Time to Update the Split-Sample Approach in Hydrological Model Calibration v1.1

Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly-used split-sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large-sample SST assessment study empirically assesses how different data splitting methods influence post-validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of the continuous calibration sub-periods (CSPs). A full-period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35-year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split-sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split-sample approach in hydrological modeling

All versions: https://doi.org/10.5281/zenodo.5915373

Dataset v1.1:
H. Shen, B. A. Tolson, and J. Mai (2022). Time to Update the Split-Sample Approach in Hydrological Model Calibration. Zenodo. http://doi.org/10.5281/zenodo.5915374

Article v1.1:
Shen, H., Tolson, B. A., & Mai, J.(2022). Time to update the split-sample approach in hydrological model calibration. Water Resources Research, 58, e2021WR031523. https://doi.org/10.1029/2021WR031523

Original CAMELS dataset v1.0:
A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR. https://dx.doi.org/10.5065/D6MW2F4D

Original Article v1.0:
A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan (2015). Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, http://doi.org/10.5194/hess-19-209-2015

The data folder contains a gauge info file (CAMELS_463_gauge_info.txt), which reports basic information of each catchment, and 463 subfolders, each having four files for a catchment, including:
(1) Raven_Daymet_forcing.rvt, which contains Daymet meteorological forcing (i.e., daily precipitation in mm/d, minimum and maximum air temperature in deg_C, shortwave in MJ/m2/day, and day length in day) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.
(2) Raven_USGS_streamflow.rvt, which contains daily discharge data (in m3/s) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.
(3) GR4J_metrics.txt, which contains reference KGE and GR4J-based KGE metrics in calibration, validation and testing periods.
(4) HMETS_metrics.txt, which contains reference KGE and HMETS-based KGE metrics in calibration, validation and testing periods.

Version 1.1
https://zenodo.org/record/6578924#.ZDiNauzMJHY

Version 1.0