HYBRID DATA-DRIVEN AND PROCESS-BASED MODELING FOR STREAMFLOW FORECASTING UNDER CLIMATE CHANGE IN THE UPPER GENALE BASIN OF ETHIOPIA

Project Documentation: Hybrid Hydrological Modeling Model Description This project implements a Hybrid Data-Driven and Process-Based Modeling framework for streamflow forecasting. It integrates the physically-based QSWATPLUS model with advanced machine learning algorithms including Random Forest (RF), XGBoost, LightGBM, and LSTM. The integration is achieved via a Stacking Ensemble (Meta-learner) and Bayesian Model Averaging (BMA), which effectively bridge the gap between physical process understanding and the pattern-recognition capabilities of ML. Key Features Feature Engineering: Incorporation of lagged flow, rolling precipitation statistics, and seasonal time-based features. Uncertainty Analysis: Normality testing of residuals and RAPS analysis to identify persistent changes. Climate Change Projections: Forecasting under CMIP6 scenarios (SSP245 and SSP585) for near-term and mid-century periods. Results Summary Superior Accuracy: The Hybrid Model (Stacking Ensemble) achieved the best performance (R² ≈ 0.968, NSE ≈ 0.968) compared to individual models. Trend Analysis: Mann-Kendall tests revealed a significant increasing trend in daily and seasonal flow for most models. Robustness: ML models significantly improved upon the baseline physically-based model by capturing non-linear hydrological dynamics. Hybrid Ensemble Model Performance Summary The Stacking Ensemble (Hybrid Model), which integrates the physically-based QSWATPLUS model with machine learning algorithms (RF, XGBoost, LightGBM, and LSTM), represents the pinnacle of this project's forecasting framework. 1. Quantitative Metrics (Validation Period 2001-2008) R² (Coefficient of Determination): ≈ 0.968 NSE (Nash-Sutcliffe Efficiency): ≈ 0.968 RMSE (Root Mean Square Error): ≈ 14.664 m³/s PBIAS (Percent Bias): ≈ 0.000% 2. Comparative Advantage Superior Accuracy: The Hybrid Model significantly outperformed the standalone QSWATPLUS (NSE ≈ 0.600) and slightly improved upon individual ML models (e.g., RF NSE ≈ 0.962). Reduced Uncertainty: Stacking effectively corrected systematic biases, as evidenced by a PBIAS near zero and residuals that are more centered and less skewed compared to individual components. Temporal Consistency: Mann-Kendall tests confirmed that while observed flows showed an increasing trend, the hybrid model residuals maintained 'no trend' (p-value > 0.05), indicating consistent performance across the entire validation timeframe.