Time Distribution of Heavy Rainfall in Brazil: Empirical Huff Curves from 290,164 Sub-Daily Storm Events
Abstract
Temporal rainfall distributions are widely used in design storm construction; however, many countries with limited sub-daily observations, such as Brazil, still rely on frameworks developed under different hydroclimatic conditions. This mismatch may introduce bias in hydrological design and water resources estimation. Here, we develop the first national-scale empirical Huff curves for Brazil using sub-daily rainfall observations. We compiled data from 3,164 stations and, after quality control, retained 290,164 storm events from 1,045 stations spanning 2010 to 2025. Empirical cumulative rainfall mass curves were constructed and fitted using seventh-degree polynomials at station, biome, state, and municipality scales. Our findings show a dominance of first-quartile (Q1; front-loaded) storm patterns, occurring at 94.4% of stations nationally, increasing to 99.2% in the Amazon and Cerrado biomes and decreasing to 88.7% in the Atlantic Forest. The national Q1 median curve closely matches the Huff (1967) reference (MAE = 0.045; Dmax = 0.097), with narrow bootstrap uncertainty. Q1 dominance is robust to inter-event time definition, with 84.7% of stations showing consistent classification across 2 to 12 h thresholds. A Soil Conservation Service Curve Number experiment across 579 headwater catchments shows that Brazilian curves increase design peak discharge by a median of 8% and up to 11% in the Cerrado relative to the Illinois reference, indicating potential underestimation when using non-local distributions. Biome-, state-, and municipality-scale parameters are provided as open data and through an interactive platform, offering locally calibrated design-storm alternatives for Brazil.
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BibTeX
@misc{junior2026time,
title = {{Time Distribution of Heavy Rainfall in Brazil: Empirical Huff Curves from 290,164 Sub-Daily Storm Events}},
author = {Marcus Nobrega Gomes Junior and André Simões Ballarin and Paulo Tarso Sanches de Oliveira},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25315},
eprint = {2609.25315},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25315},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Time Distribution of Heavy Rainfall in Brazil: Empirical Huff Curves from 290,164 Sub-Daily Storm Events
AU - Marcus Nobrega Gomes Junior
AU - André Simões Ballarin
AU - Paulo Tarso Sanches de Oliveira
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.25315
UR - https://arxiv.org/abs/2609.25315
AB - Temporal rainfall distributions are widely used in design storm construction; however, many countries with limited sub-daily observations, such as Brazil, still rely on frameworks developed under different hydroclimatic conditions. This mismatch may introduce bias in hydrological design and water resources estimation. Here, we develop the first national-scale empirical Huff curves for Brazil using sub-daily rainfall observations. We compiled data from 3,164 stations and, after quality control, retained 290,164 storm events from 1,045 stations spanning 2010 to 2025. Empirical cumulative rainfall mass curves were constructed and fitted using seventh-degree polynomials at station, biome, state, and municipality scales. Our findings show a dominance of first-quartile (Q1; front-loaded) storm patterns, occurring at 94.4% of stations nationally, increasing to 99.2% in the Amazon and Cerrado biomes and decreasing to 88.7% in the Atlantic Forest. The national Q1 median curve closely matches the Huff (1967) reference (MAE = 0.045; Dmax = 0.097), with narrow bootstrap uncertainty. Q1 dominance is robust to inter-event time definition, with 84.7% of stations showing consistent classification across 2 to 12 h thresholds. A Soil Conservation Service Curve Number experiment across 579 headwater catchments shows that Brazilian curves increase design peak discharge by a median of 8% and up to 11% in the Cerrado relative to the Illinois reference, indicating potential underestimation when using non-local distributions. Biome-, state-, and municipality-scale parameters are provided as open data and through an interactive platform, offering locally calibrated design-storm alternatives for Brazil.
ER -