Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone

Abstract

Abstract. Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how the spatial and temporal distribution of rainfall and snowmelt affects discharge in rain–snow transition zones. These zones experience large year-to-year variations in precipitation phase, cover a significant area of mountain catchments globally, and might extend to higher elevations under future climate change. We used observations from 11 weather stations and snow depths measured from one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semiarid, 1.8 km2 headwater catchment. We focused on surface water input (SWI; the summation of rainfall and snowmelt on the soil) for 4 years with contrasting climatological conditions (wet, dry, rainy, and snowy) and compared simulated SWI to measured discharge. A strong spatial agreement between snow depth from the lidar survey and model (r2 = 0.88) was observed, with a median Nash–Sutcliffe efficiency (NSE) of 0.65 for simulated and measured snow depths at snow depth stations for all modeled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the 4 years, with north-facing slopes producing 1.09–1.25 times more SWI than south-facing slopes, and snowdrifts producing up to 6 times more SWI than the catchment average. Annual discharge in the catchment was not significantly correlated with the fraction of precipitation falling as snow; instead, it was correlated with the magnitude of precipitation and spring snow and rain. Stream cessation depended on total and spring precipitation, as well as on the melt-out date of the snowdrifts. These results highlight the importance of the heterogeneity of SWI at the rain–snow transition zone for streamflow generation and cessation, and emphasize the need for spatially distributed modeling or monitoring of both snowpack and rainfall dynamics.

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

“…The iSnobal model (Havens et al, 2020; Marks et al, 1999) was chosen because of its incorporation into lidar SWE products geared toward operational water supply applications (Painter et al, 2016). iSnobal was run over Cameron Pass for both unscaled (iSn un ) and rescaled (iSn re ; e.g., Hale et al, 2023; Kiewiet et al, 2022; Voegeli et al, 2016) precipitation scenarios because the chosen atmospheric model used for meteorological forcing within iSnobal, the High Resolution Rapid Refresh (HRRR) model, has been shown to underreport SWE by up to 25% (Meyer et al, 2023). Only surveys with dry snow conditions were modelled.…”

“…The iSnobal model (Havens et al, 2020;Marks et al, 1999) was chosen because of its incorporation into lidar SWE products geared toward operational water supply applications (Painter et al, 2016). iSnobal was run over Cameron Pass for both unscaled (iSn un ) and rescaled (iSn re ; e.g., Hale et al, 2023;Kiewiet et al, 2022;Voegeli et al, 2016) When compared to snow pit measurements, the Kovacs et al (1995), Kuroiwa (1954), and Webb et al (2021) derived densities yielded overall RMSEs of 54 kg m À3 (r = 0.09), 97 kg m À3 (r = 0.07), and 83 kg m À3 (r = 0.08), respectively (Figure 2; Table S3). Both the Kovacs et al (1995) and Kuroiwa (1954) equations yielded densities with an overall negative bias, whereas the Webb et al ( 2021) equation yielded densities with a positive bias (Table S3).…”

High Resolution SnowModel Simulations Reveal Future Elevation‐Dependent Snow Loss and Earlier, Flashier Surface Water Input for the Upper Colorado River Basin

“…Additionally, in this study we analyze surface water input (SWI), which is the sum of rainfall and snowmelt available to enter the soil, to characterize the effects of a changing snowpack on the hydrologic cycle. Prior studies at plot and catchment scale have demonstrated linkages between SWI and observed and modeled streamflow (Hammond et al, 2019;Kiewiet et al, 2022;Kormos et al, 2014), and the use of SWI in this study enables linking snow changes to hydrologic effects not fully captured in prior studies using metrics including snowfall fraction (Berghuijs et al, 2014) and snowmelt rate (Barnhart et al, 2016).…”

The Spatiotemporal Variability of Snowpack and Snowmelt Water 18O and 2H Isotopes in a Subarctic Catchment

“…Changes in air temperatures, precipitation patterns, and moisture conditions are producing unprecedented changes in snow accumulation and melt in northern regions (Arp et al., 2015; Irannezhad et al., 2022; Kiewiet et al., 2022; Rasouli et al., 2022; Rixen et al., 2022; Ruosteenoja et al., 2020; Vormoor et al., 2015). The changes in magnitude and timing of snow precipitation and snowmelt runoff have gradually resulted in a hydrological regime shift from a snowmelt to a rainfall‐dominated system in cold climate regions (Berghuijs et al., 2014; Bintanja & Andry, 2017), with varying feedbacks at a catchment scale (Ala‐aho et al., 2021; Meriö et al., 2019; Pi et al., 2021).…”