TY - GEN
T1 - Precipitation Prediction for Different Return Periods Through Data Reanalysis Provided by ERA5-Land, RAIN4PE And CHIRPS In The Nanay River Basin
AU - Alonso, Cernades Palomino Diego
AU - Antonio, Meza Caysahuana Oscar
AU - Abel, Carmona Arteaga
N1 - Publisher Copyright:
© 2026, Avestia Publishing. All rights reserved.
PY - 2026
Y1 - 2026
N2 - Due to the lack of pluviometric information and water records in Peru, it is difficult to propose water works and drainage projects to mitigate damages, thus being able to have a shelter from this disaster, consequently, these areas are highly vulnerable to extreme precipitation, flooding, damage to infrastructure and proliferation of diseases. What happened in the department of Loreto in 2022 was caused by high rainfall and increased flow in the Nanay river basin. The present investigation consists of collecting data from the following gridded products RAIN4PE, ERA5-Land and CHIRPS in the Google Earth Engine platform that has the function of giving us precipitation data which we will use for the Nanay basin. A comparison will be made in different cumulative return times 10, 100 and 1000, then we will compare and determine which gridded product is more like the real values given by a known weather station.
AB - Due to the lack of pluviometric information and water records in Peru, it is difficult to propose water works and drainage projects to mitigate damages, thus being able to have a shelter from this disaster, consequently, these areas are highly vulnerable to extreme precipitation, flooding, damage to infrastructure and proliferation of diseases. What happened in the department of Loreto in 2022 was caused by high rainfall and increased flow in the Nanay river basin. The present investigation consists of collecting data from the following gridded products RAIN4PE, ERA5-Land and CHIRPS in the Google Earth Engine platform that has the function of giving us precipitation data which we will use for the Nanay basin. A comparison will be made in different cumulative return times 10, 100 and 1000, then we will compare and determine which gridded product is more like the real values given by a known weather station.
KW - ArcGIS
KW - CHIRPS
KW - ERA5-Land
KW - RAIN4PE
KW - Rainfall
KW - Return Periods
KW - Watershed
UR - https://www.scopus.com/pages/publications/105045162151
U2 - 10.11159/ffhmt26.198
DO - 10.11159/ffhmt26.198
M3 - Conference contribution
AN - SCOPUS:105045162151
SN - 9781990800740
T3 - International Conference on Fluid Flow, Heat and Mass Transfer
BT - Proceedings of the 13th International Conference on Fluid Flow, Heat and Mass Transfer, FFHMT 2026
A2 - Kruczek, Boguslaw
A2 - Ahmed, Wael H.
A2 - Ein-Mozaffari, Farhad
A2 - Wang, Huasheng
A2 - Darkwa, Jo
PB - Avestia Publishing
T2 - 13th International Conference on Fluid Flow, Heat and Mass Transfer, FFHMT 2026
Y2 - 9 June 2026 through 11 June 2026
ER -