Empirical equations for estimating the intensities of maximum annual rainfall of different durations, Huaraz – Peru
DOI:
https://doi.org/10.32911/as.2024.v17.n2.1194Keywords:
Precipitation intensity, Duration, Return period, Gumbel DistributionAbstract
The objective of the research work was to obtain empirical equations for estimating the intensities of maximum annual rainfall of different durations and return periods in the city of Huaraz, which serve for the hydrological design of hydraulic storm drainage structures. The maximum annual rainfall in 24 hours from the Santiago Antúnez de Mayolo meteorological station best fits the Gumbel probability distribution, for this purpose the MINITAB 20 software was used. With the Dyck – Peschke equation, the maximum annual rainfall was disaggregated for durations of less than 24 hours. With these disaggregated values, the empirical equations of the intensities of maximum annual rainfall of different durations at the Santiago Antúnez de Mayolo meteorological station were obtained. The Koutsoyannis equation is the most appropriate for precipitation intensities for durations less than 24 hours and different return periods for the Santiago Antúnez de Mayolo – Huaraz meteorological station.
Downloads
References
Álvaro, J. (2020). Alternative model of intense rainfall equation obtained from daily rainfall disaggregation. Revista Brasileira de Recursos Hídricos 25(2). https://acortar.link/5WzvAT
Awadallah, A., ElGamal, M., ElMostafa, ElBadry, H. (2011) Developing Intensity-Duration-Frequency Curves in Scarce Data Region: An Approach using Regional Analysis and Satellite Data. Engineering, 3, 215-226. https://acortar.link/SqfpQN
Chow, V., Maidment, D., & Mays, L. (1994). Hidrología aplicada. McGraw-Hill
Gamarra, M. (2021). Metodología para la estimación de parámetros de cálculo de tormentas de diseño con datos pluviométricos en Bolivia. Ventana Científica: 11(18). https://acortar.link/hVtg5E
Hu, H., & Ayyub, B. (2019). Machine learning for projecting extreme precipitation intensity for short durations in changing climate. Geosciences 2019, 9(5). https://acortar.link/sSJB1w
Kim, S., & Singh, V. (2015). Spatial disaggregation of areal rainfall using two different artificial neural networks models. Water. https://acortar.link/c2Xaix
Kourtis, I., & Tsihrintzis, V. (2023). Update of intensity – duration – frequency (IDF) curves under climate change: a review. Water Supply 2(3), 49 – 51. https://acortar.link/foXiiw
McCuen, R. (2005). Hydrologic analysis and design. Pearson Prentice Hall.
Monjo, R., & Mesenguer-Ruiz, O. (2024). Review: Fractal geometry in precipitation. Atmosphere 2024. 15(1). https://acortar.link/XsiV4a
Linsley, R., Kohler, M., & Paulhus, J. (1988). Hidrología para ingenieros. México: McGraw-Hill
Pereyra-Díaz (2012). Two nonlinear mathematical models to estimate the intensity – duration -return period of rainfall events. Universidad y Ciencia 28 (3). https://acortar.link/U94X4s
Priambodo, S., Montarcih, L., Suhartanto, E. (2019). Hourly rainfall distribution patterns in Java island. Conferencia Web MATEC, 276. https://doi.org/10.1051/matecconf/201927604012
Villón, M. (2020). Hidrología. Ediciones Villón.
Yamoat, N., Hanchoowong, R., Yamoad, O., Chaimoon, N., & Kangrang, A. (2023). Estimation of regional intensity – duration – frecuency relationships of extreme rainfall by simple sacaling in Thailand. Journal of Water and Climate Change 14(3). https://acortar.link/2FQkF3
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Toribio Reyes Rodríguez

This work is licensed under a Creative Commons Attribution 4.0 International License.