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：降雨是水文流域的主要水源。因此，对降雨的量化及其时空分布特征的掌握，对于水利工程的规模设计而言不可或缺。本研究旨在评估系列降雨数据对不同概率模型的拟合效果，同时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利署数据库。为估算各站点的最大日降雨量及重现期（5、10、15、25、50及100年），本研究采用了如下概率分布模型：耿贝尔（Gumbel）分布、对数正态二型（Log-Normal II）分布、对数正态三型（Log-Normal III）分布、皮尔逊三型（Pearson III）分布以及对数皮尔逊三型（Log-Pearson III）分布。针对5至1440分钟历时的降雨量估算，通过日降雨分解法完成。IDF方程的拟合通过非线性多元回归实现，采用非线性广义缩减梯度交互算法。与实测降雨数据对比后发现，多数站点的强降雨公式拟合优度优异，决定系数均高于0.99，证实了本研究所采用方法的合理性与有效性。