biostudies-literatureUnknown63(4)Levy ZFU.S. Geological Survey New York Water Science CenterDepartment of Water ResourcesCalifornia Department of Water ResourcesCalifornia State Water Resources Control BoardMissing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in "target" records by linear regression using more complete "reference" records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptGround water595-610https://www.ebi.ac.uk/biostudies/studies/S-EPMC12272003biostudies-literatureARCHI: A New R Package for Automated Imputation of Regionally Correlated Hydrologic Records.PMC12272003Stagnitta TJGlas RLTerry NLevy ZFfalseARCHI: A New R Package for Automated Imputation of Regionally Correlated Hydrologic Records.Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in "target" records by linear regression using more complete "reference" records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-accept2025-01-01T00:00:00Z2025 Jul-Aug2026-07-15T09:00:32.212Z2025-08-23T03:09:19.319ZS-EPMC122720034001909210.1111/gwat.13474