NicholasThirosLawrence Berkeley National Laboratorynthiros@lbl.gov https://orcid.org/0000-0002-1704-1031 EricaSiirila-WoodburnLawrence Berkeley National Laboratoryerwoodburn@lbl.gov https://orcid.org/0000-0001-9406-124X KennethWilliamsLawrence Berkeley National Laboratorykhwilliams@lbl.gov https://orcid.org/0000-0002-3568-1155 MatthiasSprengerLawrence Berkeley National Laboratorymsprenger@lbl.gov https://orcid.org/0000-0003-1221-2767 RosemaryCarrollDesert Research InstituteRosemary.Carroll@dri.edu https://orcid.org/0000-0002-9302-8074 PaytonGardnerUniversity of Montanapayton.gardner@mso.umt.edu https://orcid.org/0000-0003-0664-001X JamesDennedy-FrankNortheastern Universitypjdf@northeastern.edu https://orcid.org/0000-0001-9148-647X U.S. DOE > Office of Science > Biological and Environmental Research (BER) http://dx.doi.org/10.13039/100006206 fundingOrganization 2024-03-27 The partitioning of water inputs between deep and shallow groundwater flow paths is a fundamental processes, yet is challenging to observe. Numerical models provide a valuable tool to further develop insights on these groundwater mixing processes. This repository contains scripts to run the ParFlow-CLM and EcoSLIM integrated hydrologic models along the Pumphouse Hillslope in the East River Watershed and associated python scripts to process model outputs. Files includes input decks for the associated models, python scripts, and text and css files to support the model runs and processing. The 2-D hillslope model simulates from 2000 to 2021 using transient forcing conditions from the publicly available NLDAS-2 dataset. We develop an ensemble of models with variable hydrogeologic and soil parameters. Model outputs from the ensemble of runs are compared to to water level observations at the PLM1 and PLM6 wells published at (https://data.ess-dive.lbl.gov/view/doi:10.15485/1866836) and groundwater mean ages published at (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1960042. The model results are used to evaluate hillslope water mass-balance transience and mixing dynamics between groundwater flow paths with young (<10 year) and old (>10 year) ages.Sensitivity AnalysisHydrologic ModelCATEGORICAL:NONEEARTH SCIENCE > TERRESTRIAL HYDROSPHERE > GROUND WATEREARTH SCIENCE > LAND SURFACE > SOILSCATEGORICAL:GCMDResidence TimesVARIABLE:NONEEARTH SCIENCE > TERRESTRIAL HYDROSPHERE > GROUND WATER > GROUND WATER FEATURES > WATER TABLE > WATER TABLE DEPTHVARIABLE:GCMD

Groundwater Age Analysis data package:Thiros N ; Siirila-Woodburn E ; Dennedy-Frank P J ; Williams K ; Gardner W P (2023): Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package. Watershed Function SFA, ESS-DIVE repository. Dataset. doi:10.15485/1960042 accessed via https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1960042 on 2024-03-27Groundwater Level Observations:Faybishenko B ; Versteeg R ; Williams K ; Carroll R ; Dong W ; Tokunaga T ; O'Ryan D (2023): QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado (2016-2022). Watershed Function SFA, ESS-DIVE repository. Dataset. doi:10.15485/1866836 accessed via https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1866836 on 2024-03-27Additional metadata on specific locations within the watershed are provided in the following related data package:Varadharajan C ; Burrus M ; O'Ryan D ; Kakalia Z ; Alper E ; Banfield J ; Berkelhammer M ; Beutler C ; Brodie E ; Brown W ; Carbone M S ; Carroll R ; Christianson D ; Chou C ; Crystal-Ornelas R ; Chadwick K D ; Christensen J ; Dafflon B ; de Boer G ; Elbashandy H ; Enquist B J ; Feldman D ; Fox P ; Gilbert B ; Gochis D ; Henderson M ; Johnson D ; Kueppers L ; Li L ; Matheus Carnevali P ; Newman A ; Powell T ; Singha K ; Sorensen P ; Sprenger M ; Tokunaga T ; Versteeg R ; Wilkins M ; Williams K ; Worsham M ; Wong C ; Wu Y ; Zhang D ; Agarwal D (2023): Location Identifiers, Metadata, and Map for Field Measurements at the East River Watershed, Colorado, USA (Version 3.1). Watershed Function SFA, ESS-DIVE repository. Dataset. doi:10.15485/1660962

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. 2000-10-012021-09-30 The East River (ER) is a snow‐dominated, headwater basin of the Upper Colorado River Basin (UCRB) located in the western United States. The ER is the designated testbed of Lawrence Berkeley National Laboratory's Watershed Function Scientific Focus Area (WFSFA). Through WFSFA, observational networks have been established to measure stream discharge and precipitation chemistry. The ER is considered representative of many snow‐dominated headwaters of the Rocky Mountains. The study domain encompasses nearly 85 square km, a 1.4‐km vertical drop in elevation (4,120 to 2,760 m) and pristine alpine, subalpine, montane, and riparian ecosystems. The ER contains high‐energy mountain streams to low‐energy meandering floodplains and is eroding primarily into the Cretaceous, carbon‐rich, marine shale of the Mancos Formation. Additional metadata on specific locations within the watershed are provided in the following related data package: Varadharajan C. et al. (2022) doi:10.15485/1660962 -107.05-106.8839.03438.88 https://schema.org/archivedAthttps://doi.org/10.5281/zenodo.8305993 NicholasThirosLawrence Berkeley National Laboratorynthiros@lbl.gov https://orcid.org/0000-0002-1704-1031 Watershed Function SFA The first step in the hydrologic and particle tracking modeling setup is generation of the physical domain, spatial and temporally boundary conditions, and spatially variable hydrologic and geologic properties. All of these steps are automatized in provided python scripts and can be modified. The physical domain requires a land-surface digital elevation model and discretization variables in the x and y directions. Land surface boundary conditions are constructed from the NLDAS-2 meteorologic forcing dataset. Geologic properties include hydraulic conductivity, porosity, and soil-retention parameters. The second step is to simulate integrated hydrologic dynamics with the ParFlow-CLM numerical model. In this work we leverage high-performance computation resources. The model is solved many times with variable realizations of the hydraulic conductivity. Model outputs are saved for further processing. The EcoSLIM particle tracking model is used to simulate advective transport throughout the hillslope model. This involves using the outputs from the above ParFlow-CLM model. All EcoSLIM models use the same configuration.Lastly, we compare model predictions with observed groundwater levels and estimated mean groundwater ages. This analysis relies on sum-squared error metrics to quantify matches between modeled and observed groundwater levels at the PLM1 and PLM6 wells. Observed and modeled groundwater ages are qualitatively compared to eachother. Eoin Brodie Lawrence Berkeley National Laboratory elbrodie@lbl.gov principalInvestigator Thiros_2023_PLM_Model.zip application/zip