Monthly Paleostreamflow Reconstruction from Annual Dendrochronologies for Water Systems Analysis

With concerns about drought and hydroclimatic change in the Western US, water managers have increasingly sought to evaluate system vulnerability using paleo-reconstructed streamflows. However, the widespread adoption of these approaches in the water resources field has been slowed due to the temporal scale mismatch between tree-ring chronologies, measured at an annual resolution, and water systems models, which typically simulate decisions at monthly or daily scales. This study presents a novel approach to address this challenge, generating monthly paleo-streamflow reconstructions by disaggregating an existing time series of annual reconstructed streamflows. The method relies on tree-ring anomalies from regionally different species and sites to accurately adjust the seasonal hydrograph, while also incorporating reconstructions of global-scale climate patterns, such as the El Nino Southern Oscillation (ENSO) or Pacific Decadal Oscillation (PDO), as hydrograph predictors. In this way, the model combines local, regional, and global measures in an approach that is flexible enough for use at any site with sufficient tree-ring data. An example is presented using two sites in the Bear River watershed, located in northeastern Utah. Monthly flows, disaggregated using 50 tree-ring chronologies and ENSO/PDO reconstructions, accurately reproduce seasonal patterns, extreme events, and flows outside the typical tree ring growth season. This approach is unique within the paleo-hydrology field, due to its direct reconstruction of monthly flows without the need for more complex hydrologic models. Retained model covariates can also provide insights into the drivers of local hydrological droughts. Ultimately, the proposed method can greatly improve the implementation of paleo-reconstructed streamflows in water resources management, allowing for easier and more accurate simulations of water vulnerability in the Western US over a wider range of historic natural variability.