[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117333-en":3,"doc-seo-117333-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117333,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Identifying impacts of global climate teleconnection patterns on land water storage using machine learning","Study focuses on modelling global freshwater systems where climate complexity and human influence limit conventional approaches, particularly at local scales. A Gaussian process regression (GPR) routine is built to capture interactions between non-linear climatic predictors and hydrological storage, including surface water and terrestrial water storage (TWS). Twenty-three independent climate variables are tested against satellite TWS observations (Apr 2002–Jun 2017). Results link large TWS fluctuations in the Zambezi Basin to sea surface temperature shifts and global ocean teleconnection patterns, improving interpretation of climate impacts on major hydro-systems.","Identifying impacts of global climate teleconnection patterns on land water storage using machine learning  \nAuthor  \nKalu , Ikechukwu , Ndehedehe , Christopher, Okwuashi , Onuwa , Eyoh , Aniekan , Ferreira , Vagner  \nPublished 2023  \nJournal Title  \nJournal of Hydrology: Regional Studies  \nVersion  \nVersion of Record (VoR)  \nDOI  \n10.1016/j.ejrh.2023.101346  \nRights statement  \n© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BYNC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)) .  \nDownloaded from  \n[http://hdl.handle.net/10072/421830](http://hdl.handle.net/10072/421830)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nJournal of Hydrology: Regional Studies 46 (2023) 101346  \nContents lists available at ScienceDirect  \nJournal of Hydrology: Regional Studies  \njournal [homepage: www.elsevier.com/locate/ejrh](homepage: www.elsevier.com/locate/ejrh)  \n| Identifying impacts of global climate teleconnection patterns on land water storage using machine learning\u003Cbr>Ikechukwu Kalua, Christopher E. Ndehedeheb, c, *, Onuwa Okwuashia, Aniekan E. Eyoha, Vagner G. Ferreira d\u003Cbr>a Department of Geoinformatics & Surveying, University of Uyo, P.M.B. 1017, Uyo, Nigeria b School of Environment & Science, Griffith University, Nathan, QLD 4111, Australia c Australian Rivers Institute, Griffith University, Nathan, QLD 4111, Australia\u003Cbr>d School of Earth Sciences and Engineering Hohai University, Nanjing, China |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Gaussian process regression Machine learning theory surface water hydrology Zambezi River basin |  | Study Region: The Zambezi River Basin in southern Africa\u003Cbr>Study focus: Modelling global freshwater systems is difficult because of the complexities of climate and anthropogenic influence on hydrological systems, especially at local scales. The rapid changes in hydrological systems caused by the influence of global climate trigger complex processes that make traditional machine learning algorithms limited in quantifying impacts of climate variability on freshwater. In this study, we developed a novel machine learning routine based on the Gaussian process regression (GPR) technique to improve understanding of the interaction of non-linear climatic variables with hydrological stores (includes surface water and terrestrial water storage-TWS). The GPR is built on the principle of the Gaussian process, which is a stochastic process that simplifies multivariate Gaussian distribution to infinite-dimensional space, such that the distributions over function values can be defined. The prediction of the GPR is tested using twenty-three independent climate variables against satellite observations of TWS between April 2002 and June 2017. We explored the use of a characteristic length scale for the kernels, which we tagged as ‘first order kernels’, and another set of kernels having a separate length scale for each discrete predictor. The latter was implemented using the automatic relevance determination (ARD), tagged as ‘higher order kernels’. The first and higher order kernels of the GPR technique were further examined using multivariate statistical indices to reveal the close relationship among hydro-climatic similarity and predictability.\u003Cbr>New hydrological insights for the region: Our results indicate that the large fluctuations of TWS in our tentative test-bed (the Zambezi Basin) for the GPR technique are largely caused by strong changes in sea surface temperature and global teleconnection patterns of the nearby oceans. The GPR introduced in this study provides an improved modelling framework to keep track on the influence of these global climate teleconnection patterns on major hydrological systems, like the Zambezi Basin, which contributes to global hydro-climatology but curren","cbCaidaollqGqmnV","https://ap.wps.com/l/cbCaidaollqGqmnV","pdf",10331953,1,21,"English","en",105,"# Article Information\n## Abstract\n## Keywords\n# Introduction","[{\"question\":\"What is the study region and main research target?\",\"answer\":\"The study region is the Zambezi River Basin in southern Africa. The work targets how global climate teleconnection patterns affect land and hydrological water storage.\"},{\"question\":\"How does the study model climate impacts on water storage?\",\"answer\":\"It develops a machine learning routine based on Gaussian process regression (GPR) to relate non-linear climate variables to hydrological stores, including surface water and terrestrial water storage (TWS).\"},{\"question\":\"Which data and evaluation period are used for the GPR predictions?\",\"answer\":\"The GPR predictions are evaluated using twenty-three independent climate variables against satellite observations of TWS from April 2002 to June 2017.\"},{\"question\":\"What drives the large TWS fluctuations in the results?\",\"answer\":\"The results indicate that strong changes in sea surface temperature and nearby-ocean global teleconnection patterns largely cause the large TWS fluctuations in the Zambezi Basin test-bed.\"}]","Identifying impacts of global climate teleconnection patterns on land water storage using machine learning | PDF",1785675250,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"identifying-impacts-of-global-climate-teleconnection-patterns-on-land-water-storage-using-machine-learning","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identifying-impacts-of-global-climate-teleconnection-patterns-on-land-water-storage-using-machine-learning/117333/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the study region and main research target?","Question",{"text":75,"@type":76},"The study region is the Zambezi River Basin in southern Africa. The work targets how global climate teleconnection patterns affect land and hydrological water storage.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study model climate impacts on water storage?",{"text":80,"@type":76},"It develops a machine learning routine based on Gaussian process regression (GPR) to relate non-linear climate variables to hydrological stores, including surface water and terrestrial water storage (TWS).",{"name":82,"@type":73,"acceptedAnswer":83},"Which data and evaluation period are used for the GPR predictions?",{"text":84,"@type":76},"The GPR predictions are evaluated using twenty-three independent climate variables against satellite observations of TWS from April 2002 to June 2017.",{"name":86,"@type":73,"acceptedAnswer":87},"What drives the large TWS fluctuations in the results?",{"text":88,"@type":76},"The results indicate that strong changes in sea surface temperature and nearby-ocean global teleconnection patterns largely cause the large TWS fluctuations in the Zambezi Basin test-bed.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]