Estimate of intense rainfall equation parameters for rainfall stations of the Paraíba State, Brazil

ABSTRACT Rainfall is the primary water source for hydrographic basins. Hence, the quantification and knowledge of its temporal and spatial distribution are indispensable in dimensioning hydraulic projects. This study aimed at assessing the fit of a series of rainfall data to different probability models, as well as estimating parameters of the intensity-duration-frequency (IDF) equation for rain stations of the Paraíba State, Brazil. The rainfall data of each station were obtained from the Brazilian Water Agency databanks. To estimate the maximum daily rainfall of each station and return period (5, 10, 15, 25, 50 and 100 years), the following probability distributions were used: Gumbel, Log-Normal II, Log-Normal III, Pearson III and Log-Pearson III. The estimation of rainfall in durations of 5-1,440 min was carried out by daily rainfall disaggregation. The adjustment of the IDF equation was performed via nonlinear multiple regression, using the nonlinear generalized reduced gradient interaction method. When compared to the data observed, the intense rainfall equations for most stations showed goodness of fit with coefficients of determination above 0.99, which supports the methodology applied in this study.

摘要：降雨是水文流域的首要水源。因此，对降雨的量化分析及其时空分布规律的掌握，对于水利工程的设计选型而言不可或缺。本研究旨在评估系列降雨数据对不同概率分布模型的适配性，并估算巴西帕拉伊巴州各雨量站的强度-历时-频率（Intensity-Duration-Frequency, IDF）方程参数。各雨量站的降雨数据均取自巴西水务局数据库。为估算各站点的日最大降雨量与重现期（5、10、15、25、50及100年），本研究采用了耿贝尔分布（Gumbel）、对数正态II型分布（Log-Normal II）、对数正态III型分布（Log-Normal III）、皮尔逊III型分布（Pearson III）以及对数皮尔逊III型分布（Log-Pearson III）。通过日降雨时段分解，完成5~1440分钟历时下的降雨量估算。本研究采用非线性广义缩减梯度交互算法（Nonlinear Generalized Reduced Gradient Interaction Method），通过非线性多元回归实现IDF方程的参数拟合。与实测降雨数据对比后，多数站点的IDF方程拟合优度优异，决定系数均高于0.99，验证了本研究所用方法的合理性。