{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T16:03:52Z","timestamp":1789488232970,"version":"build-2803163510"},"reference-count":48,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ngwa.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Groundwater"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In the past decade the groundwater modeling industry has trended toward more computationally intensive methods that necessarily require more parallel computing power due to the number of model runs required for these methods. Groundwater modeling that requires many parallel model runs is often limited by numerical burden or by the modeler's access to computational resources. Over the last 15\u2009years the evolution of the cloud in accelerating groundwater model solutions has progressed; however, there are no apparent literature reviews of MODFLOW and PEST cloud implementation, specifically with regards to open\u2010source and efficient scalable solutions. Here we describe infrastructure as code used to develop the architecture for running PEST++ in parallel on the cloud using Docker containers and open\u2010source software to allow simple and repeatable cloud execution. The architecture utilizes Amazon Web Services and Terraform to facilitate cloud deployment and monitoring. A publicly available MODFLOW\u20106 model was used to evaluate parallel performance locally and in the cloud. Local model runs were found to have a linear 12\u2009s increase in model run time per agent on a typical office computer compared to the cloud implementation's 0.02\u2009s per model, indicating near perfect scaling even at up to 200 concurrent model runs. A consulting groundwater model was calibrated with the cloud infrastructure, which enabled acceleration of project completion at minimal cost.<\/jats:p>","DOI":"10.1111\/gwat.70052","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T12:25:25Z","timestamp":1770035125000},"page":"350-359","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improvements on Scalable and Reproducible Cloud Implementation of Numerical Groundwater Modeling"],"prefix":"10.1111","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6207-0361","authenticated-orcid":false,"given":"Martin","family":"Roth","sequence":"first","affiliation":[{"name":"Geomega  2585 Central Avenue Boulder CO 80301"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jared","family":"Grove","sequence":"additional","affiliation":[{"name":"Geomega  2585 Central Avenue Boulder CO 80301"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andy","family":"Davis","sequence":"additional","affiliation":[{"name":"Geomega  2585 Central Avenue Boulder CO 80301"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Cornell","sequence":"additional","affiliation":[{"name":"5E Advanced Materials, Inc.  9329 Mariposa Road, Suite 210 Hesperia California 92344"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Amazon Web Services. n.d.\u2010a.Amazon EC2 on\u2010demand instance pricing.https:\/\/aws.amazon.com\/ec2\/pricing\/on\u2010demand\/(accessed June 26 2025)."},{"key":"e_1_2_10_3_1","unstructured":"Amazon Web Services. n.d.\u2010b.Amazon Web Services (AWS).https:\/\/aws.amazon.com\/."},{"key":"e_1_2_10_4_1","unstructured":"Amazon Web Services. n.d.\u2010c.Array jobs\u2014AWS Batch user guide.https:\/\/docs.aws.amazon.com\/batch\/latest\/userguide\/array_jobs.html."},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/gwat.12413"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-5026-8_1"},{"key":"e_1_2_10_7_1","unstructured":"Brush C.2015.Cloud computing for groundwater modeling. Presentation at the California Water and Environmental Modeling Forum Annual Meeting 2015.https:\/\/www.cwemf.org\/AMPresentations\/2015\/CBrush_pres.pdf."},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2012.01.007"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.06.007"},{"key":"e_1_2_10_10_1","unstructured":"DHI.2025.FEFLOW Help. Cloud parallelization\u2014Microsoft Azure. FePEST 8.1 Documentation.https:\/\/download.feflow.com\/html\/help81\/fepest\/08_Parallelization\/cloud_azure.htm."},{"key":"e_1_2_10_11_1","unstructured":"Docker Inc. n.d.Docker: Empowering app development for developers. Docker.https:\/\/www.docker.com\/(accessed June 26 2025)."},{"key":"e_1_2_10_12_1","volume-title":"PEST, Model\u2010Independent Parameter Estimation: User Manual","author":"Doherty J.","year":"2018"},{"key":"e_1_2_10_13_1","doi-asserted-by":"crossref","unstructured":"Doherty J. M.N.Fienen andR.J.Hunt.2010.Approaches to highly parameterized inversion: pilot\u2010point theory guidelines and research directions. US Geological Survey Scientific Investigations Report 2010\u20135168.","DOI":"10.3133\/sir20105168"},{"key":"e_1_2_10_14_1","doi-asserted-by":"crossref","unstructured":"Doherty J. andR.J.Hunt.2010.Approaches to highly parameterized inversion: a guide to using PEST for groundwater\u2010model calibration. US Geological Survey Scientific Investigations Report 2010\u20135169.","DOI":"10.3133\/sir20105169"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/gwat.12320"},{"key":"e_1_2_10_16_1","doi-asserted-by":"crossref","unstructured":"Fienen M.N. T.C.Kunicki andD.E.Kester.2011.cloudPEST\u2014a Python module for cloud\u2010computing deployment of PEST a program for parameter estimation. 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