DOI: 10.5061/dryad.qv9s4mwp6
title: Data from: Mapping the spatial heterogeneity of watershed ecosystems and water quality in rainforest fjordlands

description: This data package corresponds to a research paper by Giesbrecht et. al.
 (2025) in the journal Ecosystems with the title "Mapping the spatial
 heterogeneity of watershed ecosystems and water quality in rainforest
 fjordlands". https://doi.org/10.1007/s10021-025-00964-x The data
 package contains: A shapefile representing sampled watershed boundaries in
 .shp format ("G2025_Wts_boundaries.shp") Table of watershed
 characteristics in .csv format ("G2025_Wts_data.csv") Table of
 quality controlled water quality data in .csv format
 ("G2025_WQ_data.csv") A README file with variable definitions
 for water quality data ("G2025_WQ_README.csv") A README file
 with variable definitions for watershed characteristics
 ("G2025_Wts_README.csv")
description: In this study, we examined spatial controls on the quality of
 freshwater exported from diverse watersheds in fjordlands of a coastal
 temperate rainforest. Samples were collected about once per month for a
 year from the outlets of 56 watersheds spanning from high mountains with
 icefields to low islands with extensive wetlands. Watershed size ranged
 from &lt; 1.5 km<sup>2</sup> to 5,782
 km<sup>2</sup> (Homathko River), yet in the regional and
 global context, all are considered “small” coastal watersheds (&lt;
 10,000 km<sup>2</sup> following Milliman and Syvitski, 1992).
 The study watersheds were spatially distributed along two fjordland
 transects on the south-central coast of British Columbia, Canada
 (51°57' N to 50°07' N and 128°09' W to 123°44'
 W). <strong>Watershed characterization and
 classification</strong> This study takes
 advantage of a previous watershed classification effort, which used four
 widely available (open access) datasets and 12 easily computed watershed
 characteristics to define 12 types of small coastal watersheds with
 cluster analysis (Giesbrecht and others 2022). These clusters separated
 watersheds by characteristic water source (glacial, snowmelt, rain),
 topography (mountains, hills, lowland), climate and geographic location
 within the NPCTR (north, central, south). For the
 present study, we assigned every sampled watershed to one of the 12 types
 of coastal watershed defined in Giesbrecht and others (2022). This
 assignment required a modelling step because 34 of our 56 sampled
 watersheds were smaller than the minimum size (20
 km<sup>2</sup>) of well delineated watersheds (DW) used in the
 regional scale classification (2022). We used a random forest (Breiman,
 2001) classifier (<em>randomForest</em> package (version
 4.6-14) in R (R Core Team, 2020)) to assign class membership to newly
 delineated (very small) watersheds.The predictor variables were the 12
 watershed characteristics originally used to define the regional watershed
 types via cluster analysis (Giesbrecht and others, 2022). The response
 variable was watershed type. The present analysis revised the previous
 regional watershed classification by better resolving the locations and
 extent of watersheds in the ~ 1 to 10 km<sup>2</sup> size
 range, particularly those with very low relief and extensive wetland
 cover. <strong>Stream chemistry
 data</strong> Water samples were collected from
 the watershed outlets roughly once every month for a year (March 2018 to
 March 2019), for a total of 405 observation site-days after quality
 control. Most watersheds were sampled eight to ten times. Each transect
 was surveyed over two to three consecutive days in order to sample under
 relatively similar weather and flow conditions. The two transects were
 surveyed as close together in time as feasible, but were often more than a
 week apart, thus not always capturing the same weather system.
 From each water sample, we measured 22 aspects of riverine water
 quality, including DOC, alkalinity, cations, organic and inorganic
 nutrients, 𝛿<sup>18</sup>O-H<sub>2</sub>O,
 𝛿<sup>2</sup>H-H<sub>2</sub>O, and handheld sensor
 (YSI ProDSS) readings of temperature, specific conductance, pH, and
 turbidity. Water samples and sensor readings were taken from the main
 flow, avoiding eddies, shallow water, loose substrates, or woody debris.
 Samples for dissolved constituents were field-filtered with a 0.45 µm
 Millipore® Millex-HP hydrophilic polyethyl sulfonate (PES) syringe filter.
 All samples were kept cool and dark during the field work. Samples were
 then preserved by freezing or acidification as appropriate, within 24
 hours of field collection. The field and laboratory procedures for this
 study follow those of St. Pierre and others (2021) and Tank and others
 (2020). Laboratory results below the detection limit were replaced by ½
 the detection limit, following common convention (e.g., EPA, 2000). In
 addition to direct measurements, we calculated several variables from the
 analytical laboratory results: the total concentration of dissolved
 inorganic nitrogen (DIN), dissolved organic nitrogen (DON), particulate
 nitrogen (PN) dissolved organic phosphorous (DOP), and particulate
 phosphorous (PP). Finally, we computed the mass ratio of sodium to calcium
 ions (Na:Ca) as a simple index of cation origin. High Na:Ca ratios can be
 caused by high inputs of cyclic marine salts (<em>via
 </em>precipitation) relative to cation inputs from rock weathering
 (Gibbs, 1970; Schlesinger, 1997) and by high inputs from silicate
 weathering relative to carbonate weathering (Gaillardet and others, 1999;
 Tank and others, 2012a). Several quality control (QC)
 and data cleaning procedures were implemented prior to the analysis, using
 a combination of visual inspection and data-based criteria. For visual
 inspection, tables and plots of the water quality measurements were
 examined while cross referencing metadata from field notes and laboratory
 notes. We omitted any suspiciously high or low values that could be
 readily explained by a procedural anomaly such as a cracked sample vial.
 For data-based QC, outlier values of sensitive species (DIN species, TN,
 and SRP) were identified (mean ± 4SD) and omitted unless supported by
 independent measurements (e.g., high DIN supported by high TDN and high
 TN). Additional quality control procedures were applied to calculated
 values to avoid use of illogical results. For example, where DIN &gt;
 TDN, the resulting negative DON value was replaced with ½ the detection
 limit of TDN to indicate a small non-zero quantity. We also omitted
 samples where specific conductance exceeded 200 µS
 cm<sup>-1</sup>, which are suspiciously high for the
 geological conditions we sampled. These samples also had high
 concentrations of Na<sup>+</sup>, K<sup>+</sup>,
 Cl<sup>-</sup>/SO<sub>4</sub><sup>2-</sup>, or Sr<sup>2+</sup> (where available), likely due to tidal mixing of brackish water. We identified seven such cases, representing five site-dates. Please refer to the corresponding research paper for a more complete description of methods: https://doi.org/10.1007/s10021-025-00964-x
description: # Data from: Mapping the spatial heterogeneity of watershed ecosystems and
 water quality in rainforest fjordlands
 [https://doi.org/10.5061/dryad.qv9s4mwp6](https://doi.org/10.5061/dryad.qv9s4mwp6) ## Description of the data and file structure This data package corresponds to a research paper by Giesbrecht et. al. (2025) in the journal *Ecosystems* with the title "Mapping the spatial heterogeneity of watershed ecosystems and water quality in rainforest fjordlands". [https://doi.org/10.1007/s10021-025-00964-x](https://doi.org/10.1007/s10021-025-00964-x) ### Files and variables #### The data package contains: 1. A geospatial dataset representing sampled watershed boundaries in .shp format ("G2025_Wts_boundaries.shp"). The .shp is contained within a .zip compressed archive. 2. Table of watershed characteristics in .csv format ("G2025_Wts_data.csv") 3. Table of quality controlled water quality data in .csv format ("G2025_WQ_data.csv") 4. A README file with variable definitions for water quality data ("G2025_WQ_README.csv") 5. A README file with variable definitions for watershed characteristics ("G2025_Wts_README.csv") The two README files define variables, data sources, and units. Missing values are indicated using "NaN". ## Code/software The .csv files can be viewed using free software such as R. The .shp file can be viewed using free software such as R or QGIS. ## Access information External data sources are defined in the published paper and the README files.