Evaluating the Functional Realism of Deep Learning Rainfall-Runoff Models Using Catchment Hydrology Principles

Evaluating the Functional Realism of Deep Learning Rainfall-Runoff Models Using Catchment Hydrology Principles | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 February 2025 V1 Latest version Share on Evaluating the Functional Realism of Deep Learning Rainfall-Runoff Models Using Catchment Hydrology Principles Authors : Ara Bayati 0009-0001-0116-1549 [email protected] , Ali A Ameli , and Saman Razavi Authors Info & Affiliations https://doi.org/10.22541/au.173862184.49073854/v1 Published Water Resources Research Version of record Peer review timeline 448 views 183 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract We propose a hydrologic-specific Explainable AI (XAI) framework to extract nonlinear and nonstationary Impulse Response Functions (IRFs) used by Long Short-Term Memory (LSTM) models to simulate streamflow. IRFs reveal how LSTMs emulate streamflow generation and celerity propagation processes in response to impulses of precipitation (P), temperature (T), and potential-evapotranspiration (PET), enabling the evaluation of LSTMs' functional realism under short-term and long-term varying climate/weather conditions. Applying this framework to 672 catchments in USA and Canada, we found that while LSTMs achieve exceptionally high predictive accuracy, their extracted functionality often contradicts established hydrologic principles. Unexpectedly, the isolated effects of T or PET short-term variations on LSTM's simulated streamflow and celerity rate are often positive in direction. For example, in over 70% of raindominated catchments, particularly along the Pacific Coast, increased T within 1-14 days prior to streamflow events is associated with higher streamflow and celerity rates. Similarly, in the southeastern USA and California, LSTMs predict increased streamflow solely in response to PET rises. In snow-dominated catchments, particularly in the Rockies, LSTMs misinterpret PET (representing evaporation function) as a proxy for temperature, the primary snowmelt's driver. These behaviors, likely influenced by seasonality in dynamic inputs, data non-homogeneity, simplicity bias, and missing causal factors, undermine LSTMs' scientific reliability for streamflow forecasting and climate impact assessments. Our XAI framework integrates deep learning with hydrologic context through Differentiable modeling, providing a screening tool for the evaluation of hydrologic models' functional realism in short-and long-term forecasting, climate change studies, and generalization to ungauged basins. Supplementary Material File (final_manuscript.pdf) Download 2.16 MB Information & Authors Information Version history V1 Version 1 03 February 2025 Peer review timeline Published Water Resources Research Version of Record 30 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords catchment hydrology hydrologic specific explainable ai framework long short-term memory (lstm) rainfall-runoff modeling trustworthy deep learning Authors Affiliations Ara Bayati 0009-0001-0116-1549 [email protected] Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia View all articles by this author Ali A Ameli Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia View all articles by this author Saman Razavi School of Environment and Sustainability, Global Institute for Water Security, University of Saskatchewan View all articles by this author Metrics & Citations Metrics Article Usage 448 views 183 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ara Bayati, Ali A Ameli, Saman Razavi. Evaluating the Functional Realism of Deep Learning Rainfall-Runoff Models Using Catchment Hydrology Principles. Authorea . 03 February 2025. DOI: https://doi.org/10.22541/au.173862184.49073854/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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