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Infiltration under snow cover: Modeling approaches and predictive uncertainty

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Meeks Jessica, Moeck Christian, Brunner Philip, Hunkeler Daniel,
Project Groundwater resources under changing climatic conditions
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Original article (peer-reviewed)

Journal Journal of Hydrology
Volume (Issue) 546
Page(s) 16 - 27
Title of proceedings Journal of Hydrology
DOI 10.1016/j.jhydrol.2016.12.042


Groundwater recharge from snowmelt represents a temporal redistribution of precipitation. This is extre-mely important because the rate and timing of snowpack drainage has substantial consequences to aqui-fer recharge patterns, which in turn affect groundwater availability throughout the rest of the year. The modeling methods developed to estimate drainage from a snowpack, which typically rely on temporally-dense point-measurements or temporally-limited spatially-dispersed calibration data, range in complex-ity from the simple degree-day method to more complex and physically-based energy balance approaches. While the gamut of snowmelt models are routinely used to aid in water resource manage-ment, a comparison of snowmelt models’ predictive uncertainties had previously not been done. Therefore, we established a snowmelt model calibration dataset that is both temporally dense and rep-resents the integrated snowmelt infiltration signal for the Vers Chez le Brandt research catchment, which functions as a rather unique natural lysimeter. We then evaluated the uncertainty associated with the degree-day, a modified degree-day and energy balance snowmelt model predictions using the null-space Monte Carlo approach. All three melt models underestimate total snowpack drainage, underesti-mate the rate of early and midwinter drainage and overestimate spring snowmelt rates. The actual rate of snowpack water loss is more constant over the course of the entire winter season than the snowmelt models would imply, indicating that mid-winter melt can contribute as significantly as springtime snow-melt to groundwater recharge in low alpine settings. Further, actual groundwater recharge could be between 2 and 31% greater than snowmelt models suggest, over the total winter season. This study shows that snowmelt model predictions can have considerable uncertainty, which may be reduced by the inclu-sion of more data that allows for the use of more complex approaches such as the energy balance method. Further, our study demonstrated that an uncertainty analysis of model predictions is easily accomplished due to the low computational demand of the models and efficient calibration software and is absolutely worth the additional investment. Lastly, development of a systematic instrumentation that evaluates the distributed, temporal evolution of snowpack drainage is vital for optimal understanding and manage-ment of cold-climate hydrologic systems.