Deciphering global patterns of forest canopy rainfall interception (FCRI): A synthesis of geographical, forest species, and methodological influences.

Forest canopy rainfall interception (FRCI) is an essential hydrological process that governs water and biogeochemical cycles in forest ecosystems. Identifying patterns and relationships of FCRI using a systematic review is key to improving our knowledge supporting new experiment research, modeling, and application. In this meta-analysis, we aimed to delineate the canopy interception (CI), throughfall (TF), and stemflow (SF) concerning geographical and forest variables and experimental methodologies. We leveraged peer-reviewed 170 articles across 234 sites globally, extracting TF, CI, SF, geographical, forest, and experimental aspects. We applied multivariate statistical procedures to discern the principal influences on TF, CI, and SF and examined their multicollinearity. In addition, we developed Generalized Linear Models (GLM) for CI and TF. Global TF experiments indicate that the predominant rainfall devices, number of sample trees, number of events, and monitoring length are 10-20 devices (81% fixed), 3-6 trees, 30-50 events, and 10-30 months. Predominant global values of TF, CI, and SF are 70-80% (median = 73%), 20%-30% (median = 23.9%), and <1.0% (median = 1.87%), respectively. Global models of CI and TF were responsive to T, LAI, and D (respectively, R2adj of 0.196** and 0.206**). Temperate forests mirrored the global model (R2adj of 0.274** and 0.31**, respectively). The Subtropical CI model was fitted based on P and DBH (R2adj = 0.245*), and the TF model was based on E, D, and LAI (R2adj = 0.532**); the Mediterranean CI model was based on T, Basal, and LAI (R2adj = 0.45*), while TF was based on P, Basal, and LAI (R2adj = 0.671**). The Tropical CI model was based on T and H (R2adj = 0.396*), and the TF model, LAI, and P (R2adj = 0.35*). This meta-analysis underscores the importance of comprehending the hydrological processes in forested areas as they are pivotal in mitigating climate change impacts.

Soil erosion assessment in the Amazon basin in the last 60 years of deforestation.

Anthropic activities in the Amazon basin have been compromising the environmental sustainability of this complex biome. The main economic activities depend on the deforestation of the rainforest for pasture cattle ranching and agriculture. This study analyzes soil erosion to understand how deforestation has impacted the Amazon basin in this context, using three land-use temporal maps (1960, 1990, 2019) through the revised universal soil loss equation (RUSLE). Our results point to a significant influence of deforestation due to the expansion of agricultural and livestock activities on soil erosion rates in the Amazon Basin. The average soil erosion rate has increased by more than 600% between 1960 and 2019, ranging from 0.015 Mg ha-1 year-1 to 0.117 Mg ha-1 year-1. During this period, deforestation of the Amazon rainforest was approximately 7% (411,857 km2), clearly the leading cause of this increase in soil erosion, especially between 1990 and 2019. The south and southeast regions are the most impacted by increasing soil erosion, in which deforestation was accelerated for expanding agriculture and livestock activities, mainly in the sub-basins of the Madeira, Solimões, Xingu, and Tapajós that present soil erosion increases of 390%, 350%, 280%, and 240%, respectively. The sub-basins with the highest sediment delivery rate (SDR) are under the influence of the Andes, highlighting Solimões (27%), Madeira (13%), and Negro (6%) due to the increase in the soil erosion rate increase in these sub-basins.

Assessment of the soil-erosion-sediment for sustainable development of South America.

One of the greatest threats to maintaining sustainable agro-ecosystems is mitigating the episodic soil loss from farm operations, further exacerbated by meteorological extremes. The Revised Universal Soil Loss Equation (RUSLE) is a model that combines the effects of rain, soil erodibility, topography, land cover, and conservation practices for estimating the annual average soil losses. This study aims to quantify soil water erosion to continental South America (S.A.) through RUSLE using available datasets and characterizing the average sediment delivery rate (SDR) to the major S.A. basins. Soil erodibility was estimated from the Global Gridded Soil Information soil database. LS-factor's topographical parameter was derived from Digital Elevation Models using the "Shuttle Radar Topography Mission" dataset. The R-factor was estimated from a previous study developed for S.A. and the C-factor from the Global Land Cover (Copernicus Global Land Services) database. We used a modeling study for SDR that simulated the annual average sediment transport in 27 basins in S.A. RUSLE set up presented a satisfactory performance compared to other applications on a continental scale with an estimated averaged soil loss for S.A. of 3.8 t ha-1 year-1. Chile (>20.0 t ha-1 year-1) and Colombia (8.1 t ha-1 year-1) showed the highest soil loss. Regarding SDR, Suriname, French Guyana, and Guyana presented the lowest values (<1.0 t ha-1 year-1). The highest soil losses were found in the Andes Cordillera of Colombia and the Center-South Region of Chile. In the former, the combination of "high" K-factor, "very high" C-factor, and "very high" LS-factor were the leading causes. In the latter, agriculture, livestock, deforestation, and aggressive R-factor explained the high soil loss. Basins with the highest SDR were located in the North Argentina - South Atlantic basin (27.73%), Mar Chiquitita (2.66%), Amazon River basin (2.32%), Magdalena (2.14%) (in Andes Cordillera), and Orinoco (1.83%).

Meteorological droughts in part of southeastern Brazil: Understanding the last 100 years.

Droughts have negatively influenced tropical regions on the planet with southeastern Brazil standing out. The objective of this study was to analyze droughts with different magnitudes since the ending of 19th and beginning of the 20th centuries based on the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI), in the locations of São Paulo city (SP - city), metropolitan regions of Belo Horizonte (MR-BH) and Campinas (MR-Campinas), Lavras (South-MG), and Piracicaba (ME-SP). Two different periods were considered: i) wet period (SPI6 and SPEI6) and; ii) summer period (SPI4 and SPEI4). Considering the SPI indexes, the hydrological year of 2013/2014 was the driest observed for South-MG, ME-SP and MR-Campinas, while for MR-BH and SP-city, 1970/1971 and 1962/1963 were the driest, respectively. MR-BH and SP city showed different variability of 1970/1971 and 1962/1963, respectively. We could detect three periods with several consecutive droughts: 1908/1918; 1968/1981 and 2013/2019. Based on SPEI, the 2013/2014 hydrological year was the driest for all the regions, except for SP city, for which 1998/1999 and 1962/1963 were the driest, and MR-BH for which 1970/1971 and 2000/2001 were the driest. Precipitation might be the main factor to evaluate the occurrence of droughts in the studied locations, which indicates SPI is a satisfactory drought indicator for the region.

Rainfall erosivity in South America: Current patterns and future perspectives.

Rainfall erosivity is the driving factor for soil erosion and can be potentially affected by climate change, impacting agriculture and the environment. In this study, we sought to project the impact of climate change on the long-term average annual rainfall erosivity (R-factor) and mean annual precipitation in South America. The CanESM2, HadGEM2-ES, and MIROC5 global circulation models (GCMs) and the average of the GCMs (GCM-Ensemble) downscaled by the Eta/CPTEC model at a spatial resolution of 20 km in the representative concentration pathway (RCP) 8.5 were applied in this study. A geographical model to estimate the R-factor across South America was fitted. This model was based on latitude, longitude, altitude, and mean annual precipitation as inputs obtained from the WorldClim database. Using this model, the first R-factor map for South America was developed (for the baseline period: 1961-2005). The GCMs projected mean annual precipitation for three 30-year time periods (time slices: 2010-2040; 2041-2070; 2071-2099). These projections were used to run the R-factor model to assess the impact of climate change. It was observed that the changes were more pronounced in the Amazon Forest region (namely, the North Region, NR, and the Andes North Region, ANR) with a strong reduction in the mean annual precipitation and R-factor throughout the century. The highest increase in the R-factor was projected on the Central and South Andes regions (CAR and SAR) because of the increase in the mean annual precipitation projected by the GCMs. The GCMs pointed contradictory projections for the Central-South Region (CSR), indicating greater uncertainty. An increase in the R-factor was projected for this region, eastern Argentina, and southern Brazil, whereas a decrease in the R-factor was expected for southeastern Brazil. In general, the GCMs projected reductions in the R-factor and annual precipitation for South America, with the highest changes projected from the baseline to the 2010-2040 time slice.

Spatial distribution of wood volume in Brazilian savannas.

Here we model and describe the wood volume of Cerrado Sensu Stricto, a highly heterogeneous vegetation type in the Savanna biome, in the state of Minas Gerais, Brazil, integrating forest inventory data with spatial-environmental variables, multivariate regression, and regression kriging. Our study contributes to a better understanding of the factors that affect the spatial distribution of the wood volume of this vegetation type as well as allowing better representation of the spatial heterogeneity of this biome. Wood volume estimates were obtained through regression models using different environmental variables as independent variables. Using the best fitted model, spatial analysis of the residuals was carried out by selecting a semivariogram model for generating an ordinary kriging map, which in turn was used with the fitted regression model in the regression kriging technique. Seasonality of both temperature and precipitation, along with the density of deforestation, explained the variations of wood volume throughout Minas Gerais. The spatial distribution of predicted wood volume of Cerrado Sensu Stricto in Minas Gerais revealed the high variability of this variable (15.32 to 98.38 m3 ha-1) and the decreasing gradient in the southeast-northwest direction.

Geomorphology-based unit hydrograph models for flood risk management: case study in Brazilian watersheds with contrasting physiographic characteristics.

Heavy rainfall in conjunction with an increase in population and intensification of agricultural activities have resulted in countless problems related to flooding in watersheds. Among the techniques available for direct surface runoff (DSR) modeling and flood risk management are the Unit Hydrograph (UH) and Instantaneous Unit Hydrograph (IUH). This study focuses on the evaluation of predictive capability of two conceptual IUH models (Nash and Clark), considering their original (NIUH and CIUH) and geomorphological approaches (NIUHGEO and CIUHGEO), and their advantages over two traditional synthetics UH models - Triangular (TUH) and Dimensionless (DUH), to estimate DSR hydrographs taking as reference two Brazilian watersheds with contrasting geomorphological and climatic characteristics. The main results and conclusions were: i) there was an impact of the differences in physiographical characteristics between watersheds, especially those parameters associated with soil; the dominant rainfall patterns in each watershed had an influence on flood modeling; and ii) CIUH was the most satisfactory model for both watersheds, followed by NIUH, and both models had substantial superiority over synthetic models traditionally employed; iii) although geomorphological approaches for IUH had performances slightly better than TUH and DUH, they should not be considered as standard tools for flood modeling in these watersheds.