Investigating regionalization techniques for large-scale hydrological modelling.

Area of Science:

• Hydrology
• Environmental Modeling
• Water Resource Management

Background:

• Large-scale hydrological models require efficient regionalization techniques for accurate water resource assessment.
• Pan-European water resource studies face challenges due to vast spatial scales and data variability.
• Understanding hydrological regions is crucial for effective model calibration and prediction.

Purpose of the Study:

• To compare unsupervised and supervised clustering-based regionalization techniques for large-scale hydrological modeling.
• To assess the efficiency of different regionalization approaches using Partial Least Squares Regression (PLSR).
• To evaluate the impact of regionalization on model calibration and computational resource requirements.

Main Methods:

• Utilized the Soil and Water Assessment Tool (SWAT) platform for hydrological modeling.
• Implemented four variants of similarity-based regionalization: unsupervised and supervised clustering.
• Employed Partial Least Squares Regression (PLSR) to identify key watershed characteristics influencing hydrological responses.

Main Results:

• PLSR indicated that physiographic characteristics well-represent average conditions but poorly predict extreme hydrological events.
• Both unsupervised and supervised clustering regionalization variants performed comparably when ample data was available.
• Supervised clustering demonstrated greater data efficiency, making it suitable for data-scarce scenarios.

Conclusions:

• Parsimonious data utilization is achievable with both unsupervised and supervised regionalization techniques.
• Effective hydrological model calibration is possible by focusing on a small subset (10%) of the model domain.
• Regionalization significantly reduces the time and computational resources needed for large-scale model calibration.