No 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.Keywords: Flood risk management; Statistical hydrology; GEV; Kappa; Mirim-São Gonçalo transboundary basin. RESUMOO 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.

The devastating effects of floods, combined with scarce data sets, have stimulated the development of hydrological regionalisation techniques. The present study proposed an evaluation of the L-moments based index-flood procedure, coupled with watershed grouping based on geographical convenience for regionalisation of maximum streamflows. A pioneer analysis for South America, addressing the geographical classification method adopted by National Water Agency of Brazil (ANA), was conducted considering over 100 watersheds in southern Brazil. Nonstationary and discordant maximum annual streamflow (MAS) series were removed with the aid of the Mann-Kendall test and discordancy measure, whereas the heterogeneity measure was used to check regional homogeneity. The best regional distribution was identified by the Z DIST goodness-of-fit measure. Finally, multiple nonlinear regressions, considering morphological and meteorological watershed characteristics, were performed to obtain better index-flood estimates. It was concluded that: (a) the proposed methodological strategy provided satisfactory estimation of design floods; (b) area, mean slope, stream gradient, and flow length, were the most satisfactory explanatory variables; (c) the fitted equations stand out as a state-of-art alternative for the scarce hydrological monitoring in southern Brazil; and (d) the hydrological boundaries defined by ANA might not be the most adequate approach from a regional point of view.

Due to the severity related to extreme flood events, recent efforts have focused on the development of reliable methods for design flood estimation. Historical streamflow series correspond to the most reliable information source for such estimation; however, they have temporal and spatial limitations that may be minimized by means of regional flood frequency analysis (RFFA). Several studies have emphasized that the identification of hydrologically homogeneous regions is the most important and challenging step in an RFFA. This study aims to identify state‐of‐the‐art clustering techniques (e.g., K‐means, partition around medoids, fuzzy C‐means, K‐harmonic means, and genetic K‐means) with potential to form hydrologically homogeneous regions for flood regionalization in Southern Brazil. The applicability of some probability density function, such as generalized extreme value, generalized logistic, generalized normal, and Pearson type 3, was evaluated based on the regions formed. Among all the 15 possible combinations of the aforementioned clustering techniques and the Euclidian, Mahalanobis, and Manhattan distance measures, the five best were selected. Several watersheds' physiographic and climatological attributes were chosen to derive multiple regression equations for all the combinations. The accuracy of the equations was quantified with respect to adjusted coefficient of determination, root mean square error, and Nash–Sutcliffe coefficient, whereas, a cross‐validation procedure was applied to check their reliability. It was concluded that reliable results were obtained when using robust clustering techniques based on fuzzy logic (e.g., K‐harmonic means), which have not been commonly used in RFFA. Furthermore, the probability density functions were capable of representing the regional annual maximum streamflows. Drainage area, main river length, and mean altitude of the watershed were the most recurrent attributes for modelling of mean annual maximum streamflow. Finally, an integration of all the five best combinations stands out as a robust, reliable, and simple tool for estimation of design floods.

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.