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Original scientific article

PRECIPITATION GENERATION FOR VARIOUS RETURN PERIODS USING RAIN4PE AND PISCOP V2.1 GRIDDED PRODUCTS IN THE SUPE RIVER BASIN

By
Rengifo-Arevalo Jhon Filips Orcid logo ,
Rengifo-Arevalo Jhon Filips

Universidad Privada del Norte , Trujillo , Peru

Agramonte-Monge Henry Fabian Orcid logo ,
Agramonte-Monge Henry Fabian

Universidad Privada del Norte , Trujillo , Peru

Campos-Vasquez Neicer Orcid logo ,
Campos-Vasquez Neicer

Grupo de Investigación Desarrollo e Innovación UPN -IDIUPN, Universidad Privada del Norte , Trujillo , Peru

Carmona-Arteaga Abel Orcid logo
Carmona-Arteaga Abel

Grupo de Investigación Desarrollo e Innovación UPN -IDIUPN, Universidad Privada del Norte , Trujillo , Peru

Abstract

This paper examines the distribution of maximum precipitation of the Supe basin at the return periods of 10, 100, 1000, and 10,000 years using Weibull technique to estimate return time. They compare PISCOp v2.1 and RAIN4PE which are two gridded rain products to evaluate their predictability in terms of extreme precipitation occurrences. These gridded products are compared with the data of the Ambar rainfall station, which is located in the upper part of Supe basin. Findings indicate that the highest values of precipitation using PISCOp v2.1 and RAIN4PE are similar with the highest amount of precipitation at 57.12 mm/day (PISCOp v2.1) and 60.07 mm/day (RAIN4PE) at a 10,000-year return period. These findings were also confirmed by the spatial analysis in ArcGIS and Google Earth Engine, in which the products were verified by means of historical data. The research is valuable to the future hydrological planning as it offers credible terms of precipitation to manage flood risks and water resource planning. The statistical analysis such as the Weibull method and the spatial interpolations (Kriging) show that both gridded products are very helpful in forecasting the rainfall intensities in the area. Nevertheless, PISCOp v2.1 indicates lower precipitation minimum values than RAIN4PE. The results demonstrate the significance of proper forecasting of precipitation in disaster management and planning of infrastructure, especially flood management and river management.

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This is an open access article distributed under the  Creative Commons Attribution Non-Commercial License (CC BY-NC) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

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