ABSTRACT The frequency of extreme weather events has increased in almost all regions of the world. In this context, it is vital to understand how the location, scale, and shape of their frequency distributions are changing over time. This study used the flexibility of neural networks to model changes in the probability of daily extremes of maximum (Tmax) and minimum (Tmin) air temperature data in Campinas, Brazil — one of South America’s longest meteorological records spanning from 1890 to 2022. Based on the Extremal Types Theorem, we employed a conditional density network to model the parameters of the generalized extreme value distribution (GEV-CDN) as functions of time. Our findings indicate that a GEV-based model, where the location and scale parameters vary over time, best described the variations in the Tmin series. However, a GEV-based model with only the location parameter varying over time best described the variations in the Tmax series. From an agrometeorological perspective, these results suggest that the probability of Tmax values leading to crop failures is rapidly increasing. The findings indicate a decrease in the probability of agronomic frost events in Campinas over the past 133 years, but the rate of this decrease has slowed in recent years. This result, combined with the negative value of the GEV’s shape parameter, suggests that it is unlikely that Campinas may become an agronomic-frost-free region. To facilitate visualization of the changes in the probability of Tmax and Tmin values from 1890 to 2022, we have developed an internet application available at https://climatology-iac.shinyapps.io/Shinny/.

ABSTRACT The improvement of probabilistic assessments of extreme air temperature events is a major goal for agrometeorological studies. The regional frequency analysis based on L-moments (RFA-Lmom) has been successfully used to improve the study of hydrometeorological variables such as extreme rainfall events. This study investigated the hypothesis that the RFA-Lmom can be applied to extreme maximum (Tmax) and minimum (Tmin) air temperature data. The RFA-Lmon was calculated considering its original algorithm (multiplicative approach) and a new procedure referred as to additive approach. The suitability of both approaches was evaluated through Monte Carlo experiments, which simulated homogeneous and heterogeneous groups of Tmin and Tmax series, and through a case study based on weather stations situated in the state of São Paulo, Brazil. The results found in this study indicated that the RFA-Lmom can be applied to Tmax and Tmin data in tropical/subtropical regions such as the state of São Paulo. In addition, the additive approach consistently outperformed the multiplicative approach. Both discordance and heterogeneous measures presented their best performances when calculated under this new approach. The original goodness-of-fit measure may also be replaced by its bivariate extension when the group is formed by more than 15 series.

RESUMO Considera-se que a vegetação ripária desempenha papel importante na retenção de sedimentos e, portanto, na diminuição da produção de sedimentos em bacias hidrográficas. A Lei Florestal (Lei 12.651/2012) determina a manutenção da vegetação ripária em faixas de largura fixa, mas permite a continuidade das atividades agrossilvipastoris em parte das Áreas de Preservação Permanente (APPs). Buscou-se, por meio deste estudo, avaliar a redução da produção de sedimentos pela recuperação da vegetação das APPs em 5, 8, 15 e 30 m, larguras admitidas pela Lei Florestal, considerando três cenários de uso do solo, que apresentam diferentes taxas de erosão: predomínio de áreas de floresta, pastagem e agricultura. O modelo SWAT (Soil and Water Assessment Tool) foi utilizado para simular a produção de sedimentos nesses cenários na bacia hidrográfica do rio Jundiaí-Mirim, localizada no estado de São Paulo, Brasil. O SWAT foi calibrado e validado para a vazão em escala mensal, obtendo-se os seguintes índices estatísticos: NS = 0,77 e 0,70, PBIAS = -10,2 e -12,5, RSR = 0,48 e 0,55 nos processos de calibração e validação, respectivamente. O maior valor de redução da produção sedimentos gerados (30%) foi observado com a recuperação total das APPs (30 m) no uso atual da bacia. Recuperando-se as APPs em 5, 8 e 15 m, a redução da produção de sedimentos ficou abaixo de 10%, nos cenários alternativos. As faixas reduzidas de APPs recuperadas mantêm alta a produção de sedimentos. E, ainda, mesmo com a recuperação total das APPs é necessário adotar práticas de conservação do solo em toda a área agrícola da bacia, a fim de minimizar os impactos nos cursos hídricos.

ABSTRACT Riparian vegetation plays an important role in sediment retention, thus reduces sediment yield in watersheds. The Brazilian Forest Law (Law 12,651/2012) requires maintenance of fixed-width buffers of riparian vegetation but allows the continuity of agriculture, livestock, and forestry farming activities in some parts of the Areas of Permanent Preservation (APP). This paper aimed to evaluate sediment reduction by recovering the APPs with vegetation strips of permitted widths (5, 8, 15, and 30 m), as per the Forest Law. We considered three land use scenarios that present distinct erosion rates - predominance of areas with forest cover, pasture, and agriculture. The Soil and Water Assessment Tool (SWAT) model was used to simulate sediment yield in these scenarios at the Jundiaí-Mirim Watershed in São Paulo, Brazil. The SWAT was calibrated and validated for monthly streamflow. We obtained statistical indices for the processes of calibration and validation, respectively, as: NS = 0.77 and 0.70, PBIAS = -10.2 and -12.5, and RSR = 0.48 and 0.55. The highest reduction in sediment yield (30%) was observed with the total recovery of the APPs (vegetation strips of 30 m) in the current land use scenario. The recovery of the APPs with vegetation strips of 5, 8, and 15 m yielded sediment reduction below 10% in the alternative land use scenarios. The APP strips with reduced recovery maintained high rates of sediment yield. Additionally, even with a total recovery of the APP it is necessary to adopt soil conservation practices throughout the basin’s agricultural area to minimize the impacts on water resources.