edu.ucar.opensky::articles:22239 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-25T15:46:14.128896 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7ht2s9c https://doi.org/10.5065/D6G73C3Q https Related Dataset #1 Catchment attributes for large-sample studies https://doi.org/10.5065/D6MW2F4D https Related Dataset #2 A large-sample watershed-scale hydrometeorological dataset for the contiguous USA A ranking of hydrological signatures based on their predictability in space 2018-11-01T00:00:00Z publication https://n2t.org/ark:/85065/d7ht2s9c https://doi.org/10.1029/2018WR022606 Addor, Nans author Nearing, G. author Prieto, C. author Newman, Andrew J. author Le Vine, N. author Clark, Martyn P. author UCAR/NCAR - Library publisher 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2018 American Geophysical Union. None eng geoscientificInformation