## 4 Results and Discussion
### Deep Learning Model Forecasting Performance
The bias-corrected synthetic discharge shows improved performance over raw synthetic inputs and approaches the accuracy of forecasts based on observed discharge. \begin{table}
\begin{tabular}{c c c c} \hline
**Forecast Horizon** & **NSE** & **KGE** & **RMSE** \\ \hline
24 h ahead & 0.84 & 0.89 & 0.301 \\ \hline
48 h ahead & 0.78 & 0.79 & 0.743 \\ \hline
72 h ahead & 0.71 & 0.74 & 0.980 \\ \hline \end{tabular}
\end{table} 
Table 3: Forecast performance of the hybrid model across different lead times. Evaluation metrics are presented for 24-, 48-, and 72-h forecast horizons using the Temporal Fusion Transformer (TFT). A gradual decline in accuracy with increasing lead time is observed. Furthermore, temporal and seasonal attention weights from the TFT model provided valuable insights into the relative importance of various input variables (Figure 9). The corrected synthetic discharge was consistently the highest weighted input feature, confirming its critical role in predictive performance. The precipitation and evapotranspiration-related variables also contributed meaningfully, highlighting the model's responsiveness to short-term meteorological drivers. Decoder importance was led by seasonal indicators such as spring and summer, aligning with periods of intensive irrigation activity. This suggests that the model effectively captures recurring human-induced flow alterations without requiring explicit irrigation inputs, further supporting its utility in semi-arid, agriculture-driven watersheds. In addition to variable importance, the attention heat map (Figure 10) offers a temporal perspective on how the model distributes focus across the input sequence. The highest attention weights are concentrated around the most recent timesteps just before the forecast window, indicating the model's reliance on immediate past conditions for making near-term predictions. As the temporal distance increases, attention diminishes--suggesting that older inputs carry less predictive influence. This pattern reflects the short-memory behavior typical in streamflow dynamics and reinforces the model's responsiveness to recent fluctuations in discharge and weather conditions. Figure 10: Variable attention heat map from the Temporal Fusion Transformer model. The plot illustrates how the model allocates attention across the input sequence relative to the prediction window. Warmer colors indicate higher attention weights, with the strongest focus placed on timesteps immediately preceding the forecast start. Figure 9: Variable importance for the Temporal Fusion Transformer model. **(Left)** Top encoder input variables, with synthetic discharge and rainfall variables contributing most to the model. **(Right)** Top decoder input variables, highlighting seasonal and temporal features influencing forecast output. These findings underscore the operational potential of the hybrid modeling approach, particularly in scenarios where direct observations are unavailable or incomplete. The model's reliable performance, combined with interpretability through feature importance, positions it as a valuable tool for operational forecasting, particularly in real-time applications or ungauged basins where synthetic inputs are often the only feasible source of continuous data.