Hopkinson, Christopher
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- ItemQuality control impacts on total precipitation gauge records for montane valley and ridge sites in SW Alberta, Canada(MDPI, 2022) Barnes, Celeste; Hopkinson, ChristopherThis paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49–76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50–76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784–1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731–1269 mm), while the valley gauge adjustments were −4% to 1% (−39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites.
- ItemMontane seasonal and elevational precipitation gradients in the southern Rockies of Alberta, Canada(Wiley, 2025) Barnes, Celeste; MacDonald, Ryan J.; Hopkinson, ChrisModelling precipitation inputs in mountainous terrain is challenging for water resource managers given sparse monitoring sites and complex physical hydroclimatic processes. Government of Alberta weather station uncorrected and bias-corrected precipitation datasets were used to examine elevational precipitation gradients (EPGs) and seasonality of EPGs for six South-Saskatchewan River headwater sites (alpine, sub-alpine, valley). January EPG from valley to alpine sites (730 m elevation difference) using uncorrected precipitation was 19 mm/100 m. Corrected EPG was approximately three times greater (61 mm/100 m). The valley received more precipitation than the alpine (inverse EPG) in late spring and summer. A seasonal signal was present whereby all sites demonstrated 50%–70% lower summertime precipitation relative to winter months, with the greatest seasonal variance at the alpine site. Winter watershed-level spatialized precipitation volume was compared to modelled snow water equivalent (SWE) associated with two late-winter airborne lidar surveys. Uncorrected volumes (2020: 64.0 × 106m3, 2021: 63.2 × 106m3) were slightly higher than modelled mean SWE (2020: 51.6 × 106m3, 2021: 44.2 × 106m3) whereas bias-corrected (2020: 120.5 × 106m3, 2021: 119.7 × 106m3) almost doubled the estimate. Corrected precipitation is assumed closer to the true value. Cumulative sublimation, evaporation and snowmelt losses result in ground-level snowpack yield that deviates from total atmospheric precipitation in an increasingly negative manner. The 2020/2021 simulations suggest wintertime atmospheric precipitation exceeds late-winter snowpack accumulation by up to 57% and 63%, respectively. A loss of 16 × 106m3 (7%) watershed SWE from the alpine zone was partially attributed to redistribution downslope to the treeline-ecotone. Physical snowpack losses from sublimation and melt, or modelling uncertainty due to precipitation correction and alpine snow-density uncertainties can also contribute to observed discrepancies between in situ SWE and cumulative precipitation. Ignoring bias-correction in headwater precipitation estimates can greatly impact headwater precipitation volume estimates and ignoring EPG seasonality is likely to result in under-estimated winter and over-estimated summer yields.
- ItemFiltering stems and branches from terrestrial laser scanning point clouds using deep 3-D fully convolutional networks(MDPI, 2018) Xi, Zhouxin; Hopkinson, Christopher; Chasmer, LauraTerrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing.
- ItemEcological impacts of shortening fire return intervals on boreal peatlands and transition zones using integrated in situ field sampling and lidar approaches(Wiley, 2022) Jones, Emily; Chasmer, Laura; Devito, Kevin; Rood, Stewart; Hopkinson, ChristopherAridity associated with rising air temperatures in northern latitudes is expected to contribute to increased frequency of wildland fires. Here, we examined regenerating vegetation following short return interval (SRI) fire (56 years post-fire) compared to long return interval (LRI) fire (>80 years post-fire) in boreal peatlands and their adjacent transitional areas. The objectives of this study were to quantify if differences exist between (1) peatland and transitional soil characteristics in LRI versus SRI areas and (2) regenerating vegetation species, structural characteristics and diversity. We also determined if patterns of vegetation structural characteristics observed using field data also occur across the broader landscape using airborne lidar data. The Utikuma Region Study Area (URSA) is located in central Alberta, Canada. Here, 19 peatlands were sampled, coincident with an airborne lidar survey of the broader region, where 120 peatlands in short and long fire return intervals were identified. We found that SRI transitional areas had significantly deeper organic soil deposits than those found in LRI (p < 0.0001). Proportions of regenerating species differed significantly between peatlands and transitional areas in SRI versus LRI, where greater proportion of coniferous species were observed in LRI. Deciduous transitional–upland species and taller post-fire vegetation heights were more commonly found SRI peatlands compared with LRI. This suggest that fires with SRIs in this region may result in enhanced deciduous succession, which may transition boreal peatlands into ecosystems that have some characteristics of transitional and upland forests.
- ItemIdentifying conifer tree vs. deciduous shrub and tree regeneration trajectories in a space-for-time boreal peatland fire chronosequence using multispectral lidar(MDPI, 2022) Enayetullah, Humaira; Chasmer, Laura; Hopkinson, Christopher; Thompson, Dan; Cobbaert, DanielleWildland fires and anthropogenic disturbances can cause changes in vegetation species composition and structure in boreal peatlands. These could potentially alter regeneration trajectories following severe fire or through cumulative impacts of climate-mediated drying, fire, and/or anthropogenic disturbance. We used lidar-derived point cloud metrics, and site-specific locational attributes to assess trajectories of post-disturbance vegetation regeneration in boreal peatlands south of Fort McMurray, Alberta, Canada using a space-for-time-chronosequence. The objectives were to (a) develop methods to identify conifer trees vs. deciduous shrubs and trees using multi-spectral lidar data, (b) quantify the proportional coverage of shrubs and trees to determine environmental conditions driving shrub regeneration, and (c) determine the spatial variations in shrub and tree heights as an indicator of cumulative growth since the fire. The results show that the use of lidar-derived structural metrics predicted areas of deciduous shrub establishment (92% accuracy) and classification of deciduous and conifer trees (71% accuracy). Burned bogs and fens were more prone to shrub regeneration up to and including 38 years after the fire. The transition from deciduous to conifer trees occurred approximately 30 years post-fire. These results improve the understanding of environmental conditions that are sensitive to disturbance and impacts of disturbance on northern peatlands within a changing climate.