Şenocak et al. (2026) Integration of Snowmelt Runoff Model (SRM) with GIS and Remote Sensing for Operational Forecasting in the Kırkgöze Watershed, Turkey

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Identification

• Journal: Water
• Year: 2026
• Date: 2026-01-29
• Authors: Serkan Şenocak, Reşat ACAR
• DOI: 10.3390/w18030335

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study demonstrates the operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with GIS and multi-platform remote sensing for discharge forecasting in the Kirkgoze Basin, Turkey, achieving excellent calibrated performance (Nash–Sutcliffe efficiency = 0.854) for the 2009 melt season.

Objective

• To demonstrate the operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing for snowmelt runoff discharge forecasting in a data-scarce mountainous basin.

Study Configuration

• Spatial Scale: Kirkgoze Basin, Eastern Anatolia, Turkey (242.7 km²), with an elevation range of 1823 m to 3140 m.
• Temporal Scale: The 2009 melt season was used for model calibration and performance evaluation.

Methodology and Data

• Models used: Snowmelt Runoff Model (SRM), Geographic Information Systems (GIS), Multiple Linear Regression.
• Data sources:
  • Meteorological forcing: Three automatic weather stations providing daily temperature and precipitation data.
  • Operational snow cover observations: MODIS MOD10A2 8-day composite products.
  • Snow cover validation data: Landsat-5/7 imagery (30 m resolution) and synthetic aperture radar imagery (RADARSAT-1 C-band, ALOS-PALSAR L-band).

Main Results

• Uncalibrated SRM performance was modest, with a coefficient of determination (R²) of 0.384 and a volumetric difference of 29.78%.
• After systematic adjustment of snowmelt and rainfall runoff coefficients, the calibrated SRM achieved excellent performance for the 2009 melt season: R² = 0.8606, correlation coefficient R = 0.927, Nash–Sutcliffe efficiency = 0.854, and volumetric difference = 3.35%.
• An enhanced temperature lapse rate of 0.75 °C/100 m was identified, reflecting the severe continental climate of the region.
• Multiple linear regression analysis identified temperature, snow-covered area, snow water equivalent, and calibrated runoff coefficients as significant discharge predictors, with an R² of 0.881.
• The study confirmed SRM’s operational feasibility for seasonal forecasting and flood warning in data-scarce, snow-dominated basins, requiring only daily temperature, precipitation, and satellite snow cover data.

Contributions

• Demonstrates the operational feasibility and robust performance of the Snowmelt Runoff Model (SRM) when integrated with remote sensing and GIS for discharge forecasting in data-scarce, snow-dominated mountainous regions.
• Provides a transferable methodology and framework for regional water resource management, seasonal forecasting, and flood warning in climatically vulnerable mountain environments.
• Validates the use of multi-platform remote sensing data (MODIS, Landsat, SAR) for operational snow cover monitoring in hydrological modeling.

Funding

Not mentioned in the provided text.

Citation

@article{Şenocak2026Integration,
  author = {Şenocak, Serkan and ACAR, Reşat},
  title = {Integration of Snowmelt Runoff Model (SRM) with GIS and Remote Sensing for Operational Forecasting in the Kırkgöze Watershed, Turkey},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18030335},
  url = {https://doi.org/10.3390/w18030335}
}