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

Abstract

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 NH4+-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 NH4+-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 NH4+-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 NH4+-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 NH4+-N and TP loads prediction. This research provided helpful insights into the influence of LULC datasets on watershed modeling.

Effects of land-use land-cover data resolution and classification methods on SWAT model flow predictive reliability

“…Also, results of hydrologic models such as the SWAT (Arnold et al, 1998) have been shown to be affected by the input LULC data (Bosch et al, 2004;Huang et al, 2013). However, there is a lack of assessment of the resolution and methods of LULC data classification on SWAT model simulations particularly in a shale-gas impacted watershed.…”

Evaluating the impact of land use uncertainty on the simulated streamflow and sediment yield of the Seyhan River basin using the SWAT model

“…Better estimation of land cover parameters improves the performance of the hydrologic model used. Appropriate spatial and temporal resolution of the used land cover improves the prediction of the hydrologic model (Huang et al, 2013). Several studies have been conducted to study the impact of land cover change on hydrology and water quality by (1) using readily available data (Cai et al, 2012;Yan et al, 2013), (2) using artificial land cover scenarios including farming practices (Chaplot et al, 2004;De Girolamo and Lo Porto, 2012;Mbonimpa et al, 2012), and (3) generating land use change scenarios using the land use change models (one such land use change model is the conversion of land use and its effects model (CLUE-s, Verburg et al,…”

“…Many studies have shown that streamflow is not highly affected by either the origin of the chosen dataset or the spatial resolution of the land cover dataset; however, sediment, nitrogen and total phosphorus loads are highly affected by which land cover dataset is used as well as the spatial resolution of that data. For example, Huang et al, (2013) compared 3 types of land cover datasets derived from Landsat thematic mapper satellite imagery from 2007 and 2010 and an ETM+ image of 2002. They used 3 different categories of land cover detail (10, 5, and 3) in the SWAT model.…”

Simulation of surface runoff in Karaj dam basin, Iran

“…This model has been widely used to land-use change effect assessment (Shen et al 2010;De Girolamo and Lo Porto 2012;Yang et al 2012;Du et al 2013;Huang et al 2013;Niu and Sivakumar 2014), sediment prediction (Shen et al 2010), climate change 1 3 147 Page 2 of 10 (Andersson et al 2006;Zhang et al 2012;Huang et al 2015), water quality (Debele et al 2008;Zhang et al 2011) and simulation of evapotranspiration (Wang et al 2006). Many computer programs have been developed by hydrologists for parameters uncertainty analysis in river basin model, such as, generalized likelihood uncertainty estimation (GLUE; Beven and Binley 1992), sequential uncertainty fitting (SUFI-2; Abbaspour et al 2004), parameter solution (ParaSol; Van Griensven and Meixner 2006) and Markov chain Monte Carlo (MCMC; Kuczera and Parent 1998;Vrugt et al 2008).…”