20.500.12556/DiRROS-28556 Robust discharge prediction of seasonal snow-influenced karst systems through hybridization of process-based and data-driven models Hydrological modeling of karst systems is difficult due to their unique recharge, drainage and discharge behavior, which is often highly dynamic and nonlinear. It becomes even more challenging for elevated karst catchments, where the recharge process is additionally influenced by snow accumulation and melting. In this study, an innovative modelling approach was developed that hybridizing a process-based model and a datadriven model for the karst systems influenced by seasonal snow cover and its application was tested to a large, complex karst system in the Unica River catchment in Slovenia. For this purpose, the process-based model G´enie Rural ` a 6 param`etres Journalier, including the CemaNeige snow routine (CemaNeige GR6J), was hybridized with the Stacked Autoencoder Deep Neural Networks (SAE-DNN). A 60-year period of catchment discharge observations, from 1962 to 2021, was used for model development, testing and evaluation. The performance of the stand-alone models, CemaNeige GR6J and SAE-DNN, as well as the hybrid model CemaNeige GR6J-SAE-DNN, was systematically compared. The results show that the hybrid model clearly outperforms both stand-alone models, especially during the extreme flow conditions. Additionally, the hybrid model performs better for more recent modelling periods than for longer ones. This is due to changes in climate conditions in historical datasets, which the hybrid model is limited to capture. Overall, the proposed hybrid modeling approach offers an innovative way to robustly predict the daily discharge behavior of karst systems influenced by seasonal snow cover, especially during extreme flow conditions, and could be applied to other karst systems with similar complexity and characteristics to support robust decision making in karst water resource management. true false true Angleški jezik Ni določen © 2026 The Authors Neznano 2026-03-23 19:46:23 2026-03-23 19:46:23 2026-05-09 09:27:24 0000-00-00 00:00:00 2026 0 0 20 str. [article no.] ǂ135002 Vol. 669 Apr. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2026-01-29 556:551.44 0022-1694 10.1016/j.jhydrol.2026.135002 271689475 https://www.sciencedirect.com/science/article/pii/S0022169426000995?via%3Dihub 1 98299d47-26e8-11f1-aa40-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=42373 1-s2.0-S0022169426000995-main.pdf 1-s2.0-S0022169426000995-main.pdf 1 78D925E24A23DB66E102CDBAC124DFD6 86edc67dcfa8d36d93d2f0b69dbc9dc89898fc5de75730b7d51c28eb4e0d12d5 3005db76-26eb-11f1-aa40-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=42374 1-s2.0-S0022169426000995-mmc1.docx 1-s2.0-S0022169426000995-mmc1.docx Supplementary Data 1 F4528D5CFA5407E7E58ACEA8C509D2F4 035d406c0351271f5123247557f89d931d65b1de78d984c80acf889d9aa03bd0 fcc07d69-26eb-11f1-aa40-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=42375 Znanstvenoraziskovalni center Slovenske akademije znanosti in umetnosti 0 0 0