<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>42</volume><submitter>Wilkins A</submitter><funding>CSIRO</funding><funding>ACARP</funding><pubmed_abstract>Chloride deposition-rate measurements at points within Australia are upscaled to the entire continent on a regular 0.05° grid. The upscaling uses a double-exponential correlation between deposition rate and distance to the coast, where the parameters in the double-exponential are spatially varying. These parameters are estimated using least-squares with Tikhonov regularisation to ensure minimal spatial variability. A calibration-constrained, null-space Monte-Carlo analysis is used to quantify uncertainty in the prediction. The resulting dataset consists of the best-fit chloride deposition rates across Australia, as well as estimates of uncertainty. The dataset can be used for various purposes including: estimating groundwater recharge through the use of the chloride mass-balance method; catchment salt balance estimates; regional investigations of groundwater hydrochemistry; and, corrosion prediction.</pubmed_abstract><journal>Data in brief</journal><pagination>108189</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9065633</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Australian gridded chloride deposition-rate dataset.</pubmed_title><pmcid>PMC9065633</pmcid><pubmed_authors>Davies P</pubmed_authors><pubmed_authors>Louth-Robins T</pubmed_authors><pubmed_authors>Dawes W</pubmed_authors><pubmed_authors>Gao L</pubmed_authors><pubmed_authors>Crosbie R</pubmed_authors><pubmed_authors>Raiber M</pubmed_authors><pubmed_authors>Wilkins A</pubmed_authors></additional><is_claimable>false</is_claimable><name>Australian gridded chloride deposition-rate dataset.</name><description>Chloride deposition-rate measurements at points within Australia are upscaled to the entire continent on a regular 0.05° grid. The upscaling uses a double-exponential correlation between deposition rate and distance to the coast, where the parameters in the double-exponential are spatially varying. These parameters are estimated using least-squares with Tikhonov regularisation to ensure minimal spatial variability. A calibration-constrained, null-space Monte-Carlo analysis is used to quantify uncertainty in the prediction. The resulting dataset consists of the best-fit chloride deposition rates across Australia, as well as estimates of uncertainty. The dataset can be used for various purposes including: estimating groundwater recharge through the use of the chloride mass-balance method; catchment salt balance estimates; regional investigations of groundwater hydrochemistry; and, corrosion prediction.</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Jun</publication><modification>2025-04-18T12:12:01.95Z</modification><creation>2025-04-06T21:49:44.231Z</creation></dates><accession>S-EPMC9065633</accession><cross_references><pubmed>35515987</pubmed><doi>10.1016/j.dib.2022.108189</doi></cross_references></HashMap>