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Channel types predictions for the South Fork Eel River basin

Citation

Guillon, Hervé et al. (2020), Channel types predictions for the South Fork Eel River basin , Dryad, Dataset, https://doi.org/10.25338/B8VG83

Abstract

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, we leveraged machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data.

Methods

A bottom-up machine learning approach selects the most accurate and stable model among ~96,000 and derives the relationship between 147 predictors and labels corresponding to regional channel types in a three-tiered framework which: (i) define a tractable problem; assess model performance (ii) in statistical learning; and (iii) in prediction. In the present application to the South Fork Eel River catchment (California, USA), the developed framework selects a Random Forest model to predict 7 channel types previously determined from 96 field-surveys over 8,022 200-m stream interval.

Performance in statistical learning is high with a 88% median cross-validation accuracy and a 0.98 mean multiclass Area Under Curve value. Furthermore, the predictions coherently capture the expected geomorphic organization of the landscape. As main metric of uncertainty, we include for each stream-segment the entropy calculated from the posterior probabilities output from the machine learning algorithm. For completeness, evenness and richness are also reported.

The predictions included in this dataset corresponds to an aggregated version of the output from the machine learning framework. Each initial 200-m stream interval was aggregated by using their COMIDs from the National Hydrography Dataset corresponding to the most common identifier used by stake-holders. For each resulting NHD stream line, the 200-m scale probabilities associated to each channel types are summed and normalized to sum to one.

Usage Notes

  • Format: shapefile (.shp)
  • Spatial reference:
    • Projection: California Albers
    • Datum: NAD83
    • Units: meters
  • Attributes:
    • COMID: Common identifier of the NHD feature     
    • FDATE: Feature Currency Date     
    • RESOLUTION: Always "Medium"
    • GNIS_ID: Geographic Names Information System ID for the value in GNIS_Name   
    • GNIS_NAME: Feature Name from the Geographic Names Information System
    • LENGTHKM:  Feature length in kilometers
    • REACHCODE: Reach Code assigned to feature
    • FLOWDIR: Flow direction is “WithDigitized” or “Uninitialized”  
    • WBAREACOMI: ComID of an NHD polygonal water feature through which an NHD “Artificial Path” flowline flows
    • FTYPE: NHD Feature Type    
    • FCODE: Numeric codes for various feature attributes in the NHDFCode lookup table    
    • SHAPE_LENG: Feature length in decimal degrees
    • ENABLED: Always "True"   
    • GNIS_NBR: Internal field for data processing
    • group: most probable channel types    
    • shannon: Shannon's entropy calculated from the posterior probabilities  
    • richness: Richness calculated from posterior probabilities
    • evenness: Evenness calculated from posterior probabilities
    • SFE01: posterior probability for channel type SFE01 (i.e. membership)     
    • SFE02: posterior probability for channel type SFE02 (i.e. membership)     
    • SFE03: posterior probability for channel type SFE03 (i.e. membership)     
    • SFE04: posterior probability for channel type SFE04 (i.e. membership)     
    • SFE05: posterior probability for channel type SFE05 (i.e. membership)     
    • SFE06: posterior probability for channel type SFE06 (i.e. membership)     
    • SFE07: posterior probability for channel type SFE07 (i.e. membership) 

 

The seven channel types are:

  • SFE01: Confined high width-to-depth, gravel cobble, rifle-pool
  • SFE02: Unconfined, gravel, riffle-pool
  • SFE03: Confined, gravel-cobble, bed-undulating
  • SFE04: Confined, high width-to-depth, gravel-boulder, uniform
  • SFE05: Confined, low width-to-depth, gravel-cobble, uniform
  • SFE06: Partly-confined, gravel-cobble, uniform
  • SFE07: Confined, high-gradient, cobble-boulder, step-pool/cascade

Funding

California State Water Resources Control Board, Award: 16-062-300

U.S. Department of Agriculture, Award: CA‐D‐LAW‐7034‐H

U.S. Department of Agriculture, Award: CA‐D‐LAW‐2243‐H