{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:16:53Z","timestamp":1760231813995,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["DMS-1555072","DMS-1736364","DMS-2053746","DMS-2134209","382247","DE-SC0021142"],"award-info":[{"award-number":["DMS-1555072","DMS-1736364","DMS-2053746","DMS-2134209","382247","DE-SC0021142"]}]},{"DOI":"10.13039\/100006231","name":"Brookhaven National Laboratory Subcontract","doi-asserted-by":"publisher","award":["DMS-1555072","DMS-1736364","DMS-2053746","DMS-2134209","382247","DE-SC0021142"],"award-info":[{"award-number":["DMS-1555072","DMS-1736364","DMS-2053746","DMS-2134209","382247","DE-SC0021142"]}],"id":[{"id":"10.13039\/100006231","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy (DOE) Office of Science Advanced Scientific Computing Research","doi-asserted-by":"publisher","award":["DMS-1555072","DMS-1736364","DMS-2053746","DMS-2134209","382247","DE-SC0021142"],"award-info":[{"award-number":["DMS-1555072","DMS-1736364","DMS-2053746","DMS-2134209","382247","DE-SC0021142"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input\u2013output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities.<\/jats:p>","DOI":"10.3390\/computation10050072","type":"journal-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T13:10:26Z","timestamp":1651756226000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2677-1474","authenticated-orcid":false,"given":"Georgios","family":"Karagiannis","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, Durham University, Stockton Road, Durham DH1 3LE, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9388-6060","authenticated-orcid":false,"given":"Zhangshuan","family":"Hou","sequence":"additional","affiliation":[{"name":"Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA"}]},{"given":"Maoyi","family":"Huang","sequence":"additional","affiliation":[{"name":"National Oceanic and Atmospheric Administration, Washington, DC 20230, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0976-1987","authenticated-orcid":false,"given":"Guang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Mathematics, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA"},{"name":"Department of Statistics (Courtesy), Purdue University, West Lafayette, IN 47907, USA"},{"name":"Department of Earth, Atmospheric, and Planetary Sciences (Courtesy), Purdue University, West Lafayette, IN 47907, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1002\/joc.893","article-title":"The evolution of, and revolution in, land surface schemes designed for climate models","volume":"23","author":"Pitman","year":"2003","journal-title":"Int. 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