Identification of Dominant Runoff Controls Using Hydrologically Informed Machine Learning Approach

Abstract Relative dominance of the runoff controls, such as topography, geology, soil types, land use, and climate, may differ from catchment to catchment due to spatial and temporal heterogeneity of landscape properties and climate variables. Understanding dominant runoff controls is an essential task in developing unified hydrological theories at the catchment scale. Semi-distributed rainfall-runoff models are often used to identify dominant runoff controls for a catchment of interest. In most such applications, the model selection is based on either expert's judgement or experimental and fieldwork insights. Model selection is the most important step in any hydrological modelling exercise as the findings are largely influenced by the selected model. Hence, a subjective model selection without sufficient expert's knowledge or experimental insights may result in biased findings, especially for comparative studies like identification of dominant runoff controls. In this study, we use a physics informed machine learning toolbox based on genetic programming Machine Induction Knowledge Augmented - System Hydrologique Asiatique (MIKA-SHA) to identify the relative dominance of runoff controls. We find the quantitative and automated approach based on MIKA-SHA to be highly appropriate for the intended task. MIKA-SHA does not require explicit user selections and relies on data and fundamental hydrological processes. The approach is tested using the Rappahannock River basin at Remington, Virginia, United States. Two rainfall-runoff models are learnt to represent the runoff dynamics of the catchment using topography-based and soil-type-based hydrologic response units independently. Based on prediction capabilities, in this case, the topography is identified as the dominant runoff driver.