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Comparison of Flood Scenarios in the Cunas River Under the Influence of Climate Change

  • Carlos Enrique Torres-Mercado
  • , Jhordan Anderson Villafuerte-Jeremias
  • , Giancarlo Paul Guerreros-Ollero
  • , Giovene Perez-Campomanes

Research output: Contribution to journalOriginal Articlepeer-review

3 Scopus citations

Abstract

Climate change has a significant impact on river flows, leading to overflows and floods that affect populations, especially in Andean regions. This study examines flood scenarios in the Cunas River Basin (Junín, Peru) through hydrological and hydraulic simulations under various climate projections. A Reliability Ensemble Averaging (REA) approach was employed using CMIP6 climate models. In this analysis, precipitation data were processed, basin parameters were calculated, and peak flows and the extent of flood-prone areas were estimated. HEC-HMS software was used to simulate peak flows corresponding to return periods of 25, 50, 100, 139, and 200 years, while HEC-RAS was employed to determine flood zones. Model calibration and validation relied on historical precipitation data from nearby stations. The results indicate a considerable increase in peak flows and flood-prone areas due to climate change. A 3.32% increase in peak flow, a 55.35% expansion in flood-prone areas, and a 34.12% rise in flood depth are observed. These findings highlight the importance of implementing riverine protection structures. This study provides key information for flood risk management in the Peruvian highlands, using widely accepted tools to understand the hydrological response to climate change.

Original languageAmerican English
Article number117
JournalHydrology
Volume12
Issue number5
DOIs
StateIndexed - May 2025

Bibliographical note

Publisher Copyright:
© 2025 by the authors.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • CMIP6 climate scenarios
  • Cunas River
  • HEC-HMS
  • HEC-RAS
  • Reliability Ensemble Averaging
  • climate change
  • flood modeling
  • hydrological simulation

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