The Soil and Water Assessment Tool (SWAT) model has been widely applied for simulating the water cycle and quantifying the influence of climate change and anthropogenic activities on hydrological processes. A major uncertainty of SWAT stems from poor representation of vegetation dynamics due to the use of a simplistic vegetation growth and development module. Using long-term remote sensing-based phenological data, we improved the SWAT model’s vegetation module by adding a dynamic growth start date and the dynamic heat requirement for vegetation growth rather than using constant values. We verified the new SWAT model in the Han River basin, China, and found its performance was much improved in comparison with that of the original SWAT model. Specifically, the accuracy of the leaf area index (LAI) simulation improved notably (coefficient of determination (R2) increased by 0.193, Nash–Sutcliffe Efficiency (NSE) increased by 0.846, and percent bias decreased by 42.18%), and that of runoff simulation improved modestly (R2 increased by 0.05 and NSE was similar). Additionally, we found that the original SWAT model substantially underestimated evapotranspiration (Penman–Monteith method) in comparison with the new SWAT model (65.09 mm (or 22.17%) for forests, 92.27 mm (or 32%) for orchards, and 96.16 mm (or 36.4 %) for farmland), primarily due to the inaccurate representation of LAI dynamics. Our results suggest that accurate representation of phenological dates in the vegetation growth module is important for improving the SWAT model performance in terms of estimating terrestrial water and energy balance.

Hydrological modeling of wetlands is important for reliable estimation of biogeochemical processes in soils subject to periodically inundating conditions. The present study has developed a wetland module in the Richards-equation-based SWAT model to fully couple the surface water storage and soil water dynamics. The wetland module was tested using observed daily water level data from four wetlands (including restored and natural wetlands with and without impermeable soil layers) in the Choptank River Watershed, Maryland, USA. After the wetland module was calibrated, simulated daily water level and observed data were compared and evaluated using three statistics, i.e., percent bias (Pbias), coefficient of determination (R2), and Nash-Sutcliffe coefficient (NS) from 2016 to 2017. The results showed that, in general, the wetland module regenerated hydroperiods for both restored and natural wetlands with and without impermeable soil layers; specifically, the module was able to accurately model saturation conditions for different soil layers corresponding to wet and dry periods in plant growing seasons; the wetland module had the tendency to generate better results for natural wetlands because restored wetlands tended to have mixed plant types which caused difficulty for accurate estimation of evapotranspiration; the ability to accurately describe inundation conditions for wetlands is important for biogeochemical modeling so that the newly developed wetland module has a great potential in enhancing simulation of biogeochemical cycles not only at the site scale but also at the watershed scale.