The influence of diurnal snowmelt and transpiration on hilllslope throughflow and stream response

Abstract

Abstract. Daily stream flow and groundwater dynamics in forested subalpine catchments during spring are to a large extent controlled by hydrological processes that respond to the day-night energy cycle. Diurnal snowmelt and transpiration events combine to induce pressure variations in the soil water storage that are propagated to the stream. In headwater catchments these pressure variations can account for a significant amount of the total pressure in the system and control the magnitude, duration, and timing of stream inflow pulses at daily scales, especially in low flow systems. Changes in the radiative balance at the top of the snowpack can alter the diurnal hydrologic dynamics of the hillslope-stream system with potential ecological and management consequences. We present a detailed hourly dataset of atmospheric, hillslope, and streamflow measurements collected during one melt season from a semi-alpine headwater catchment in western Montana, US. We use this dataset to investigate the timing, pattern, and linkages among snowmelt-dominated hydrologic processes and assess the role of the snowpack, transpiration, and hillslopes in mediating daily movements of water from the top of the snowpack to local stream systems. We found that the amount of snowpack cold content accumulated during the night, which must be overcome every morning before snowmelt resumes, delayed water recharge inputs by up to 3 hours early in the melt season. These delays were further exacerbated by multi-day storms (cold fronts), which resulted in significant depletions in the soil and stream storages. We also found that both diurnal snowmelt and transpiration signals are present in the diurnal soil and stream storage fluctuations, although the individual contributions of these processes is difficult to discern. Our analysis showed that the hydrologic response of the snow-hillslope-stream system is highly sensitive to atmospheric drivers at hourly scales, and that variations in atmospheric energy inputs or other stresses are quickly transmitted and alter the intensity, duration and timing of snowmelt pulses and soil water extractions by vegetation, which ultimately drive variations in soil and stream water pressures.

Quantifying contributions of snowmelt water to streamflow using graphical and chemical hydrograph separation

“…Further, SC can be measured continuously at relatively low costs using automated loggers, which can provide improved hydrograph separation results compared to discretely sampled chemistry (Caissie et al, 1996), and has been used successfully to separate perennial contributions in similar watersheds in the Southern Rocky Mountains (Caine, 1989; Liu et al, 2004; Miller et al, 2014; Rumsey et al, 2015). Diurnal streamflow cycles have been previously analysed to gain process understanding on the hydrologic response to snowmelt (Caine, 1992; Kobayashi, 1986; Kurylyk & Hayashi, 2017; Loheide & Lundquist, 2009; Lundquist & Cayan, 2002; Lundquist & Dettinger, 2005; Mutzner et al, 2015; Pellerin et al, 2012; Woelber et al, 2018). In addition, monitoring the natural diurnal discharge responses of headwater mountain systems to snowmelt “pulses” has been proposed as a diagnostic tool for analysing how climate change affects watersheds (Lundquist & Cayan, 2002), and graphical techniques have previously been used to isolate changes in the fast flow pathways (Buttle et al, 2019; Hewlett & Hibbert, 1967; Scaife et al, 2020).…”

Conceptualizing catchment storage dynamics and nonlinearities

“…Specifically, the reconstruction of the phase space of a hydrologic system enables visualization of system attractors and state trajectories that offer qualitative insight into the stability, complexity, and nonlinearity of the system. Data‐driven approximations of phase space and attractors of hydrologic systems based on streamflow time‐series analysis have been extensively explored by Porporato and Ridolfi (); Sivakumar, Jayawardena, and Li (); Sivakumar and Singh (); and Woelber et al (), among many others. However, reconstructions of the phase space using modeled storage are less common (Duffy, ; Brandes, Duffy, & Cusumano, ; and more recently Beven & Davies, ).…”