An approach for assessing impact of land use and biophysical conditions across landscape on recharge rate and nitrogen loading of groundwater.
Li, Q., Qi, J., Xing, Z.S., Li, S., Jiang, Y., Danielescu, S., Zhu, H., Wei, X.H., and Meng, F. (2014). "An approach for assessing impact of land use and biophysical conditions across landscape on recharge rate and nitrogen loading of groundwater.", Agriculture, Ecosystems and Environment, 196, pp. 114-124. doi : 10.1016/j.agee.2014.06.028 Access to full text
Assessing the impact of agricultural practices on groundwater quality is a must for environment management, however, such assessment is difficult because of dynamics of land use in interaction with topographic and climatic conditions. In this study, a multiple regression approach for assessing land use impact on groundwater quality and quantity was developed by using the ANCOVA test to select regression model variables from key input variables to a widely used and well-calibrated SWAT (Soil and Water Assessment Tool) model system. This approach can upscale model prediction for a small watershed with the calibrated SWAT model to a large watershed, generating spatial and temporal estimations of groundwater recharge and nitrate loading for the large watershed. It can also be used to evaluate the impacts of land use, soil types and climatic factors on water quality and quantity. Precipitation, air temperature, evapotranspiration, land use and soil types are determined as the most important factors for estimating monthly groundwater recharge rates and nitrate loading with the developed approach. Among various agricultural crops examined, potato is determined as the critical crop to have the highest impact on groundwater nitrate loading. The predictions of the groundwater monthly recharge multiple regression models developed in this study show good agreement with the SWAT model prediction (R2 > 0.77). The monthly nitrate loading models perform a little bit poorly but still show reasonable agreement with the SWAT model with R2 values > 0.60. Furthermore, the developed approach can be easily plugged into large-scale groundwater simulation models (e.g., MODFLOW) to address spatial variability of landscape characteristics in terms of non-point source pollution.
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