Assessment of spatiotemporal variability of ultraviolet index (UVI) over Kerala, India, using satellite remote sensing (OMI/AURA) data.

The spatial and temporal dynamics of daily ultraviolet index (UVI) for a period of 18 years (2004-2022) over the Indian state of Kerala were statistically characterised in the study. The UVI measurements used for the study were derived from the ultraviolet-B (UVB) irradiance measured by the Ozone Monitoring Instrument (OMI) of the AURA satellite and classified into different severity levels for analysis. Basic statistics of daily, monthly and seasonal UVI as well as Mann-Kendall (MK) statistical trend characteristics and the rate of change of daily UVI using Theil-Sen's slope test were also evaluated. A higher variability of UVI characteristics was observed in the Kerala region, and more than 79% of the measurements fell into the categories of very high and extreme UVI values, which suggests the need of implementation of appropriate measures to reduce health risks. Although the UVI measured during the study period shows a slight decrease, most of the data show a seasonal variation with undulating low and peak values. Higher UVI are observed during the months of March, April and September. The region also has higher UVI during the southwest monsoon (SWM) and summer seasons. Although Kerala region as a single whole unit, UVI show a non-significant decreasing trend (-0.83), the MK test revealed the increasing and decreasing trends of UVI ranging from -1.96 to 0.41 facilitated the delineation of areas (domains) where UVI are increasing or decreasing. The domain of UVI increase occupies the central and southern (S) parts, and the domains of decrease cover the northern (N) and S parts of the Kerala region. The rate of change of daily UVI in domain of increase and decrease shows an average rate of 0.34 × 10-5 day-1 and -2 × 10-5 day-1, respectively. The parameters (rainfall, air temperature, cloud optical depth (COD) and solar zenith angle (SZA)) that affect the strength of UV rays reaching the surface indicate that a cloud-free atmosphere or low thickness clouds prevails in the Kerala region. Overall, the study results indicate the need for regular monitoring of UVI in the study area and also suggest appropriate campaigns to disseminate information and precautions for prolonged UVI exposure to reduce the adverse health effects, since the study area has a high population density.

Bioclimatic Zoning for Sheep Farming through Geostatistical Modeling in the State of Pernambuco, Brazil.

The Intergovernmental Panel on Climate Change (IPCC) has pointed out the high vulnerability of developing countries to climate change, which is expected to impact food and income security. Sheep farming is one of the main animal productions among the families located in the most vulnerable regions of the semiarid region of Pernambuco state, a Brazilian territory known for its high temperatures, low relative humidity, and high net solar radiation. Therefore, the objective of this study was to identify different regions of Pernambuco that may be more suitable for different breeds of sheep, based on non-parametric statistics and kriging maps of the temperature and humidity index (THI). THI values were determined based on mean annual temperature and wind speed extracted from the TerraClimate remote sensing database. Pernambuco state presented THI values ranging from 66 to 79, with the hair breeds having a high potential for exploitation in almost all territories, including the main meat-producing breeds. The East Friesian breed, a high milk producer, would be well suited to the Agreste mesoregion, a territory that, like the Pajeú and Moxotó microregions, also proved favorable for the introduction of three wool breeds (Suffolk, Poll Dorset, and Texel) known as major meat producers. The kriging maps of the THI values successfully allowed the identification of strategic development regions of Pernambuco state with high potential for sheep breeding.

Evaluation of daily average temperature trends in Kerala, India, using MERRA-2 reanalysis data: a climate change perspective.

Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) data for a period of 40 years (1980-2019) was used to detect the trend characteristics of daily average temperature in the state of Kerala, India. Data extracted from a total of fifty locations in the state were statistically processed using Mann-Kendall (MK) and Spearman's Rho (SR) tests to detect the trend, Pettitt test to identify the single change point, and Theil-Sen's method for the calculation of the rate of change. The MERRA-2 product is validated for the study region according to statistical indicators. The daily average temperature in the state during the period of study varies between 16.56 and 32.64 °C. The spatial pattern of daily average maximum temperature shows higher temperature domains in the central and southern parts of the state. Trend characteristics of daily average temperature assessed through MK and SR tests show a significant increasing trend in all stations, with maximum values in stations located in the northern part of the state. Change point detected through the Pettitt test divided the sampling stations into three groups based on the change in daily average temperature characteristics in the years 2002 (north zone), 2009 (south zone), and 2012 (central zone), indicating nonunique spatial variability in temperature characteristics in the state. The rate of change in the daily average temperature assessed indicates an increase at the rate of an average of 0.013 °C.year-1. During the whole study period, the daily average temperature showed an overall increase of 0.54 °C, and for the 100-year futuristic prediction, the daily average temperature in the state shows an average increase of 1.35 °C. Among the stations, a higher rate of increase in daily average temperature is shown by stations located in the eastern part of the Pathanamthitta, Idukki, and Kollam districts. Though the rise in daily average temperature is not much higher, its spatial characteristics require more attention because, in recent times, the study area has faced repeated, severe, and long drought conditions along with sunburn incidents. As an agrarian state, a change in the temperature domain will adversely affect the overall agricultural production and will evoke not only a food crisis but also economic as well as water resources issues. The result obtained can be used as holistic basic information for understanding the impending effect of climate change in the state to frame better mitigation as well as management strategies.

Investigation of pre and post environmental impact of the lockdown (COVID-19) on the water quality of the Capibaribe and Tejipió rivers, Recife metropolitan region, Brazil.

The coronavirus pandemic has seriously affected human health, although some improvements on environmental indexes have temporarily occurred, due to changes on socio-cultural and economic standards. The objective of this study was to evaluate the impacts of the coronavirus and the influence of the lockdown associated with rainfall on the water quality of the Capibaribe and Tejipió rivers, Recife, Northeast Brazil, using cloud remote sensing on the Google Earth Engine (GEE) platform. The study was carried out based on eight representative images from Sentinel-2. Among the selected images, two refer to the year 2019 (before the pandemic), three refer to 2020 (during a pandemic), two from the lockdown period (2020), and one for the year 2021. The land use and land cover (LULC) and slope of the study region were determined and classified. Water turbidity data were subjected to descriptive and multivariate statistics. When analyzing the data on LULC for the riparian margin of the Capibaribe and Tejipió rivers, a low permanent preservation area was found, with a predominance of almost 100% of the urban area to which the deposition of soil particles in rivers are minimal. The results indicated that turbidity values in the water bodies varied from 6 mg. L-1 up to 40 mg. L-1. Overall, the reduction in human-based activities generated by the lockdown enabled improvements in water quality of these urban rivers.

Temporal and spatial patterns of fire activity in three biomes of Brazil.

The trade-off between conservation of natural resources and agribusiness expansion is a constant challenge in Brazil. The fires used to promote agricultural expansion increased in the last decades. While studies linking annual fire occurrence and rainfall seasonality are common, the relationship between fires, land use, and land cover remains understudied. Here, we investigated the frequency of the fires and performed a trend analysis for monthly, seasonal, and annual fires in three different biomes: Cerrado, Pantanal, and Atlantic Forest. We used burned area and integrated models in distinct scales (interannual, intraseasonal, and monthly) using Probability Density Functions (PDFs). The best fitting was found for Generalized Extreme Values (GEV) distribution at all three biomes from the several PDFs tested. We found the most fire in the Pantanal (wetlands), followed by Cerrado (Brazilian Savanna) and Atlantic Forest (Semideciduous Forest). Our findings indicated that land use and land cover trends changed over the years. There was a strong correlation between fire and agricultural areas, with increasing trends pointing to land conversion to agricultural areas in all biomes. The high probability of fire indicates that expanding agricultural areas through the conversion of natural biomes impacts several natural ecosystems, transforming land cover and land use. This land conversion is promoting more fires each year.

Modeling of the air temperature using the Extreme Value Theory for selected biomes in Mato Grosso do Sul (Brazil).

This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007-2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov-Smirnov test, the corrected Akaike criterion AIC c , the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R 2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28-32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02206-1.

MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region.

Hydrological analyses based on precipitation records in the Amazon are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, there are limitations related to data series with short periods and many gaps and failures at the daily scale. Thus, a hybrid model was developed based on an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) coupled with the maximum overlap discrete wavelet (MODWT) method to obtain precipitation estimates. Six rainfall gauge stations located in different biomes within the studied region were adopted, and satellite data (CMORPH) were used. The interval of data that was have used is 1998-2016. The precipitation data were evaluated by seasonal (wet and dry) periods. The results obtained demonstrated the good capacity of the MODWT-ANFIS model to simulate the daily precipitation. In this case, data entries lagged by 4 days and 5 days performed better, with Nash values close to 1.0 and mean square errors (MSE) below 0.1.

Variations in the tropospheric concentration of NO2 in the central west of Brazil, MS, and their relationship with the COVID-19.

COVID-19 (coronavirus disease 2019) started in late 2019 in Wuhan, China. Subsequently, the disease was disseminated in several cities around the world, where measures were taken to control the spread of the virus through the adoption of quarantine (social isolation and closure of commercial sectors). This article analyzed the environmental impact of the COVID-19 outbreak in the state of Mato Grosso do Sul, Brazil, regarding the variations of nitrogen dioxide (NO2) in the atmosphere. NO2 data from the AURA satellite, in the period before the beginning of the epidemic (2005-2019) and during the adoption of the preventive and control measures of COVID-19 in 2020, were acquired and compared. The results obtained from the analysis showed that the blockade from COVID-19, beginning in March 2020, improved air quality in the short term, but as soon as coal consumption in power plants and refineries returned to normal levels (since June 2020), due to the resumption of works, the pollution levels returned to the level of the previous years of 2020. NO2 levels showed a significant decrease, since they were mainly associated with the decrease in economic growth and transport restrictions that led to a change in energy consumption and a reduction in emissions. This study can complement the scientific community and policy makers for environmental protection and public management, not only to assess the impact of the outbreak on air quality, but also for its effectiveness as a simple alternative program of action to improve air quality.

Modeling of land use and land cover change dynamics for future projection of the Amazon number curve.

The hydrological parameter Curve Number (CN) was projected in the future in a 30 m spatial resolution grid for the Amazon. Through the DINAMICA EGO platform, Land Use and Land Cover (LULC) were calibrated, simulated, validated, and projected for 2049 in a five-year time frame from 2009. The reclassified LULCs of 2009, 2014, and 2019 of the MapBiomas 5.0 project were used as input to DINAMICA EGO. Calibration was prepared using the 2009 and 2014 maps and the 2014 simulated map; the validation was carried out using the 2014 map, 2019, and 2019 simulated. In the calibration, the multiple window similarity values were all above 50% for the models of each basin, except for the Tapajós which was 40% in spatial resolution of 255 m. Validation values ranged between 36% and 76% at a spatial resolution of 255 m. Concerning the future projection of CN, the average CN of the Amazon region is equal to 77. The highest values of CN were found in the southern regions of the basins of the Xingu, Tapajós, Madeira, and throughout the basins of the Araguaia and Tocantins. In this Amazon region, in 2049, the areas of high CN will increase due to forest conversion to pasture/agriculture, implying larger runoff and flooding, including the urban areas, which will also expand. These floods will be intensified concerning those that already occur in the Amazon.

Application of remote sensing in environmental impact assessment: a case study of dam rupture in Brumadinho, Minas Gerais, Brazil.

The collapse of mining tailing dams in Brumadinho, Minas Gerais, Brazil, that occurred in 2019 was one of the worst environmental and social disasters witnessed in the country. In this sense, monitoring any impacted areas both before and after the disaster is crucial to understand the actual scenario and problems of disaster management and environmental impact assessment. In order to find answers to that problem, the aim of this study was to identify and analyze the spatiality of the impacted area by rupture of the tailing dam of the Córrego do Feijão mine in Brumadinho, Minas Gerais, by using orbital remote sensing. Land use and land occupation, phytoplankton chlorophyll-a, water turbidity, total suspended solids on water, and carbon sequestration efficiency by vegetation (CO2Flux) were estimated by orbital imagery from the Landsat-8/OLI and MSI/Sentinel-2 sensors in order to assess the environmental impacts generated by the disaster. Data were extracted from spectral models in which the variables that best demonstrated the land use variation over the years were sought. Mean comparison by t-test was performed to compare the time series analyzed, that is, before and after the disaster. Through the analysis of water quality, it was observed that the environmental impact was calamitous to natural resources, especially water from Córrego do Feijão.

Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications.

Rainfall is a climatic variable that dictates the daily rhythm of urban areas in Northeastern Brazil (NEB) and, therefore, understanding its dynamics is fundamental. The objectives of the study were (i) to validate the CHELSA product with data in situ, (ii) assess the spatial-temporality of the rains, and (iii) assess the trends and socio-environmental implications in the Metropolitan Region of Maceió (MRM). The monthly rainfall data observed between 1960 and 2016 were flawed and were filled with the imputation of data. These series were subjected to descriptive and exploratory statistics, statistical indicators, and the Mann-Kendall (MK) and Pettitt tests. CHELSA product was validated for MRM, and all stations obtained satisfactory determination coefficients (R2) and Pearson correlation (r). The standard error of the estimate (SEE), root mean square error (RMSE), and mean absolute error (MAE) were satisfactory. The highest annual rainfall accumulated occurred near the Mundaú and Manguaba lagoons. The Pettitt test identified that abrupt changes occur in El Niño and La Niña years (strong and weak). The monthly rain boxplots showed high variability in the rainy season (April-July). Outliers have been associated with extreme rainfall at MRM. The drought period was 5 months in all MRM seasons, except in Satuba and Pilar. The Mann-Kendall test and the Sen method showed a tendency for a significant increase in rainfall in Satuba and not significant in the Pilar, while in the others, there was a tendency for a decrease in rainfall. The MRM rainfall depends on physiographic factors, multiscale meteorological systems, and the coastal environment. These results will assist in planning conservationist practices, especially in areas of socio-environmental vulnerability.

Fire foci dynamics and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Northeast Brazil.

The objective is to evaluate the fire foci dynamics via environmental satellites and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Brazil. Data considered the period between 2000 and 2017 and was obtained from CPTEC/INPE. Annual and monthly analyzes were performed based on descriptive, exploratory (boxplot) and multivariate statistics analyzes (cluster analysis (CA), principal component analysis (PCA)) and Poisson regression models (based on 2000 and 2010 census data). CA based on the Ward method identified five fire foci homogeneous groups (G1 to G5), while Coruripe did not classify within any group (NA); therefore, the CA technique was consistent (CCC = 0.772). Group G1 is found in all regions of Alagoas, while G2, G5, and NA groups are found in Baixo São Francisco, Litoral, and Zona da Mata regions. Most fire foci were observed in the Litoral region. Seasonally, the largest records were from October to December months for all groups, influenced by the sugarcane harvesting period. The G4 group and Coruripe accounted for 60,767 foci (32.1%). The highest number of fire foci occurred in 2012 and 2015 (between 8000 and 9000 foci), caused by the action of the El Niño-Southern Oscillation. The Poisson regression showed that the dynamics of fire foci are directly associated with the Gini index and Human Development Index (models 1 and 3). Based on the PCA, the three components captured 78.8% of the total variance explained, and they were strongly influenced by the variables: population, GDP, and demographic density. The municipality of Maceió has the largest contribution from the fire foci, with values higher than 40%, and in PC1 and PC2 are related to urban densification and population growth.

Non-parametric tests and multivariate analysis applied to reported dengue cases in Brazil.

Dengue is among the largest public health problems in Brazil. Reported dengue cases via DATASUS were correlated with reanalysis data from NCEP (rainfall and air temperature) and Brazil's population data (2000 and 2010) from 1994 to 2014. The aim of this study was to evaluate relational patterns between climate variables together with population data from the last census and reported cases of dengue in Brazil from 1994 to 2014 by using statistical techniques. Several statistical methods [descriptive and exploratory statistics; simple and multiple linear regressions; Mann-Kendall (MK), Run, and Pettit nonparametric tests; and multivariate statistics via cluster analysis (CA)] were applied to time series. The highest percentages of Dengue cases were in Brazil's Southeast (47.14%), Northeast (29.86%), and Central West (13.01%). Upon CA of the Brazilian regions, three homogeneous dengue groups were formed: G1 (North and Central West), G2 (Southeast and Northeast), and G3 (South). Run testing indicated that the time series is homogenous and persistence free. MK testing showed a nonsignificant trend of increase of dengue cases in 23 states with positive trends and in four states with negative trends of Brazil. A significant increase in the magnitude of dengue at the regional level was recorded in the North, Southeast, South, and Central West regions. Statistical methods showed that dengue variability in Brazil is cyclical (2- to 3-year cycles), but not repetitive of El Niño-Southern Oscillation (ENSO) in the moderate, strong, and neutral categories. ENSO interferes with the action of weather systems, changing or intensifying rainfall and air temperatures in Brazil. The population increase in recent decades and the lack of effective public policies together with the action of ENSO contributed to the increase in dengue cases reported in Brazil.