Summary
This paper addresses the lack of guidance for selecting hydrological signatures by proposing that spatial predictability provides a robust criterion for evaluation. Using three complementary approaches across the CAMELS dataset of 600+ U.S. catchments, the authors ranked 15 commonly used hydrological signatures and demonstrated that signatures with poor spatial structure are poorly simulated, weakly related to catchment attributes, and sensitive to measurement noise—findings with implications for catchment classification, process understanding, and model calibration studies.
Regional applicability
The methodological framework and ranking approach could be applied to UK catchments and hydrological monitoring networks to improve signature selection for national and regional water resource management and flood characterisation studies. However, the specific signature rankings would need validation against UK catchment attributes and hydroclimate conditions.
Key measures
Random forest feature importance scores; spatial autocorrelation (Moran's I); Sacramento Soil Moisture Accounting model predictions; sensitivity to discharge uncertainties
Outcomes reported
The study ranked 15 commonly used hydrological signatures based on their spatial predictability across 600+ U.S. catchments using machine learning, hydrological modelling, and spatial autocorrelation analysis. Signatures with noisier spatial patterns were found to be poorly captured by hydrological simulations, less explained by catchment attributes, and more sensitive to discharge uncertainties.
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