Data from: Watershed classification predicts streamflow regime and organic carbon dynamics in the Northeast Pacific Coastal Temperate Rainforest

Watershed classification has long been a key tool in the hydrological sciences, but few studies have been extended to biogeochemistry. We developed a combined hydro-biogeochemical classification for watersheds draining to the coastal margin of the Northeast Pacific coastal temperate rainforest (1,443,062 km2), including 2,695 small coastal rivers (SCR) and 10 large continental watersheds. We used cluster analysis to group SCR watersheds into 12 types, based on watershed properties. The most important variables for distinguishing SCR watershed types were evapotranspiration, slope, snowfall, and total precipitation. We used both streamflow and dissolved organic carbon (DOC) measurements from rivers (n = 104 and 90 watersheds respectively) to validate the classification. Watershed types corresponded with broad differences in streamflow regime, mean annual runoff, DOC seasonality, and mean DOC concentration. These links between watershed type and river conditions enabled the first region-wide empirical characterization of river hydro-biogeochemistry at the land-sea margin, spanning extensive ungauged and unsampled areas. We found very high annual runoff (mean > 3000 mm, n = 10) in three watershed types totaling 59,024 km2 and ranging from heavily glacierized mountain watersheds with high flow in summer to a rain-fed mountain watershed type with high flow in fall-winter. DOC hotspots (mean > 4 mg L-1, n = 14) were found in three other watershed types (48,557 km2) with perhumid rainforest climates and less-mountainous topography. We described four patterns of DOC seasonality linked to watershed hydrology, with fall-flushing being widespread. Hydro-biogeochemical watershed classification may be useful for other complex regions with sparse observation networks.This data package contains several datasets corresponding with the manuscript Watershed classification predicts streamflow regime and organic carbon dynamics in the Northeast Pacific Coastal Temperate Rainforest, in review at Global Biogeochemical Cycles (Giesbrecht et al., 2022). 1. Watershed properties including the watershed type defined by cluster analysis 2. Metadata for streamflow gauging station assessment and selection 3. Streamflow data from gauging stations 4. Metadata for Dissolved Organic Carbon (DOC) site assessment and selection 5. DOC data from river observation sites 6. Streamflow hydrographs for individual watersheds 7. Monthly DOC concentration plots for individual watersheds All variables are defined in the README file. For full details, see the corresponding manuscript and the source datasets. These files include both primary data sources and processed data derived from other publicly accessible data sources. The source is named for each case of streamflow or DOC data. Funding provided by: Tula FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100014446Award Number: Funding provided by: Simon Fraser UniversityCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004326Award Number: Funding provided by: Natural Sciences and Engineering Research Council of CanadaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000038Award Number:For a complete description of methods, please see the manuscript Watershed classification predicts streamflow regime and organic carbon dynamics in the Northeast Pacific Coastal Temperate Rainforest, in review at Global Biogeochemical Cycles (Giesbrecht et al., 2022). Watershed polygons were taken from Gonzalez Arriola et al. (2018) which can be accessed through the Related Works section below. Watershed properties were extracted from published geospatial datasets that were freely available, peer-reviewed, and spatially consistent across all three geopolitical regions with respect to variable definitions and estimation approaches. Source datasets are defined in the README file. We used two separate approaches to place all watersheds of the NPCTR margin into discrete groups or types. First, we set aside 10 large continental watersheds because we expected them to be fundamentally different from the SCR watersheds in terms of climate, terrain, landcover, and catchment controls on river hydro-biogeochemistry. Second, we grouped the remaining 2,695 SCR watersheds into 12 statistically similar clusters using k-means partitioning. The watershed types were subsequently assigned descriptive labels that reflect their defining characteristics. Discharge data were acquired for near-outlet gauging stations in 104 independent watersheds. To achieve the maximum spatial coverage, we included all active and deactivated gauges that met basic quality criteria. We considered all gauges operated by the Water Survey of Canada (WSC) and the US Geological Survey (USGS). We compiled a dataset of 4,674 riverine DOC concentration (mgL-1) measurements at near-outlet stream locations in 90 watersheds. We omitted sites that were clearly unrepresentative of the watershed outlet (e.g., headwaters) and sites with obviously poor data quality or small sample size. The DOC dataset was derived from an exhaustive search of six different water-quality databases