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