Prediction of regional streamflow frequency using model tree ensembles

- A method for regionalization of streamflow statistics is proposed and evaluated.
- The method predicts streamflow frequency better than global regression models.
- Inclusion of human variables increases R2 for low flow frequencies.
- Implications for predictions in ungaged basins and basins undergoing change.

SummaryThis study introduces a novel data-driven method called model tree ensembles (MTEs) to predict streamflow frequency statistics based on known drainage area characteristics, which yields insights into the dominant controls of regional streamflow. The database used to induce the models contains both natural and anthropogenic drainage area characteristics for 294 USGS stream gages (164 in Texas and 130 in Illinois). MTEs were used to predict complete flow duration curves (FDCs) of ungaged streams by developing 17 models corresponding to 17 points along the FDC. Model accuracy was evaluated using ten-fold cross-validation and the coefficient of determination (R2). During the validation, the gages withheld from the analysis represent ungaged watersheds. MTEs are shown to outperform global multiple-linear regression models for predictions in ungaged watersheds. The accuracy of models for low flow is enhanced by explicit consideration of variables that capture human interference in watershed hydrology (e.g., population). Human factors (e.g., population and groundwater use) appear in the regionalizations for low flows, while annual and seasonal precipitation and drainage area are important for regionalizations of all flows. The results of this study have important implications for predictions in ungaged watersheds as well as gaged watersheds subject to anthropogenically-driven hydrologic changes.