Şen et al. (2026) Prediction of present and future flood discharges in catchments with sparse data coverage

Identification

• Journal: Natural Hazards
• Year: 2026
• Date: 2026-04-01
• Authors: Olgay Şen, Ercan Kahya, Ahmet Faruk İpek
• DOI: 10.1007/s11069-026-08105-w

Research Groups

• Faculty of Civil Engineering, Hydraulics and Water Resources Division, Istanbul Technical University, Istanbul, Turkey

Short Summary

This study developed a novel empirical approach for curve number (CN) estimation in data-scarce mountainous catchments to predict present and future flood discharges under climate change scenarios. The findings indicate a general decreasing trend in flood discharges until 2069, followed by an increase by the end of the century, though remaining below present levels, with significant basin sensitivity to precipitation changes.

Objective

• To develop a novel and practical approach for curve number (CN) estimation in data-scarce mountainous catchments to represent infiltration processes and address input limitations for hydrological models.
• To predict present and future flood discharges (100-year and 500-year return periods) in the basins of Rize Province, Turkey, under three climate change projection periods.
• To analyze the sensitivity of basin hydrological response to future climate variability using elasticity analysis.

Study Configuration

• Spatial Scale: Ten small to mid-sized mountainous basins in Rize Province, Eastern Black Sea region of Turkey, with annual average precipitation exceeding 2300 mm. Basin areas range from 83 km² to 231 km².
• Temporal Scale:
  • Calibration period: 1983–1993 (based on 12 peak flow observations).
  • Climate model reference period: 1961–1990.
  • Future projection periods: 2013–2039, 2040–2069, and 2070–2099.

Methodology and Data

• Models used:
  • Hydrological models: HEC-1 (Hydrologic Engineering Center-1) for rainfall-runoff simulation, WMS (Watershed Modeling System) for model construction and parameter derivation.
  • Unit Hydrograph (UH) methods: SCS, Snyder, Mockus, and DSI.
  • Loss method: SCS Curve Number (CN) method.
  • Rainfall calculation methods: Thiessen polygon and Isohyetal methods.
  • Modeling approaches: Lumped and semi-distributed.
  • Climate models: ECHAM5/A2 (Global Circulation Model) dynamically downscaled by RegCM3 (Regional Climate Model).
• Data sources:
  • Meteorological data: Daily precipitation from the Turkish State Meteorological Service (MGM).
  • Hydrological data: Daily mean streamflow and instantaneous maximum flow data (12 peak flow records) from the State Hydraulic Works (DSI).
  • Spatial data: GIS-based land use maps and 30 m resolution Digital Elevation Model (DEM) data from the Geomatics Division of Istanbul Technical University.
  • Derived data: Soil types derived from slope classes, composite CN values, and design storms (100-year and 500-year) for reference and future periods (from Kahya et al. 2015).

Main Results

• A novel empirical framework for CN estimation was developed and applied to data-scarce mountainous basins.
• Estimated runoff curve numbers (CN) for the Rize basins ranged from 72.9 to 81.8, with a mean of 77.6, consistent with values from similar mountainous regions.
• During calibration, the lumped hydrological modeling approach with the SCS Unit Hydrograph method yielded better results (lowest mean absolute percentage error of 20.0%) compared to semi-distributed approaches (20.2% for Thiessen, 44.7% for Isohyets), especially given the sparse rain gauge network.
• Future flood discharge predictions show a decreasing trend across all basins until 2069 (approximately 50% reduction by 2039 and 60% by 2069 for peak flows).
• An increasing trend in flood discharges is projected for the period until 2099, but peak flows are still approximately 35% lower than present conditions.
• Basins 9 and 10 in eastern Rize Province were found to be the least affected by climate change.
• Peak flow elasticity analysis revealed that a 1% change in precipitation can lead to approximately a 1.5% change in peak discharge (elasticity values ranging from 0.82 to 1.84), indicating significant basin sensitivity to climate shifts.
• Most basins exhibited U-shaped elasticity trends across projection periods, suggesting evolving water retention mechanisms under prolonged climate stress.
• 100-year floods generally showed higher elasticity values than 500-year floods, indicating that moderate flood events may be more responsive to climate variability.
• Statistical analysis confirmed strong positive correlations (r ≈ 0.85, p < 0.002) between CN values and predicted peak flows, highlighting the dominant role of land surface properties in runoff generation.

Contributions

• Development of a novel and practical empirical framework for Curve Number (CN) estimation specifically tailored for mountainous, data-scarce basins, integrating multiple rainfall calculation methods, modeling approaches, and unit hydrograph methods.
• First comprehensive assessment of present and future flood discharges (100-year and 500-year return periods) under climate change scenarios (ECHAM5/A2–RegCM3) for the Rize Province, a highly flood-prone and data-limited region in Turkey.
• Demonstration that simpler lumped hydrological models can outperform more complex semi-distributed models in regions with sparse rain gauge networks, providing practical guidance for hydrological modeling in data-limited environments.
• Quantification of basin sensitivity to climate change through peak flow elasticity analysis, revealing U-shaped trends and higher sensitivity of moderate (100-year) floods compared to extreme (500-year) floods.
• Provision of a transferable range of calibrated CN values (72.9 to 81.8) for use in other basins with similar characteristics and data limitations.

Funding

• Scientific and Technological Research Council of Turkey (TUBITAK) under Project No. 112Y204.
• Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK).

Citation

@article{Şen2026Prediction,
  author = {Şen, Olgay and Kahya, Ercan and İpek, Ahmet Faruk},
  title = {Prediction of present and future flood discharges in catchments with sparse data coverage},
  journal = {Natural Hazards},
  year = {2026},
  doi = {10.1007/s11069-026-08105-w},
  url = {https://doi.org/10.1007/s11069-026-08105-w}
}