Testing Machine Learning and Traditional Models for Tree-Ring-Based scPDSI Streamflow Reconstruction: A 1500-Year Record of the French Broad River, Tennessee, USA

The French Broad River in eastern Tennessee is a critical water resource for the Tennessee Valley Authority’s hydropower and drought relief, yet its instrumental record spans less than a century. To evaluate new dendrochronological tools and examine long-term streamflow trends, we extended the stream record by 1500 years using linear regression and machine learning reconstruction models informed by the tree-ring-derived self-calibrating Palmer Drought Severity Index (scPDSI). Linear regression models provided skillful reconstruction and stable performance across calibration and validation periods. Random Forest and Deep Learning achieved higher skill but lost some of their skill advantage with validation periods, indicating overfitting. All models captured drought years more reliably than flood years, reflecting the sensitivity of scPDSI to soil moisture but its limitations for high-flow extremes in the Appalachian region. Trend analyses identified a significant change point in 1271 CE, separating a drought-dominated early period (500–1272 CE) from a wetter, less variable regime (1273–1970 CE). An emerging trend shows higher average flow interrupted by severe single-year droughts, consistent with regional evidence and projected changes to hydrologic regimes in Appalachia. These findings provide a millennial perspective on hydrologic extremes and guidance on using paleohydrology tools for water resource planning in a changing climate.