Future Effective Runoff Under CMIP6 Scenarios in a Data-Scarce Monsoon Basin: A Regionalized SCS-CN Approach

The monsoon-dependent Amaravathi River Basin (ARB) of southern India lacks long-term streamflow observations. This study assesses projected changes in precipitation and effective runoff (Qe) for 2030–2090 under SSP2–4.5 and SSP5–8.5 using five NEX-GDDP-CMIP6 models. An Earth-observation-based framework was implemented in Google Earth Engine, integrating the Soil Conservation Service Curve Number (SCS-CN) with daily CMIP6 precipitation, hydrologic soil groups, MODIS landcover, and five-day antecedent moisture. Potential runoff was converted to Qe using a regional yield factor (RYF = 0.129), calibrated in a single gauge’s sub-basin over three Northeast Monsoon seasons; its cross-validated skill is modest (NSE = 0.67), single-season errors are large, and it is transferred to the whole basin only at the annual scale, and its transferability to other seasons and the future is untested, so absolute Qe values are indicative. Relative to the CHIRPS baseline (2001–2023), ensemble-mean annual Qe changed by −11.5% (SSP2–4.5) and +12.64% (SSP5–8.5), but neither change was significant (95% confidence intervals include zero); relative to the models’ own historical simulations, Qe increased by 13.6% and 44.6%. Southwest Monsoon Qe increased by 26–72% relative to CHIRPS and 33–81% relative to the models’ own runs, raising its annual share from 29% to 41–44%. Pre-monsoon and Northeast Monsoon Qe decreased, which reflects the GCM dry bias. The SSP5–8.5 displays an increase in annual precipitation from CanESM5 (+9.7%; −0.2% without it). Future runoff changes in the ARB are driven by seasonal timing shifts rather than by changes in annual totals.