A ranking of hydrological signatures based on their predictability in space

Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N. and Clark, M. P. (2018) A ranking of hydrological signatures based on their predictability in space. Water Resources Research, 54 (11). pp. 8792-8812. ISSN 0043-1397

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Abstract

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, 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 671 US catchments from the CAMELS data set (Catchment Attributes and MEteorology for Large‐sample Studies). Firstly, 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. Secondly, we use simulations of a conceptual hydrological model (Sacramento) to benchmark the random forest predictions. Thirdly, we take advantage of the large sample of CAMELS catchments to characterize the spatial auto‐correlation (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 correlated to climatic indices) and iii) that they are particularly sensitive to discharge uncertainties. We suggest that a better understanding of their drivers and better characterization of their uncertainties would increase their value in hydrological studies.

Item Type: Article
Uncontrolled Keywords: hydrological signatures,large‐sample hydrology,catchment behaviour,machine learning,spatial auto‐correlation
Faculty \ School: Faculty of Science > School of Environmental Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 13 Sep 2018 10:30
Last Modified: 22 May 2020 23:55
URI: https://ueaeprints.uea.ac.uk/id/eprint/68270
DOI: 10.1029/2018WR022606

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