dankovacek / hysets_basin_delineation

Replication of basin polygons from the HYSETS dataset.

BC Streamflow Monitoring Network Analysis

Introduction

The importance of validating inputs to any modelling effort is important. Large sample hydrometric datasets have a key role as input in hydrological studies (Addor et al. 2020; Gupta et al. 2014; Klingler, Schulz, and Herrnegger 2021) in both process-based and machine learning approaches. Kratzert et al. (2019) used the large sample hydrologic dataset CAMELS (Addor et al. 2017) in a machine learning model for rainfall-runoff prediction in ungauged basins. Other efforts in generating large sample datasets attribute sets similar to CAMELS quickly followed (Alvarez-Garreton et al. 2018; Coxon et al. 2020; Fowler et al. 2021).

Arsenault et al. (2020) developed HYSETS, a large-sample dataset of over 14K hydrometric monitoring stations in North America and Mexico. This repo presents a method of replicating the attributes derived for the Arsenault et al. (2020) study, from collecting the source spatial data, to deriving catchments, to deriving basin attributes and comparing to the values derived in the study.

Basin delineation is critical to acquiring representative values for many attribute indices, and here we discuss several challenges in automated basin delineation and highlight the information added to the process manually by experienced GIS practitioners, geographers, and hydrologists.

Notes

Setup

See the README.md under setup_scripts/ for setup of the validation scripting.

References

Addor, Nans, Hong X Do, Camila Alvarez-Garreton, Gemma Coxon, Keirnan Fowler, and Pablo A Mendoza. 2020. “Large-Sample Hydrology: Recent Progress, Guidelines for New Datasets and Grand Challenges.” Hydrological Sciences Journal 65 (5): 712–25.

Addor, Nans, Andrew J Newman, Naoki Mizukami, and Martyn P Clark. 2017. “The Camels Data Set: Catchment Attributes and Meteorology for Large-Sample Studies.” Hydrology and Earth System Sciences 21 (10): 5293–5313.

Alvarez-Garreton, Camila, Pablo A Mendoza, Juan Pablo Boisier, Nans Addor, Mauricio Galleguillos, Mauricio Zambrano-Bigiarini, Antonio Lara, et al. 2018. “The Camels-Cl Dataset: Catchment Attributes and Meteorology for Large Sample Studies–Chile Dataset.” Hydrology and Earth System Sciences 22 (11): 5817–46.

Arsenault, Richard, François Brissette, Jean-Luc Martel, Magali Troin, Guillaume Lévesque, Jonathan Davidson-Chaput, Mariana Castañeda Gonzalez, Ali Ameli, and Annie Poulin. 2020. “A Comprehensive, Multisource Database for Hydrometeorological Modeling of 14,425 North American Watersheds.” Scientific Data 7 (1): 1–12.

Barnes, Richard. 2016. “Parallel Priority-Flood Depression Filling for Trillion Cell Digital Elevation Models on Desktops or Clusters.” Computers & Geosciences 96: 56–68.

Coxon, Gemma, Nans Addor, John P Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas JK Howden, et al. 2020. “CAMELS-Gb: Hydrometeorological Time Series and Landscape Attributes for 671 Catchments in Great Britain.” Earth System Science Data 12 (4): 2459–83.

Fowler, Keirnan JA, Suwash Chandra Acharya, Nans Addor, Chihchung Chou, and Murray C Peel. 2021. “CAMELS-Aus: Hydrometeorological Time Series and Landscape Attributes for 222 Catchments in Australia.” Earth System Science Data 13 (8): 3847–67.

Gleeson, Tom, Nils Moosdorf, Jens Hartmann, and LPH Van Beek. 2014. “A Glimpse Beneath Earth’s Surface: GLobal Hydrogeology Maps (Glhymps) of Permeability and Porosity.” Geophysical Research Letters 41 (11): 3891–8.

Gupta, Hoshin Vijai, C Perrin, G Blöschl, A Montanari, R Kumar, M Clark, and Vazken Andréassian. 2014. “Large-Sample Hydrology: A Need to Balance Depth with Breadth.” Hydrology and Earth System Sciences 18 (2): 463–77.

Huscroft, Jordan, Tom Gleeson, Jens Hartmann, and Janine Börker. 2018. “Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal Hydrogeology Maps 2.0 (Glhymps 2.0).” Geophysical Research Letters 45 (4): 1897–1904.

Klingler, Christoph, Karsten Schulz, and Mathew Herrnegger. 2021. “LamaH-Ce: LArge-Sample Data for Hydrology and Environmental Sciences for Central Europe.” Earth System Science Data 13 (9): 4529–65.

Kratzert, Frederik, Daniel Klotz, Mathew Herrnegger, Alden K Sampson, Sepp Hochreiter, and Grey S Nearing. 2019. “Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning.” Water Resources Research 55 (12): 11344–54.

Robinson, Natalie, James Regetz, and Robert P Guralnick. 2014. “EarthEnv-Dem90: A Nearly-Global, Void-Free, Multi-Scale Smoothed, 90m Digital Elevation Model from Fused Aster and Srtm Data.” ISPRS Journal of Photogrammetry and Remote Sensing 87: 57–67.