Hydrologic flow path development varies by aspect during spring snowmelt in complex subalpine terrain

Webb, Ryan; Fassnacht, S. R.; Gooseff, M. N.

doi:10.5194/tc-12-287-2018

Cited by 30 publications

(30 citation statements)

References 59 publications

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“…In addition, we did not allow for frozen soils in our simulations, but this can be a strong influence on soil input partitioning in places where snow depth was <50 cm and incapable of insulating the soil (Slater et al, 2017). The 1-D model does not incorporate lateral surface or subsurface flow, which can be redistribute water downslope along the soil snow interface (Webb et al, 2018) and within the shallow subsurface (Kampf et al, 2015). Lateral redistribution of water thus leads to spatially variable patterns of input, storage, runoff generation, and ET at the hillslope to watershed scales .…”

Section: Uncertaintiesmentioning

confidence: 99%

Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties

Hammond¹,

Harpold²,

Weiss³

et al. 2019

Preprint

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Abstract. Streamflow generation and deep groundwater recharge in high elevation and high latitude locations may be vulnerable to loss of snow, making it important to quantify how snowmelt is partitioned between soil storage, deep drainage, evapotranspiration, and runoff. Based on previous findings, we hypothesize that snowmelt produces greater streamflow and deep drainage than rainfall and that this effect is greatest in dry climates. To test this hypothesis we examine how snowmelt and rainfall partitioning vary with climate and soil properties using a physically based variably saturated subsurface flow model, HYDRUS-1D. To represent climate variability we use historical inputs from five SNOTEL sites in each of three mountain regions with humid to semiarid climates: Northern Cascades, Sierra Nevada, and Uinta. Each input scenario is run with three soil profiles of varying hydraulic conductivity, soil texture, and bulk density. We also create artificial input scenarios to test how the concentration of input in time, conversion of snow to rain input, and soil profile depth affect partitioning of input into deep drainage and runoff. Results indicate that event-scale runoff is higher for snowmelt than for rainfall due to higher antecedent moisture and input rates in both wet and dry climates. At the annual scale, surface runoff also increases with snowmelt fraction, whereas deep drainage is not correlated with snowmelt fraction. Deep drainage is less affected by changes from snowmelt to rainfall because it is controlled by deep soil moisture changes over longer time scales. However, extreme scenarios with input highly concentrated in time, such as during melt of a deep snowpack, yield greater deep drainage below the root zone than intermittent input. Soil texture modifies daily wetting and drying patterns but has limited effect on annual scale partitioning of rain and snowmelt, whereas increases in soil depth decrease runoff and increase deep drainage. Overall these results indicate that runoff may be substantially reduced with seasonal snowpack decline in all climates. These mechanisms help explain recent observations of streamflow sensitivity to changing snowpack and emphasize the need to develop strategies to mitigate impacts of reduced streamflow generation in places most at risk for shifts from snow to rain.

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Section: Uncertaintiesmentioning

confidence: 99%

Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties

Hammond¹,

Harpold²,

Weiss³

et al. 2019

Preprint

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“…To gain a predictive understanding of how the plant community will respond to climate change over space, it is important to understand key environmental factors controlling their spatial arrangement (Zimmermann & Kienast, 1999). Several research efforts have recognized a significant topographical effect-particularly the slope aspect (i.e., slope orientation; Pelletier et al, 2018)-on hydrological (Allen-Diaz, 1991;Webb et al, 2018) and pedological (Collins et al, 2004;Marston, 2010) processes. Such processes, on the other hand, have a significant impact on the local biogeochemistry activity (Amundson et al, 2015;Collins et al, 2004;Marston, 2010), leading to a heterogeneous composition of plant communities (Allen-Diaz, 1991;Zimmermann & Kienast, 1999).…”

Section: Introductionmentioning

confidence: 99%

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High‐Resolution Remote Sensing and Surface Geophysical Data

et al. 2019

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This study aims to investigate the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain‐hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain‐hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain‐hillslope transect at 0.50‐m resolution and extracted LiDAR‐derived metrics to model the rapid change in microtopography. We then investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long‐term monitoring to capture community‐specific physiological responses to perturbations, offering the possibility of bridging local plot‐scale observations with large landscape monitoring.

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“…These results indicate that accurately simulating the snowmelt input averaged across an entire HRU may not necessarily result in accurate simulation of snowmelt runoff generation. Recent studies showing that substantial snowmelt can be localized to specific areas of a watershed because of redistribution of meltwater and that greater runoff can be generated than if snowmelt were evenly distributed across landscape further highlight the importance of the sub‐grid variability of snowmelt input (Eiriksson et al, ; Webb, Fassnacht, & Gooseff, ; Webb, Williams, & Erickson, ). Therefore, as suggested by Luce et al (), an important challenge for future hydrologic modelling applications is relating the watershed‐wide surface water input rate to runoff generation processes that are spatially dependent on snowmelt variability.…”

Section: Discussionmentioning

confidence: 99%

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

Sexstone

Driscoll

Hay

et al. 2020

Hydrological Processes

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The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of sub-grid or sub-watershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the precipitation-runoff modelling system (NHM-PRMS) represents the sub-grid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-mean SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the sub-grid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as decreases in simulated snowmelt rate by 1 mm day −1 resulted in decreases in runoff ratio by 1.8% on average in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water-limited dryness index was associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modelling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modelling applications.

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Hydrologic flow path development varies by aspect during spring snowmelt in complex subalpine terrain

Cited by 30 publications

References 59 publications

Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties

Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High‐Resolution Remote Sensing and Surface Geophysical Data

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

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