{"id":6,"date":"2017-10-11T10:32:19","date_gmt":"2017-10-11T08:32:19","guid":{"rendered":"http:\/\/wp.unil.ch\/gaia\/?page_id=6"},"modified":"2025-02-04T15:04:51","modified_gmt":"2025-02-04T14:04:51","slug":"6-2","status":"publish","type":"page","link":"https:\/\/wp.unil.ch\/gaia\/","title":{"rendered":"Overview"},"content":{"rendered":"<p>The main research interests of the <a href=\"https:\/\/wp.unil.ch\/gaia\/team\/\">research group<\/a> reside in the d<a href=\"https:\/\/wp.unil.ch\/gaia\/team\/\"><img alt=\"\" loading=\"lazy\" decoding=\"async\" class=\" wp-image-42 alignright\" src=\"https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image005-300x169.jpg\" alt=\"\" width=\"375\" height=\"212\" srcset=\"https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image005-300x169.jpg 300w, https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image005-768x432.jpg 768w, https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image005-1024x576.jpg 1024w, https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image005-391x220.jpg 391w, https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image005.jpg 1920w\" sizes=\"auto, (max-width: 375px) 100vw, 375px\" \/><\/a>evelopment of methods to understand the variability inherent to natural systems. We use advanced spatio-temporal modeling to study problems related to water resources and climate change.<\/p>\n<p>The statistical and machine learning approaches we develop are leveraging large amounts of data such as those coming from satellite observations or climare models. As such, we work at the frontier between Environmental Science and Computer Science, with a strong emphasis on stochastic models, training images and analog-based modeling.\u00a0Our contributions include <a href=\"https:\/\/wp.unil.ch\/gaia\/research\/\">algorithms<\/a> to perform missing data reconstruction and stochastic simulations, broadly known as\u00a0 <a href=\"https:\/\/wp.unil.ch\/gaia\/mps\/\">Multiple-Point Statistics (MPS)<\/a>. Please check out the <a href=\"https:\/\/eu.wiley.com\/WileyCDA\/WileyTitle\/productCd-111866275X.html\">book<\/a> on this topic.<\/p>\n<p>The main research themes are:<img alt=\"\" loading=\"lazy\" decoding=\"async\" class=\" wp-image-95 alignright\" src=\"https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image094.gif\" alt=\"\" width=\"358\" height=\"169\" \/><\/p>\n<ul>\n<li><span style=\"text-decoration: underline\">Remote Sensing<\/span> (Image processing, gap-filling, data fusion, statistical downscaling, pattern analysis).<\/li>\n<li><span style=\"text-decoration: underline\">Geostatistics<\/span> (Multiple-point geostatistics as well as variogram-based geostatistics, training images, model inference, spatial variability, texture synthesis, parallel computing).<\/li>\n<li><span style=\"text-decoration: underline\">Climate<\/span> (rainfall measurement and uncertainty quantification, climate change indicators, predictions from multiple climate models).<\/li>\n<li><span style=\"text-decoration: underline\">Hydrology<\/span> (Spatio-temporal rainfall analysis, rainfall-runoff modeling, rainfall measurement, time series analysis, water resources management using Agent-Based Models).<\/li>\n<li><span style=\"text-decoration: underline\">Hydrogeology<\/span> (Aquifer heterogeneity, flow and transport modeling, inverse problems, pore-scale models, karst infiltration processes, groundwater usage optimization).<img alt=\"\" loading=\"lazy\" decoding=\"async\" class=\"wp-image-44 aligncenter\" src=\"https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image009-300x135.png\" alt=\"\" width=\"455\" height=\"204\" srcset=\"https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image009-300x135.png 300w, https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image009-489x220.png 489w, https:\/\/wp.unil.ch\/gaia\/files\/2017\/12\/image009.png 676w\" sizes=\"auto, (max-width: 455px) 100vw, 455px\" \/><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The main research interests of the research group reside in the development of methods to understand the variability inherent to natural systems. We use advanced spatio-temporal modeling to study problems related to water resources and climate change. The statistical and machine learning approaches we develop are leveraging large amounts of data such as those coming &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/wp.unil.ch\/gaia\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Overview&#8221;<\/span><\/a><\/p>\n","protected":false},"author":108,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"class_list":["post-6","page","type-page","status-publish"],"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages\/6","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/users\/108"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/comments?post=6"}],"version-history":[{"count":3,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages\/6\/revisions"}],"predecessor-version":[{"id":1491,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages\/6\/revisions\/1491"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/media?parent=6"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}