{"paper_id":"2de51b49-e48c-4a7a-b257-400f85e1aeed","body_text":"Causal Analysis of Streamflow, Evapotranspiration and Snow Dynamics in Large Sample Hydrology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Analysis Causal Analysis of Streamflow, Evapotranspiration and Snow Dynamics in Large Sample Hydrology David Strahl, Urmi Ninad, Sebastian Gnann, Karoline Wiesner, Thorsten Wagener This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9105235/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract A key challenge in hydrological science lies in establishing causal relationships among environmental variables across very diverse systems, given only limited observations. Without such causal understanding, it is difficult to determine whether simulation models adequately represent hydrological processes and their responses to climate change or other anthropogenic forcings. To address this challenge, we tailor a combination of causal discovery and effect estimation to hydrological systems and apply it to daily observations and simulations from 671 heterogeneous catchments across the USA. The inferred causal networks resolve the direction, strength, lag, and seasonal timing of key interactions and quantify shifts in process controls along gradients of water and energy availability. When applied to multiple versions of a widely used hydrological model, the approach reveals structural differences overlooked by conventional statistical metrics. More broadly, our results establish causal inference as a unifying approach that connects observations, theory, and models in heterogeneous environmental systems. Earth and environmental sciences/Hydrology Earth and environmental sciences/Environmental sciences Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.pdf Supplementary Figures and Tables Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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