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%).

Minimum streamflow regionalization in a Brazilian watershed under different clustering approaches

Estimating the minimum streamflows in rivers is essential to solving problems related to water resources. In gauged watersheds, this task is relatively easy. However, the spatial and temporal insufficiency of gauged watercourses in Brazil makes researchers rely on the hydrological regionalization technique. This study's objective was to compare different hierarchical and non-hierarchical clustering approaches for the delimitation of hydrologically homogeneous regions in the state of Rio Grande do Sul, Brazil, aiming to regionalize the minimum streamflow that is equaled or exceeded in 90% of the time (Q90). The methodological development for the regionalization of Q90 consisted of using regression analysis supported by multivariate statistics. With respect to independent variables for regionalization, this study considered the morphoclimatic attributes of 100 watersheds located in southern Brazil. The results of this study highlighted that: (i) the clustering techniques had the potential to define hydrologically homogeneous regions, in the context of Q90 in the Rio Grande do Sul State, mostly the Ward algorithm associated with the Manhattan distance; (ii) drainage area, perimeter, centroids X and Y, and mean annual total rainfall aggregated important information that increased the accuracy of the cluster; and (iii) the refined mathematical models provided excellent performance and can be used to estimate Q90 in ungauged rivers.

Influence of different relief information sources on the geomorphological characterization of small watersheds

Mathematical models have been widely used to quantify hydrological processes for various practical purposes. These models depend on geomorphological attributes which are derived from relief information represented by Digital Elevation Models (DEM). The objective of this study was to evaluate the influence of relief information sources (ASTER, SRTM-30, SRTM-90, and TOPO) over geomorphological characterization of five Brazilian watersheds. Geoprocessing tools were applied for extraction of the following geomorphological attributes for each DEM: drainage area, perimeter, and watershed slope; length and slope of the main stream; total length of streams; bifurcation, stream length and stream area ratios; and length of the highest order stream. The differences in the values of attributes were calculated in relation to the reference DEM (TOPO). It was found that: i) slope of main stream and bifurcation ratio were the most sensitive parameters regarding the relief information source; ii) flat watersheds were more susceptible to altimetric errors; iii) ASTER did not adequately represent drainage networks for flat watersheds; and iv) the differences in the geomorphological attributes increased as drainage area decreased. The results indicate that DEM may exert influence on the use of hydrological models that depend on geomorphological attributes.

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.