Search for: All records

Abstract Prioritizing watershed management interventions relies on delineating homogeneous precipitation regions. In this study, we identify these regions in the Brazilian Legal Amazon based on the magnitude of Sen’s Slope trends using annual precipitation data from September to August, employing the Google Earth Engine platform. Utilizing the silhouette method, we determine four distinct clusters representing zones of homogeneous precipitation patterns. Cluster 0 exhibits a significant median increase in precipitation of 3.20 mm year⁻¹over the period from 1981 to 2020. Cluster 1 shows a notable increase of 8.13 mm year⁻¹, while Clusters 2 and 3 demonstrate reductions in precipitation of − 1.61 mm year⁻¹and − 3.87 mm year⁻¹, respectively, all statistically significant. Notably, the region known as the arc of deforestation falls within Cluster 2, indicating a concerning trend of reduced precipitation. Additionally, our analysis reveals significant correlations between Sea Surface Temperature (SST) in various oceanic regions and precipitation patterns over the Brazilian Legal Amazon. Particularly noteworthy is the strong positive correlation with SST in the South Atlantic, while negative correlations are observed with SST in the South Pacific and North Atlantic. These findings provide valuable insights for enhancing climate adaptation strategies in the Brazilian Legal Amazon region. 

Abstract Water is redistributed from evaporation sources to precipitation sinks through atmospheric moisture transport. In the Brazilian Amazon, the spatial and temporal variability of dry season moisture sources for key agricultural regions has not been investigated. This study investigates moisture sources for dry season rainfall in the state of Rondônia in Brazil, especially during drought years. Using a precipitationshed framework, we quantified the variability of moisture contributions to rainfall in the state of Rondônia (Brazilian Amazon) and the influence of synoptic circulation patterns. Ocean evaporation accounts for 58% of mean dry season precipitation while continental recycling contributed 42%. During drought years, although forests maintain or increase evapotranspiration, the moisture contribution of both ocean and forests to dry season rainfall decreases due to the synoptic circulation changes, reducing the moisture transport into Rondônia. 

Abstract Tropical rainforests provide essential ecosystem services to agricultural areas, including moisture recycling. In the Amazon basin, drought frequency has increased in the late 20th and early 21st centuries, but the role of forests, ocean, and nonforested areas in causing or mitigating drought has not been determined. Using a precipitationshed moisture tracking framework, we quantify the contribution sources of evaporation to rainfall in Rondônia in the Brazilian Amazon. Forests account for ∼48% of annual rainfall on average, and more than half of the forest source is from protected areas (PAs). During droughts in 2005 and 2010, moisture supply decreased from oceans and nonforested areas, while supply from forests was stable and compensated for the decrease. Remote sensing and land surface models corroborate the relative insensitivity of forest evapotranspiration to droughts. Forests mitigate drought in the agricultural study region, providing an important ecosystem service that could be disrupted with further deforestation. 

Abstract Rainforest in protected areas in the Brazilian Amazon is at risk due to increasing economic pressures and recent weakening of environmental agencies and legislation by the federal administration. This study examines the impacts of deforestation in protected areas on dry‐season precipitation in the Brazilian state of Rondônia located in the southwestern Brazilian Amazon. Regional‐climate model simulations indicate that clearing protected forests in Rondônia would result in substantial changes to the surface energy balance, including increased sensible and decreased latent heat flux. Consequent changes to low‐level wind circulation would enhance moisture flux convergence and convection over the newly deforested areas, leading to enhanced rainfall in those areas. However, deforestation of protected areas would decrease dry season rainfall up to 30% in the existing agricultural region, with potentially important negative impacts on agricultural production. Additionally, our results indicate that following deforestation, the newly degraded areas will experience warmer and drier afternoons that could place the remaining natural vegetation under vapor deficit stress. 

This study examines the lived experiences and adaptation strategies of small-scale farmers in the southwestern Brazilian Amazonian state of Rondônia, amidst escalating climate challenges. Through nine in-depth interviews, it uncovers the impact of unpredictable weather, increased temperatures, and shifting precipitation on agriculture and livelihoods. Participants, ranging from family farmers to agricultural collective members, detail shifts from traditional crop cultivation to more resilient practices like cattle ranching and dairy production. The narratives reveal a deep understanding of local climate volatility and its direct effects on water availability, crop viability, and livestock productivity. Farmers describe adaptation measures including new crop varieties, irrigation systems, and strategic land use to enhance biodiversity and mitigate climate change effects. Despite these adaptations, challenges like water scarcity, high input costs, and the need for technical assistance remain prominent. Farmers emphasize the need for stronger support systems, highlighting community solidarity, governmental aid, and access to sustainable technologies and education as essential for climate adaptation. They call for policies providing equitable resources and support, underscoring the importance of inclusive climate governance that acknowledges the unique vulnerabilities and contributions of Rondônia’s agricultural sector. This research contributes to understanding how climate change reshapes rural Amazonian communities, arguing that ongoing deforestation and climatic changes threaten regional agricultural stability. It advocates for targeted policy interventions to provide technical assistance for sustainable farming and climate adaptation, alongside mechanisms to support fair market pricing. These measures are essential for enhancing the resilience and sustainability of local farming communities amidst climate change. 

This study uses a combined research approach based on remote-sensing and numerical modeling to quantify the effects of burned areas on the surface climate in the two Brazilian biomes most affected by fires: the tropical savanna and the Amazon rainforest. Our estimates indicate that between 2007 and 2020, approximately 6% of the savanna and 2% of the rainforest were burned on average. Non-parametric regressions based on 14-year climate model simulations indicate that latent heat flux decreases on average by approximately 0.17 W m−2 in the savanna and 0.60 W m−2 in the rainforest per each 1 km2 burned, with most of the impacts registered during the onset of the wet season. Sensible and ground heat fluxes are also impacted but at less intensity. Surface air is also warmer and drier, especially over rainforest burned sites. On average, fire reduced gross primary production in the savanna and rainforest by 12% and 10%, respectively, in our experiments. 

The Brazilian Amazon provides important hydrological cycle functions, including precipitation regimes that bring water to the people and environment and are critical to moisture recycling and transport, and represents an important variable for climate models to simulate accurately. This paper evaluates the performance of 13 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This is done by discussing results from spatial pattern mapping, Taylor diagram analysis and Taylor skill score, annual climatology comparison, cumulative distribution analysis, and empirical orthogonal function (EOF) analysis. Precipitation analysis shows: (1) This region displays higher rainfall in the north-northwest and drier conditions in the south. Models tend to underestimate northern values or overestimate the central to northwest averages. (2) The southern Amazon has a more defined dry season (June, July, and August) and wet season (December, January, and February) and models simulate this well. The northern Amazon dry season tends to occur in August, September, and October and the wet season occurs in March, April, and May, and models are not able to capture the climatology as well. Models tend to produce too much rainfall at the start of the wet season and tend to either over- or under-estimate the dry season, although ensemble means typically display the overall pattern more precisely. (3) Models struggle to capture extreme values of precipitation except when precipitation values are close to 0. (4) EOF analysis shows that models capture the dominant mode of variability, which was the annual cycle or South American Monsoon System. (5) When all evaluation metrics are considered, the models that perform best are CESM2, MIROC6, MRIESM20, SAM0UNICON, and the ensemble mean. This paper supports research in determining the most up-to-date CMIP6 model performance of precipitation regime for 1981–2014 for the Brazilian Amazon. Results will aid in understanding future projections of precipitation for the selected subset of global climate models and allow scientists to construct reliable model ensembles, as precipitation plays a role in many sectors of the economy, including the ecosystem, agriculture, energy, and water security.