Monitoring the timing of snowmelt and the initiation of streamflow using a distributed network of temperature/light sensors

Abstract

The loss of snow cover and the initiation of streamflow are key triggers for both terrestrial and aquatic biota. Landscape-scale snowmelt and streamflow dynamics are difficult to estimate, however, because they integrate large spatial extents and can vary rapidly in time. Remotely sensed observations are often temporally discontinuous and point observations lack sufficient spatial density (e.g. point measures from data-logging piezometers). In this study, we employ inexpensive temperature/light sensors to monitor the distribution of snowmelt and headwater stream discharge as a proxy for hydrological state of the landscape with high spatial and temporal resolution. This study was conducted at the Redondo Peak, a large (local relief over 1100 m) resurgent dome within the caldera complex of the Valles Caldera National Preserve, 30 km west of Los Alamos, New Mexico, USA. The first-order streams that drain the Redondo Peak encompass the full spectrum of terrain aspects, resulting in significant variability in wind exposure, turbulent and radiative fluxes, snow cover duration, and vegetation structure. Complex interactions between these variables impact groundwater recharge through variations in sublimation, evaporation, and transpiration. To monitor the role this variation plays with respect to spatio-temporal dynamics of snowmelt and headwater stream discharge, we have installed 128 temperature/light sensors in eight different streambeds draining through unique aspects of the peak. The variation of daily temperature/light levels relative to seasonal change in mean temperature/light levels provides a metric of the spatial distribution of surface waters and snow cover. This metric correctly identified flowing lengths of headwater streams with 80% accuracy during an early summer period (10 May 2007) and 79% of the time in late summer period (2 October 2007). Between these two observation periods, the percentage length of headwater streams flowing decreased by 12%, indicating a general drying of the landscape. On the basis of a conceptual model of a groundwater 'mound' within the peak that roughly follows the shape of the land surface, such a metric relates directly to the amount of water in the landscape, providing insight to the complex ecohydrological interactions between snowmelt, rainfall, and vegetative uptake. This serves as a precursor to developing a broader ecohydrological perspective of the interrelationships between ecology and hydrological processes.

Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

“…Forest disturbance can have significant impacts on snow processes, whose effects can range from immediate (Boon, 2009) to decadal (Lyon et al, 2008;Gleason and Nolin, 2016). At the stand scale, forests attenuate wind speeds, thereby suppressing turbulent mixing of the near-surface atmosphere (Liston and Sturm, 1998); modify the radiation received at the snow surface through shifts in shortwave and longwave contributions and reduced surface albedo (Sicart et al, 2004;O'Halloran et al, 2012;Gleason et al, 2013); and temporally shift seasonal-and event-scale accumulation and ablation patterns through canopy snowfall interception .…”

Mountain ecohydrology: quantifying the role of vegetation in the water balance of montane catchments

“…The papers in this second section examine patterns resulting from both of these precipitation sources. Beginning this group of papers, Lyon et al (2008) use inexpensive temperature and light sensors to describe distributed patterns of snow melt and stream flow at the scale of headwater catchments, which serve as naturally defined ecosystems that integrate the lateral redistribution of water and nutrients. These basic hydrological measures provide spatial and temporal representations of key triggers for montane ecosystems: the beginning of the growing season and the snowmelt-initiated recharge of soil moisture.…”

“…Liu et al's results identify non-linear thresholds in hydrologic residence time, the redistribution of precipitation within the ecosystem, and thus ecosystem-scale patterns of water availability. Sources of streamflow were related to soil moisture and precipitation, conceptually linking lateral redistribution of water to the work of Lyon et al (2008) and Vivoni et al (2008), but the type of precipitation (rain versus snow), soil type, and subsurface geology resulted in non-linear behaviour in space and time. For example, lateral subsurface flow dominated water redistribution during all seasons in the well-drained soils at higher elevations.…”

“…In contrast, forest vegetation on adjacent hillslopes reduces the scattering of incoming solar radiation prolonging snow cover in the basin. Combining these results with topographic shading, the distributed model is used to classify portions of the landscape into various snow accumulation zones providing an opportunity for subsequent measurement and model comparisons with the field data on snow persistence described in Lyon et al (2008). Given the strong associations between snow and streamflow demonstrated by both Lyon et al (2008) and Liu et al (2008), the albedo condition and relative position of distant slopes may be more important to ecohydrological interactions that control both ecosystem structure and catchment water balance than has previously been recognized.…”

Hydrological effects of Norway spruce and European beech on snow cover in a mid-mountain region of the Polana mts., Slovakia / Hydrologický vplyv smreka obyčajného a buka lesného na snehovú pokrývku v stredohorských polohách pohoria poľana na slovensku

“…Therefore, it is important to understand how particular types of forest stands perform within this context. Numerous research results Lyon et al, 2008;Mindas, 2003;Molotch et al, 2009;Pobedinskij and Krecmer, 1984;Rinehart et al, 2008;Veatch et al, 2009) point out that the accumulation and melting of snow depends on a large number of factors (geographic area, size of mountains, elevation, slope, exposition, stand age and canopy cover, oceanic/continental climate etc. ), hence, the obtained knowledge is not easy to interpret and generalize.…”