COVID-19 Downgraded Already Impaired Mental Health of Medical Undergraduates during the Pandemic Years

Background: Measures to contain the COVID-19 transmission reached teaching routines of universities worldwide with possible mental health consequences for anxiety. This study assessed prevalence and risk factors for stress, depression, and anxiety (SDA) in medical students during quarantine by COVID-19. Methods: A cross-sectional observational study of medical students by means of the DASS-21 questionnaire. Risk factors for SDA were assessed based on epidemiologic questions related to COVID-19. Receiver Operating Characteristics (ROC) curves were calculated for each predictor, as well as sensitivity and specificity. Results: This survey reached 1009 responses. A prevalence of 77.5% for some SDA disorder was found, 63% being severe. Previous diagnosis of psychiatric disorder was a factor of risk for anxiety (OR 2.78 CI95% 1.44 - 14.25, p = 0.044), as well as for depression (OR 3.37 CI95% 1.98 - 6.02, p Conclusion: Psychiatric conditions as well as chronic illnesses were risk factors for high prevalence of anxiety, depression and stress during the COVID-19 pandemic among medical students.

Curupira V1.0: Joint Inversion of VES and TEM for Environmental and Mass Movements Studies

An innovative inversion code, named “Curupira v1.0”, has been developed using Matlab to determine the vertical distribution of resistivity beneath the subsoil. The program integrates Vertical Electrical Sounding (VES), successful in shallow subsurface exploration and Time Domain Electromagnetic (TEM) techniques, better suited for deeper exploration, both of which are widely employed in geophysical exploration. These methodologies involve calculating subsurface resistivity through appropriate inversion processes. To address the ill-posed nature of inverse problems in geophysics, a joint inversion scheme combining VES and TEM data has been incorporated into Curupira v1.0. The software has been tested on both synthetic and real-world data, the latter of which was acquired from the Parana sedimentary basin which we summarise here. The results indicate that the joint inversion of VES and TEM techniques offers improved recovery of simulated models and demonstrates significant potential for hydrogeological studies.

Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process;thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems;hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Downscaling Climate Projections over La Plata Basin

Regional Climate Models are important tools, which are increasingly being used in studies of impacts and adaptation to climate change at local scale. The goal of this work is to assess the climate change over the La Plata Basin, using the Eta Regional model with a resolution of 10 km. Initial and boundary conditions used by the model are provided by the Eta-20 km model and the HadGEM2-ES Global model. The RCP 4.5 scenario was used for simulations of the future climate. The evaluation of the present climate (1961-1990) shows that the model represents well the spatial and temporal distribution of precipitation and temperature in the region. The model underestimates precipitation over large areas in summer, and overestimates in Southern Brazil in winter. Simulated temperature shows a good correlation with CRU data, with bias less than 1°C. The bias of temperature and precipitation in this simulation setup for the La Plata Basin is substantially reduced in comparison with previous literature using regional models. The climatic projections are shown in timeslices: 2011-2040, 2041-2070, and 2071-2099. In the three timeslices, the simulation project has a trend for an increase in precipitation during summer in Argentina, Uruguay, and southernmost Brazil. This increase is only projected in Southern Brazil during winter. The negative anomaly of precipitation appears in a large portion of the model domain during summer and is limited to some states in Southeast and Central-West Brazil in winter. The area with largest warming is projected in the northern portion of the domain. The projected increase in temperature reaches about 4°C in 2071-2099.

More Erratic and More Extreme: Trends in Precipitation in the State of São Paulo, Brazil

The study assessed changes in rainfall variability and the frequency of extreme events (very wet and very dry) in the state of S?o Paulo, Brazil, for a 40-year period that divided into two sub-groups: 1973-1992 (P1) and 1993-2012 (P2). Data of 79 rain gauge stations were selected to represent the different climatic and geomorphological domains of the state. The annual pattern was evaluated through the scale and the shape parameters of the gamma distribution and the 95th and the 5th percentiles thresholds, the latter also employed to evaluate the seasonal spatial patterns (rainy season, Oct.-Mar. and sub-humid to dry season, Apr.-Sep.). Results showed that the average precipitation was similar in P1 and P2, but S?o Paulo evolved to a pattern of increased irregularity in the rainfall distribution, with a rise of approximately 10% in the number of extremes between 1973 and 2012, especially in the very dry occurrences, and in the north and west of the state, which are the least rainy regions. Moreover, while 55% of the evaluated rain gauges recorded more extreme wet episodes in P2, 76% registered more dry extreme episodes in the same period. Some very dry or very wet events recorded after the 40-year period evaluated were discussed in terms of the associated weather patterns and their impacts on society and attested to the validity of the results found in the quantitative assessment. The qualitative analysis indicates that if the trends of more irregular distribution of rain and increase in extreme events persist, as pointed out by the gamma and percentile analyses, they would continue to bring serious effects on the natural and social systems in the state, which is the most populous and has the strongest and most diversified economy in Brazil.

A New Approach for Assessing the Drought Risk Management Capacity at the Municipal Level in Brazil

Long-term drought has occurred in all regions of Brazil, and its effects have been more intense in recent decades. Poor management of drought can exacerbate significant consequences, severely compromising water, food, energy, economic security, natural systems, and high fire risk that can affect biomes. It also slowly and indirectly affects the society living on vulnerable geographic space. This article discusses a methodology for assessing the drought risk management capacity at the municipal level in Brazil, and this new approach is statistically based using environmental data provided by the municipalities, from observational networks to data banks and remotely sensed data. It presents a method to indicate the steps of priority actions for the phases of drought management. It also characterized the long-term drought in Brazil (hydrological drought) between 1982 and 2022. The proposed approach provides a better understanding and the use of various drought indices to develop the most appropriate action steps for mitigation and adaptation. The final goal is to increase the resilience for those affected by drought. The work was developed based on the actions defined by the Brazilian Federal Government (Preparation, Prevention, Mitigation, Response, Recovery, and Restoration). This aims to improve the management of risk and disaster typified as drought in Brazil and to contribute with scientific knowledge to legislators regarding adaptation and resilience policies to drought extremes in parts of the country. At the end, we expect to highlight to managers and decision-makers the critical points in the government’s proactive and reactive actions to drought that need to be better managed.

Obtaining 2D Soil Resistance Profiles from the Integration of Electrical Resistivity Data and Standard Penetration Test (SPT) and Light Dynamic Penetrometer (DPL) Resistance Tests—Applications in Mass Movements Studies

In Brazil and various regions globally, the initiation of landslides is frequently associated with rainfall;yet the spatial arrangement of geological structures and stratification considerably influences landslide occurrences. The multifaceted nature of these influences makes the surveillance of mass movements a highly intricate task, requiring an understanding of numerous interdependent variables. Recent years have seen an emergence in scholarly research aimed at integrating geophysical and geotechnical methodologies. The conjoint examination of geophysical and geotechnical data offers an enhanced perspective into subsurface structures. Within this work, a methodology is proposed for the synchronous analysis of electrical resistivity geophysical data and geotechnical data, specifically those extracted from the Light Dynamic Penetrometer (DPL) and Standard Penetration Test (SPT). This study involved a linear fitting process to correlate resistivity with N10/SPT N-values from DPL/SPT soundings, culminating in a 2D profile of N10/SPT N-values predicated on electrical profiles. The findings of this research furnish invaluable insights into slope stability by allowing for a two-dimensional representation of penetration resistance properties. Through the synthesis of geophysical and geotechnical data, this project aims to augment the comprehension of subsurface conditions, with potential implications for refining landslide risk evaluations. This endeavor offers insight into the formulation of more effective and precise slope management protocols and disaster prevention strategies.

Thresholds of Instability: Precipitation, Landslides, and Early Warning Systems in Brazil

Rainfall accumulation thresholds are crucial for issuing landslide warnings by identifying when soil saturation from rain could potentially trigger a landslide. Two essential types of thresholds are considered: environmental and operational. The environmental threshold indicates the minimum rainfall level required to potentially initiate a landslide. Conversely, the operational threshold is set lower to enable agencies to issue alerts before reaching environmental thresholds. Establishing these thresholds improves the accuracy of landslide predictions in terms of location and timing. This study introduces an innovative approach for determining these thresholds. Our approach employs cluster analysis and historical landslide data from the Metropolitan Region of Recife, Pernambuco State, Brazil. We applied our defined values to a significant landslide event in 2022, validating their robustness as the foundation for the operational threshold used by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

A Review on Clustering Methods for Climatology Analysis and Its Application over South America

South America’s climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influence of distinct atmospheric systems. While some studies have characterized the prevailing systems over South America, they often lacked the utilization of statistical techniques for homogenization. On the other hand, other research has employed multivariate statistical methods to identify homogeneous regions regarding temperature and precipitation, but their focus has been limited to specific areas, such as the south, southeast, and northeast. Surprisingly, there is a lack of work that compares various multivariate statistical techniques to determine homogeneous regions across the entirety of South America concerning temperature and precipitation. This paper aims to address this gap by comparing three such techniques: Cluster Analysis (K-means and Ward) and Self Organizing Maps, using data from different sources for temperature (ERA5, ERA5-Land, and CRU) and precipitation (ERA5, ERA5-Land, and CPC). Spatial patterns and time series were generated for each region over the period 1981-2010. The results from this analysis of spatially homogeneous regions concerning temperature and precipitation have the potential to significantly benefit climate analysis and forecasts. Moreover, they can offer valuable insights for various climatological studies, guiding decision-making processes in diverse fields that rely on climate information, such as agriculture, disaster management, and water resources planning.

Sensitivity of Amazon Regional Climate to Deforestation

It is known that the Amazon region plays an important role in the global energy, hydrological cycle and carbon balance. This region has been suffering from the course of the past 40 years intense land use and land cover changes. With this in mind, this study has examined possible associations between change in spatial and temporal rainfall variability and land cover change in the Amazon, using the PRECIS regional modelling system. It has been found that the impacts of land cover change by forest removal are more intense in the so-called “Arc of deforestation” over central and southern Amazonia. However, the relative impact of the simulated rainfall changes seems to be more important in the JJA dry season. In addition, the simulations under the deforestation scenarios also show the occurrence of extreme rainfall events as well as more frequent dry periods. Therefore, the results found show to be potentially important in the modulation of regional climate variations which have several environmental and socio-economic impacts.

Hydrological Processes and Climate Change in Hydrographic Regions of Brazil

The objective of this work is to assess the impacts of IPCC AR5 climate change scenarios on water resources and hydrological processes across the entire Brazilian territory. Hydrological simulations are carried out in total drainage area of about 11,535,645 km2 and average stream flow of about 272,460 m3/s. The study area consists of different climates and land covers such as the Amazon Forest, Northeast Semiarid, Brazilian Savannah, Pantanal wetlands and temperate climate in the South. The atmospheric forcing to drive the large-scale hydrological model MGB-IPH is derived from the downscaling of two global climate models, HadGEM2-ES and MIROC5, by the Eta Regional Climate Model, at 20 km resolution. The Eta model provided the downscaling of the baseline (1961-1990) and three time-slices (2011-2040, 2041-2070 and 2071-2099). These projections adopted two emission scenarios, the RCP 4.5 and RCP 8.5. The change in the average and extremes of precipitation, evapotranspiration, rates of river discharge and soil moisture were assessed. The simulations showed the response of the hydrographic regions due to change of precipitation and potential evapotranspiration in the scenarios. Water availability decreases in almost the entire study area (exception for the South) and the major basins for hydroelectric power generation are affected. The Northwest, Amazon and a small area along the Northeast Atlantic coast exhibited intensification of the extremes discharges, where the anomaly is positive for high-flow (Q10) and negative for low-flow (Q95). The results highlight the most climatic sensitive regions in Brazil in terms of hydrological variables and water resources.

Validation of Novel Model for Identification of Thermal Conditions in the Low Corona

The electron density and temperature key properties of the neutral-magnetized plasma in the solar corona, which are predicted with a novel model, provide an interesting window along the whole solar cycle. In this work, we test the quantitative validity of the model and prove that the Coronal Density and Temperature (CODET) is reliable. Furthermore, this work contrasts the CODET model results with alternative observational remote and in-situ datasets during the simplest conditions of the quiescent corona near the solar minimum. This successful outcome/validation of the CODET model allowed a good qualitative density and temperature retrieval in the solar corona covering a large portion of time interval from solar cycles 23 and 24.

Spatial Modeling of Soil Lime Requirements with Uncertainty Assessment Using Geostatistical Sequential Indicator Simulation

This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Paraná state, Brazil.

Natural Disaster Risk in Municipal Solid Waste Disposal Sites Using GIS: A Case Study in São Paulo State, Brazil

Natural disasters and finding suitable areas to dispose of municipal solid waste (MSW) are two major global challenges, both of which have several influences in the water resources and are related to climatic change, especially in developing countries like Brazil. Currently the risk of natural disasters is not utilized in the process of locating areas to dispose of MSW. This study, using geographic information system (GIS), analyzed spatially the susceptibility for landslide and flooding hazards in MSW disposal sites in S?o Paulo state, Brazil. The results showed that more than 90% of sanitary and ditch landfills are located in null, very low and low hazard classes (P0, P1 and P2). However, in this remaining 10% of landfills we still have approximately 55% of the total municipal solid waste disposed into susceptible areas with middle, high and very high hazard classes (P3, P4 and P5). This shows that S?o Paulo MSW disposal sites need more attention and control to prevent the occurrence of landslide/flooding and consequently the mitigation of environmental, economic and social impacts. In addition, the use of GIS is proved to be an appropriate tool to carry out this type of analysis, so we recommend that the risk of natural disasters should be taken into consideration in the process of finding landfill areas. Mainly, because, there is a tendency for extreme weather events to increase causing more flood and landslide events and consequently contributing to water scarcity and water degradation.

How Accurately Contemporary Models Can Predict Monsoons?

Seasonal changes exhibit climate changes, so models can predict future climate change accurately only if they can reproduce seasonal cycle accu-rately. Further, seasonal changes are much larger than the changes even in long period of centuries. Thus it is unwise to ignore large ones compared to small climate change. In this paper, we determine how accurately a suite of ten coupled general circulation models reproduce the observed seasonal cycle in rainfall of the tropics. The seasonal cycles in rainfall of global tropics are known as monsoons. We found that the models can reasonably reproduce the seasonal cycle in rainfall, thus are useful in climate prediction and simulation of global monsoons.

Use of the Wavelet Transform for Digital Terrain Model Edge Detection (Special Issue—Wavelet Analysis)

The purpose of this work is to analyze the feasibility of using the wavelet transform in the edge detection of digital terrain models (DTM) obtained by Laser Scanner. The Haar wavelet transform and the edge detection method called Wavelet Transform Modulus Maxima (WTMM), both implemented in Matlab language, were used. In order to validate and verify the efficiency of WTMM, the edge detection of the same DTM was performed by the Roberts, Sobel-Feldman and Canny methods, chosen due to the wide use in the scientific community in the area of Image Processing and Remote Sensing. The comparison of the results showed superior performance of WTMM in terms of processing time.

Data Mining for Flooding Episode in the States of Alagoas and Pernambuco—Brazil

The increasing volume of data in the area of environmental sciences needs analysis and interpretation. Among the challenges generated by this “data deluge”, the development of efficient strategies for the knowledge discovery is an important issue. Here, statistical and tools from computational intelligence are applied to analyze large data sets from meteorology and climate sciences. Our approach allows a geographical mapping of the statistical property to be easily interpreted by meteorologists. Our data analysis comprises two main steps of knowledge extraction, applied successively in order to reduce the complexity from the original data set. The goal is to identify a much smaller subset of climatic variables that might still be able to describe or even predict the probability of occurrence of an extreme event. The first step applies a class comparison technique: p-value estimation. The second step consists of a decision tree (DT) configured from the data available and the p-value analysis. The DT is used as a predictive model, identifying the most statistically significant climate variables of the precipitation intensity. The methodology is employed to the study the climatic causes of an extreme precipitation events occurred in Alagoas and Pernambuco States (Brazil) at June/2010.

Electrical Resistivity Tomography and TDEM Applied to Hydrogeological Study in TaubatéBasin, Brazil

This research applies Electrical Resistivity Tomography (ERT) and Time Domain Electromagnetic Method (TDEM) to study the hydrogeology of the Taubaté basin, which is characterized by half-grabens with about 850 m of maximum sediments thickness. The study area is in Taubaté city, São Paulo State, Brazil, where the Taubaté aquifer is an important water source. The Taubaté Group is the main sedimentary package of the basin;it is formed mainly by shales that form aquicludes, and thin layers of sandstones that form the aquifer. There are 40 groundwater exploration wells in Taubaté city that provide important information. The study purpose is to characterize the geoelectrical stratigraphy of the subsurface to locate the contact between the Quaternary and Tertiary sediments and to identify the Taubaté aquifer. The ERT is used for shallow investigations (tens of meters) and the TDEM can reach a great investigation depth (hundreds of meters). Therefore, these geophysical methods are complementary. The ERT data were acquired with the pole-dipole array with 20 m of electrodes spacing and 400 m length, and the TDEM data with the central-loop array with a 200 × 200 m transmitter loop. The results permit to define the contact between the Quaternary and Tertiary sediments around 15 m depth, the Pindamonhangaba Formation between 15 m and 30 m depth and the Taubate Group between 30 m and 300 m depth. The TDEM method defined the Taubaté Group as a single geoelectric layer because the shale and the sandstone layers are all very conductive. The basement is formed by gneiss, which is a very resistive rock. The TDEM method is not able to identify a high conductor/resistor contrast. Overall, the results are consistent with the known geology and the wells information.

Impacts of Climate Change on the Hydrology of a Small Brazilian Headwater Catchment Using the Distributed Hydrology-Soil-Vegetation Model

Climate change is one of the greatest issues for human society. The objective of this study is to assess the impacts of future climate change on seasonal average discharge and monthly water budget in a small headwater catchment, located on the Grande River basin, in Minas Gerais, Brazil. The assessment is carried out using the hydrology model, DHSVM. The atmospheric forcing to drive the Distributed Hydrology-Soil-Vegetation Model (DHSVM) is derived from the downscaling of the HadGEM2-ES projections by the Eta Regional Climate Model, at 5-km high resolution. The projections assume the RCP4.5 and RCP8.5 IPCC AR5 emission scenarios. Baseline period was taken between 1961 and 1990. The projections are assessed in three time slices (2011-2040, 2041-2070 and 2071-2099). The climate change is assessed in time slices of 30 years and in comparison against the baseline period to evaluate the hydrological changes in the catchment. The results showed differences in the hydrological behavior between the emission scenarios and though time slices. Reductions in the magnitude of the seasonal average discharge and monthly water budget may alter the water availability. Under the RCP4.5 scenario, results show greater reductions in the water availability in the first time slice, whereas under RCP8.5 scenario greater reductions are indicated in the third time slice.