Hindcasting Water Availability in Semi-Arid Systems
Authors: Kolarik Nicholas E. 
Published: February 2025

Dryland systems cover one third of the earth’s land surface and are becoming
increasingly drier, but existing datasets do not capture all of the types of
available water that sustain these systems. In semi-arid environments, small
surface water bodies and areas of mesic vegetation (wetlands, wet meadows,
riparian zones) function as critical water resources. However, the most
commonly-used maps of water availability are derived from the Landsat time
series or single date aerial photographs, and are either too coarse spatially or
temporally to effectively monitor dynamics of available water. In Chapter 1, I
produced a Sentinel Fusion (SF) water resources time series for a semi-arid
mountainous region of the western United States, which includes monthly maps of
both a) surface water and b) mesic vegetation at 10-m spatial resolution using
freely available Earth observation data on an open access platform. I applied
random forest classifiers to multispectral data from the Sentinel-2 time series,
synthetic aperture radar (SAR) data from the Sentinel-1 time series, and
topographic variables. I compared our SF product with three commonly used and
publicly available datasets in the western U.S. and demonstrated improvements in
mapping both surface water and mesic vegetation. With nine times finer spatial
resolution and more frequent image collection, the SF maps characterize
intra-annual dynamics of smaller water bodies (< 30-m wide) and mesic
vegetation integral to ecosystem functions in semi-arid systems compared to
leading Landsat-derived products. Further, this workflow is easily reproducible,
uses freely available data on an open access platform, and can be adopted to
help guide land use decisions related to water availability by farmers,
ranchers, and conservationists in semi-arid environments.

Newer earth observation datasets, including those from the Sentinel
constellation, provide opportunities to monitor mesic ecosystems at meaningful
spatial scales, but are insufficient for measuring decadal-scale changes.
Conversely, the Landsat time series has decades of data, but images are
spatially coarse relative to many of the mesic ecosystem areas that sustain
dryland systems, resulting in classifications with mixed pixels inadequate for
effective monitoring. In Chapter 2, I developed a workflow that uses 10-m
classifications produced from fusion of the Sentinel-1 and −2 time series
(2017–2020) to estimate sub-pixel proportions of Landsat time series
observations (2004–2020). Using random forest regression models, I quantified
water resource proportions (WRP) of surface water, mesic vegetation, and upland
land covers within each 30-m Landsat pixel. I incorporated ancillary covariates
to account for varying topographic conditions, land cover, and climate. Results
indicate that this approach consistently estimates sub-pixel proportions of
Landsat pixels more accurately compared to spectral mixture analysis (SMA). I
then demonstrated the ability of this time series to characterize historical
water availability at a case study site with a wel-documented restoration
history by qualitatively examining the mesic vegetation dynamics time series to
identify system responses to restoration efforts. This approach allows analysts
to hindcast observations of Sentinel products and measure water resource
dynamics with greater precision over larger temporal scales. I envision these
WRP data to be useful for measuring the impacts of conservation interventions,
disturbance recovery, or land use changes that pre-date the Sentinel time
series.

Private land protection is essential for achieving biodiversity conservation
goals and mitigating the effects of climate change. Methods of protection focus
on maximizing acres conserved to achieve these goals, but their ability to do so
may be overestimated if they do little to improve key ecosystems in need of
protection. Mesic ecosystems are integral to conservation in dryland systems
because they sustain wildlife, livelihoods, and landscape connectivity. These
areas are known to have disproportionate ecological importance, but are at risk
as periods of drought increase in both frequency and duration and extractive
land use activities continue to degrade them. In Chapter Three, I used time
series maps of mesic vegetation proportion to estimate water availability and
analyzed development and wetness dynamics in varying management contexts within
the High Divide, a semi-arid region of Idaho and Montana with diverse land
tenure characteristics typical in the American West. I used difference in
differences panel regressions to estimate likelihood of development and the
proportion of mesic vegetation late in the water year indicative of a healthy
riverscape on private lands where conservation easements are implemented
compared to non-easements. The results emphasize that the current process of
easement implementation is effective in targeting undeveloped, healthy
riverscapes on private lands worth conserving, but this analysis shows no
evidence that they are better able to create or conserve mesic habitats in
semi-arid landscapes. Similarly, I did not find strong evidence that lands
placed under easement are less likely to be developed than those that are not.
These results contribute to the discussion regarding the importance of rigorous
monitoring protocols for conservation easements and other conservation
tools.

This dissertation provides evidence for using freely available satellite imagery
for monitoring restoration efforts and varying management contexts. I
demonstrate how petabytes of publicly owned data, freely accessible cloud
computing platforms, and knowledge shared by stakeholders can be used to improve
the way we interact with the Earth and how they can play a vital role in
repairing our relationship with the planet that sustains us.