Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds.

Area of Science:

• Hydrology
• Water Resource Management
• Environmental Science

Background:

• Accurate streamflow prediction is crucial for water resource management, especially in ungauged watersheds where data is scarce.
• Regionalization methods offer a viable approach to transfer hydrological information from gauged to ungauged basins.
• The Ardabil Province in Iran faces challenges in managing water resources due to limited streamflow data.

Purpose of the Study:

• To propose and validate a regionalization method for streamflow prediction in ungauged watersheds.
• To assess the accuracy of the developed method using established hydrological performance metrics.
• To provide a tool for effective water quantity and quality management in an intensive agricultural region.

Main Methods:

• Utilized Fuzzy c-means clustering (FCM) to group 46 watersheds (19 gauged, 27 ungauged) into homogeneous units based on topographical and climatic factors.
• Employed the Soil and Water Assessment Tool (SWAT) for model calibration and validation using data from gauged watersheds within each cluster.
• Tested the calibrated parameters in pseudo-ungauged watersheds to evaluate the regional model's predictive capability.

Main Results:

• Calibration and validation achieved R-Squared and Nash-Sutcliffe efficiency (NSE) values of ≥0.70.
• Testing in pseudo-ungauged watersheds yielded R-Squared ≥0.80 and NSE ≥0.74, indicating satisfactory performance.
• The validated regional models demonstrated reliable streamflow prediction for ungauged areas within each identified group.

Conclusions:

• The proposed regionalization method, integrating FCM and SWAT, provides a robust approach for streamflow prediction in ungauged watersheds.
• The validated models are suitable for supporting water resource management decisions in the study area.
• This approach can be applied to similar regions facing data limitations for hydrological modeling.