From Patch to Catchment: A Statistical Framework to Identify and Map Soil Moisture Patterns Across Complex Alpine Terrain

Frontiers in Water

Noah P. Molotch²  Oliver Wigmore³  Eve-Lyn S. Hinckley⁵  Anna L. Hermes⁵  Haruko M. Wainwright⁶  Nicola Falco⁶ 

[1] Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand;Department of Geography, University of Colorado at Boulder, Boulder, CO, United States;Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, CO, United States;Environmental Studies Program, University of Colorado at Boulder, Boulder, CO, United States;Institute of Arctic and Alpine Research, University of Colorado at Boulder, Boulder, CO, United States;Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Division, Berkeley, CA, United States;

关键词: hydrology;    critical zone;    topography;    random forest;    Niwot Ridge LTER;    Rocky Mountains;   

DOI  :  10.3389/frwa.2020.578602

来源: DOAJ

【 摘 要 】

Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (102->103 m) and microtopography (<100-102 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships at the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems.

【 授权许可】

Unknown