Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions - Wainwright, Haruko M.; Dafflon, Baptiste; Siirila-Woodburn, Erica R.; Falco, Nicola; Wu, Yuxin; Breckheimer, Ian; Carroll, Rosemary W. H. (Frontiers in Water, 2024)
In this study, we develop a machine-learning (ML) enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds with a particular focus on snow-soil-plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is defined as the dependency of the annual peak plant productivity on the Palmer drought severity index in early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil-moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant for the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, USA), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, nonlinear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpack, lateral flow and soil storage limitation. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to more broadly increase the use of ML and model-simulation results to guide critical zone and watershed-monitoring activities.