A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales |
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| Affiliation: | 1. Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, Austria;2. Department of Civil Engineering, Queen''s School of Engineering, University of Bristol, Bristol, UK;1. School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei 430079, China;2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;1. School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China;2. Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, North Park Road, Harrison Building, Exeter EX4 4QF, UK;1. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA;2. College of Tropical Agriculture and Human Resources, University of Hawaii at Manoa, Honolulu, HI 96822, USA;1. College of Geographic Sciences, Fujian Normal University, Fuzhou, 350007, Fujian, China;2. Cultivation Base of State Key Laboratory of Humid Subtropical Mountain Ecology, Fuzhou, 350007, Fujian, China;3. Dorset Environmental Science Centre, Ontario Ministry of Environment and Climate Change, 1026 Bellwood Road, Dorset, P0A 1E0, Ontario, Canada;4. Department of Geography, Nipissing University, 100 College Drive, Box 5002, North Bay, Ontario, P1B 8L7, Canada;1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710054, China;2. School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China |
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| Abstract: | Assessing the time-varying sensitivity of environmental models has become a common approach to understand both the value of different data periods for estimating specific parameters, and as part of a diagnostic analysis of the model structure itself (i.e. whether dominant processes are emerging in the model at the right times and over the appropriate time periods). It is not straightforward to visualize these results though, given that the window size over which the time-varying sensitivity is best integrated generally varies for different parameters. In this short communication we present a new approach to visualizing such time-varying sensitivity across time scales of integration. As a case study, we estimate first order sensitivity indices with the FAST (Fourier Amplitude Sensitivity Test) method for a typical conceptual rainfall–runoff model. The resulting plots can guide data selection for model calibration, support diagnostic model evaluation and help to define the timing and length of spot gauging campaigns in places where long-term calibration data are not yet available. |
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| Keywords: | Global sensitivity analysis FAST Sensitivity indices visualization |
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