Erhai Lake was highly concerned. Since 2017, protection and rescue actions have been implemented in the Erhai Lake basin which significantly improved the water quality of Erhai Lake. But little attention...
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Veröffentlicht in: | Open Geosciences 2023-03, Vol.15 (1), p.12-5 |
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Sprache: | eng |
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Zusammenfassung: | As a typical lake in the early stage of eutrophication, the non-point source pollution (NSP) in Erhai Lake was highly concerned. Since 2017, protection and rescue actions have been implemented in the Erhai Lake basin which significantly improved the water quality of Erhai Lake. But little attention has been paid to the interception effects of these actions on NSP. Based on high resolution datasets, including refinement land use/land cover (LU/LC) data, China Meteorological Assimilation Driving Database for the SWAT (CMADS), and Advanced Land Observing Satellite-1 (ALOS-12) data, the interception effects of ecological ponds newly built in 2018 on NSP was discussed with the support of the soil and water assessment (SWAT) model. These three high resolution datasets can meet the needs for simulating NSP by SWAT model, which was conductive to better reveal the interception effects of ecological ponds on NSP. Besides, the pollution load of shrubbery on Total nitrogen (TN)/Total phosphorous (TP), which has never been involved in similar research works in the Erhai Lake basin, was measured. Compared the temporal and spatial variations in TN/TP concentration before and after 2018, the interception effects of ecological ponds on NSP in the Erhai Lake basin were revealed by a sharp decrease in TN/TP concentration and a delayed presence of annual peak value about 1 month late in TN concentration before and after 2018. The interception intensity of NSP was determined by the number and volume of ecological ponds in each sub-basin and their corresponding upstream. |
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ISSN: | 2391-5447 2391-5447 |
DOI: | 10.1515/geo-2022-0339 |