Hydrological behaviour of an ice‐layered snowpack in a non‐mountainous environment

Paquotte, Andrea; Baraër, Michel

doi:10.1002/hyp.14433

Cited by 10 publications

(8 citation statements)

References 67 publications

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“…The higher performance at Sainte-Marthe could be due to a combination of different factors. Early melt of snow due to frequent rain-on-snow events in this region (Paquotte and Baraer, 2021) and the presence of basal ice as observed in the field campaign might have contributed to a more structured snowpack in the Sainte-Marthe forest and hence improved the prediction of snow depth compared to the other agro-forested site. Reasonably high R 2 values in agricultural fields at both agro-forested sites (0.39 in Sainte-Marthe and 0.45 in Saint-Maurice) indicate that the models captured the relevant processes through the predictor variables considered.…”

Section: Relationship Of Snow Depth To Topographic and Vegetation Cha...mentioning

confidence: 82%

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV-lidar

Dharmadasa

Kinnard

Baraër

2022

Preprint

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Abstract. Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. Understanding and assessing the effect of vegetation and topographic conditions on the snow depth variability is useful for the accurate prediction of snow depths. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by Unmanned Aerial Vehicle Light Detection and Ranging (UAV-lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance less than 10 m in the forested area at all three sites, whereas open areas showed scale invariance or comparatively large scale break distances (i.e., 18 m). The effect of vegetation and topographic variables on the spatial variability of snow depths at each site was investigated with random forest models. Results show that including wind-related forest edge proximity effects improved the model accuracy by more than 50 % in agro-forested sites, whereas incorporating canopy characteristics improved the model accuracy by more than 60 % in the coniferous site. Hence the underlying topography and the wind-redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in process-based models for accurate estimates of snowpack dynamics. This study also demonstrates the usefulness of UAV-lidar to resolve and understand high-resolution snow depth heterogeneity in agro-forested environments and boreal forests.

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Section: Relationship Of Snow Depth To Topographic and Vegetation Cha...mentioning

confidence: 82%

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV-lidar

Dharmadasa

Kinnard

Baraër

2022

Preprint

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“…Three sites that represent the main land use and cover patterns in southern Québec were selected to test the ability of UAV lidar to measure snow depths in open and vegetated areas (Figure 1). Sainte-Marthe (45.4 • N, 74.2 • W) is a paired agricultural and dense deciduous forested site [44], where the forested area comprises sugar maple (Acer saccharum), red maple (Acer rubrum) with no or sparse understory, and a small conifer plantation to the Southwest. Saint-Maurice (46.4 • N, 72.5 • W) is a paired agricultural and high to moderate dense mixed forested site, and the forested area comprises poplar (Populus x canadensis), red maple (Acer rubrum), white pine (Pinus strobus), and balsam fir (Abies balsamea) with sparse understory.…”

Section: Study Sitesmentioning

confidence: 99%

An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar

2022

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This study assesses the performance of UAV lidar system in measuring high-resolution snow depths in agro-forested landscapes in southern Québec, Canada. We used manmade, mobile ground control points in summer and winter surveys to assess the absolute vertical accuracy of the point cloud. Relative accuracy was determined by a repeat flight over one survey block. Estimated absolute and relative errors were within the expected accuracy of the lidar (~5 and ~7 cm, respectively). The validation of lidar-derived snow depths with ground-based measurements showed a good agreement, however with higher uncertainties observed in forested areas compared with open areas. A strip alignment procedure was used to attempt the correction of misalignment between overlapping flight strips. However, the significant improvement of inter-strip relative accuracy brought by this technique was at the cost of the absolute accuracy of the entire point cloud. This phenomenon was further confirmed by the degraded performance of the strip-aligned snow depths compared with ground-based measurements. This study shows that boresight calibrated point clouds without strip alignment are deemed to be adequate to provide centimeter-level accurate snow depth maps with UAV lidar. Moreover, this study provides some of the earliest snow depth mapping results in agro-forested landscapes based on UAV lidar.

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“…Accuracy is either given by the manufacturer or estimated for worst-case scenarios. Adapted from Paquotte and Baraer (2022)…”

Section: Study Sitementioning

confidence: 99%

“…-SWE can be calculated based on manual snow coring to estimate sample volume and mass. The manual method is time-consuming, destructive and of moderate precision (Goodison et al, 1987;Morris and Cooper, 2003;Sturm and Holmgren, 2018;Paquotte and Baraer, 2022). Automatic monitoring makes it possible to capture SWE temporal variability.…”

Section: Introductionmentioning

confidence: 99%

Drone-based ground-penetrating radar (GPR) application to snow hydrology

Valence¹,

Baraër²,

Rosa³

et al. 2022

The Cryosphere

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Abstract. Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, the structure and properties of the different snow and ice layers dictate the quantity and timing of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences of winter melting episodes and increases in the frequency of ROS events. This study develops a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repeated and non-destructive way. Snowpack evolution during the winter of 2020–2021 was evaluated using a drone-based, ground-penetrating radar (GPR) coupled with photogrammetry surveys conducted at the Ste-Marthe experimental watershed in Quebec, Canada. Drone-based surveys were performed over a 200 m2 area with a flat and a sloped section. In addition, time domain reflectometry (TDR) measurements were used to follow water flow through the snowpack and identify drivers of the changes in snowpack conditions, as observed in the drone-based surveys. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements over the ablation period, served as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis were used to generate georeferenced snow depth, density, snow water equivalent and bulk liquid water content maps. Despite some limitations, the results show that the combination of drone-based GPR, photogrammetric surveys and TDR is very promising for assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. For instance, the tested method allowed for measuring marked differences in snow pack behaviour between the first and second weeks of the ablation period. A ROS event that occurred during the first week did not generate significant changes in snow pack density, liquid water content and water equivalent, while another one that happened in the second week of ablation generated changes in all three variables. After the second week of ablation, differences in density, liquid water content (LWC) and snow water equivalent (SWE) between the flat and the sloped sections of the study area were detected by the drone-based GPR measurements. Comparison between different events was made possible by the contact-free nature of the drone-based measurements.

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Hydrological behaviour of an ice‐layered snowpack in a non‐mountainous environment

Cited by 10 publications

References 67 publications

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV-lidar

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV-lidar

An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar

Drone-based ground-penetrating radar (GPR) application to snow hydrology

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