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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 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]

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]

Extreme rainfall events are likely to become more frequent according to recent scenarios of climate change. This issue is especially important over regions with complex topography, which enhances rainfall variability when associated with weather patterns. The state of Rio de Janeiro (SRJ), southeastern Brazil, is characterized by altitudes ranging from the mean sea level up to 2500 m.a.s.l, in mountain ranges and valleys covering significant parts of the region. Time series data of annual maximum daily rainfall were obtained from 110 stations with a data coverage of at least 20 years, from 1960 to 2010. The Probability Distribution Functions (PDFs) normal, log-normal, exponential, gamma, Gumbel, Weibull, and Generalized Extreme Value (GEV) were fitted to maximum rainfall series. Goodness-of-fit tests (Chi-squared - χ2 and Anderson-Darling) revealed that the Gumbel, GEV, and log-normal were found to be the best choices. However,the Gumbel and GEV PDFs were the best ranking by the χ2 and Anderson-Darling test, respectively. Extreme rainfall events with different recurrence intervals (5, 10, 25, 50 and 100 years) were calculated based on the Gumbel and GEV Cumulative Distribution Function (CDF). The differences between extreme values from Gumbell and GEV function increased as the shape parameter increases from zero, with higher probability and extreme value. Five regions with homogeneous patterns of extreme rainfall were identified using clustering analysis (Ward's method) and different recurrence intervals. Overall, the regions with higher values of extreme rainfall in all scenarios and CDFs were the ones close to the coast, within 40 km, and south of Serra dos Órgãos mountain range, located in the middle of the state. The mountain range separates the state in two halves, concentrating higher values of extreme rainfall in the lower part, where the city of Rio de Janeiro, the state's capital, is located. Scenarios for both CDF (GEV and Gumbel) indicated daily rainfall events up to 200 mm, with recurrence intervals of 50 to 100 years. In addition, the southernmost part of the state is subjected to rainfall extremes up to 260 mm in scenarios of 50 to 100 years of recurrence interval. This region, and the state's capital, are characterized by complex topography and a high fraction of population living in slums over hills, or lowlands near the ocean, increasing the vulnerability to events such as landslides and floods associated with extreme rainfall. Unlabelled Image • Seven probability distribution functions were fitted to annual maximum daily rainfall series in Rio de Janeiro State, Brazil • The best-fit distributions (Gumbel and GEV) were used to estimate extreme rainfall with different recurrence intervals • The cluster analysis and spline tension method were applied to assess the spatial patterns of the extreme rainfall • The regions with higher extreme rainfall were close to the coast and south of mountains located in the middle of the State • In these regions, extreme daily rainfall range between 200 and 300 mm, with return times of 50 to 100 years were expected [ABSTRACT FROM AUTHOR]

Abstract Drought indices allow for the characterization of drought events, being the most widely used the Standardized Precipitation Index (SPI), which is recommended by the World Meteorological Organization (WMO) as the standard drought index. The objectives of this study are: i) to characterize the variation of annual SPI index in the state of Rio de Janeiro from 1979 to 2009; ii) to relate ENSO events with the variation of the annual SPI; iii) to identify possible significant trends of rainfall increase and reduction based on the annual SPI; and iv) characterize drought evolution in the SRJ in the 1979–2009 period (Appendix A). For the SPI calculation, a monthly time series with 31 consecutive years was used, based on rainfall data from 99 rainfall stations distributed in eight government regions. Annual SPI data was presented using maps created with ArcGIS software version 10.2.2 and all statistical procedures were calculated in the R environment software. The Shapiro-Wilk (SW) and Bartlett (B) tests characterized rainfall data as being mostly normal and non-homogeneous. The annual SPI showed that the Center South region experienced the highest continuous dry period between 1999 and 2000 and 2002–2003. The annual SPI was assessed with the Mann-Kendall (MK), Curvature Sen (magnitude) and Pettitt (abrupt inflection) tests. The MK test indicated significant trends of annual rainfall increase in the North and Mountainous regions, and significant reduction trends in the Mountainous and Center South regions. Pettitt's test indicated significant abrupt changes in the North, Center South and Mountainous regions. The Curvature Sen test revealed that stations Dois Rios (North) and Cordeiro (Mountainous) registered a greater magnitude in the increase and reduction of the annual SPI, respectively. Graphical abstract Unlabelled Image Highlights • The SPI index is an important tool in the spatiotemporal characterization of drought in the state of Rio de Janeiro. • Events of ENSO were not directly related to the increase or reduction of rainfall in any of the regions of the state. • The influence of ENSO seems to be less representative in terms of rainfall regime alteration in Rio de Janeiro. • Significant trends of annual SPI increase and reduction were identified in regions Center south, Mountainous and North. [ABSTRACT FROM AUTHOR]

Dry spell studies are of vital importance for agricultural planning and water management. This study characterized dry spells in the state of Rio de Janeiro (SRJ) - southeastern Brazil - based on statistical tests, multivariate analysis and spatial distribution. Daily rainfall data from 1995 to 2017 were obtained from 86 rainfall stations located in the SRJ and neighbouring states. The data were submitted to quality analysis and gap filling of data using the simple linear regression method. The start of a dry spell was considered after three consecutive days with rainfall < 1 mm during the rainy season (November to March). A dry spell was considered the period with at least three consecutive dry days (CDD) and is divided in four classes of dry spells - Class A (3 - 6 days), B (7-10 days), C (11-14 days) and D (15 days or more) – were established for the SRJ. The Shapiro-Wilk (SW), Anderson-Darling (AD), Kolmogorov-Smirnov (KS), Jarque-Bera (JB) and Bartlett (B) tests were also applied to the time series to validate data. The SW (83.72%), AD (74.42%), KS (55.81%) and JB (80.23%) tests indicated non-normality of the data. The classes of dry spells registered different frequencies of occurrence, with Class A (70.03%), B (17.98%), C (6.08%) and D (5.91%). Spatially, there was a high variability of dry spells in the south of the state with the shortest prolonged dry spells, while in the north dry spells are usually longer, with emphasis on February and March. Principal Component Analysis (PCA) was applied to eight variables for Class A (most frequent), and identified latitude, longitude and, particularly elevation, as variables that influence the spatial distribution of dry spells, with highlights for the summer (December and January) season. The high spatial-temporal variability of dry spells in Rio de Janeiro is influenced by multi-scale meteorological systems, with an emphasis on frontal systems and physiographic factors. The applied methodology and presented results can be used to improve public policies regarding water management and mitigate the effects of droughts assuring the quantity and quality of water resources in the development of the SRJ. • Spatial-temporal variability of dry spells in Rio de Janeiro is influenced by meteorological systems and physiographic factors. • Dry spells of classes B (7 to 10 days), C (11 to 14 days) and D (15 days or more) are influenced by physiographic factors. • Class A (3 to 6 days) dry spells are influenced by latitude. • The North of Rio de Janeiro has a higher occurrence of long-term dry spells compared to the South. [ABSTRACT FROM AUTHOR]

Climate Data analysis has become a fundamental tool for scientists who seek to better evaluate changes in climatic variables worldwide. When it comes to rainfall there are many datasets publicly available, and orbital products have been gradually sharpening its results. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR) annual product is used in this study to characterize rainfall variation over the state of Rio de Janeiro (SRJ), considering the period of 1983 to 2017. A rainfall dataset with 35 year long series for each of the 92 municipalities of the SRJ was created using GIS software. Several statistical tests were then applied to the datasets of each municipality in order to verify normality (Shapiro-Wilk, Anderson-Darling, Lilliefors and Jarque-Bera), homogeneity (Pettitt, SNHT, Buishand and von Neumann), trends (Mann-Kendall) and intensity (Şen) of reduction or increase in annual rainfall. The estimated rainfall datasets were classified mainly as normal and homogenous (non-significant breakpoints), but significant breakpoints were registered by the Buishand's test in the dataset of twenty seven (29.34%) municipalities. Twenty municipalities had their estimated datasets compared to local meteorological stations in order to verify PERSIANN-CDR performance over the SRJ. Municipalities in the Middle Paraíba and Center South regions are the wettest of the state, while locations that presented lower average annual rainfalls in the state are concentrated in the North, Coastal Flats and Northwest regions. Alarming trends of reduction in annual rainfall were identified for all municipalities using the MK test, but to the threshold of 95% reliability, results show fifty four (58.69%) municipalities located in the central and western parts of the SRJ. According to Şen ´s test the intensity of annual rainfall reduction is greater in municipalities of the Middle Paraíba and Green Coast regions, but Center South, Metropolitan and Coastal Flats regions also registered disquieting results. PERSIANN-CDR analysis can be considered an efficient methodology in the characterization of rainfall variability and trend detection for the SRJ, being encouraged for future studies addressing rainfall and drought variability over the state. The analysis of the PERSIANN-CDR products should also be applied in other regions of the country, especially considering the remarkable interannual and intraseasonal variability of rainfall in Brazil. • The state of Rio de Janeiro presents decreasing trends of annual rainfall. • The south region of the state presents significant reduction. • Abrupt changes were identified in the years of 1996 and 1998. • Urgent measures are needed in order to guarantee future potable water supply. [ABSTRACT FROM AUTHOR]