The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: Insights From Machine Learning Methods and Sensitivity Analysis

Abstract

AbstractThe effect of groundwater nutrient inputs on river and estuary water quality and the potential impacts of urbanization on groundwater are central concerns in many coastal areas. It has been previously identified that dissolved organic nitrogen (DON) can be the dominant form of total dissolved nitrogen (TDN) in some aquifers. However, there is a paucity of evidence about the sources and flow paths of DON, relative to inorganic nitrogen in groundwater. DON and dissolved organic carbon/DON were first compared against different landscape variables in this study, and no significant relationships were found. However, the relationships became statistically significant when shallow samples (sampling depth < 10 m) were separated from deep samples. A random forest model and sensitivity analysis were then applied to further our understanding of the ecohydrological drivers and seasonal patterns that shape DON variability. The random forest algorithm was built to classify 171 groundwater wellbores into three classes (low: <0.5 mg/L; medium: 0.5–2.5 mg/L; and high: >2.5 mg/L) which achieved 72% classification accuracy using landscape characteristics, hydrological conditions, and temporal information. The results indicated that the effects of landscapes on sandy shallow groundwater DON were controlled both by certain landscape characteristics and depth to groundwater. A conceptual model of groundwater DON is therefore proposed where the balance of exposure and processing time scales from the surface to groundwater is the critical control on the preservation of landscape signatures; we expect that this conceptual model would be applicable for other sandy, shallow groundwater areas.