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The Guanabara Bay hydrographic region (GBHR) has served as a central hub for human settlement and resource utilization throughout Brazil's history. However, the region's high population density and intense industrial activity have come at a cost, leading to a significant decline in water quality. This work aimed to identify homogeneous regions in GBHR according to water quality parameters in dry and rainy periods. The following water quality monitoring variables were monitored at 49 gauge stations: total phosphorus (TP), nitrate (NO3−), dissolved oxygen (DO), hydrogenionic potential (pH), turbidity (Turb), thermotolerant coliforms (TCol), total dissolved solids (TDS), biochemical oxygen demand (BOD), water temperature (Tw), and air temperature (Ta). The statistical analysis consisted of determining principal components, cluster analysis, seasonal differences, and Spearman's correlation. The water quality parameter correlations were not expressively influenced by seasonality, but there are differences in the concentrations of these parameters in the dry and rainy periods. In the dry period, urban pressure on water quality is mainly due to fecal coliforms. The resulting clusters delimited areas under urban, agricultural, and forestry influence. Clusters located in areas with high demographic density showed high concentrations of TCol and TP, while clusters influenced by forestry and agriculture had better water quality. In the rainy season, clusters with urban influence showed problems with TCol and TP, in addition to some characteristics in each group, such as high TDS, NO3−, and BOD. Forested areas showed high DO, and clusters under agricultural influence had higher concentrations of TCol, BOD, and NO3− concerning forested regions. The troubling state of sanitation in GBHR occurs in metropolitan regions due to lack of a formal sanitation system. [ABSTRACT FROM AUTHOR]

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series. [ABSTRACT FROM AUTHOR]

Disasters caused by rainfall extremes represent social challenges in several sectors, especially in climate change scenarios. Along with changes in land use and land cover, this relationship may become more complex. Thus, the present study aimed to detect recent trends in the pattern of rainfall indicators and associate them with records of natural disasters and the evolution of changes in land use and land cover for the state of Espírito Santo, southeastern Brazil. Daily rainfall data from 77 rain gauge stations from 1970 to 2018 were used to analyze climate trends in different rainfall indicators obtained using the RClimDex software. Records of natural disasters from 1991 to 2020 in the state of Espírito Santo were gathered from the Brazilian Integrated Disaster Information System. Additionally, land use and land cover data from 1985 and 2019 were also used. The Mann–Kendall (MK) test and the Sen slope were used to detect and quantify trends. In the Litoral Norte Espírito-santense and Noroeste Espírito-santense mesoregions of the State, trends of drought intensification are expected mainly in the regions further north. Associated with the great recent agricultural expansion in the region, this trend is concerning. The Central Espírito-santense and Sul Espírito-santense mesoregions tend to become wetter, which explains the recent increase in floods and heavy rainfall. No significant trends were found in the other regions studied. The approach adopted in this study has the potential to assist decision-making, implement mitigation and/or adaptation measures, and reduce the impacts of natural disasters in different regions of the world. [ABSTRACT FROM AUTHOR]

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 variations 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, nonhomogeneous 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 reference information for public policies on water resources and future studies for Alagoas and similar regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aimed to assess monthly average daily global solar irradiation ( H g m) estimates in Southeastern Brazil from empirical models and artificial intelligence (AI) techniques using extreme air temperatures (maximum—Tx and minimum—Tn) and extraterrestrial solar irradiation ( H 0 m ) and evaluate the geographic and climate factors in the performance of the models or the AI techniques. The H g m , Tx and Tn data series from 95 automatic weather stations of the Brazilian National Institute of Meteorology (INMET) were used. The Hargreaves-Samani (HS) and Bristow-Campbell (BC) models were fitted, and AI techniques (artificial neural network—ANN and support vector machines—SVM) were trained, and assessed according to local climate conditions. Three input schemes in the AI techniques were assessed: (i) H 0 m , Tx, and Tn; (ii) H 0 m and air temperature amplitude (ΔT); and (iii) H 0 m , Tx, Tn, and ΔT. The coefficients of the HS (0.134 ≤ kr ≤ 0.262) and BC (0.539 ≤ β0 ≤ 0.796; 0.004 ≤ β1 ≤ 0.843; 0.35 ≤ β2 ≤ 2.60) models and their performance were influenced by climate, continentality/maritime effects, altitude and weather systems. The performance of the HS model was inferior to that of BC model and the SVM and ANN techniques. Overall, the SVN and ANN techniques performed better than the BC method. However, BC had similar or superior precision and accuracy to SVM and ANN for some climatic conditions and combinations of input variables. The quality of the H g m series, the combination of input variables, and climate influenced the estimates of the SVM and ANN techniques. [ABSTRACT FROM AUTHOR]

The probable rainfall is an excellent climatic parameter of information since it consists of the highest/lowest expected rainfall for a particular period of the year considering a certain level of probability. This study aims to evaluate traditional PDF performance using different goodness-of-fit tests to establish a criterion for choosing them on a monthly rainfall scale, in Mato Grosso do Sul, Brazil. The Cramer-von Misses (CVM) and Anderson–Darling (AD) tests were the most rigorous in rejecting the hypothesis of PFD suitable for monthly rainfall data, while the Kolmogorov–Smirnov (KS) was the least rigorous. The application of stricter goodness-of-fit tests as the CVM implies the use of up to 60% fewer series compared to the KS test. However, suitable series by the KS test presented erroneous estimates of probable monthly rainfall. The CVM and AD tests indicate the PDF with the best statistical performance (higher precision and accuracy between the observed and estimated frequency by the PDF) in more than 60% of situations. The most suitable PDFs for total monthly rainfall by the goodness-of-fit tests was gamma (12 months of the year). The exponential and Generalized Extreme Values (GEV) can be used for both the dry and rainy periods, respectively. The parameters of PDF are correlated with geographical variables, describing the total monthly rainfall distribution such as, for example, the influence of the South Atlantic Subtropical Anticyclone, the South Atlantic Convergence Zone in the rainy period, and the orographic effect in the dry period. [ABSTRACT FROM AUTHOR]

Evapotranspiration is an important parameter to evaluate soil water deficit and water use efficiency, especially in locations with irregularly distributed precipitation. The aim of this study was to assess the daily actual evapotranspiration (ETa) estimated by the Thornthwaite and Mather soil water balance adapted for crops (ThM) and by the dual Kc method with the crop coefficients optimized from inverse modeling and by the adjustment suggested in the FAO-56 bulletin. The models were optimized and evaluated with actual evapotranspiration determined by the Bowen ratio – energy balance method (ETβ) for sugarcane at full canopy closure grown in Alagoas state, northeastern Brazil. The objective function of the inverse problem was defined in terms of ETβ and ETa estimated by the ThM or dual Kc method by optimizing the single crop coefficient (Kc) and the basal crop coefficient Kcb, respectively. Optimized Kc (1.05) and Kcb (1.03) were lower than those adjusted by the Kc FAO56 method (Kc = 1.25 or Kcb = 1.20), with optimized Kc slightly higher than the Kc obtained experimentally (1.01 ± 0.08). ETa estimated by the ThM and dual Kc method with optimized crop coefficients had similar high precision (r2 > 0.79) and accuracy (dm > 0.93 and RMSE < 0.30 mm d−1). However, using the coefficients adjusted from the FAO56 method overestimated ETa in both models. [ABSTRACT FROM AUTHOR]

Desertification is a land degradation phenomenon with dire and irreversible consequences, affecting different regions of the world. Assessment of spatial climate susceptibility to desertification requires long-term averages of precipitation (P) and potential evapotranspiration (PET). An alternative to desertification susceptibility analysis is the use of spatially gridded climate data. The aim of this study was to assess an approach based on gridded climate data and cartographic modeling to characterize climate susceptibility to desertification over Southeast Brazil. Two indices were used to identify climate desertification susceptibility: the aridity index Ia (P/PET) and D (PET/P). Precipitation gridded data from the Global Precipitation Climatology Centre (GPCC), and air temperature from the Global Historical Climatology Network (GHCN) were used. The PET was estimated by the Thornthwaite's method using air temperature data. The assessment of these gridded climate series, PET and indices was performed using independent observed climate series (1961–2010) from the National Institute of Meteorology (INMET) of Brazil—(68 weather stations). Determination coefficient (r2) and the Willmott's coefficient (d) between gridded and observed data revealed satisfactory precision and agreement for grids of precipitation (r2 > 0.93, d > 0.90), air temperature (r2 > 0.94, d > 0.53) and PET (r2 > 0.93, d > 0.63). Overall, the aridity indices based on climate gridded presented good performance when used to identify areas susceptible to desertification. Susceptible areas to desertification were identified by the index Ia over the Northern regions of Minas Gerais and Rio de Janeiro states. No susceptible areas to desertification were identified using the index D. However, both indices indicated large areas of sub-humid climate, which can be strongly affected by desertification in the future. [ABSTRACT FROM AUTHOR]

The characterization of spatial and temporal patterns of wind is essential to several sectors, including energy, urban climate, and applied meteorology. However, few studies describe the regional characteristics of the wind regime over the Brazilian Southeast (SEB), the most developed and populated part of the country. The objectives of the current work were (a) to assess the spatial patterns of the wind regime using cluster analysis (CA) and (b) to apply principal components analysis (PCA) to investigate which meteorological systems influence the spatial and temporal patterns of the wind regime. The dataset consisted of wind speed and direction from 70 automatic weather stations with records from 2008 to 2019. According to the CA method, four groups of homogeneous wind speed (G1–G4) were identified; G4 presented the highest magnitudes of wind speed (wind speed >5 m·s−1, with maxima of 7.2 m·s−1). Seasonal well‐defined minima (March–June) and maxima (July–October) were observed only for G2 and G3. The systems South Atlantic Subtropical Anticyclone (SASA), Frontal Systems (FS), and the South Atlantic Convergence Zone (SACZ) influenced these groups. In addition, the mesoscale meteorological systems likely influence other groups such as breezes from land/sea (G1, G3, and G4), lakes (G2 and G3), and valley/mountain (G2 and G3). The topography had a strong influence on G3, mainly due to the mountain ranges of Mantiqueira and Serra do Mar and Paraíba River valley. Dominant wind directions were E, N/NE, and NW, associated with SASA, in addition to S and SE/SW, influenced by FS and SACZ. The wind speed range of 1.4–3.9 m·s−1 was dominant in all groups, except for G4 (range of 2.4–5.5 m·s−1). According to PCA analysis, two PCs are enough to explain the wind pattern in the SEB with 69.9% of the total variance explained. The surface roughness accounted for 29% of the total variance explained in PC1. The latitude (37.6%) and distance to the coast (30.8%) are the most important variables for PC2. Region's physiography and meteorological systems strongly influenced the wind regime over the Brazilian Southeast. The assessment of wind regime variability presented in this work is expected to support public policies on renewable energy applications, air pollution, and climate change mitigation. [ABSTRACT FROM AUTHOR]

In state of Rio de Janeiro, the Southeast Region of Brazil, 97.91% of the reforested areas contain exclusively species of Pinus and Eucalyptus. Due to the growing demand for raw forest materials, and low diversity of planted species, the market needs to explore new ones. Paricá (Schizolobium amazonicum Huber ex Ducke), Brazilian firetree (Schizolobium parahyba), Cedar (Toona ciliate), and Teak (Tectona grandis) are species with high economic value and are already found in small reforestation areas in the state of Rio de Janeiro. The goal of this study was to find the suitable environmental areas to good productivity each of these four forest species in the State of Rio de Janeiro. Suitable environmental areas were determined by classifying climate maps (water deficit, rainfall, and air temperature) and digital elevation models (DEM) of the State of Rio de Janeiro, based on the climatic and altitude demands of each species. Maps of suitable areas were generated using spatial analysis as raster map algebra and raster map reclassification. The species under study showed great potential for growth throughout the State of Rio de Janeiro. Brazilian firetree (known as Guapuruvu in Brazil) presented the largest environmentally suitable area for planting in the state (47%), especially in regions of low maximum rainfall, associated with high temperatures and low altitudes in Rio de Janeiro (e.g., Baixadas Litorâneas with 36.9% of suitable area). Paricá presented the smallest environmentally suitable area (19%), with suitable areas in regions of altitude less than 700 m (e.g., Médio Paraíba and Serrana, within 27.4% and 20.8% of the suitability area, respectively.). Future studies could refine the suitable areas by examining the soil conditions of each area and researching the economic and social factors that are unique to each region. [ABSTRACT FROM AUTHOR]

This study aims at fitting polynomial models based on latitude, longitude and altitude coordinates to estimate the monthly and annual mean rainfall in Mato Grosso do Sul state, Midwest region of Brazil. Furthermore, its target is to verify whether hydrologically similar regions provide statistical improvement in the regression fit for rainfall estimation. To create the monthly and annual rainfall for 32 rain gauge stations, there were used at least 15 years long data records for analysis with a percentage of gaps at most 10%. Generally, the monthly and annual rainfall models present suitable statistical validation coefficients. The number of predictor variables enhances the performance of the regression method when estimating monthly and annual rainfall. Fitting regressions in hydrologically similar groups through cluster analysis tends to increase regression performance; however, the limited number of rain gauge stations in Brazil makes this technique difficult to apply because the number of parameters of the regression models may be greater than the number of rain gauge stations in the cluster. The first degree polynomial regression proved to be the most adequate to represent the mean monthly and annual rainfall because of the equivalence between observed and predicted values of rainfall and because of the statistical analysis. Fitting polynomial models presents suitable method for practical applications, forming an important tool for environmental management in the Mato Grasso do Sul State. [ABSTRACT FROM AUTHOR]

Desertification is a serious threat to human development and sustainability. This process is characterized by land degradation over areas with dry sub‐humid, semi‐arid and arid climate. In this work, climate susceptibility to desertification was identified for the state of Rio de Janeiro (SRJ), Brazil, the third most populated state in the country. The state is characterized by complex topography, and diverse climate and land uses. Two formulations for the aridity index were used: Ia, defined by Thornthwaite (1948, Geographical Review, 38(1), 55–94) and Penman (1953, Horticultural Congress, 2, 913–924), and D, a modification of Ia by Hare (1983, Climate and Desertification: A Revised Analysis, Geneva: World Meteorological Organization). Both estimates of the aridity require annual rainfall and potential evapotranspiration, which were calculated from the climatological water balance using times series of rainfall and air temperature (1961–2010) from 98 stations over the SRJ. Annual rainfall, evapotranspiration, water deficit and the aridity indices were then spatially interpolated over a 5 × 5‐km grid using a spline tension method. Both indices agree on the driest regions of the SRJ: Norte and Baixada Litorânea, near the northern coast, both under sub‐humid and semi‐arid climate. The index Ia indicated susceptibility to desertification over 2,305 km2 on the Norte and Baixada Litorânea regions, which correspond to 5% of SRJ area; similarly, the index D classified a smaller area over the Norte region under desertification (431 km2). These subregions, among the others also considered in this study, are of great importance for the agricultural, forestry and tourism industries and contribute immensely to the economic development of the state. Hence, the evaluation of susceptibility to desertification in those areas is essential to help prevent further land degradation and support policies formulation on long‐term sustainable management of land and water resources, especially under regional and global climate change scenarios. The methodology used in this work can be applied to studies of desertification in other regions of the world. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Five deterministic methods of spatial interpolation of monthly rainfall were compared over the state of Rio de Janeiro, southeast Brazil. The methods were the inverse distance weight (IDW), nearest neighbor (NRN), triangulation with linear interpolation (TLI), natural neighbor (NN), and spline tension (SPT). A set of 110 weather stations was used to test the methods. The selection of stations had two criteria: time series longer than 20 years and period of data from 1960 to 2009. The methods were evaluated using cross-validation, linear regression between values observed and interpolated, root mean square error (RMSE), coefficient of determination (r2), coefficient of variation (CV, %), and the Willmott index of agreement (d). The results from different methods are influenced by the meteorological systems and their seasonality, as well as by the interaction with the topography. The methods presented higher precision (r2) and accuracy (d, RMSE) during the summer and transition to autumn, in comparison with the winter or spring months. The SPT had the highest precision and accuracy in relation to other methods, in addition to having a good representation of the spatial patterns expected for rainfall over the complex terrain of the state and its high spatial variability. [ABSTRACT FROM AUTHOR]

ABSTRACT Several of the methods used to estimate actual evapotranspiration ( ETa) consider the crop evapotranspiration ( ETc) to be affected by a soil water stress co-efficient (Ks) such that ETa = Ks ETc. The aim of this study was to assess the ETa estimation in maize in Alagoas State (09 ° 38 ′ S and 36 ° 40 ′ W; 260 m msl) in northeastern Brazil using the method of the single crop co-efficient from the FAO-56 paper, combined with three different approaches to determine Ks. The Ks was expressed as either a linear (Ks,lin) or a logarithmic (Ks,log) function of the soil water depletion. Additionally, the method proposed in the FAO-56 (Ks, FAO) was used when the impact was linear, but it was switched on after only one offset. Meteorological and soil water measurements were conducted in a maize crop from July to October 2005. The estimates of ETa were compared with the ETa determined from soil water balance. The ETa estimated with Ks,lin had the highest precision ( r2 = 0.67) and accuracy ( d = 0.89 and root mean square error ( RMSE) = 0.67 mm d−1). However, the ETa determined from Ks,log ( r2 = 0.41, d = 0.57 and RMSE = 1.69 mm d−1) or Ks, FAO ( r2 = 0.46, d = 0.62 and RMSE = 1.73 mm d−1) was not estimated successfully. When the parameters of Ks, FAO were fitted to the local edaphoclimatic conditions, the precision (0.67) and accuracy (0.89 and 0.82 mm d−1) were similar to the estimates with Ks,lin but with a better performance for ETa > 3.0 mm d−1. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum: Agronomy is the property of Universidade Estadual de Maringa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)