Cluster-based eXtreme gradient boosting and conceptual event-based model for short-term rainfall–runoff modeling under uncertainty
Author(s)
Date Issued
2026
Type
article
Volume
64
Issue
103160
Journal or Series
Abstract
Study region
The study focuses on the Sieber River watershed in northern Germany, a small mountainous catchment characterized by rapid rainfall–runoff response, limited hydrological data availability, and substantial short-term flow variability. These characteristics make the region an ideal testbed for developing robust, data-efficient short-term runoff prediction models.
Study focus
This research proposes a novel hybrid modeling framework combining eXtreme Gradient Boosting (XGBoost) with clustering algorithms (K-means and X-means) to improve multi-step-ahead rainfall–runoff forecasting under uncertainty. Hourly precipitation–runoff data and lagged precipitation inputs (Pt to Pt–36) are used to generate predictions at 1-, 2-, 3-, and 6-hour horizons. The hybrid models are benchmarked against standalone XGBoost, Principal Component Regression (PCR), and the conceptual Event-Based Approach for Small and Ungauged Basins (EBA4SUB). Model performance is evaluated using RMSE, MAE, NSE, R², and uncertainty bounds.
New hydrological insights for the region
Clustering rainfall–runoff conditions into homogeneous hydrometeorological regimes considerably enhances prediction accuracy. The XGBoost–K-means model provides the best performance, achieving low predictive error (6-hour ahead: RMSE = 0.580 m³/s, NSE = 0.954) and the narrowest uncertainty range (WUCB = 2.274). These findings demonstrate that cluster-enhanced machine learning models offer a reliable and computationally efficient solution for operational short-term forecasting in small catchments like the Sieber watershed. The hybrid approach supports improved flood early warning, real-time water management, and decision-making in data-scarce environments.