Publicações

O trabalho analisa a percepção da paisagem na bacia do Paraíba do Sul (trecho paulista), comparando dados de mapas de uso e cobertura com dados da pesquisa de percepção ambiental realizada na bacia. A análise na paisagem da região é importante para ajudar a identificar as atividades humanas ocorridas e como estas transformaram o território. Além da pesquisa de percepção socioambiental, realizada em 2019, foram utilizados dados de uso e cobertura da terra do projeto MapBiomas (1985 e 2019) e outros estudos envolvendo a bacia, verificando as principais mudanças ocorridas na paisagem nesses 34 anos e se isso foi percebido pelos entrevistados. Os resultados mostram que algumas mudanças na paisagem da região que foram percebidas pelos entrevistados também foram observadas nos mapas de uso e cobertura e foram corroboradas por pesquisas em todo o Vale do Paraíba. É o caso do aumento da monocultura do Eucalipto observada por eles como reflorestamento, aumento da área urbanizada e de queimadas que podem ter relação com a pecuária e contribuem para a poluição do ar. Uma parcela significativa dos entrevistados apresentou sugestões para melhoria da situação ambiental da sua região, sendo que das respostas obtidas a Educação Ambiental foi a solução mais mencionada pelos participantes. Espera-se que este estudo incentive políticas públicas mais incisivas em educação ambiental para a população da bacia, levando-os a se mobilizar e reconhecer a importância de se preservar a natureza para o futuro da humanidade.

Large-scale forest restoration is vital for delivering a broad array of ecosystem services benefits to society. However, it is often perceived as an economically noncompetitive land use choice. Integrating economic opportunities into restoration aligns socioeconomic and environmental goals, reducing conflicts between forest production and conservation-oriented management decisions. Supply chains focusing on high-value goods can enhance the reach of forest restoration efforts and unite ecological and economic benefits in a multifunctional manner. The bioeconomy has emerged as a potential but critical driver for attracting investments in restoration. We outline the challenges and solutions to reconcile forest restoration and bioeconomy, specifically about (i) native timber production, (ii) non-timber forest products, (iii) biotechnological products, and (iv) intangible ecosystem services. This requires collaborative and multidisciplinary efforts to improve investment in large-scale projects. The intricacies of these issues intersect with research development, market dynamics, legal frameworks, and regulatory paradigms, underscoring the necessity for nuanced and tailored public policy interventions. These integrated approaches should enable tropical countries to lead the global forest-based economy and usher in a new era of forest restoration.

Amazonia’s net biome exchange (NBE), the sum of biogenic and wildfire carbon fluxes, is a fundamental indicator of the state of its ecosystems. It also quantifies the magnitude and patterns of short- and long-term carbon dioxide sources and sinks but is poorly quantified and out of equilibrium (non-zero) due to both direct (deforestation) and indirect (climate-related) anthropogenic disturbance. Determining trends in Amazonia’s carbon balance, shifts in carbon exchange pathways of NBE, and timescales of ecosystem sensitivity to disturbance requires reliable biogenic flux models that adequately capture fluxes from diurnal to seasonal and annual timescales. Our study assimilates readily available observations and a derived solar-induced fluorescence (SIF) product to estimate hourly biogenic carbon dioxide (CO2) fluxes (here in units of μmolCO2m-2s-1) as net ecosystem exchange (NEE), as well as its photosynthesis and respiration constituents, at 12 km resolution using four versions of the data-driven diagnostic Vegetation Photosynthesis and Respiration Model (VPRM). The VPRM versions are all calibrated with ground-based eddy flux data and vary based on whether (1) the photosynthesis term incorporates SIF (VPRM-SIF) or traditional surface reflectance (VPRM-TRA) and (2) the respiration term is modified beyond a simple linear air temperature dependence (VPRM-SIFg; VPRM-TRG). We compare the VPRM versions with each other and with hourly fluxes from the bottom-up mechanistic Simple Biosphere 4 (SiB4 v4.2) model. We also use NASA’s Orbiting Carbon Observatory (OCO-2) CO2 column observations to optimize the VPRM and SiB4 models during the 2016 wet season which occurred at the tail of the 2015/2016 severe El Niño. The wet season 2016 case study suggests that relative to SiB4 and the SIF-based VPRMs, the traditional VPRM versions can underestimate uptake by a factor of 3. In addition, the VPRM-SIFg version better captures biogenic CO2 fluxes at hourly to seasonal scales than all other VPRM versions in both anomalously wet and anomalously dry conditions. We also find that the VPRM-SIFg model and the independent bottom-up mechanistic hourly SiB4 model converge in NEE, although there are differences in the partitioning of the photosynthesis and respiration components. We further note that VPRM-SIFg describes greater spatial heterogeneity in carbon exchange throughout the Amazon. Despite the paucity of OCO-2 CO2 column observations (X CO2) over the Amazon in the wet season, incorporating X CO2 into the models significantly reduces near-field model-measurement mismatch at aircraft vertical profiling locations. Finally, a qualitative analysis of the unoptimized biogenic models from 2010-2020 agrees with the wet season 2016 case study, where the traditional VPRM formulations significantly underestimate photosynthesis and respiration relative to VPRM-SIFg. Overall, the VPRM-SIFg biogenic flux model shows promise in its ability to capture Amazonian carbon fluxes across multiple timescale and moisture regimes, suggesting its suitability for larger studies evaluating interannual and seasonal carbon trends in fire as well as the biogenic components of the region’s NB

The authors would like to point out that unfortunately, a factual error has been detected in the original published article which requires correction. This error appears on page 589, in the following statement:

“Notwithstanding the inherent value this Action Track represents, at the time of writing it has not yet been possible to define an indicator or benchmark for teachers and the teaching profession as stipulated in this Action Track, despite this being an essential point for the transformation of education. Participants preferred to avoid creating another multistakeholder structure, as there is already an International Task Force on Teachers for Education 2030 (TTF).”

This statement is only partially accurate, as indicator 4.c.1 currently monitors the number of pre-primary teachers who receive training. However, this indicator does not correspond to all the aspects that were discussed within Action Track 3 on teachers, teaching, and the teaching profession. The corrected statement should therefore read as follows

“Notwithstanding the inherent value of this Action Track to transforming education, at the moment there is one indicator that monitors solely the proportion of teachers with the minimum required qualifications in pre-primary education (Indicator 4.c.1).

The increased intensity and frequency of future climate extremes might invalidate current water policies, particularly in the case of basins with competing water uses. To analyze these challenges, the nexus approach was proposed as an alternative to integrated resources management by simultaneously taking into account synergies and trade-offs on water, energy, and food security. However, the application of nexus approaches is incipient, and scarcity events highlighted challenges in implementing its principles, which are crucial for meeting Sustainable Development Goals (SDGs). Therefore, this study

proposes a set of water, energy, and food indicators that reflect the SDGs, focusing on water use in the Paraiba do Sul River Basin, Brazil for the period 2016-2050. Results showed a limited influence of land use policies on future resource availability, with differences of less than 5% among scenarios. Projections revealed an increased frequency of extreme discharge compared with the baseline period. Moreover, the analysis of the scenarios revealed a higher probability of failing to meet one of the water transfer targets of the basin. The efficiency of wastewater treatment will significantly impact freshwater quality, indicating low water quality 90% of the time considering the current level of wastewater treatment.

A quarter of the deforested Amazon has regrown as secondary tropical forest and yet the climatic importance of these complex regenerating landscapes is only beginning to be recognised. Advances in satellite remote-sensing have transformed our ability to detect and map changes in forest cover, while detailed ground-based measurements from permanent monitoring plots and eddy-covariance flux towers are providing new insights into the role of secondary forests in the climate system. This review summarises how progress in data availability on Amazonian secondary forests has led to better understanding of their influence on global, regional and local climate through carbon and non-carbon climate benefits. We discuss the climate implications of secondary forest disturbance and the progress in representing forest regrowth in climate models. Much remains to be learned about how secondary forests function and interact with climate, how these processes change with forest age, and the resilience of secondary forest ecosystems faced with increasing anthropogenic disturbance. Secondary forests face numerous threats: half of secondary forests in the Brazilian legal Amazon were 11 years old or younger in 2023. On average, 1%-2% of Amazon secondary forests burn each year, threatening the permanence of sequestered carbon. The forests that burn are predominantly young (in 2023, 55% of burned secondary forests were <6 years old, <4% were over 30 years old). In the context of legally binding international climate treaties and a rapidly changing political backdrop, we discuss the opportunities and challenges of encouraging tropical forest restoration to mitigate anthropogenic climate change. Amazon secondary forests could make a valuable contribution to Brazil’s Nationally Determined Contribution provided there are robust systems in place to ensure permanence. We consider how to improve communication between scientists and decision-makers and identify pressing areas of future research.

Brazil is a country highly dependent on hydroelectricity. Because electricity generation is programmed monthly, one of the most critical information is the inflow of the main hydropower plants of the country. In this context, this study evaluates the predictability and accuracy of raw and bias-corrected ECMWF S2S forecasts as input of a hydrological model, focusing on high and low-flows within the Tocantins-Araguaia Basin in Brazil. Rainfall forecasts were also evaluated against observations across 22 sub-basins of the Tocantins-Araguaia River, considering lead times ranging from 1 to 28 days. Results indicated that the predictability of S2S forecasts vary with the sub-basin area and through seasons (wet and dry periods). Bias corrections were able to improve the accuracy of the forecasts for all lead-times and across sub-basins of different sizes. In the case of low-flows, using raw ECMWF S2S rainfall forecasts as input of the hydrological model resulted in streamflow forecast errors that increase with the scale of the sub-basin. For high-flows results are the opposite, errors are larger in small sub-basins and smaller at larger scales. This is due to the overestimation of dry season rainfall in the ECMWF S2S forecasts. When using bias-corrected rainfall forecasts as input, river flow forecasts are useful up to 28 days of lead-time. The results of this study revealed the potential of using subseasonal forecasts for decision-making in the Tocantins-Araguaia Basin, and encouraging further developments.

The future state of the global water cycle and the prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several

United Nations Sustainable Development Goals. The Global Precipitation Experiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing, and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling, and experimental research and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here, we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2-3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multiscale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

Linking knowledge of energy fluxes and evapotranspiration (ET) in cacti is useful for understanding plant growth, which acts as an extremely important feed source for dairy farming. However, there is little quantitative understanding of the latent (LE) and sensible (H) heat fluxes in drylands of cactus. We therefore investigated the feedback from plants of Nopalea and Opuntia on an interannual and seasonal scale, of the surface energy balance (SEB) components, morphophysiological parameters, biomass, water relations and photochemical relationships during the wet and dry seasons of 20182021 in the semi-arid region of Brazil. To analyse the data, four periods were selected (dry season, wet season, and dry-wet and wet-dry transitions). Our results showed that the LE (105.26 W m−2) of Opuntia was 77 % greater than that of Nopalea. In all seasons, H was the SEB component with the highest energy consumption in the two cacti, with LE the second most affected. Under Nopalea, the soil heat flux increased at the surface (112 %) during the wet and dry seasons (a mean of 1.61 MJ m−2 day−1). The mean ET of Nopalea and Opuntia was 1.71 and 1.96 mm day−1 respectively. The H/Rn ratio showed decreasing behaviour from the dry to the wet season, with a reduction of 37 % in Nopalea and 14 % in Opuntia. The NDVI ranged from 0.19 to 0.67 (Nopalea), and 0.170.70 for Opuntia, similar to the Chlorophyll Index, which maintained the seasonality of the NDVI. Overall, we found spatial patterns for the Photochemical Reflectance Index of −0.01 to 0.14 for Nopalea and Opuntia. Under the same conditions, Opuntia showed a higher growth rate and net assimilation rate. The mean cladode water content was 86 % in Nopalea and 89 % in Opuntia. Biomass and water use efficiency were greater in Opuntia (56.01 Mg ha−1 and 7.54 kg m−3 respectively). We also found that these comparisons indicate greater sensitivity in Nopalea, and more-significant quantification of the SEB in Opuntia.

A Ciência do Sistema Terrestre tem uma abordagem interdisciplinar, que permite analisar fenômenos complexos com rigor científico, uma vez que o planeta é um sistema dinâmico e complexo. A abordagem também permite que as instituições públicas sejam uma espécie de proxy para avaliar a atuação em relação aos temas socioambientais, uma vez que o poder público tem papel de protagonismo na emanação de diretrizes e na implantação de políticas públicas nessa área. No caso em específico dessa tese, foram analisados dados da capacidade institucional de enfrentamento às mudanças climáticas. Para isso, foi construída uma matriz de indicadores baseada nos dados obtidos pelas edições de 2013, 2017 e 2020 da MUNIC, a Pesquisa de Informações Básicas Municipais do IBGE, especificamente dos suplementos Meio Ambiente e Gestão de Riscos. Para verificar a capacidade de análise da matriz de indicadores construída, utilizou-se o estudo de caso da Região Metropolitana de São Paulo (RMSP), com o objetivo de verificar, em âmbito metropolitano e municipal, a evolução da capacidade institucional entre diferentes levantamentos da MUNIC. Foi constatada a deterioração da capacidade institucional para enfrentamento às mudanças climáticas nos municípios da RMSP entre 2013 e 2020. Esse processo de deterioração foi desigual e atingiu de forma mais intensa municípios de menor porte. Também foi possível perceber que, apesar de terem um papel importante, as políticas da cidade de São Paulo em relação ao tema das mudanças climáticas, como centralidade da RMSP, não conseguem influenciar a região toda. Com base no diagnóstico, recomenda-se a criação de um Sistema Nacional de Enfrentamento às Mudanças Climáticas com iniciativas regionalizadas para monitorar e melhorar a capacidade institucional de municípios, sobretudo os de pequeno e médio porte.

The generation of electric energy through renewable sources, such as solar photovoltaic (PV) systems, has emerged as one solution to the climate change crisis. To avoid fluctuations in energy generation on the electricity grid, reliable and accurate forecasts are required. Statistical downscaling methodologies often rely on data from General Circulation Model (GCM) to provide information on the local level. In contrast to traditional down scaling approaches, the present study focuses on a data-driven approach using the Global Data Assimilation System (GDAS) database in three different ways to feed the machine learning (ML) models, based on boosting and bagging, to provide medium-term Global Horizontal Irradiance (GHI) forecasts, i.e. up to seven days ahead. In addition, feature selection and feature augmentation techniques are also applied. The results showed that Extra Trees method outperformed the other tested in the present study, with a RMSE of 99.6 and 137.6 W/m2 , and correlation 0.31 and 0.52 for June and October, respectively. Thus far, this work marks the initial suggestion of incorporating GDAS analysis data into a data-driven forecasting approach employing bagging and boosting models. This innovative approach demonstrates the potential for integrating global-scale data assimilation sys tems with local measurements using ML techniques. The outcomes of this research contribute to advancing the field of traditional downscaling methods and provides more robust and specialized forecasting.

The UN sustainable development goals (SDGs) and the Paris climate target require a holistic transformation towards human well-being within planetary boundaries. However, there are growing debates on how to best pursue these targets. Proposed transformation strategies include market- and technology-driven green-growth, shifting towards a sufficiency-oriented post-growth economy, and a transformation driven primarily by strong government action. Here we quantify three alternative sustainable development pathways (SDPs), Economy-driven Innovation, Resilient Communities, and Managing the Global Commons, that reflect these different societal strategies. We compare the quantifications from two integrated assessment models and two sectoral models of the buildings and materials sectors across a broad set of indicators for sustainable development and climate action. Our global multi-scenario and multi-model analysis shows that all three SDPs enable substantial progress towards the human development goals of the SDGs. They simultaneously limit global warming and prevent further environmental degradation, with the sufficiency-oriented Resilient Communities scenario showing the lowest peak warming and lowest reliance on carbon dioxide removal as well as the largest improvements in biodiversity intactness. The SDPs also alleviate the concerns about the biogeophysical and technological feasibility of narrowly-focused climate change mitigation scenarios. However, the shifts in energy and food consumption patterns assumed in the SDPs, ranging from moderate in Economy-driven Innovation to very ambitious in Resilient Communities, also lead to increased challenges regarding socio-cultural feasibility.