National classification of surface–groundwater interaction using random forest machine learning technique

Abstract

AbstractCharacterization of surface–groundwater interaction is an increasingly useful skill for riverine ecologists and water resource managers interested in the dynamics of water, nutrient, and micro‐organism exchange at the reach scale, as it can be used to better represent point‐scale processes within larger catchment‐scale models. This study describes a method for predicting the nature of reach‐scale surface–groundwater interaction, using the random forest (RF) machine learning technique with national‐scale geology, hydrology, and land use data. Observed stream flow depletion and accretion surveys from riparian areas and spring‐line flow accretion surveys were also used. The RF model allowed prediction of observed losing and gaining reaches with a high prediction accuracy (“out‐of‐bag” error < 10%). The performance of the model, however, was found to be dependent on the geographic administrative region. The model was also found to be sensitive to slope, distance to headwater, distance to coast, and underlying geological characteristics. When applied in New Zealand, this approach yielded a realistic conceptual representation of national surface–groundwater dynamics that are subsequently being used to inform a national‐scale hydrological model.