Enhancing Runoff Forecast Performance in the Yellow River Source Region: Roles of Meteorological Factors, Future Information, and Spatial Differentiation

Enhancing Runoff Forecast Performance in the Yellow River Source Region: Roles of Meteorological Factors, Future Information, and Spatial Differentiation | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 13 September 2025 V1 Latest version Share on Enhancing Runoff Forecast Performance in the Yellow River Source Region: Roles of Meteorological Factors, Future Information, and Spatial Differentiation Authors : Wu Ruiyan , Wang Jingyang 0009-0009-1472-5479 , Xiang Li [email protected] , Dongqin Yin , and Xi Xinyue Authors Info & Affiliations https://doi.org/10.22541/au.175777081.15851129/v1 167 views 84 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Source Area of the Yellow River (YRSA) serves as a critical water conservation region for the Yellow River Basin, and its hydrological processes exert profound impacts on basin-wide water resources management. However, the runoff yielding mechanisms in this region remain insufficiently understood, the underlying surface conditions are highly complex, and the monitoring infrastructure is relatively sparse, together posing substantial challenges to accurate runoff forecasting. Focusing on the Jungong (JG) hydrological station in the YRSA, this study develops a runoff forecasting model based on a Back-Propagation Neural Network (BPNN). Experiments are conducted at both daily and ten-day timescales during the flood and non-flood seasons to systematically investigate the impacts of meteorological factors, future meteorological information, and spatial heterogeneity on forecasting accuracy. The results are compared against the practical forecasting model, namely Multiple Linear Regression (MLR), currently used by the Hydrology Bureau of Yellow River Conservancy Commission (YRCC). The findings indicate that: (1) Incorporating meteorological factors significantly improves forecast accuracy. Using a three-factor combination of precipitation, air temperature, and runoff as inputs increased the Deterministic Coefficient (DC) by 14.3%, 109.8%, and 35.2% at the daily timescale during flood season, ten-day timescale during flood season, and ten-day timescale during non-flood season, respectively, compared with using runoff alone. (2) Considering future meteorological information further enhances runoff forecasting performance, with DC values at the forecast end of the three-factor combination model increasing by 5.5%, 149.3%, and 23.8%, respectively, compared to without consideration. (3) After subdividing the entire study area according to natural geographical characteristics and considering the spatial heterogeneity of meteorological data, the DC values at the forecast end can be further improved to 0.85, 0.70, and 0.83, respectively. (4) Compared with the MLR model used by the Hydrology Bureau of YRCC, the BPNN model developed in this study demonstrates superior performance in most scenarios and provides methodological references and technical support for practical runoff forecasting. Supplementary Material File (manuscript for hp.docx) Download 20.09 MB Information & Authors Information Version history V1 Version 1 13 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords flood season machine learning non-flood season runoff forecasting yellow river Authors Affiliations Wu Ruiyan Qinghai University View all articles by this author Wang Jingyang 0009-0009-1472-5479 Qinghai University View all articles by this author Xiang Li [email protected] Qinghai University View all articles by this author Dongqin Yin China Agricultural University College of Land Science and Technology View all articles by this author Xi Xinyue The Hong Kong Polytechnic University Department of Civil and Environmental Engineering View all articles by this author Metrics & Citations Metrics Article Usage 167 views 84 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Wu Ruiyan, Wang Jingyang, Xiang Li, et al. Enhancing Runoff Forecast Performance in the Yellow River Source Region: Roles of Meteorological Factors, Future Information, and Spatial Differentiation. Authorea . 13 September 2025. DOI: https://doi.org/10.22541/au.175777081.15851129/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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