Gustavo Sueiro Medeiros

Sustainable Development Reserves are organized units in the Amazon that are essential for the proper use and sustainable management of the region’s natural resources and for the livelihoods and economy of the local communities. This study aims to provide a climatic characterization of the Madeira River Sustainable Development Reserve (MSDR), offering scientific support to efforts to assess the feasibility of implementing adaptation measures to increase the resilience of isolated Amazon communities in the face of extreme climate events. Significant statistical analyses based on time series of observational and reanalysis climate data were employed to obtain a detailed diagnosis of local climate variability. The results show that monthly mean two-meter temperatures vary from 26.5 °C in February, the coolest month, to 28 °C in August, the warmest month. Monthly precipitation averages approximately 250 mm during the rainy season, from December until May. July and August are the driest months, August and September are the warmest months, and September and October are the months with the lowest river level. Cold spells were identified in July, and warm spells were identified between July and September, making this period critical for public health. Heavy precipitation events detected by the R80, Rx1day, and Rx5days indices show an increasing trend in frequency and intensity in recent years. The analyses indicated that the MSDR has no potential for wind-energy generation; however, photovoltaic energy production is viable throughout the year. Regarding the two major commercial crops and their resilience to thermal stress, the region presents suitable conditions for açaí palm cultivation, but Brazil nut production may be adversely affected by extreme drought and heat events. The results of this study may support research on adaptation strategies that includethe preservation of local traditions and natural resources to ensure sustainable development.

Evapotranspiration from the Amazon Forest significantly contributes to rainfall over the La Plata Basin. This study aims to evaluate the influence of different components of Amazonian evapotranspiration on precipitation over the La Plata Basin, associated with the moisture transport by the Low‐Level Jet east of the Andes and the South Atlantic Convergence Zone. Four experiments were conducted using the Eta regional climate model from 1998 to 2009. The Control Experiment is set up with the roots of Amazonian trees reaching 2 m deep. The Rootless Experiment is set up without roots in the Amazon Forest. The Deep Root Experiment is set up with the roots of Amazonian trees reaching 7.2 m deep. The Interception Experiment is set up without water storage in the tree canopies of the Amazon Forest. The results of this study show that the transpiration of Amazonian trees is much more important to mean precipitation over the La Plata Basin than interception by tree canopies. Moreover, the deep soil water storage in the southern and eastern portions of the Amazon Basin constitutes a source of moisture to mean rainfall over the La Plata Basin during the austral winter and spring seasons, which is mainly possible by the Low‐Level Jet east of the Andes and the South Atlantic Convergence Zone, respectively. This study recommends that the deep soil water uptake by Amazonian tree roots be considered in weather and climate simulations over the La Plata Basin.

This study aimed to evaluate the performance of the Eta Regional Climate Model in reproducing the seasonal climate over South America for the rainy season from November until April, with emphasis on the Madeira, São Francisco, and Paraná river basins. For this purpose, a 10-year set of 6-month range seasonal hindcasts was produced using the Eta Regional Climate Model at 20-km horizontal resolution driven by the CFSv2 forecasts. In addition to dynamical downscaling, the precipitation and 2-meter temperature were statistically downscaled by applying a Quantile Mapping bias correction. The Eta model forecasts reasonably reproduced the precipitation and temperature patterns in the region, with some errors that were reduced by the statistical downscaling. Precipitation skill scores are higher in the northern and central areas of the continent. Although it has shown mixed performance for extreme events—low in the Paraná basin and limited but useful in the Madeira and São Francisco basins—the dynamical-statistical system developed with the Eta model shows higher skill and added value over the driver model, indicating potential to support water resources management in South America.

Transmission line towers are highly exposed to weather hazards. In southern Brazil, several damages to transmission lines have been reported due to severe weather events. Climate change projections indicate that the frequency and magnitude of extreme events may increase, making planning and adaptation of the transmission system exposure even more critical. In this work, we propose a severe weather index (SWI) as a proxy for detecting severe storms that can potentially result in extreme weather events. The SWI combines thresholds of instability indices based on observed severe events around the transmission tower-line systems. These indices were calculated using the downscaling by the Eta model at 20 km resolution of three global climate model projections and at 5 km of one global climate model for the historical period and the near future. The results show that the SWI captures the severe storms observed in the region. All downscaling projections agree with the increase of extreme weather events in the near future and the expansion of vulnerable areas. The constructed index can be employed to assess the risks and to plan the power transmission system better.