Campomanes et al. (2026) Assessment of Flood-Prone Areas in the Lacramarca River Basin in the Santa Clemencia and Pampadura Region, Peru, Under Climate Change Effects

Identification

• Journal: Hydrology
• Year: 2026
• Date: 2026-03-26
• Authors: Giovene Pérez Campomanes, Karla Karina Romero-Valdez, Victor Manuel Martínez García, Carlos Cacciuttolo, Jesús Manuel Bernal-Camacho, Carlos Carbajal Llosa
• DOI: 10.3390/hydrology13040103

Research Groups

• Escuela de Ingeniería Civil, Facultad de Ingeniería, Universidad Continental, Huancayo, Peru
• Facultad de Ingeniería y Tecnología de Mazatlán, Universidad Autónoma de Sinaloa, Culiacán Rosales, Mexico
• Facultad de Arquitectura y Diseño, Universidad Autónoma de Sinaloa, Culiacán Rosales, Mexico
• Departamento de Ingeniería Civil, Universidad de Castilla-La Mancha, Ciudad Real, Spain
• Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Lima, Peru

Short Summary

This study assesses flood-prone areas in the Lacramarca River basin, Peru, under historical and 2050 climate change scenarios, revealing a significant increase in flood extent due to projected climate variability and highlighting the inadequacy of current protection infrastructure.

Objective

• To identify flood-prone areas in the Lacramarca River basin (Santa Clemencia and Pampadura region) under extreme discharge conditions and climate change effects (ENSO), hypothesizing that climate variability will increase peak discharges and intensify flood risk to agricultural land and human settlements.

Study Configuration

• Spatial Scale: Lacramarca River basin, Ancash region, Peru (712.89 km² basin area, 196 km² study area for satellite imagery). Focus on the river section between Pampadura and Santa Clemencia, discretized into 51 cross-sections at 100 m intervals.
• Temporal Scale: Historical climate records and climate scenarios projected for 2050. Analysis of return periods of 50, 100, and 140 years. Satellite imagery from January 2017 (before Coastal El Niño) and March 2017 (after Coastal El Niño).

Methodology and Data

• Models used:
  • Hydrological modeling: HEC-HMS (version 4.12)
  • Hydraulic simulation: HEC-RAS (version 6.7 beta 3), IBER (version 3.3.1)
  • Land cover classification: Random Forest (RF) algorithm (implemented via ranger R package)
  • GIS software: QGIS (versions 3.36.2, 3.x, 3.40.7), RiverGIS extension
  • Data processing/visualization: Google Earth Engine, terra R package, sf R package, tmap R package, R (version 4.x), AutoCAD 2023
  • Runoff method: Soil Conservation Service Curve Number (SCS-CN) method
  • Time of concentration: Kirpich formula
• Data sources:
  • Maximum precipitation data: Historical climate records and climate scenarios (ACCESS 1-3, HadGEM2-ES, MPI-ESM-MR, median 2050 projection) from the National Meteorology and Hydrology Service of Peru (SENAMHI) data platform (Intensity–Duration–Frequency (IDF) curves).
  • Hydrometric data (for calibration): Discharge records from the Quitaracsa hydrometric station (Santa River basin) from the National Water Authority (ANA) database.
  • Topographic data: ASTER GDEM v3 satellite product (10 m spatial resolution).
  • Satellite imagery: Sentinel-2 Level-2A surface reflectance imagery (Blue (B2, 490 nm), Green (B3, 560 nm), Red (B4, 665 nm), Near-Infrared (B8, 842 nm), all resampled to 10 m spatial resolution).
  • Derived spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI).

Main Results

• Land Cover Classification (2017 Coastal El Niño event): Overall Accuracy of 75.9% (κ = 0.59) before and 77.6% (κ = 0.68) after the event.
• Hydrological Model Validation (HEC-HMS): Achieved Nash–Sutcliffe efficiency (NSE) of 0.881, Coefficient of determination (R²) of 0.88, and Percent Bias (PBIAS) of -3.78%, indicating very good model performance.
• Estimated Design Peak Discharges (HEC-HMS):
  • For a 100-year return period, peak discharge increased from 125.4 m³/s (historical) to 319.6 m³/s (2050 projection).
  • For a 140-year return period, peak discharge increased from 210 m³/s (historical) to 460 m³/s (2050 projection).
• Hydraulic Flow Simulation (HEC-RAS & IBER) - Water Levels:
  • In Santa Clemencia, 2050 projections for a 140-year return period (HEC-RAS: 2.29 m, IBER: 2.81 m) exceeded the 2 m riverbank protection height.
  • In Pampadura, 2050 projections for both 100-year (HEC-RAS: 3.16 m, IBER: 3.36 m) and 140-year (HEC-RAS: 3.71 m, IBER: 3.71 m) return periods significantly exceeded the 2 m riverbank height, indicating widespread flooding of agricultural areas.
• Flood-Prone Areas (IBER model):
  • Under the historical scenario, flood-prone areas were 149,000 m² (100-year return period) and 172,000 m² (140-year return period).
  • Under the 2050 projection scenario, these areas increased to 242,000 m² (100-year return period) and 323,000 m² (140-year return period).
  • This represents an increase in flood-prone areas of 62.42% for the 100-year return period and 87.79% for the 140-year return period under the 2050 projection compared to historical data.

Contributions

• Provides a comprehensive flood risk assessment for the Lacramarca River basin, a region with limited prior hydrological studies, under current and future climate change scenarios.
• Quantifies the projected increase in peak discharges and flood-prone areas by 2050 due to climate variability, particularly extreme ENSO events.
• Highlights the potential inadequacy of existing riverbank protection structures (2 m height) to withstand future extreme flood events.
• Integrates advanced hydrological (HEC-HMS) and hydraulic (HEC-RAS, IBER) modeling with satellite imagery analysis (Sentinel-2, Random Forest) for robust flood mapping.
• Contributes to Sustainable Development Goal 13 by providing data for flood risk mapping and planning of climate-resilient infrastructure.

Funding

This research received no external funding.

Citation

@article{Campomanes2026Assessment,
  author = {Campomanes, Giovene Pérez and Romero-Valdez, Karla Karina and García, Victor Manuel Martínez and Cacciuttolo, Carlos and Bernal-Camacho, Jesús Manuel and Llosa, Carlos Carbajal},
  title = {Assessment of Flood-Prone Areas in the Lacramarca River Basin in the Santa Clemencia and Pampadura Region, Peru, Under Climate Change Effects},
  journal = {Hydrology},
  year = {2026},
  doi = {10.3390/hydrology13040103},
  url = {https://doi.org/10.3390/hydrology13040103}
}