A Ranking of Hydrological Signatures Based on Their Predictability in Space

Hydrological signatures are now used for a wide range of purposes, including catchment classification, process exploration, and hydrological model calibration. 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. Third, we take advantage of the large sample of CAMELS catchments to characterize the spatial autocorrelation (using Moran's I) of the signature field. These three approaches lead to remarkably similar rankings of the signatures. We show (i) that signatures with the noisiest spatial pattern tend to be poorly captured by hydrological simulations, (ii) that their relationship to catchments attributes are elusive (in particular they are not well explained by climatic indices), and (iii) that they are particularly sensitive to discharge uncertainties. We suggest that a better understanding of the drivers of hydrological signatures and a better characterization of their uncertainties would increase their value in hydrological studies.