Watershed-scale hydrological models have become important tools to understand, assess, and predict the impacts of natural and anthropogenic-driven activities on water resources. However, model predictions are associated with uncertainties stemming from sources such as model input data. As an important input to most watershed models, land use/cover (LULC) data can affect hydrological predictions and influence the interpretation of modeling results. In addition, it has been shown that the use of soft data will further ensure the quality of modeling results to be closer to watershed behavior. In this study, the ecologically relevant flows (ERFs) are the primary soft data to be considered as a part of the modeling processes. This study aims to evaluate the impacts of LULC input data on the hydrological responses of the rapidly urbanizing Upper Cahaba River watershed (UCRW) located in Alabama, USA. Two sources of LULC data, i.e., National Land Cover Database (NLCD) and Digitized Landsat 5 Thematic Mapper (TM) images, were used as input in the Soil and Water Assessment Tool (SWAT) model for the years 1992 and 2011 using meteorological data from 1988 to 2013. The model was calibrated at the watershed outlet against daily streamflow from 1988 to 1993 using the 1992 LULC data and validated for the 2008–2013 period using the 2011 LULC datasets. The results show that the models achieved similar performances with both LULC datasets during the calibration and validation periods according to commonly used statistical rating metrics such as Nash Sutcliffe efficiency coefficient (NSE), coefficient of determination (R²), and model percent bias (PBIAS). However, LULC input information had substantial impacts on simulated ERFs such as mean monthly streamflow, maximum and minimum flows of different durations, and low flow regimes. This study demonstrates that watershed models based on different sources of LULC and applied under different LULC temporal conditions can achieve equally good performances in predicting streamflow. However, substantial differences might exist in predicted hydrological regimes and ERF metrics depending on the sources of LULC data and the LULC year considered. Our results reveal that LULC data can significantly impact the simulated flow regimes of the UCRW with underlaying influences on the predicted biotic and abiotic structures of aquatic and riparian habitats.

Headwater streams drain over 70% of the land in the United States with headwater wetlands covering 6.59 million hectares. These ecosystems are important landscape features in the southeast United States, with underlying effects on ecosystem health, water yield, nutrient cycling, biodiversity, and water quality. However, little is known about the relationship between headwater wetlands’ nutrient function (i.e., nutrient load removal (R_L) and removal efficiency (E_R)) and their physical characteristics. Here, we investigate this relationship for 44 headwater wetlands located within the Upper Fish River watershed (UFRW) in coastal Alabama. To accomplish this objective, we apply the process-based watershed model SWAT (Soil and Water Assessment Tool) to generate flow and nutrient loadings to each study wetland and subsequently quantify the wetland-level nutrient removal efficiencies using the process-based wetland model WetQual. Results show that the calculated removal efficiencies of the headwater wetlands in the UFRW are 75–84% and 27–35% for nitrate (${\mathrm{N}\mathrm{O}}_3^{-}$) and phosphate ${(\mathrm{P}\mathrm{O}}_4^{+})$, respectively. The calculated nutrient load removals are highly correlated with the input loads, and the estimated ${\mathrm{P}\mathrm{O}}_4^{+}$E_R shows a significant decreasing trend with increased input loadings. The relationship between ${\mathrm{N}\mathrm{O}}_3^{-}$E_R and wetland physical characteristics such as area, volume, and residence time is statistically insignificant (p > 0.05), while for ${\mathrm{P}\mathrm{O}}_4^{+}$, the correlation is positive and statistically significant (p < 0.05). On the other hand, flashiness (flow pulsing) and baseflow index (fraction of inflow that is coming from baseflow) have a strong effect on ${\mathrm{N}\mathrm{O}}_3^{-}$ removal but not on ${\mathrm{P}\mathrm{O}}_4^{+}$ removal. Modeling results and statistical analysis point toward denitrification and plant uptake as major ${\mathrm{N}\mathrm{O}}_3^{-}$ removal mechanisms, whereas plant uptake, diffusion, and settling of sediment-bound P were the main mechanisms for ${\mathrm{P}\mathrm{O}}_4^{+}$ removal. Additionally, the computed nutrient E_R is higher during the driest year of the simulated period compared to during the wettest year. Our findings are in line with global-level studies and offer new insights into wetland physical characteristics affecting nutrient removal efficiency and the importance of headwater wetlands in mitigating water quality deterioration in coastal areas. The regression relationships for ${\mathrm{N}\mathrm{O}}_3^{-}$ and ${\mathrm{P}\mathrm{O}}_4^{+}$ load removals in the selected 44 wetlands are then used to extrapolate nutrient load removals to 348 unmodeled non-riverine and non-riparian wetlands in the UFRW (41% of UFRW drains to them). Results show that these wetlands remove 51–61% of the ${\mathrm{N}\mathrm{O}}_3^{-}$ and 5–10% of the ${\mathrm{P}\mathrm{O}}_4^{+}$ loading they receive from their respective drainage areas. Due to geographical proximity and physiographic similarity, these results can be scaled up to the coastal plains of Alabama and Northwest Florida.