Supplementary material to "Hydrometeorology and landscapes control sediment and organic matter mobility across a diverse and changing glacier-sourced river basin"
Year: 2025
Type: supplementary-materials
Abstract: Supporting information Catchment structural unitsCatchment structural units (CSUs) describe an integrated landscape condition code for 400 m 2 landscape areas based on various geospatial datasets (Orwin et al., 2022).Geospatial datasets used to construct CSUs include: land cover (Alberta Biodiversity Monitoring Institute, 2013), human footprint (Alberta Biodiversity Monitoring Institute, 2016), surficial geology (Alberta Geological Survey, 2015), wetland cover (Government of Alberta, 2019), and topography (Altalis, 2017).Each dataset has a listing of codes in a particular position of the typically six-digit code.However, it is possible for entire categories to not be present within a CSU and thus are zeroed in that digit position.For land cover, absence of leading sixth digit may indicate agricultural cover (i.e., cross referenced with land use) or wetland cover.Absence of or zero in the fifth position indicates no specific anthropogenic land uses in the CSU.See Table S1 for details about the coding approach and the dominant codes by area for the North Saskatchewan River basin tributary catchments in the current study.used with flow to quantify daily export.Loadflex includes estimates of daily standard error of prediction for each export model and are shown in Tables S9 and S10.To produce annual water year export for each site and each model, we summed all daily export estimates for each water year and associated daily errors via summing propagation of error rules.For errors related to unmonitored flow, we used the mean proportional error of the river(s) used to calculate unmonitored flows via their yields (see Methods).Errors related to the change in storage (ΔS) term were determined by simple error propagation rules.In most cases, errors surrounding annual export estimates were reasonably low reflecting the close relationships between TSS and river flow, and DOC and river flow.However, at stations where those relationships were poor, error surrounding estimates was much higher.These errors were then propagated to calculated terms like change in storage (ΔS).However, these high errors occurred in the log-log regression model only and did not occur in other models at a given site.
Authors: Craig A. Emmerton, John F. Orwin, Cristina Buendia, Mike Christensen, Jennifer A. Graydon, Brian Jackson, Elynne Murray, Steph Neufeld, Brandi W. Newton, Ryan Ozipko, Rick Pickering, Nadine Taube, Chris Ware
Institution: United States Department of the Interior
Language: English
Subfield: Soil Science
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4411428756