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Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

Journal Article · · Frontiers in Water
 [1];  [2];  [2];  [2];  [2];  [3];  [4]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  3. Rocky Mountain Biological Lab., Crested Butte, CO (United States)
  4. Desert Research Institute (DRI), Reno, NV (United States)
+ Show Author Affiliations

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 used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the 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 to 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, United States), 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, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the 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 significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
2349067
Journal Information:
Frontiers in Water, Journal Name: Frontiers in Water Vol. 5; ISSN 2624-9375
Publisher:
Frontiers Media S.A.Copyright Statement
Country of Publication:
United States
Language:
English

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Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions
Journal Article · Sun Jan 30 23:00:00 EST 2022 · Hydrology and Earth System Sciences (Online) · OSTI ID:1842887

Related Subjects

54 ENVIRONMENTAL SCIENCES
Gaussian mixture model
clustering
hillslope
machine learning
random forest
watershed
zonation