A ranking of hydrological signatures based on their predictability in space

The recent boost in the popularity and number of signatures has however not been accompanied by the development of clear guidance on signature selection. Here we propose that exploring the predictability of signatures in space provides important insights into their drivers and their sensitivity to data uncertainties and is hence useful for signature selection.

We use three complementary approaches to compare and rank 15 commonly used signatures, which we evaluate in 600+ U.S. catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set. First, we employ machine learning (random forests) to explore how attributes characterizing the climatic conditions, topography, land cover, soil, and geology influence (or not) the signatures. Second, we use simulations of the Sacramento Soil Moisture Accounting model to benchmark the random forest predictions.