Subseasonal Streamflow Forecasting in the Godavari River Basin: Assessing Meteorological and Hydrological Uncertainties under Monsoon Conditions

Reliable subseasonal streamflow forecasting is essential for flood risk management, reservoir operation, and water allocation in large monsoon-dominated river basins such as the Godavari River Basin, India. However, forecast skill remains constrained by uncertainties in meteorological forcing and hydrological model structure, particularly at longer lead times. This study evaluates the relative contributions of precipitation input uncertainty and hydrological model uncertainty within a 1–4 week (up to 30-day) streamflow forecasting framework by integrating subseasonal-to-seasonal (S2S) precipitation forecasts with a physics-based distributed hydrological model. Deterministic and ensemble precipitation forecasts from the S2S Hydrological Simulation System are used to drive the Soil and Water Assessment Tool (SWAT), with precipitation bias correction implemented through empirical quantile mapping using the India Meteorological Department (IMD) 0.25° × 0.25° gridded rainfall dataset. The Godavari basin is discretized into headwater, midstream, and large downstream sub-basins, and simulated streamflow forecasts are evaluated against Central Water Commission (CWC) daily discharge observations. Forecast performance is assessed across lead times using both deterministic and probabilistic skill metrics, including coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), percent bias (PBIAS), and flow-regime-specific diagnostics for high-flow (≥90th percentile) and low-flow (≤10th percentile) conditions. Results show a systematic decline in forecast skill with increasing lead time, with substantial variability across precipitation products, basin scales, and flow regimes. Bias correction of S2S precipitation significantly reduces systematic discharge errors and enhances forecast skill up to 3–4 weeks, particularly for low-flow conditions and larger downstream sub-basins. While hydrological model structure dominates forecast uncertainty at shorter lead times, precipitation forcing uncertainty becomes the primary source of error at longer lead times. Overall, the study demonstrates the value of jointly evaluating meteorological and hydrological uncertainties and highlights the potential of subseasonal hydrological forecasting to support operational flood early warning and water management decisions in large, regulated, monsoon-driven river basins.