One of the actions to mitigate the impacts of hydrological extremes is to issue warnings as far in advance as possible. This article reports the application of neural networks for water level forecast to three small watersheds in Brazil that are susceptible to flash floods. First, the physical characteristics and land use and cover maps of the watersheds were surveyed. Next, Multilayer Perceptrons were trained with observed water level and rainfall data covering the period 2014 to 2022 to make water level forecasts 1, 2 and 3 h in advance. To design the neural networks, different combinations of activation functions in the hidden and output layers were tested and also variations in the number of neurons in the hidden layer. The neural networks forecasts for the three watersheds test data were quite good for the three forecast horizons, highlighting the forecasts 3 h in advance that reached a Nash–Sutcliffe index greater than 0.9. In future work, neural networks will be trained with rainfall estimates obtained from numerical weather forecast models data and observed rainfall data, enabling their operational use in the National Center for Monitoring and Early Warning of Natural Disasters situation room. The operational neural models can semi-automate the flash flood warning process for the studied watersheds. As a result, the warnings effectiveness, concerning the advance-assertiveness trade-off, is expected to improve.
The United Nations Office for Disaster Risk Reduction and the World Meteorological Organization launched in 2022 the executive plan of the world program "Early Warning Systems for All" to be implemented from 2023 to 2027. This program champions an investment of USD 3.1 billion into the four pillars of warning systems and calls for multi-hazard and people-centered warning systems (PCWS). However, there is a scientific gap concerning interdisciplinary approaches to promoting them. Motivated by the call for action of "Early Warning Systems for All" and warning research gaps on the lack of interdisciplinarity, a workshop series "Interdisciplinary Approaches for Advancing People-Centered Warning Systems" was held in early 2023. This short article shares the preliminary findings and recommendations of this research, which involved a transnational virtual dialogue between one scientific organization in Brazil and one from the United States. The findings and recommendations discussed in three virtual sessions and one collective working paper were centered on three aspects: promoting interdisciplinary integration in research; the need to discuss the characteristics of a PCWS; and promoting problem and solution-based programs with people to integrate them at all phases of the warning system.
Disaster forensic approaches aim to identify the causes of disasters to support disaster risk management. However, few studies have conducted a systematic literature review of scientific articles that labeled themselves as a forensic approach to disasters. This article provides a qualitative analysis of these forensic studies, focusing on five main issues: (1) the methodologies applied; (2) the forensic approaches used in the disaster risk management phases; (3) the hazards addressed; (4) if the methodologies involve social participation, and using what types of participation; and (5) if there are references to urban planning in the scientific studies analyzed. Our results showed a predominance of the Forensic Investigations of Disasters (FORIN) and Post-Event Review Capability (PERC) methodologies used in isolation or combination. There is a need for methodologies that engage people in participatory FORIN, fostering the co-production of knowledge and action research approaches.
The analytical treatment of some of the primary environmental controls on landslides has demonstrated that the landslide size distribution in the Paraitinga River basin, southeastern Brazil, are influenced by parameters such as triggering rainfall, lithology, slope gradient, upslope area, and land cover. Through the mapping of the surface of rupture of 1102 landslides triggered during an intense rainfall event in the summer of 2009/2010 and the fitting of a probability density function, we have discovered that the probability of large landslides occurring was significantly disturbed by lithology, slope gradients > 0.56 m/m, and triggering rainfall exceeding 180 mm within 48 h. In contrast, the probability density function for small landslides was affected by slope gradients less than < 0.31 m/m, rainfall less than 120 mm within 48 h, and higher values of upslope area. Moreover, the area of landslides in the probability peaks substantially increased when lithology consisted of schist or quartzite and in forested areas. These findings corroborate previous studies that establish a relationship between landslide size distributions and triggering processes, as well as the mechanics of landslides based on stability models, indicating a variation in triggering processes between the two segments of the curve: rollover (small landslides) and tail (large landslides). Full text available for visualization at: https://rdcu.be/dpFHC
Soil nitrous oxide (N2O) fluxes comprise a significant part of the greenhouse gas emissions of agricultural products but are spatially and temporally variable, due to complex interactions between climate, soil and management variables. This study aimed to identify the main factors that affect N2O emissions under sugarcane, using a multi-site database from field experiments. Greenhouse gas fluxes, soil, climate, and management data were obtained from 13 field trials spanning the 2011–2017 period. We conducted exploratory, descriptive and inferential data analyses in experiments with varying fertiliser and stillage (vinasse) type and rate, and crop residue rates. The most relevant period of high N2O fluxes was the first 46 days after fertiliser application. The results indicate a strong positive correlation of cumulative N2O with nitrogen (N) fertiliser rate, soil fungi community (18S rRNA gene), soil ammonium (NH4⁺) and nitrate (NO3⁻); and a moderate negative correlation with amoA genes of ammonia-oxidising archaea (AOA) and soil organic matter content. The regression analysis revealed that easily routinely measured climate and management-related variables explained over 50% of the variation in cumulative N2O emissions, and that additional soil chemical and physical parameters improved the regression fit with an R² = 0.65. Cross-wavelet analysis indicated significant correlations of N2O fluxes with rainfall and air temperature up to 64 days, associated with temporal lags of 2 to 4 days in some experiments, and presenting a good environmental control over fluxes in general. The nitrogen fertiliser mean emission factors ranged from 0.03 to 1.17% of N applied, with urea and ammonium nitrate plus vinasse producing high emissions, while ammonium sulphate, ammonium nitrate without vinasse, calcium nitrate, and mitigation alternatives (nitrification inhibitors and timing of vinasse application) producing low N2O-EFs. Measurements from multiple sites spanning several cropping seasons were useful for exploring the influence of environmental and management-related variables on soil N2O emissions in sugarcane production, providing support for global warming mitigation strategies, nitrogen management policies, and increased agricultural input efficiency. Supplementary Information The online version contains supplementary material available at 10.1007/s10705-023-10321-w.
The study of rainfall trends is crucial for food security and water availability in Alagoas state, Northeast of Brazil. In this work, monthly, seasonal and annual rainfall trends have been studied (1960–2016) for homogeneous rainfall regions over the eastern part of the Northeast Brazil (ENEB) and later related to climate variability. Cluster analysis was applied to identify homogeneous rainfall regions while the Mann–Kendall (MK), modified Mann–Kendall (MMK) and Pettitt tests were used in the analysis and identification of trends on a spatial and temporal scale. To relate rainfall and climate variability modes, Spearman's correlation was used in each homogeneous region. The rainfall series provided evidence of a general decrease in rainfall in the rainy period and an increase in the dry period, mainly over the driest region. The break points of time series occurred mostly in periods of great varia- tions in values of modes of climate variability, especially the Monthly Niño3.4 Index and the Southern Oscillation Index (SOI), both having a robust influence across the region. Moreover, the probable rainfall in the time series with trends was different in most months before and after the breakpoint. After the breakpoint, probable rainfall was lower, influenced by the breakpoint year (size of the series before and after the breakpoint), which mainly occurred in the 1980s and 1990s and presented a warm phase and a greater number of El Niño events. The MK and MMK trend tests showed the ability to detect trends, although there is no established standard on which test or version to use due to self-correlated, nonhomo- geneous series with nonrandom or nonindependent data. Rainfall is an important variable for water and food security and in the monitoring of natural disasters. The changes detected in this study can be used as refer- ence information for public policies on water resources and future studies for Alagoas and similar regions.
In this study, we examine the impact of human mobility on the transmission of COVID-19, a highly contagious disease that has rapidly spread worldwide. To investigate this, we construct a mobility network that captures movement patterns between Brazilian cities and integrate it with time series data of COVID-19 infection records. Our approach considers the interplay between people’s movements and the spread of the virus. We employ two neural networks based on Graph Convolutional Network (GCN), which leverage spatial and temporal data inputs, to predict time series at each city while accounting for the influence of neighboring cities. In comparison, we evaluate LSTM and Prophet models that do not capture time series dependencies. By utilizing RMSE (Root Mean Square Error), we quantify the discrepancy between the actual number of COVID-19 cases and the predicted number of cases by the model among the models. Prophet achieves the best average RMSE of 482.95 with a minimum of 1.49, while LSTM performs the least despite having a low minimum RMSE. The GCRN and GCLSTM models exhibit mean RMSE error values of 3059.5 and 3583.88, respectively, with the lowest standard deviation values for RMSE errors at 500.39 and 452.59. Although the Prophet model demonstrates superior performance, its maximum RMSE value of 52,058.21 is ten times higher than the highest value observed in the Graph Convolutional Networks (GCNs) models. Based on our findings, we conclude that GCNs models yield more stable results compared to the evaluated models.
The municipality of Ubatuba, São Paulo State, Southeast Brazil, is known for its strong orographic rainfall as a result of moisture fronts in the Atlantic Ocean, morphological structure, and abrupt relief of Serra do Mar. These conditioning combinations reinforce the occurrence of natural disasters such as runoff, floods, and landslides. There are several indices to assess flood hazard with different individual characteristics, but not an integrated index, based on traditional approaches. We used the compactness coefficient, circularity ratio, sinuosity index, drainage density, and curve number to propose an integrated index based in a multi-criteria assessment. Hence, the aim of this study was to propose and discuss an integrated index for morphometric and land use and land cover analysis of the Ubatuba municipality watersheds based on a decision matrix. Overall, the integrated multi-criteria index was efficient in discriminating hazard areas to flooding on the north coast of São Paulo, with 82.35% of the watersheds studied being identified with moderate hazard. The use of an integrated multi-criteria index using relief, LULC and soil type, an empirical parameter used in the determination of direct surface runoff from excess rainfall, and hydrography features helps in the interpretation of local hazard to flooding and contributes to urban planning, flood warning systems, and water resources management.
In this study, a comprehensive multiscale analysis of compounding drought-heat events in the Pantanal region is presented. The goal is to assess the multiscale nature of drought and determine whether the combined effects of drought and heatwaves, as driving factors, are more relevant than the effects of each event separately. The study describes a persistent interannual extreme event characterized by drought and heatwaves in the Pantanal, lasting from 2019 to 2021. The extreme event involved a prolonged dry season, a shortened and delayed rainy season, and persistent heatwaves, resulting in the emergence of drought-heat compound events. Despite experiencing consecutive months of increasing drought hazards and a delayed rainy season in late 2020 to 2021, the northern Pantanal region was unable to recover from the water deficit accumulated due to water stress in the previous year. This emphasizes the long-lasting impacts of compound events on water availability and ecosystem health. Furthermore, the study suggests that interannual water stress played a crucial role in explaining the context that led to record-breaking daily maximum temperatures during the austral spring of 2020. The regions most at risk for such compound extreme events are the northern and central Pantanal. Looking at longer timescales, the analysis of compound drought-heat events can provide insights essential for understanding and preventing their impacts, particularly those that could trigger fire outbreaks.
In March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2,800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two distinct runs from the UK Met Office Hadley Centre HadGEM3-GA6 model: one with all forcings and the other with natural forcings only, considering antecedent conditions and soil moisture (extreme 60-day rainfall, Rx60day) and heavy rainfall (extreme 3-day rainfall, Rx3day) which may trigger landslides. The long-term set-up became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20-42% of the total losses and damages. The greatest economic losses occurred in Guarujá (42%), followed by São Vicente (30%) and Santos (28%). Landslides were responsible for 47% of homes damaged, 85% of homes destroyed, all reported injuries, and 51% of deaths associated with heavy rainfall. Changes in land cover and increased urbanization showed a pronounced increase in urbanized area in Guarujá (107%), São Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. Population growth estimates also indicate higher exposure to extreme rainfall events. The proportion of irregular and/or precarious housing indicates vulnerability, with Guarujá being the municipality with the highest number (34.8%) of dwellings of this type. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.
In the last few years, the world has experienced numerous extreme droughts with adverse direct, cascading, and systemic impacts. Despite more frequent and severe events, drought risk assessment is still incipient compared to that of other meteorological and climate hazards. This is mainly due to the complexity of drought, the high level of uncertainties in its analysis, and the lack of community agreement on a common framework to tackle the problem. Here, we outline that to effectively assess and manage drought risks, a systemic perspective is needed. We propose a novel drought risk framework that highlights the systemic nature of drought risks, and show its operationalization using the example of the 2022 drought in Europe. This research emphasizes that solutions to tackle growing drought risks should not only consider the underlying drivers of drought risks for different sectors, systems or regions, but also be based on an understanding of sector/system interdependencies, feedbacks, dynamics, compounding and concurring hazards, as well as possible tipping points and globally and/or regionally networked risks.
Brazilian biomes have been experiencing an increase in fires during the whole year, but fires increase substantially during drier periods. Several indexes might be good indicators of the severity of the droughts, such as The Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI) and the Vegetation Health Index (VHI). This study therefore aimed to understand the dynamics of climate, using some indexes and fires in Paraíba do Sul River Valley, Paulista portion, to verify whether fire is more likely to spread in hotter and drier years. We hypothesized that fire events are more frequent and burned areas are larger in hotter and drier years in the region. By conducting a cross-correlation analysis and separating the monthly data into dry and rainy seasons, it was possible to establish a correlation between climate parameters and fire. A significant correlation was found between RAI and fires in both seasons. Additionally, we observed that high occurrences of fire events and burning areas were more explained by RAI, VHI and SPI-3 in dry and wet seasons than by temperature and SPI-1, SPI-6 and SPI-12. We noticed a complex dynamic between fire events, burned area, the environment, and climatic variables. However, the studied indexes proved to be effective tools for detecting drought conditions in the region and their relationship to fire. Keywords: burning, climatic indexes, Paraíba do Sul River Valley.
Brazil’s Pantanal wetland is one of the most threatened Brazilian ecosystems from direct anthropogenic pressures and climate change. In this study, the overarching research question is to explore whether compound drought-heat events (CDHEs) have become more recurrent, intense, and widespread over Brazil’s Pantanal wetland in recent decades. For this, we purpose and tested two different approaches using validated long-term time series of monthly precipitation, temperature, and the satellite-based Vegetation Health Index (VHI) to characterize the spatiotemporal pattern of CDHEs over Pantanal. Firstly, we assessed global gridded precipitation and temperature data sets against ground measurements to choose an appropriate dataset for this study. Then, we calculated the Standardized Precipitation Index (SPI), Standardized Temperature Index (STI), and Standardized Precipitation Evapotranspiration Index (SPEI) from 1981 to 2021. The results showed that using both approaches (CDHE-M1 and CDHE-M2), the frequency of events is higher considering the moderate category, which is expected since the criteria are less restrictive. In addition, the highest frequency of CDHE events occurs between September and November (the end of the dry season). The results also indicated that CDHE events have been more recurrent and widespread since 2000 in Pantanal. Besides, considering all methods for identifying the CDHEs, the probability density function indicates a shift pattern to warmer and drier conditions in the last 40 years. The Mann–Kendall tests also confirmed the assumption that there is a significantly increasing trend in the compound drought-heat events in the Pantanal. Developing methodologies for monitoring compound climate events is crucial for assessing climate risks in a warming climate. Besides, it is expected that our results contribute to the convincing of the urgent need for environmental protection strategies and disaster risk reduction plans for the Pantanal.
Resumo Este artigo propõe uma metodologia de análise das capacidades institucionais de enfrentamento das mudanças climáticas em âmbito municipal e metropolitano. A metodologia foi aplicada na Região Metropolitana de São Paulo (RMSP), utilizando os dados relativos a Meio Ambiente e Gestão de Riscos, da Pesquisa Nacional de Informações Municipais do IBGE (Munic), edições de 2013, 2017 e 2020. Os resultados indicam perda consistente das capacidades institucionais de enfrentamento das mudanças climáticas entre 2013 e 2020. Há uma diferença considerável no padrão dessa perda quando a análise incide separadamente sobre a capacidade institucional para lidar com os temas da gestão ambiental e da gestão de riscos de desastres.
This article proposes a methodology of analysis of the institutional capabilities that are employed to cope with climate change at the municipal and metropolitan levels. The methodology was applied to the São Paulo Metropolitan Region using Environmental and Disaster Risk Management data, collected by the Brazilian Institute of Geography and Statistics (IBGE) through the National Survey of Municipal Information (MUNIC), years 2013, 2017, and 2020. The results indicate that municipal institutions consistently lost capabilities to cope with climate change between 2013 and 2020. There is a considerable difference in the pattern of this loss when the analysis focuses separately on institutional capacity to deal with environmental management and with disaster risk management.
This study aims to assess the changes in the atmospheric conditions favorable to storm surges over the Santos Coast in Southeast Brazil. Storm surges can favor high sea level rises and coastal erosion, affecting people and strategic structures in coastal areas. The assessment of the atmospheric conditions was based on the downscaling of climate simulations of the Brazilian Earth System Model by the Eta regional climate model at higher spatial resolution. The detection scheme used by the model was able to reproduce the three observed atmospheric patterns favorable to storm surges found by recent studies: Pattern 1 is characterized by a cyclone on the synoptic scale over the ocean; Pattern 2 presents an intense wind fetch from the southeast; Pattern 3 is characterized by winds parallel to the coast. The simulations underestimated the number of cases in Patterns 1 and 2. However, it overestimated the number of days in Pattern 3. The model presented more intense winds in the three patterns. The storm surges characterized by Pattern 1 will become more intense. However, it will be equal to or less frequent. In Pattern 2, the number of events will decrease. Nevertheless, these episodes will be associated with more precipitation along the coastline. Pattern 3 will have a similar number of storm surges.
The Amazon forest carbon sink is declining, mainly as a result of land-use and climate change1-4. Here we investigate how changes in law enforcement of environmental protection policies may have affected the Amazonian carbon balance between 2010 and 2018 compared with 2019 and 2020, based on atmospheric CO2 vertical profiles5,6, deforestation7 and fire data8, as well as infraction notices related to illegal deforestation9. We estimate that Amazonia carbon emissions increased from a mean of 0.24 ± 0.08 PgC year-1 in 2010-2018 to 0.44 ± 0.10 PgC year-1 in 2019 and 0.52 ± 0.10 PgC year-1 in 2020 (± uncertainty). The observed increases in deforestation were 82% and 77% (94% accuracy) and burned area were 14% and 42% in 2019 and 2020 compared with the 2010-2018 mean, respectively. We find that the numbers of notifications of infractions against flora decreased by 30% and 54% and fines paid by 74% and 89% in 2019 and 2020, respectively. Carbon losses during 2019-2020 were comparable with those of the record warm El Niño (2015-2016) without an extreme drought event. Statistical tests show that the observed differences between the 2010-2018 mean and 2019-2020 are unlikely to have arisen by chance. The changes in the carbon budget of Amazonia during 2019-2020 were mainly because of western Amazonia becoming a carbon source. Our results indicate that a decline in law enforcement led to increases in deforestation, biomass burning and forest degradation, which increased carbon emissions and enhanced drying and warming of the Amazon forests.
In hydrological modelling, it is important to consider the uncertainties related to a model’s structures and parameters when different hydrological models are used to represent a system. Therefore, an adequate analysis of daily discharge forecasts that takes into account the performance of hydrological models can assist in identifying the best extreme discharge forecasts. In this context, this study aims to evaluate the performance of three hydrological models—Lavras Simulation of Hydrology (LASH), Variable Infiltration Capacity (VIC), and Distributed Hydrological Model (MHD-INPE) in the Verde River basin. The results demonstrate that LASH and MHD can accurately simulate discharges, thereby establishing them as crucial tools for managing water resources in the study region’s basins. Moreover, these findings could serve as a cornerstone for future studies focusing on food and water security, particularly when examining their connection to climate change scenarios.
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