On the similarity of hillslope hydrologic function: a clustering approach based on groundwater changes

Abstract. Hillslope similarity is an active topic in hydrology because of its importance improving our understanding hydrologic processes and enabling comparisons paired studies. In this study, we propose a holistic bottom-up hillslope clustering based on region's integrative hydrodynamic response quantified by the seasonal changes groundwater levels ΔP. The main advantage ΔP ability to capture recharge discharge processes. We test performance comparing it seven other common approaches. These include approaches aridity index, topographic wetness elevation, land cover, machine-learning that jointly integrate multiple data. assess these identify categorize hillslopes with similar static characteristics, hydroclimate, surface processes, subsurface dynamics mountainous watershed – East River located headwaters Upper Colorado Basin. performs very well identifying six out nine characteristics studied. variability among clusters as coefficient variation (0.2) less machine learning than others (> 0.3 for TWI, cover). further demonstrate robustness testing predict responses wet dry conditions, which when average conditions.