Hydrological models are generally calibrated and validated using a suite of well-known statistical metrics, which sometimes lack clear connection and tailoring to the local users’ need and therefore limits the evaluation, especially in the case of global climate services. Therefore, in this study, two types of application-based evaluation metrics are introduced, addressing (i) temporal matching of quantile extremes and (ii) relative bias in flow signatures, which supplements commonly used model performance assessment metrics. The introduced metrics are compared to conventional statistical metrics, at seven case study areas across the world, with three model settings representing different datasets and calibrations, generated from the global hydrological model World-Wide HYPE (WW HYPE). The results suggest that different performance results can occur when comparing application-based metrics to conventional ones. This implies that different evaluation metrics reveal models’ capability in various aspects, supporting their application under the corresponding circumstances. Finally, these metrics enable us to propose two model applicability scenarios: generally applicable models and conditionally applicable models. For instance, the WW HYPE with global dataset and local calibration can yield optimal estimates concerning the timing of quantile extremes and temporal variations in flow signatures, despite its suboptimal performance in conventional evaluation metrics. Therefore, it may be considered as a conditionally applicable model which can be used in regions with limited local datasets, supplying reliable information for decision-makers in formulating strategic plans for water resources management.

Assessing the relative contribution of Land Use and Cover Changes (LUCC) and climate changes on runoff still represents a great challenge for water resources management. This issue is particularly critical for the Upper Paraná River Basin (UPRB), one of the most important basins in South America and responsible for most of the production of food, ethanol, and electricity generation in Brazil. In this paper, we used the Soil and Water Assessment Tool (SWAT) to quantitatively assess the relative contribution of both forcings. The simulation period included a time of great importance for climate studies, known as the 1970s global climate shift, and of great impact on river discharge within the UPRB. Three land use and cover scenarios were assigned to the 1961-1990 period of simulations, representing land use and cover during a pristine period (around the Year 1500), 1960, and 1985. Thirteen years of precipitation before and after the climate shift (considered to be the period 1974-1977) were analyzed and compared. Results showed a precipitation increase for the basin in general after the climate shift. The increase in rainfall reached up to 15% in many northern areas and more than 20% in the southern parts of the basin. By comparing all simulations, results indicate that both LUCC and precipitation increase due to the climate shift had a significant effect on the changes in annual discharge of the largest rivers of the UPRB. However, the results suggest that the impact of the precipitation increase on the discharge exceeded that of the LUCC. Between 1960 and 1985 the LUCC accounts for about 16% of the increase of the median annual discharge, whereas climate shift accounts for an increase of about 32%. These findings, suggesting a more relevant role for the climate, are consistent with two recent water crisis experienced by the country in the last decades, caused by prolonged below-normal rainfall throughout 2001/2002 and again in 2014/2015.

Within a single region, it is possible to identify opposite changes in flow production. This proved to be the case for several basins in southeastern South America. It remains challenging to the causes this behavior and whether changes in streamflow will continue at current levels or decline in the coming decades. In this study, we used the Soil Water Assessment Tool to simulate monthly river discharge in the Ivaí River Basin, an unregulated medium-sized catchment and tributary of the Upper Paraná River Basin. After calibration, the simulated flow regime for the five streamflow stations based on the Nash-Sutcliffe Efficiency index (NSE) rated four of the streamflow stations Very Good (NSE between 0.86 and 0.89) and only one in the Good index (0.70). The overall flow behavior was well represented, although an underestimation was identified in four monitoring stations. Through assessment of its functionality and limitations in terms of specific flow duration curves percentages, the calibrated model could provide (to managers) the reliability needed for a realistic intervention. The results of this study may assist managers and support public policies for the use of water resources at the Ivaí River basin.

This study aimed to analyse the spatio‐temporal variability of wet and drought events in the Paraná River Basin and their association with the El Niño—Southern 20 Oscillation (ENSO) phenomenon. A dataset of daily precipitation from 953 pluviometric stations from 1982 to 2016 was used. Anomalies of sea surface temperature (SST) from the four Niño regions of the Equatorial Pacific were also used. Homogeneous rainfall regions were defined using cluster analysis and principal component analysis (PCA). To describe the drought or wet conditions the standardized precipitation index (SPI) for 1‐, 3‐, and 12‐months time scales were used. The relationship between the ENSO and the wet and drought conditions were also analysed. The results suggest that two regions are enough to represent the whole Paraná River Basin during different rainfall patterns. In the 1‐month SPI, the precipitation extremes of the region that is mainly composed of the state of Paraná and southern region of Mato Grosso do Sul (Region 2), showed a statistically significant correlation at the level of 1% with ENSO events. In the 12‐month SPI, the most intense drought and wet events and with greater spatial coverage, in general, were observed in years with ENSO occurrences. In general, it is observed that although the ENSO phenomenon is not the main factor that causes the drought and wet events in the region, El Niño contributes to the rainfall excess and La Niña to the rainfall deficit in the Paraná River Basin.

In the present work, the trend of extreme rainfall indices in the Macro-Metropolis of São Paulo (MMSP) was analyzed and correlated with largescale climatic oscillations. A cluster analysis divided a set of rain gauge stations into three homogeneous regions within MMSP, according to the annual cycle of rainfall. The entire MMSP presented an increase in the total annual rainfall, from 1940 to 2016, of 3 mm per year on average, according to Mann-Kendall test. However, there is evidence that the more urbanized areas have a greater increase in the frequency and magnitude of extreme events, while coastal and mountainous areas, and regions outside large urban areas, have increasing rainfall in a better-distributed way throughout the year. The evolution of extreme rainfall (95 th percentile) is significantly correlated with climatic indices. In the center-north part of the MMSP, the combination of Pacific Decadal Oscillation (PDO) and Antarctic Oscillation (AAO) explains 45% of the P95 th increase during the wet season. In turn, in southern MMSP, the Temperature of South Atlantic (TSA), the AAO, the El Niño South Oscillation (ENSO) and the Multidecadal Oscillation of the North Atlantic (AMO) better explain the increase in extreme rainfall (R 2 = 0.47). However, the same is not observed during the dry season, in which the P95 th variation was only negatively correlated with the AMO, undergoing a decrease from the ‘70s until the beginning of this century. The occurrence of rainy anomalous months proved to be more frequent and associated with climatic indices than dry months.

This work presents an analysis of the observed trends in extreme precipitation events in the Paraná River basin (PRB) from 1977 to 2016 (40 yr) based on daily records from 853 stations. The Mann–Kendall test and inverse-distance-weighted interpolation were applied to annual and seasonal precipitation and also for four extreme precipitation indices. The results show that the negative trends (significance at 95% confidence level) in annual and seasonal series are mainly located in the northern and northeastern parts of the basin. In contrast, except in the autumn season, positive trends were concentrated in the southern and southeastern regions of the basin, most notably for annual and summer precipitation. The spatial distributions of the indices of annual maximum 5-day precipitation and number of rainstorms indicate that significant positive trends are mostly located in the south-southeast part of the basin and that significant negative trends are mostly located in the north-northeast part. The index of the annual number of dry days shows that 88% of significant trends are positive and that most of these are located in the northern region of the PRB, which is a region with a high number of consecutive dry days (>90). The simple daily intensity index showed the highest number of stations (263) with mostly positive significant trends.

Social distancing policies put in place during COVID-19 epidemic in addition to helping to limit the spread of the disease also contributed to improving urban air quality. Here we show a decrease in air pollutant concentration as a consequence of mobility reduction in São Paulo during the containment measure which began on 22nd March 2020. When comparing to foregoing weeks to equivalent periods of 2019, the concentration of most air pollutants sharply decreased in the first days of mobility restriction, to then increase again after government officials downplayed the threat of the disease. This trend is also followed by a decrease in hospital admissions by SARS-influenza. Therefore, despite the great economic and social unrest caused by the pandemic, this unique situation shows that large-scale mobility reduction policy had a significant impact on air quality, benefiting, directly and indirectly, the public health system.

This study presents a new land cover map for the Upper Parana River Basin (UPRB-2015), with high spatial resolution (30 m), and a high number of calibration and validation sites. To the new map, 50 Landsat-8 scenes were classified with the Support Vector Machine (SVM) algorithm and their level of agreement was assessed using overall accuracy and Kappa coefficient. The generated map was compared by area and by pixel with six global products (MODIS, GlobCover, Globeland30, FROM-GLC, CCI-LC and, GLCNMO). The results of the new classification showed an overall accuracy ranging from 67% to 100%, depending on the sub-basin (80.0% for the entire UPRB). Kappa coefficient was observed ranging from 0.50 to 1.00 (average of 0.73 in the whole basin). Anthropic areas cover more than 70% of the entire UPRB in the new product, with Croplands covering 46.0%. The new mapped areas of croplands are consistent with local socio-economic statistics but don't agree with global products, especially FROM-GLC (14,9%), MODIS (33.8%), GlobCover (71.2%), and CCI (67.8%). In addition, all global products show generalized spatial disagreement, with some sub-basins showing areas of cropland varying by an order of magnitude, compared to UPRB-2015. In the case of Grassland, covering 25.6% of the UPRB, it was observed a strong underestimation by all global products. Even for the Globeland30 and MODIS, which show some significant fraction of pasture areas, there is a high level of disagreement in the spatial distribution. In terms of general agreement, the seven compared mappings (including the new map) agree in only 6.6% of the study area, predominantly areas of forest and agriculture. Finally, the new classification proposed in this study provides better inputs for regional studies, especially for those involving hydrological modeling as well as offers a more refined LU/LC data set for atmospheric numerical models.

The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non‐stationary for location parameter), and Model 3 (non‐stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non‐stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb), and October (Aw, Cfa, and Cfb). Approximately 90% of the 1,112 stations presented no trend regarding the GEV parameters. The non‐stationarity showed by other stations (Models 2 and 3) might be associated with several factors, such as the alteration of land use due to the north expansion of the agricultural border of the Paraná River basin.

The Upper Paraná River Basin (UPRB) has undergone many rapid land use changes in recent decades, due to accelerating population growth. Thus, the prediction of water resources has crucial importance in improving planning and sustainable management. This paper presents a large-scale hydrological modelling of the UPRB, using the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated for 78 outlets, over a 32-year simulation period between 1984 and 2015. The results and the comparison between observed and simulated values showed that after the calibration process, most of the outlets performed to a satisfactory level or better in all objective functions analyzed with 86%, 92%, 76%, 88%, and 74% for Percent bias, Coefficient of determination, Nash-Sutcliffe efficiency, Kling-Gupta efficiency, and the Ratio of Standard deviation of observations to root mean square error, respectively. The model output provided in this work could be used in further simulations, such as the evaluation of the impacts of land use change or climate change on river flows of the Upper Paraná Basin.

The aim of this study is to assess personal exposure to Particle Number Concentrations (PNC) in four size ranges between 0.3 and 10 mu m, and particulate matter (PM1; PM2.5; PM4; PM10) in order to evaluate possible genotoxic effects through a comet assay in buccal cells. A convenience cohort of 30 individuals from a Brazilian medium-sized city was selected. These individuals aged between 20 and 61 and worked in typical job categories (i.e., administrative, commerce, education, general services and transport). They were recruited to perform personal exposure measurements during their typical daily routine activities, totaling 240 h of sampling. The 8-h average mass concentrations in air for volunteers ranged from 2.4 to 31.8 mu g m(-3) for PM1, 4.2-45.1 mu g m(-3) for PM2.5, 7.9-66.1 mu g m(-3) for PM4 and from 23.1 to 131.7 mu g m(-3) for PM10. The highest PNC variation was found for 0.3-0.5 range, between 14 and 181 particles cm(-3), 1 to 14 particles cm(-3) for the 0.5-1.0 range, 0.2 to 2 particles cm(-3) for the 1.0-2.5 range, and 0.06 to 0.7 particles cm(-3) for the 2.5-10 range. Volunteers in the 'education' category experienced the lowest inhaled dose of PM2.5, as opposed to those involved in 'commercial' activities with the highest doses for PM10 (1.63 mu g kg(-1) h(-1)) and PM2.5 (0.61 mu g kg(-1) h(-1)). The predominant cause for these high doses was associated with the proximity of the workplace to the street and vehicle traffic. The comet assay performed in buccal cells indicated that the volunteers in 'commerce' category experienced the highest damage to their DeoxyriboNucleic Acid (DNA) compared with the control category (i.e. 'education'). These results indicate the variability in personal exposure of the volunteers in different groups, and the potential damage to DNA was much higher for those spending time in close proximity to the vehicle sources (e.g. commercial services) leading to exposure to a higher fraction of fine particles. This study builds understanding on the exposure of people in different job categories, and provide policy makers with useful information to tackle this neglected issue.

This paper evaluates the contributions of the emissions from mobile, stationary and biogenic sources on air pollution in the Amazon rainforest by using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. The analyzed air pollutants were CO, NOx, SO2, O-3, PM2.5, PM10 and volatile organic compounds (VOCs). Five scenarios were defined in order to evaluate the emissions by biogenic, mobile and stationary sources, as well as a future scenario to assess the potential air quality impact of doubled anthropogenic emissions. The stationary sources explain the highest concentrations for all air pollutants evaluated, except for CO, for which the mobile sources are predominant. The anthropogenic sources considered resulted an increasing in the spatial peak-temporal average concentrations of pollutants in 3 to 2780 times in relation to those with only biogenic sources. The future scenario showed an increase in the range of 3 to 62% in average concentrations and 45 to 109% in peak concentrations depending on the pollutant. In addition, the spatial distributions of the scenarios has shown that the air pollution plume from the city of Manaus is predominantly transported west and southwest, and it can reach hundreds of kilometers in length.

Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.

Sugar cane bagasse is one of the largest fuels used for electricity generation in Brazil and its usage has continuously increased to supply the energy demand. This paper presents emission inventory based on power plants burning sugar cane bagasse. The inventory involves the spatial distribution and the estimated flows for the following major pollutants: nitrogen oxides (NOx), particulate material (PM), carbon dioxide (CO2) and total organic carbon (TOC). A total of 384 power plants were inventoried, representing a generated power of 9.9 GW, about 26% of the energy produced by thermal power plants sector. The plants are concentrated in two main poles: one of them in S?o Paulo State and nearby areas and the other one in coast of Brazilian Northeast. The limits proposed by the AP-42 Regulations of the US Environmental Protection Agency (USEPA) for the emission factors were applied. Additional emission factors identified in the scientific literature were also included in the analysis in order to assess the uncertainties associated to the estimative. The estimated emissions showed values in the range 16.0 - 20.5 Gg?year?1 for NOx, 18.0 - 267.0 Gg?year?1 for MP and 20.5 - 26.7 Tg?year?1 for CO2. The contribution of TOC showed a minor contribution around 10 - 20 Mg?year?1. PM showed to be the most representative pollutant emitted by the thermal plants burning sugar cane bagasse, but with a large range of uncertainty. There is a high level of uncertainty associated to the preparation of cane as well as the use of collectors to control particulate emissions. The adequate control over all stages could reduce the bagasse ash content in 90% or more.