Deriving hydrological signatures from soil moisture data

• Published in: Hydrological processes Vol. 34; no. 6; pp. 1410 - 1427
• Main Authors: Branger, Flora, McMillan, Hillary K.
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 15.03.2020; Wiley
• Subjects: Data; data collection; Diagnostic systems; discriminatory power; Evaluation; Field capacity; Hydrologic studies; hydrological signatures; Hydrology; Land use; model validation; Moisture distribution; New Zealand; Ocean, Atmosphere; regional models; representativeness; robustness; Sciences of the Universe; Sensitivity analysis; Signatures; Soil; Soil moisture; Soil moisture distribution; Soil types; soil water; Soils; Watersheds; New Zealand; New Zealand; hydrological signatures; soil moisture; discriminatory power; representativeness; robustness; regional models
• ISSN: 0885-6087; 1099-1085
• DOI: 10.1002/hyp.13645

Soil moisture is an important variable in hydrological studies, but has been little used for model evaluation due to its high sensitivity to local conditions. We explore the possibility to derive hydrological signatures from soil moisture data that could overcome this limitation and be helpful for model evaluation. A set of eight hydrological signatures was built, encompassing long‐term to short‐term time scales. These signatures were tested according to robustness, representativeness and discriminatory power, using in situ data sets from New Zealand, including national network and experimental watershed data. Field capacity, type of soil moisture distribution, and starting dates of seasonal transitions typically meet the criteria, subject to uniform sensor depths and homogeneous land uses. Durations of seasonal transitions and event‐based signatures showed higher variability and lower discriminatory power. In general, long‐term signatures are more robust, more representative of large areas, and have a high discriminatory power, thus showing a good potential for use in diagnostic evaluation of regional models. We derive eight hydrological signatures from in situ soil moisture data. These signatures are tested for robustness, spatial representativeness, and discriminatory power using a national data set from New Zealand. Long‐term scales show a better potential for use in diagnostic evaluation of regional hydrological models.