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Assessing the Influence of Land Use and Land Cover Datasets with Different Points in Time and Levels of Detail on Watershed Modeling in the North River Watershed, China

Bibliographic Data

ID15465950
AuthorsJinliang Huang (0000-0002-0895-3418, Xiamen University, corresponding author), Pei Zhou (0000-0002-7823-3416, Xiamen University), Zhou Pei (0000-0001-5936-3736, Xiamen University), Zengrong Zhou (Huaqiao University), Yaling Huang (0009-0000-0410-1739, Xiamen University)
Year2012
Volume10
Issue1
Pages144-157
Publication date2012-12-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph10010144
PMID23271303
PMCIDPMC3564134
OpenAlexW2050246904
LanguageEN
Citations received2
References cited34

Land use and land cover (LULC) information is an important component influencing watershed modeling with regards to hydrology and water quality in the river basin. In this study, the sensitivity of the Soil and Water Assessment Tool (SWAT) model to LULC datasets with three points in time and three levels of detail was assessed in a coastal subtropical watershed located in Southeast China. The results showed good agreement between observed and simulated values for both monthly and daily streamflow and monthly NH(4)+-N and TP loads. Three LULC datasets in 2002, 2007 and 2010 had relatively little influence on simulated monthly and daily streamflow, whereas they exhibited greater effects on simulated monthly NH(4)+-N and TP loads. When using the two LULC datasets in 2007 and 2010 compared with that in 2002, the relative differences in predicted monthly NH(4)+-N and TP loads were -11.0 to -7.8% and -4.8 to -9.0%, respectively. There were no significant differences in simulated monthly and daily streamflow when using the three LULC datasets with ten, five and three categories. When using LULC datasets from ten categories compared to five and three categories, the relative differences in predicted monthly NH(4)+-N and TP loads were -6.6 to -6.5% and -13.3 to -7.3%, respectively. Overall, the sensitivity of the SWAT model to LULC datasets with different points in time and levels of detail was lower in monthly and daily streamflow simulation than in monthly NH(4)+-N and TP loads prediction. This research provided helpful insights into the influence of LULC datasets on watershed modeling

Cartography · Drainage basin · Geography · Hydrology (agriculture · Land cover · Land use · Soil and Water Assessment Tool · Streamflow · Subtropics · SWAT model · Watershed · Environmental Science · Hydrological Forecasting Using AI · Hydrology and Watershed Management Studies · Soil and Water Nutrient Dynamics · Ecology · Geology

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Unique citing works2
Citations per year0,17
Citation span2014 - 2019 (6)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 2

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