ABSTRACT Erosion process occurs naturally, shaping the Earth’s surface. Soil loss can cause harmful effects to the environment when intensive anthropic activities occur. Mathematical models have been used as effective and less costly alternatives for identifying sites highly prone to soil loss, especially at the watershed scale. In Brazil, the Revised Universal Soil Loss Equation (RUSLE) is one of the most commonly used soil loss prediction models. The RUSLE requires information on soil erodibility, rainfall erosivity, topography, land use and cover (C), and conservation practices (P) to estimate average annual soil losses. Images derived from remote sensing techniques are generally used to quantify the spatialization of C factor; however, the variation in land use throughout the year is not usually considered. This study aimed to estimate soil losses in an important subwatershed of Candiota river watershed (CRWsub) by using RUSLE, considering land use and rainfall erosivity in different periods of the year. The periods considered were P1 (January, February and March), P2 (April, May and June), P3 (July, August and September) and P4 (October, November and December). Based on the results, the lowest soil losses occurred in P1. Probably, the high vegetation cover in the soil increases its protection against rainfall erosivity. In P3, the heavy rainfall events are predominantly frontal, occurring in the same months as those when the preparation of the soil for later planting takes place; that is, there is no vegetation cover in this period, thus making the soil more prone to erosion. The use of different images to classify and identify land uses is the best way to understand soil losses throughout the year in the study area. It was possible to observe that agricultural areas are generally associated with greater soil losses in the subwatershed. In addition, the land uses were considered to vary quarterly, thereby making it possible to identify the periods most prone to erosion processes throughout the year. Finally, the erosion percentages in the subwatershed can be linked to the tolerance index for different land-uses, soil classes, and slope categories.

Resumo O Modelo do Número da Curva do Serviço de Conservação do Solo é um modelo conceitual destinado a estimar a precipitação efetiva (ER). Esse modelo está embasado em um parâmetro - denominado Curve Number (CN), que é determinado a partir de informações sobre as características da bacia hidrográfica. O Método Padrão (M1) para determinar o CN é baseado em tabelas de solo e uso da terra, no entanto, alguns autores propuseram metodologias alternativas para definir o valor de CN de eventos monitorados de chuva-escoamento, como aqueles descritos por Hawkins (1993) (M2), Soulis and Valiantzas (2012) (M3) e Soulis and Valiantzas (2013) (M4). O objetivo deste trabalho foi avaliar o impacto da utilização desses métodos para determinação do parâmetro CN na estimativa da ER, tomando como referência quarenta eventos pluviométricos monitorados entre 2015 e 2018 na bacia do rio Cadeia, que possui características de Bioma Pampa. Os diferentes métodos avaliados para definição do parâmetro CN resultaram em desempenhos contrastantes no que diz respeito à estimativa de ER para CRW, como os seguintes achados: i) M1 deu valores de ER com pouca confiabilidade, principalmente devido à classificação das classes de teor de umidade antecedentes; ii) M3 apresentou os melhores resultados na determinação de ER, seguido de M2; e iii) os valores de ER estimados de acordo com o M4 diferiram dos observados, principalmente para eventos com menores profundidades de chuva.

Abstract The Soil Conservation Service Curve Number Model is a conceptual model intended for estimating effective rainfall (ER). This model is grounded in a parameter - referred to as Curve Number (CN), which is determined from information on the characteristics of the watershed. The Standard Method (M1) for determining the CN is based on soil and land-use tables; however, some authors have proposed alternative methodologies for defining the CN value from monitored rainfall-runoff events, such as those described by Hawkins (1993) (M2), Soulis and Valiantzas (2012) (M3), and Soulis and Valiantzas (2013) (M4). The objective of this study was to evaluate the impact of using these methods for determination of the CN parameter on the estimation of ER, taking as reference forty rainfall-runoff events monitored between 2015 and 2018 in the Cadeia River Watershed, which has characteristics of the Pampa biome. The different methods assessed for definition of the CN parameter resulted in contrasting performances with respect to the estimation of ER for CRW, as the following findings: i) M1 gave ER values with little reliability, mainly due to the classification of antecedent moisture content classes; ii) M3 provided the best results in determining ER, followed by M2; and iii) the ER values estimated according to M4 differed from those observed, mainly for events with lower rainfall depths.

RESUMO O dimensionamento adequado de estruturas hidráulicas é dependente das estimativas de vazões máximas. A escassez no monitoramento hidrológico no Brasil tem levado ao uso de métodos de regionalização. O principal objetivo do presente estudo foi desenvolver uma ferramenta via função regional para a estimativa das vazões máximas e seus respectivos tempos de retorno, com o suporte de técnicas baseadas no método dos momentos-L, com vistas a aplicações adequadas da engenharia hidrológica e da gestão de risco de cheias. As séries de vazão máxima diária anual foram ajustadas às funções densidade de probabilidade tradicionais de 2 parâmetros (Normal, 2-parameter Log-Normal, Gumbel, Gamma) e multiparâmetros (GEV and Kappa), baseando-se no método dos momentos-L, as quais foram utilizadas no desenvolvimento da função regional pelo método da curva adimensional. A capacidade preditiva da função regional foi determinada por validação cruzada para diferentes tempos de retorno. Pode-se concluir que a abordagem baseada nos momentos-L no ajuste da função regional foi satisfatória. Além disso,a função regional: i) foi aperfeiçoada quando as distribuições multiparâmetros acima mencionadas foram usadas e ii) foi classificada como ótima para tempos de retorno de até 100 anos sendo útil a projetos práticos de engenharia e na gestão de cheias.

ABSTRACT The proper design of hydraulic structures depends on estimates of maximum stream flows. The scarce stream flow monitoring in Brazil has led to the use of regionalization methods. The main objective of this study was to develop a tool via regional function to estimate maximum stream flows and their corresponding return periods (RP) with the aid of techniques based on the L-moments method, seeking for adequate hydrologic engineering applications and flood risk management. Annual maximum stream flow historical series were adjusted to traditional 2-parameter probability density functions (PDFs) (Normal, 2-parameter Log-Normal, Gumbel, Gamma) and multiparameter PDFs (GEV and Kappa), based on the L-moments method, which were used in the development of the regional function employing the dimensionless curve method. The regional function’s predictive capability was determined by cross-validation for different RPs. It can be concluded that the approach based on L-moments was successfully used to adjust the regional function. In addition, the regional function: i) was improved when using the aforementioned multiparameter PDFs and ii) was framed as optimum for RP of up to 100 years and considered useful for practical engineering projects and flood risk management.