Soil water content and net precipitation spatial variability in an Atlantic forest remnant

Abstract

This study was carried out in an Atlantic forest remnant in Southeastern Brazil and aimed to spatially model the soil water content (SWC) and net precipitation (NP) on a monthly time scale and to assess the spatial behavior of these hydrological variables in the different seasons. NP is defined by summing throughfall and stemflow, which have been collected after each rain event and accumulated monthly. Soil moisture measurements were carried out monthly up to a depth of 1.00 m and then integrated to obtain the SWC. The exponential semivariogram model was fitted for both hydrological variables, and the goodness-of-fit was assessed by a cross-validation procedure, spatial dependence degree (SDD) and spatial dependence index (SDI). This model provided adequate performance for SWC and NP mapping according to the cross-validation statistics. Based on the SDD, both variables have been classified as a ‘strong spatial dependence structure’. Nevertheless, when the SDI was assessed, NP showed less spatial dependence, while the SWC maintained almost the same performance. Kriging maps pictured the regional climate seasonality due to higher values of both variables in spring and summer than in autumn and winter seasons. However, correlations between NP and SWC are not expressive in the studied period.

Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms

“…In this regard, geostatistical maps were developed to highlight the performance of RF in modelling the spatial structure of PMA (Figure 7). Ordinary kriging was applied, relying on the exponential model, because this is a suitable approach for modelling soil moisture and its hydrological characteristics in tropical and subtropical forests (Carvalho Junior, Chagas, Lagacherie, Calderano Filho, & Bhering, 2014; Liang, Hung, Chan, & Lu, 2014; Mello et al, 2011; Rodrigues et al, 2020). Although the spherical and Gaussian models may also be suitable for modelling soil moisture in the Atlantic forest (Mello et al, 2011), the geostatistical analysis has been carried out to assess the RF's ability to keep the spatial structure (i.e., to indicate the regions with higher and lower moisture) and not to point out the best semivariogram model.…”

“…Eight dry and wet events (16 events) with different soil moisture conditions and TF values were selected to represent the spatiotemporal variability considering the best model. The spatial pattern of PMA was modelled through the exponential model, and the maps were created by means of ordinary kriging (Rodrigues et al, 2020). The Geostatistical Wizard tool of ArcGIS 10.1 was applied to perform both geostatistical modelling and ordinary kriging.…”

“…The previously discussed interactions between rainfall and the forest canopy add to the heterogeneity of the climate, soil physical, chemical and biological attributes, and topography. Forest traits affect the spatiotemporal distribution of the soil moisture in this environment (Gao et al, 2011; Rodrigues et al, 2020; Suo, Huang, Zhang, Duan, & Shan, 2018). Conversely, the soil moisture spatiotemporal variability drives species diversity, tree growth and mortality, evapotranspiration, preferential flow paths (favouring infiltration and groundwater recharge) and soil moisture redistribution.…”

Water balance of an Atlantic forest remnant under a prolonged drought period

“…Moreover, the net precipitation ranged from 0 to 73.2 mm (14.8 mm ± 15.5 mm) across the monitoring sets with a significant spatial variability (Table 2). The scattered canopy with three distinguished layers summed to the species diversity (Terra et al, 2018) cause heterogeneity on the rainfall partitioning, and therefore drive the spatial variability of net precipitation (Rodrigues et al, 2020). The different water inputs are the main cause of the soil moisture variability observed at 0.1 m depth since throughfall is the main hydrological variable driving the spatiotemporal pattern of soil moisture in this forest (Oliveira et al, 2021).…”

Spatial and Temporal Patterns in Carbon and Nitrogen Inputs by Net Precipitation in Atlantic Forest, Brazil

“…The high incidence of lianas and the canopy gaps caused by the fall of trees further increase the intrinsic heterogeneity of this type of forest. In the same forest remnant, Rodrigues et al (2018) related the spatial variability of net precipitation to the semideciduous characteristics. However, Terra et al (2018a) did not found a spatial pattern in stem ow, associating it to the Atlantic Forest remnant heterogeneity of species.…”