Disentangling Sources of Uncertainty in Hydrologic Projections Using Multiple Climate Forcings, Bias-Correction Techniques, and Shared Socioeconomic Pathways

Meteorological forcings under different climate scenarios exert substantial control over hydrologic-hydrological processes in watersheds and river systems. This study presents a comprehensive assessment of uncertainty in hydrologic projections by integrating a wide range of climate forcings, multiple bias-correction approaches, and several Shared Socioeconomic Pathways (SSPs). Specifically, we (i) quantify the total uncertainty in projected hydrologic responses, (ii) attribute uncertainty to individual sources, and (iii) examine how uncertainty propagates along the hydroclimatic modeling chain. The analysis is demonstrated for a range of watersheds using a fully calibrated Soil and Water Assessment Tool (SWAT) model. The hydrologic simulations are forced by outputs from thirty global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset at a spatial resolution of 0.25° (~25 km) under two SSPs. To further refine the climate inputs, a linear bias-correction method is applied to daily temperature and precipitation time series to align long-term mean monthly values during the reference period (1985–2014) with PRISM observations. A total of four bias-correction scenarios are considered: (1) original NEX-GDDP-CMIP6 data, (2) precipitation-corrected data, (3) temperature-corrected data, and (4) jointly corrected temperature and precipitation data. This framework yields four forcing scenarios for each GCM–SSP combination, resulting in a total of 240 simulations (4 × 30 GCMs × 2 SSPs) for each watershed. Streamflow changes are evaluated for the near-future period (2031-2060) and far future period (2061-2090), relative to the historical baseline (1985-2014). Changes in probability distributions and cumulative distribution functions are analyzed across climate models, bias-correction methods, and SSPs. In addition, the relative contributions of individual uncertainty sources are quantified at monthly, seasonal, and annual time scales. By systematically accounting for uncertainties arising from climate forcings, bias-correction techniques, and socioeconomic pathways, this study provides a robust characterization of the range of plausible hydrologic futures. Such uncertainty-informed streamflow projections are essential for water-resources planning, flood and drought risk management, and the development of effective long-term water-management strategies.