The Influence of Cloudiness on Hydrologic Fluctuations in the Mountains of the Western United States

Abstract

This study investigates snowmelt and streamflow responses to cloudiness variability across the mountainous parts of the western United States. Twenty years (1996–2015) of Geostationary Operational Environmental Satellite‐derived cloud cover indices (CC) with 4‐km spatial and daily temporal resolutions are used as a proxy for cloudiness. The primary driver of nonseasonal fluctuations in daily mean solar insolation is the fluctuating cloudiness. We find that CC fluctuations are related to snowmelt and snow‐fed streamflow fluctuations, to some extent (correlations of <0.5). Multivariate linear regression models of daily snowmelt (MELT) and streamflow (ΔQ) variations are constructed for each month from February to July, when snowmelt is most active. Predictors include CC from five antecedent days up to the current day. The CC‐MELT and CC‐ΔQ associations vary with time and location. The results show the dominance of negative correlations between CC and MELT, exemplifying the cloud‐shading (or clear‐sky) effect on snowmelt. The magnitude of the CC‐MELT association (R2) amounts to 5–61%, typically peaking in May. These associations fade earlier in summer during dry years than wet years, indicating the differing responses of thicker versus thinner snowpack. The CC‐ΔQ association displays a less consistent pattern, with R2 amounting to 2–47%. Nevertheless, MELT and ΔQ fluctuations exhibit spatially extensive patterns of correlations with daily cloudiness anomalies, indicating that the effects of cloudiness often operate over regional spatial scales.

Drivers and projections of ice phenology in mountain lakes in the western United States

“…In our Random Forest analysis, cumulative mean daily winter/spring air temperature were stronger predictors of ice breakup date than downward surface shortwave radiation, which included the effects of cloud cover (Mitchell et al 2004;Abatzoglou 2013). Cloud cover and shortwave radiation are well documented primary controls on snow and ice melt (Marks and Dozier 1992;Marks et al 1999;Sumargo and Cayan 2018) and moderately improved the predictions of our linear mixed effects model (LMEM). However, we elected not to include downward shortwave radiation in our predictive model because GCM predictions of cloud cover are less certain than the more certain outcome of reduced snow fraction.…”

A comparison of snowmelt‐derived streamflow from temperature‐index and modified‐temperature‐index snow models

“…Topography (Blöschl, Kirnbauer, & Gutknecht, ; Kirnbauer, Blöschl, & Gutknecht, ) and vegetation influence the spatial pattern of the snowpack because they affect the net radiation, which is usually the primary energy source for snowmelt (Aguado, ). Air temperature and cloud cover (Aguado, ; Sicart, Pomeroy, Essery, & Bewley, ) also contribute to the net radiation, thus affecting the spatial distribution of the snowpack and the resultant streamflow (Sumargo & Cayan, ).…”

Monitoring the Daily Evolution and Extent of Snow Drought

“…Dry periods have differing snowpack outcomes during both the accumulation and ablation season depending on temperature (Hatchett and McEvoy, 2018;Xu et al, 2019) as well as how the snowpack energy budget is influenced by the deposition of dust or other light-absorbing particles on snow (Skiles and Painter, 2016;Skiles et al, 2018), cloud cover (Sumargo and Cayan, 2018), and moisture (Harpold and Brooks, 2018). How best to include these additional parameters that help to describes changes in the phase diagram trajectories is an area of future research.…”