Abstract
Abstract. Precipitation orographic enhancement is the result of both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement, as well as how they affect precipitation lapse rates, remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged over 11 000 snow course data upstream of two reservoirs in the western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and, consequently, improve predictions of a snow hydrologic modeling chain (Flood-PROOFS). We found that snow water equivalent (SWE) above 3000 m a.s.l. (above sea level) was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m a.s.l., with gradients up to 1000 mm w.e. km−1. Enhancement factors, estimated by blending precipitation gauge and snow course data, were consistent between the two hydropower headwaters (median values above 3000 m a.s.l. between 4.1 and 4.8). Including blended gauge course lapse rates in an iterative precipitation spatialization procedure allowed Flood-PROOFS to remedy underestimations both of SWE above 3000 m a.s.l. (up to 50 %) and – importantly – of precipitation vs. observed streamflow. Annual runoff coefficients based on blended lapse rates were also more consistent from year to year than those based on precipitation gauges alone (standard deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to the essential role of high-elevation headwaters in supporting water security and ecosystem services worldwide.
Highlights
Orographic precipitation is a critical driver of the Earth’s water budget (Jiang, 2003), as it affects amount and distribution of snowpack at high elevations and, freshwater supply and water security during the warm season (Serreze et al, 1999; Bales et al, 2006; Viviroli et al, 2007b; Blanchet et al, 2009; Mott et al, 2014; Sarmadi et al, 2019)
Compared to stand-alone devices like snow pillows (Cox et al, 1978), courses allow operators to capture spatial variability in snow cover and derive a more representative estimate of snow water equivalent (SWE) across the landscape (Malek et al, 2017). This is why courses are a cornerstone of water supply forecasting in the western USA (Pagano et al, 2004; Harrison and Bales, 2016) and elsewhere (Metsämäki et al, 2005). In addition to their century-old role as an indicator of snow water resources, in this paper we hypothesized that snow courses could be rethought as natural precipitation gauges in the hope that they could provide new information about precipitation totals and their orographic trends at elevations that are usually ungauged
We did so by hypothesizing that snow courses could be rethought as natural precipitation gauges and, be leveraged to reconstruct blended precipitation gauge snow course lapse rates
Summary
Orographic precipitation is a critical driver of the Earth’s water budget (Jiang, 2003), as it affects amount and distribution of snowpack at high elevations and, freshwater supply and water security during the warm season (Serreze et al, 1999; Bales et al, 2006; Viviroli et al, 2007b; Blanchet et al, 2009; Mott et al, 2014; Sarmadi et al, 2019). Orographic precipitation may concur in generating floods (Buzzi et al, 1998; Galewsky and Sobel, 2005; Panziera et al, 2015) and in triggering landslides and avalanches (Roe, 2005). A feature of both stratiform and convective systems (Roe, 2005), orographic precipitation introduces sharp transitions between wet, windward, and dry leeward slopes that are ubiquitous across continents (rain shadows, see Houston and Hartley, 2003; Anders et al, 2006; Galewsky, 2009; Viale and Nuñez, 2011).
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