Uncertainty in seasonal snow reconstruction: Relative impacts of model forcing and image availability |
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| Institution: | 1. Dipartimento di Scienze, Sezione di Nanoscienze, Università degli Studi “Roma Tre”, Via della Vasca Navale 84, 00146 Rome, Italy;2. CNR-IPCF and Dipartimento di Fisica, Università di Roma “La Sapienza”, Piazzale A. Moro 2, I-00185, Rome, Italy;3. ISIS Facility, STFC Rutherford Appleton Laboratory, Harwell Oxford, Didcot Oxon, OX11 0QX, UK |
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| Abstract: | There are many areas of uncertainty when solving the inverse problems of snow water equivalent (SWE) reconstruction. These include (i) the ability to infer the Final Date of the Seasonal Snow (FDSS) cover, particularly from remote sensing; (ii) errors in model forcing data (such as air temperature or radiation fluxes); and (iii) weaknesses in the snow model used for the reconstruction, associated with both the fidelity of the equations used to simulate snow processes (structural uncertainty) and the parameter values selected for use in the model equations. We investigate the trade-offs among these sources of uncertainty using 10,000 station-years worth of data from the western US SNOTEL network. Model structural and parameter uncertainty are eliminated by using a perfect model scenario i.e. comparing results to modelled control runs. The model was calibrated for each station-year to ensure that the model simulations reflect reality. Results indicate that for a temperature index model, a ±5 days error in FDSS gives a median ?25%/+32% error in maximum SWE. A 1 °C air temperature bias produces a SWE error larger than a 5 days error in the FDSS for 50% of the 10,000 cases. Similarly, a 5 days error in FDSS could be accounted for by a net radiation error of 13 W m?2 or less during the melt period, in 50% of cases. Mean absolute errors of 1 °C or more are typically reported in the literature for air temperature interpolations at high elevations. Observed solar radiation during the melt season can differ by 30 W m?2 over relatively short distances, while estimates from reanalysis (NARR, ERA-Interim, MERRA, CFSRR) and GOES satellites typically span more than 40 W m?2. Using data from both MODIS sensors (Terra & Aqua) at all snow covered points in the western US, a consecutive 5 days gap in imagery at time of FDSS is likely to occur only 5–10% of the time. This work shows that errors in model forcing data are at least as important, if not more, than image availability when reconstructing SWE. |
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