Landslides are highly dangerous phenomena that occur
in different parts of the world and pose significant threats to human
populations. Intense rainfall events are the main triggering process for
landslides in urbanized slope regions, especially those considered high-risk
areas. Various other factors contribute to the process; thus, it is essential
to analyze the causes of such incidents in all possible ways. Soil moisture
plays a critical role in the Earth’s surface-atmosphere interaction systems; hence, measurements and their
estimations are crucial for understanding all processes involved in the water
balance, especially those related to landslides. Soil moisture can be estimated
from in-situ measurements using
different sensors and techniques, satellite remote sensing, hydrological
modeling, and indicators to index moisture conditions. Antecedent soil moisture
can significantly impact runoff for the same rainfall event in a watershed. The
Antecedent Precipitation Index (API) or “retained
rainfall,” along with the antecedent moisture condition from the Natural
Resources Conservation Service, is generally applied to estimate runoff in
watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and
establish thresholds based on landslide occurrences. The estimated soil
moisture will be compared and calibrated using measurements obtained through
multisensor capacitance probes installed in a high-risk area located in the
mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API
used in the calculation has been modified, where the recession
coefficient depends on air temperature variability as well as the
climatological mean temperature, which can be considered as losses in the water
balance due to evapotranspiration. Once the API is calibrated, it will be used
to extrapolate to the entire watershed and consequently estimate soil moisture.
By utilizing recorded mass movements and comparing them with API and soil
moisture, it will be possible to determine thresholds, thus enabling
anticipation of landslide
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