OPTIMIZATION OF RIPARIAN ZONE NITROGEN MANAGEMENT THROUGH THE DEVELOPMENT OF RIPARIAN MODEL

This thesis addresses the modeling approach to benefit the riparian zone nutrient management related to water quality in the Northeast and Midwest of USA. Nutrient (primarily Nitrogen (N)) loss from agricultural watersheds through runoff and drainage water continues to be a water quality concern of global importance. Since N is a crucial input for the sustainability of agriculture, the use of N has increased dramatically in recent decades and the excessive nutrient losses have increased too. Like global concern, agriculture (cropland, pasture, managed forest) is an important component of many watersheds of the USA Northeast where N flux to major estuaries is of substantial concern. In this circumstance, the finding from almost 30 years of research on riparian zone hydrology and biogeochemistry demonstrates that riparian zones can serve as best management practices (BMPs) to minimize the adverse agricultural impact on water quality.\nRiparian zones have been used as one of the most important practices for water quality improvement in agricultural settings due to its ability to perform multi functions including reducing NO3- concentrations in subsurface flow, trapping sediments and pesticides in overland flow, and control erosion. They are often characterized as “filters” or “buffers” and are vital elements in watershed management schemes for water quality maintenance and stream ecosystem habitat protection. Nevertheless, the buffering capacity of riparian zones (mostly for N) varies enormously due to the hydrogeomorphic setting such as topography, soil type, and surficial geology of the riparian zone. Upland land use/land cover affects both the water quantity and quality of the water entering the riparian zone. Hydrogeomorphic setting can influence the flowpaths and hydrologic connections be-tween upland sources of nitrate and the biologically active (i.e., upper 1-2 m) portions of the riparian zone. Thus, a number of key attributes related to location are critical in determining the potential impact of a riparian zone on water. These attributes are incorporated in models like the Riparian Ecosystem Management Model (REMM; Altier et al., 2002; Lowrance et al., 2000). Given the interest in expanding riparian zone BMPs, there is a critical need to advance our understanding of riparian functions at the site scale. Sitespecific models can improve riparian zone management decisions that seek to place, restore and protect riparian zones more effectively.\nREMM has been used to simulate managed riparian ecosystems in a number of settings in USA including Chesapeake Bay Watershed, Delaware, Mississippi, North Carolina, Georgia, California, and Puerto Rico. Globally, SWAT-REMM integration has been used in a glaciated landscape in New Brunswick, Canada by Zhang et al., 2017 to examine the effect of different levels of dividing up the watershed into sub-watershed for SWAT on the performance of the model. Liu et al., 2017 used REMM in China for the evaluation of riparian zones as BMP. However, REMM has not yet been integrated with AnnAGNPS model and applied to evaluate management at the field scale in the glaciated settings of the Northeast and Midwestern regions, even though the agricultural lands are linked to excessive nutrient pollution and riparian zones are widely used in these regions to mitigate N losses to streams. So, our focus on field scale analyses with AnnGNPS provides more insight into site scale behavior.\nThe objective of this work is to develop a set of Riparian Model parameters for the USA Midwest, USA Northeast to facilitate the use of REMM in these regions and improve its functionality with respect to N and N2O. The work has been described in the following five manuscripts, as per the Graduate School Manual guidelines:\nChapter 1. Manuscript I (published in Water, 2020)\nThe objective of this work was to: (i) evaluate the performance of the AnnAGNPS model in simulating the runoff volume at three separate watersheds with glacial setting of Northeast and Midwest USA; (ii) improve the model's runoff prediction capacity through calibration; (iii) validate the model’s runoff prediction with the improved calibrated parameters; (iv) conduct a parameter sensitivity analysis for runoff simulation; (v) conduct an analysis of the spatial distribution of runoff depth for three watersheds; (vi) provide a discussion of the model’s performance in order to estimate event peak discharge.\nChapter 2. Manuscript II (published in Agriculture, 2021)\nThe objective of this work was to test the application of REMM in formerly glaciated setting of Rhode Island (RI), USA for riparian zone nitrate dynamics.\nChapter 3. Manuscript III (In preparation for Nutrient Cycling in Agroecosystems, 2021)\nThe objective of this work was to test the ability of REMM model for riparian zone nitrogen simulation in two agricultural watersheds from the glacial setting of Indiana (IN), USA Midwest.\nChapter 4. Manuscript IV (In preparation for Jounral of Contaminant Hydrology, 2021).\nThe objective of this work was to evaluate the potential of REMM model in a glaciated watershed of New York (NY), USA Northeast for riparian zone nitrogen estimation.\nChapter 5. Manuscript V (In preparation for Journal of Hydrologic Engineering – ASCE, 2021)\nThe objective of this work was to assess the climate change impact on runoff coming from field edge (upland) towards the riparian zone (stream edge) in the glaciated landscape of the Northeast and Midwest USA.\nIn conclusion, this study provides an evaluation of the ability of the REMM model for nutrient management in the glaciated setting of USA Northeast and USA Midwest and establishes a base of site specific parameters for water resources managers. Model performance during calibration and validation phases shows that REMM model can be successfully coupled with upland inputs from a distributed model (AnnAGNPS) with field-measured hydrologic and N data from multiple buffers. Both the hydrologic and nutrient testing of REMM showed that it captured well the daily measured data (WTDs and groundwater NO3-N concentrations in stream edge) for both calibration and validation periods.

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