Fienen, Michael N. Long, Andrew J. Markovich, Katherine H. Haj, Adel E. Barker, Matthew I. 2025 publication https://doi.org/10.5066/P13KUGYI History matching of large hydrologic models is challenging due to data sparsity and non-unique process combinations (and associated parameters) that can produce similar model predictions. We developed an ensemble-based history matching and uncertainty quantification approach using an iterative ensemble smoother (iES) method for three cutouts of the National Hydrologic Model (NHM) and qualitatively compared the results and performance to the stepwise history matching approach. In the latter approach, subsets of parameters and observations were sequentially calibrated to a diverse range of observations to mitigate non-uniqueness and local minima. In iES, localization simulates the same causal connections between parameters and observations without the need (and computational cost) of sequential history matching steps. iES uses a weighted sum-of-squared errors objective function which allows differential weighting of multiple data sources. Formal adoption of range observation also pushes results to within ranges of observation values rather than discrete values. Overall, the ensemble approach performs similarly to the stepwise approach. Both approaches performed poorly for the cutout representing a snowmelt-dominated watershed, indicating a structural issue in the process representation of the model. The main advantage of iES is quantification of uncertainty in both the history matching and the predictions of interest. Plain language summary: Parameter estimation, or history matching, for large hydrologic models to best align with real-world observations is difficult because there are often limited data and many ways to adjust the parameters that can produce similar results. This study tests a statistical approach, called the iterative ensemble smoother (iES), in three National Hydrologic Model cutouts. The iES method is compared with a traditional step-by-step parameterization approach. Both methods produced similar results overall, though both struggled in a watershed where snowmelt plays a major role, suggesting the model needs improvement there. The main advantage of the iES approach is that it provides an estimate of uncertainty, which helps scientists better understand how confident they can be in both the history matching and its predictions. Workflow for connecting PEST++ with the pywatershed implementation of the National Hydrologic Model for parameter estimation and uncertainty analysis. 19991001 20090930 Simulation Time Complete None planned -125.0000 -66.9000 49.0000 24.5000 None uncertainty analysis python PEST++ parameter estimation uncertainty analysis pywatershed iES National Hydrologic Model NHM USGS Metadata Identifier USGS:6931dc50d4be024058c06064 None Conterminous United States Turtle Creek in Wisconsin Perkiomen Creek in Wisconsin East River in Colorado No access constraints. Please see 'Distribution Information' for details. These data are marked with a Creative Commons Zero v1.0 Universal (CC0-1.0) public domain dedication and do not have any use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Michael N. Fienen U.S. Geological Survey, MIDCONTINENT REGION Research Hydrologist mailing address

Mail Stop Office 164, One Gifford Pinchot Drive

Madison WI 53726 US 608-821-3894 mnfienen@usgs.gov Formal history matching was performed to estimate model parameters that result in adequate correspondence between model outputs and observations of streamflow and ranges of other water budget components collocated in space and time. This data release was reviewed to make sure that the output files herein produce the outputs described in the report, and that the files in this archive can be run to produce the same output as was used in the report This data release presents all the inputs needed to run the PEST++ workflow to generate files needed to run PEST++. Parallel runs must be run on a high-throughput or high-performance computing resource. Ultimate PEST++ outputs are also provided for comparison with reported results. A formal accuracy assessment of the horizontal positional information in the data set has not been conducted. A formal accuracy assessment of the vertical positional information in the data set has not been conducted. Model input files were generated using custom python codes that are included in this data release. This workflow consists of: 1) a series of scripts to run the pywatershed model and to extract and post-process results from the model to align with observations. 2) a series of notebooks to connect the pywatershed model to iES. 3) a combination of scripts and notebooks to post-process and visualize results from iES. View included README for detailed instructions. 2024 U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov The data have been approved for release by the U.S. Geological Survey (USGS). Although the data have been subjected to rigorous review and are substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, the data are released on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from authorized or unauthorized use. Although the data, software, and related material have been processed successfully on a computer system at the USGS, no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. The USGS or the U.S. Government shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. figures.zip none zipfile Additional plots of all history matching results in PDF form. 41.5 https://www.sciencebase.gov/catalog/item/6931dc50d4be024058c06064?name=figures.zip None scripts.zip none zipfile Supporting Python scripts used for both pre- and post-processing files for PEST. 0.03549 https://www.sciencebase.gov/catalog/item/6931dc50d4be024058c06064?name=scripts.zip None notebooks.zip none zipfile Six jupyter notebooks used to create input files for PEST++. 0.05881 https://www.sciencebase.gov/catalog/item/6931dc50d4be024058c06064?name=notebooks.zip None NHM_extractions.zip none zipfile Starting cutout models extracted from the NHM. 10.5 https://www.sciencebase.gov/catalog/item/6931dc50d4be024058c06064?name=NHM_extractions.zip None NHM_extractions_PST_reference.zip none zipfile PST files generated from the notebooks, meant to be run with pestpp-ies. 4.27 https://www.sciencebase.gov/catalog/item/6931dc50d4be024058c06064?name=NHM_extractions_PST_reference.zip None Supporting_information.zip none zipfile Six supporting files used by the notebooks that contain tabular information. 0.00364 https://www.sciencebase.gov/catalog/item/6931dc50d4be024058c06064?name=Supporting_information.zip None results.zip none zipfile Result files from pest-ies for local processing. 896.08 https://www.sciencebase.gov/catalog/item/6931dc50d4be024058c06064?name=results.zip None README.md none markdown README file outlining contents of this data release. 0.0012 https://www.sciencebase.gov/catalog/item/6931dc50d4be024058c06064?name=README.md None 20251222 Michael N. Fienen U.S. Geological Survey, MIDCONTINENT REGION Research Hydrologist mailing address

Mail Stop Office 164, One Gifford Pinchot Drive

Madison WI 53726 US 608-821-3894 mnfienen@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998