Publicações

Human activities in river basins profoundly affect the environmental balance, causing deforestation, pollution, and erosion, and threatening water quality. One of the most significant impacts of high rates of nutrient transport to aquatic systems is eutrophication. Eutrophication is the excessive enrichment of nutrients in aquatic ecosystems, leading to issues such as toxic algal blooms, uncontrolled growth of aquatic plants, oxygen depletion, and the death of aquatic organisms, ultimately resulting in the degradation of water bodies. The objective of this study was to develop the Water Pollution Risk (RPA) indicator for the municipalities within the Paraíba do Norte River basin. To calculate the RPA, the specialized balance of soil phosphorus and the nutrient load from urban areas were considered. Our results demonstrate that the amount of phosphorus exported is related to the land use and occupation of each region. More urbanized areas with high agricultural activity presented higher RPA. The RPA is an important tool for understanding phosphorus (P) losses at the watershed scale and is essential for guiding the implementation of water resource preservation measures, as well as identifying the regions most vulnerable to impacts on the integrity of ecosystem services.

Identifying the status of excavated fishponds (inactive or productive) remains a significant challenge in remote sensing using visible spectrum data. This study, conducted at the Embrapa experimental farm in Palmas, Tocantins (Brazilian Legal Amazon) between November 2023 and February 2024, assessed 15 excavated fishponds with different treatments (inactive, monoculture, and polyculture). Our objective was to identify inactive ponds using the remote sensing reflectance data from the smartphone application Hydrocolor across various fish production treatments. With the data obtained, we used broadband reflectance data (RGB) and unsupervised clustering analysis (k-means). To define the optimal number of clusters, we used the silhouette score. Three distinct classes were identified: one with higher red band values, another with higher green band values, and a third with heterogeneous spectra lacking spectral dominance. Notably, 80% of the inactive ponds aligned with the class exhibiting high green band values. The results highlight the potential of remote sensing to identify inactive and productive ponds. However, more studies are needed to differentiate between monoculture and polyculture treatment. This study contributes to the project “AI for a Thriving Amazon: Unlocking the Potential of Sustainable Aquaculture.” Our findings highlight the potential of remote sensing as a democratic tool through smartphone use, promoting citizen science among fish farmers. The Hydrocolor app successfully collected data for the presented techniques, demonstrating promising and replicable results. Integrating the app into fish farming practices offers an innovative approach to monitoring and managing aquaculture environments. The successful identification of pond usage status underscores the feasibility of using broadband reflectance data for scalable aquaculture management solutions.

A avaliação dos riscos de impactos das mudanças climáticas associada às questões de resíduos é crucial devido à importância econômica, social e ambiental. Principalmente quando analisamos o setor da construção civil, que por um lado contribui para a economia local e geração de empregos, e por outro é muitas vezes considerado um poluidor do meio ambiente. O objetivo deste trabalho foi avaliar possíveis indicadores de resíduos sólidos urbanos e de construção civil, para análise de riscos de impactos a mudanças climáticas relacionado as chuvas intensas, aplicando a mesma metodologia da plataforma AdaptaBrasil para todos os municípios brasileiros, considerando o momento presente. Foram utilizados 29 indicadores, distribuídos nas dimensões de exposição, vulnerabilidade (sensibilidade e capacidade adaptativa) e ameaça climática. O primeiro resultado observado foi em relação a disponibilidade de indicadores, que são escassos para resíduos de construção civil quando comparado com outros tipos de resíduos. Em relação ao risco, os resultados mostram que a maioria dos municípios apresentam risco baixo (57%) ou médio (41%), e o mesmo comportamento foi verificado para ameaça climática com 75% dos municípios apresentando baixa ameaça. Mesmo com risco baixo, é preciso avaliar as demais dimensões. A vulnerabilidade destacou que 37,4% dos municípios brasileiros estão com alta vulnerabilidade, sendo situados na região nordeste e sudeste do país. Para o índice de sensibilidade destaca-se a região sul, sudeste e nordeste classificado como alta sensibilidade. Já para o índice de capacidade adaptativa, 50% dos municípios brasileiros possuem uma classificação de muito baixa. Isso leva a pensar que se outros indicadores fossem selecionados como tipo e distância de aterros dos municípios juntamente com outros de ameaça climática como mudanças de temperatura, talvez o valor do risco aumentasse devido a alta vulnerabilidade em regiões que estão mais sujeitas a impactos de riscos climáticos (alta exposição) aumentando as consequências ao meio ambiente e a saúde humana. Nessa perspectiva, seria ideal aumentar o nível de capacidade adaptativa, reduzir a sensibilidade, para diminuir a vulnerabilidade. Isso pode ser feito ao melhorar a gestão institucional, com políticas de incentivo à reciclagem tanto para resíduos sólidos como para resíduos de construção civil, equilibrar as receitas orçadas com as receitas recebidas, incentivar mais empresas especializadas em coleta e tratamento de resíduos de construção civil, reduzir a produção de resíduos com utilização de materiais mais sustentáveis. Outra sugestão seria que a gestão municipal tivesse um maior comprometimento ao gerar ou preencher os dados compartilhados com o sistema nacional de informação sobre saneamento e resíduos sólidos, o que facilitaria as análises futuras e auxiliaria o município inclusive a buscar recursos a nível estadual e federal.

Resumo: The southeastern region of Brazil has experienced an increase in extreme precipitation events, with more than a thousand events occurring between 2015 and 2020. Several studies have shown an increase in the frequency and intensity of these events, triggering natural hazards with catastrophic consequences. This is related to the location of the South Atlantic Convergence Zone and deep convection associated with low-level anticyclonic circulations. In addition, blocking systems, cold frontal passages, orographic lift and local convective instability are the main rain producing systems near the coast with heavy and persistent rain. The last event occurred on February 18-19, 2023, on the coast of São Paulo State and became the highest rainfall event in the history of Brazil, when almost 700 mm of rain were recorded in less than 24 hours on the North Coast of São Paulo, causing several damages and dozens of deaths (65) in the region. Given the impact and recurrence of these events, the need for accurate numerical weather prediction (NWP) models became critical to assist decision makers and governments in planning effective actions to avoid or mitigate catastrophic consequences. Several studies have highlighted the challenges of accurately predicting rainfall intensity and location, the dependence on appropriate initial conditions, the importance of microphysical schemes, and the appropriate choice of parameterizations. Our main objective was to apply the Model for Prediction Across Scales Atmosphere (MPAS) model with a variable-resolution mesh under convection-permitting resolution to perform the simulation for the extreme rainfall event over the north coast of São Paulo. A 60-3 km variable resolution mesh with refinement centered on the city of São Sebastião was configured. Model simulations were driven by GDAS/FNL analysis and ERA5 reanalysis to investigate sensitivity to initial conditions. Gridded data from MERGE for daily accumulated precipitation were used to evaluate the simulated daily accumulated precipitation. Data from CEMADEN automatic rain gauges distributed over the coast of São Paulo are also used for precipitation evaluation, while INMET automatic weather stations provide weather variables over Brazil for general evaluation. The ERA5-driven simulation showed more precipitation activity on the north coast according to the simulated radar reflectivity compared to the GDAS/FNL-driven simulations. The bias computed with MERGE data as reference shows a general underestimation for all model runs over the main region. The comparison of simulated hourly precipitation with CEMADEN rain gauge data showed in general a better representation of the observed precipitation by the ERA5-driven simulation, but all simulations were not able to reproduce the precipitation intensity and timing exactly. Three microphysical schemes were also tested: Thompson (convection-permitting suit), WSM6, and Kessler. The results show similar performance in reproducing weather variables, especially for Thompson and WSM6, with slightly better results for the WSM6 run. All simulations predicted rainfall in excess of 250 mm/day in the most affected area. These preliminary results provide valuable information on the performance of MPAS, and the evaluation needs to be extended to other physical parameterizations and model configurations.

This study aims to analyze the synoptic patterns responsible for the different systems development that led to extreme precipitation events in Brazilian Northeast (BNE) on three different dates – May 27, 2017, February 20, 2020, and March 14, 2023. Despite having the driest region of Brazil (The Sertão) with rainfall values not exceeding 400 mm per year, BNE is characterized by the influence of several synoptic-scale systems throughout the year that contribute to extreme precipitation events over the region. ERA5 global reanalysis data have been used for synoptic and vertical structure evaluation as a first step of analysis. MERGE precipitation product and pluviometric stations were used to document accumulated precipitation produced by these events. The following step will consist of conducting simulations at a very high-resolution using the Model for Prediction Across Scales (MPAS) to assess its ability to represent the circulation patterns associated with the extreme precipitation events analyzed. The three cases were situated in different sectors of BNE and were caused by different synoptic-scale systems: Easterly Wave Disturbances (EWD), Upper Tropospheric Cyclonic Vortex (UTCV) and Intertropical Convergence Zone (ITCZ), respectively. The first analyzed event was caused by an EWD, where the axis of a trough at the 850 hPa level was observed along with intense moisture transport along the eastern coast of BNE, resulting in an intense rainfall of 173 mm/24h. The second event, considered more atypical, was triggered by an UTCV, also along the BNE eastern coast. The precipitation from this system resulted in 68 mm in just 4 hours, surpassing the historical February average in Maceió, Alagoas. Finally, the third event was caused by the ITCZ and a Mesoscale Convective System (MCS) convection, resulting in heavy rains (140 mm/24h) over the Maranhão state, especially in the city of São Luís. Despite the different circulation patterns of the analyzed synoptic systems, some similar characteristics were identified, such as intense moisture convergence and strong wind shear, in addition to negative surface relative vorticity. Topography played a significant role. The first case analyzed (on the east coast) recorded the highest rainfall among the three cases, despite not presenting cloudiness with great vertical development. This occurred due to the high convergence of moisture transport combined with the elevated relief to the west, which facilitated increased convective activity. Vertical profiles of specific humidity and vertical air movements proved to be very important fields for analyzing the circulation patterns of synoptic scale systems, which in turn have quite different characteristics. Furthermore, it demonstrated its significance as a valuable instrument for enhancing comprehension of the vertical thermodynamic structures of the extreme precipitation events.

This study focuses on solar energy generation as an alternative source of renewable energy in Brazil, driven by the growing demand in this sector. In recent years, photovoltaic energy technology has undergone significant advancements, resulting in increased efficiency and cost reduction. However, in Brazil, the availability of observed solar irradiance data, essential for validating the generation potential of this form of energy, is extremely limited. Given that in some regions of the country the only available data is extrapolated from weather stations in other locations, this study adopts the approach of using data from meteorological models to fill these gaps. The study focused on using the second generation of the MERRA-2 reanalysis, which has the positive feature of assimilating aerosol data provided by GMAO/NASA, to estimate solar irradiance in tropical and subtropical regions of Brazil. This approach allowed an evaluation of the impact of these scenarios on solar irradiance estimation, validated through observational data collected at surface stations by INMET (National Institute of Meteorology). The variables used from the MERRA-2 reanalysis and analyzed in this work are Surface Net Downward Shortwave Flux (SWGNT), Surface Net Downward Shortwave Flux assuming no aerosol (SWGNTCLN), Surface Net Downward Shortwave Flux assuming clear sky (SWGNTCLR), and Surface Net Downward Shortwave Flux assuming clear sky and no aerosol (SWGNTCLRCLN). Seeking a better understanding of the effects of clouds and aerosols on the solar irradiance of the studied model, data from all regions of Brazil will be considered. Forty-two weather stations were compared with reanalysis data estimates, considering cloud cover and aerosol inclusion. The study revealed a high correlation between observations and the MERRA-2 reanalysis, highlighting the relevance of cloud effects in solar irradiance estimation. MERRA-2 achieved the best results when considering aerosol effects in situations of high concentration, as during the months of September in Campo Grande – MS, one of the regions most affected by fires in the Amazon region.

When sources of climate change information do not agree or are misaligned to decision contexts, there is a lack of consensus on how to resolve these two problems to inform local-to-regional decision-making that leads to consequences: recently the new World Climate Research Programme (WCRP) Core Project on Regional Information for Society (RIfS) held an expert meeting to address this problem. The members of the program committee for that meeting, on Robustness of Climate Change Information for Decisions, will share the ideas, intentions and outcomes of this event, which occured in April 2024. In particular, we will focus on the topics of defining “robustness”, dialogues among communities, and lessons from the past. Our process for organizing and running the meeting was focused around active participation and dialogue. We will also summarize some of the key outcomes and discuss next steps for our community, including discussions of guidance and minimum standards, North-South partnerships, the ethical dimensions of transparency and accountability, and research to reduce epistemic uncertainties with regional consequences.