Soil moisture signatures provide a promising solution to overcome the
difficulty of evaluating soil moisture dynamics in hydrologic models.
Soil moisture signatures are metrics that represent catchment dynamics
extracted from time series of data and enable process-based model
evaluations. To date, soil moisture signatures have been tested only
under limited land-use types. In this study, we explore soil moisture
signatures’ ability to discriminate different dynamics among contrasting
land-uses. We applied a set of nine soil moisture signatures to datasets
from six in-situ soil moisture networks worldwide. The dataset covers a
range of land-use types, including forested and deforested areas,
shallow groundwater areas, wetlands, housing areas, grazed areas, and
cropland areas. These signatures characterize soil moisture dynamics at
three temporal scales: event, seasonal, and time-series scales.
Statistical and visual assessment of extracted signatures showed that
(1) storm event-based signatures can distinguish different dynamics for
most land-uses, (2) season-based signatures are useful to distinguish
different dynamics for some types of land-uses (forested vs. deforested
area, greenspace vs. housing area, and deep vs. shallow groundwater
area), (3) timeseries-based signatures can distinguish different
dynamics for some types of land-uses (forested vs. deforested area, deep
vs. shallow groundwater area, non-wetland vs. wetland area, and ungrazed
vs. grazed area). We compared signature-based process interpretations
against literature knowledge: event-based and time series-based
signatures were generally matched well with previous process
understandings from literature, but season-based signatures did not.
This study demonstrates the best practices of extracting soil moisture
signatures under various land-use and climate environments and applying
signatures for model evaluations.
InSAR Time-Series Analysis of Land Subsidence under Different Land Use Types in the Eastern Beijing Plain, China