Tropical woodland moisture cycling responses to climate stochasticity
using eddy covariance and machine learning
Abstract
Brazilian tropical woodlands, such as the wooded Cerrado, perform
hydrological functions that need to be well understood by field data
acquisition and mathematical modeling. Here, we aimed to assess the
water partitioning behavior and variability of a wooded Cerrado fragment
located in Southeastern Brazil by (i) measuring fluxes using eddy
covariance; (ii) applying machine learning techniques to obtain a model
to estimate the evapotranspiration (ET) using meteorological data as
input: solar radiation (Rg), wind speed (WS), temperature (T), relative
humidity (RH), and rainfall (P); and (iii) simulating a long-term water
balance using stochastic climate generator inputs and the previously
calibrated ET model. The average observed ET was 3.12±0.93 mm d) and P
(1227±208 mm yr-1) have similar standard deviations;
differently from the ET (1054±46 mm yr-1), which
presented higher annual rates with a small variability throughout the
simulation.