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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.

 

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.

 

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 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. 

Evapotranspiration (ET) connects the land to the atmosphere, linking water, energy, and carbon cycles. ET is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in ET are needed from regional to continental scales. This study compared eight global actual ET datasets (ETgl) and the average actual ET ensemble (ETens) based on remote sensing, climate reanalysis, land-surface, and biophysical models to ET computed from basin-scale water balance (ETwb) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of ET, comparing ETgl and ETens with ETwb. Global ET datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using ETwb as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (ETens), and RMSE ranged from 35.6 (ETens) to 40.5 mm·month−1 (MOD16). Overall, ETgl estimates ranged from 0 to 150 mm·month−1 in most basins in South America, while ETwb estimates showed maximum rates up to 250 mm·month−1. ETgl varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly ETgl and ETwb, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between ETgl and ETwb in the humid tropics (in the Amazon), with ETgl showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between ETgl and ETwb for interannual variability. Assessing ET datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of ET estimation and monitoring for water management.