Continental-scale prediction of hydrologic signatures and processes

Understanding hydrologic processes is essential for developing effective hydrologic models and management strategies. However, we lack a continental-scale, comprehensive knowledge of which processes dominate and how their drivers vary across diverse landscapes.

To address this gap, we synthesize large-sample precipitation and streamflow datasets, from Caravan and USGS GAGES-II, to identify spatial patterns in hydrologic behavior. Then, we apply a random forest machine learning model to examine the predictability of hydrological processes and to understand their climatic and landscape drivers. We use a hydrologic signature approach, where signatures—metrics derived from observed hydroclimatic time series—capture key aspects of hydrologic dynamics. 

Using these hydrologic signatures, we developed a “dominant process map” that highlights the spatial variability of baseflow, overland flow, water balance loss, and storage capacity across the conterminous United States. The map demonstrates clear regional gradients from baseflow to overland flow regimes, as well as transitions from water-retaining to low-storage regions. 

In contrast to previous studies emphasizing climate as the primary driver of these processes, our map highlights substantial influences from landscape features. In the eastern half of the US, baseflow is primarily influenced by soils and geology, while stormflow is controlled by topography. In the western US, climate remains the dominant control of most processes. Metropolitan areas emerged as hotspots influenced by anthropogenic factors.

Our dominant process maps serve as a valuable hypothesis-generating tool for model builders and water managers to estimate regional hydrological processes a priori. Our approach to training random forest models to predict hydrologic signatures is readily applicable to other datasets; this facilitates extrapolating hydrological process knowledge from well-studied catchments to ungaged basins or other large-sample datasets.