Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-3165-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-3165-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A hybrid Kolmogorov–Arnold Networks-based model with attention for predicting Arctic river streamflow
Department of Environmental and Geosciences, Sam Houston State University, Huntsville, TX 77340, USA
Shiqi Liu
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China
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Cited articles
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., Duan, Y., Abdullah, A., Farhan, L., Lu, Y., Gupta, A., Albu, F., Abbosh, A., and Gu, Y.: A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications, J. Big Data, 10, 46, https://doi.org/10.1186/s40537-023-00727-2, 2023.
Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R., and Shuckburgh, E.: Seasonal arctic sea ice forecasting with probabilistic deep learning, Nat. Commun., 12, 5124, https://doi.org/10.1038/s41467-021-25257-4, 2021.
Bakhshi Ostadkalayeh, F., Moradi, S., Asadi, A., Moghaddam Nia, A., and Taheri, S.: Performance improvement of LSTM-based deep learning model for streamflow forecasting using kalman filtering, Water Resour. Manage., 37, 3111–3127, https://doi.org/10.1007/s11269-023-03492-2, 2023.
Basu, B., Morrissey, P., and Gill, L. W.: Application of nonlinear time series and machine learning algorithms for forecasting groundwater flooding in a lowland karst area, Water Resour. Res., 58, e2021WR029576, https://doi.org/10.1029/2021WR029576, 2022.
Bring, A., Fedorova, I., Dibike, Y., Hinzman, L., Mård, J., Mernild, S. H., Prowse, T., Semenova, O., Stuefer, S. L., and Woo, M.-K.: Arctic terrestrial hydrology: a synthesis of processes, regional effects, and research challenges, J. Geophys. Res.-Biogeo., 121, 621–649, https://doi.org/10.1002/2015JG003131, 2016.
Chang, S. Y., Schwenk, J., and Solander, K. C.: Deep learning advances arctic river water temperature predictions, Water Resour. Res., 61, e2024WR039053, https://doi.org/10.1029/2024WR039053, 2025.
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv [preprint], https://doi.org/10.48550/ARXIV.1406.1078, 2014.
Cinkus, G., Mazzilli, N., Jourde, H., Wunsch, A., Liesch, T., Ravbar, N., Chen, Z., and Goldscheider, N.: When best is the enemy of good – critical evaluation of performance criteria in hydrological models, Hydrol. Earth Syst. Sci., 27, 2397–2411, https://doi.org/10.5194/hess-27-2397-2023, 2023.
DeWalle, D. R. and Rango, A.: Principles of snow hydrology, 1st edn., Cambridge University Press, https://doi.org/10.1017/CBO9780511535673, 2008.
Ernakovich, J. G., Hopping, K. A., Berdanier, A. B., Simpson, R. T., Kachergis, E. J., Steltzer, H., and Wallenstein, M. D.: Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change, Glob. Change Biol., 20, 3256–3269, https://doi.org/10.1111/gcb.12568, 2014.
Feng, D., Gleason, C. J., Lin, P., Yang, X., Pan, M., and Ishitsuka, Y.: Recent changes to arctic river discharge, Nat. Commun., 12, 6917, https://doi.org/10.1038/s41467-021-27228-1, 2021.
Gao, S., Huang, Y., Zhang, S., Han, J., Wang, G., Zhang, M., and Lin, Q.: Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation, J. Hydrol., 589, 125188, https://doi.org/10.1016/j.jhydrol.2020.125188, 2020.
Gelfan, A., Gustafsson, D., Motovilov, Y., Arheimer, B., Kalugin, A., Krylenko, I., and Lavrenov, A.: Climate change impact on the water regime of two great arctic rivers: modeling and uncertainty issues, Climatic Change, 141, 499–515, https://doi.org/10.1007/s10584-016-1710-5, 2017.
Granata, F., Zhu, S., and Di Nunno, F.: Advanced streamflow forecasting for central european rivers: the cutting-edge kolmogorov-arnold networks compared to transformers, J. Hydrol., 645, 132175, https://doi.org/10.1016/j.jhydrol.2024.132175, 2024.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gusev, E. M., Nasonova, O. N., and Dzhogan, L. Y.: Physically based simulating long-term dynamics of diurnal variations of river runoff and snow water equivalent in the kolyma river basin, Water Resour., 42, 834–841, https://doi.org/10.1134/S0097807815060056, 2015.
Häkkinen, S. and Mellor, G. L.: Modeling the seasonal variability of a coupled arctic ice-ocean system, J. Geophys. Res.-Oceans, 97, 20285–20304, https://doi.org/10.1029/92JC02037, 1992.
Harpold, A. A., Kaplan, M. L., Klos, P. Z., Link, T., McNamara, J. P., Rajagopal, S., Schumer, R., and Steele, C. M.: Rain or snow: hydrologic processes, observations, prediction, and research needs, Hydrol. Earth Syst. Sci., 21, 1–22, https://doi.org/10.5194/hess-21-1-2017, 2017.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput., 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9, 2003.
Holmes, R. M., McClelland, J. W., Peterson, B. J., Tank, S. E., Bulygina, E., Eglinton, T. I., Gordeev, V. V., Gurtovaya, T. Y., Raymond, P. A., Repeta, D. J., Staples, R., Striegl, R. G., Zhulidov, A. V., and Zimov, S. A.: Seasonal and annual fluxes of nutrients and organic matter from large rivers to the Arctic Ocean and surrounding seas, Estuar. Coast., 35, 369–382, https://doi.org/10.1007/s12237-011-9386-6, 2012.
Jin, A., Wang, Q., Zhan, H., and Zhou, R.: Comparative performance assessment of physical-based and data-driven machine-learning models for simulating streamflow: a case study in three catchments across the US, J. Hydrol. Eng., 29, 5024004, https://doi.org/10.1061/JHYEFF.HEENG-6118, 2024a.
Jin, A., Wang, Q., Zhou, R., Shi, W., and Qiao, X.: Hybrid multivariate machine learning models for streamflow forecasting: a two-stage decomposition–reconstruction framework, J. Hydrol. Eng., 29, 4024026, https://doi.org/10.1061/JHYEFF.HEENG-6254, 2024b.
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang, L.: Physics-informed machine learning, Nat. Rev. Phys., 3, 422–440, https://doi.org/10.1038/s42254-021-00314-5, 2021.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper danube basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019.
Krogh, S. A., Pomeroy, J. W., and Marsh, P.: Diagnosis of the hydrology of a small arctic basin at the tundra–taiga transition using a physically based hydrological model, J. Hydrol., 550, 685–703, https://doi.org/10.1016/j.jhydrol.2017.05.042, 2017.
Kůrková, V.: Kolmogorov's theorem and multilayer neural networks, Neural Networks, 5, 501–506, https://doi.org/10.1016/0893-6080(92)90012-8, 1992.
LeCun, Y., Bottou, L., Orr, G. B., and Müller, K.-R.: Efficient BackProp, in: Neural networks: tricks of the trade, vol. 1524, edited by: Orr, G. B. and Müller, K.-R., Springer Berlin Heidelberg, Berlin, Heidelberg, 9–50, https://doi.org/10.1007/3-540-49430-8_2, 1998.
Liu, S., Wang, P., Yu, J., Zhou, R., Bai, B., Gabysheva, O. I., Frolova, N. L., and Pozdniakov, S. P.: Changes in hydrological regime regulate POC export across permafrost-dominated arctic river basins, Geosci. Front., 102208, https://doi.org/10.1016/j.gsf.2025.102208, 2025.
Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y., and Tegmark, M.: KAN: Kolmogorov–Arnold Networks, arXiv [preprint], https://doi.org/10.48550/ARXIV.2404.19756, 2024.
McClelland, J. W., Holmes, R. M., Peterson, B. J., and Stieglitz, M.: Increasing river discharge in the eurasian arctic: consideration of dams, permafrost thaw, and fires as potential agents of change, J. Geophys. Res.-Atmos., 109, 2004JD004583, https://doi.org/10.1029/2004JD004583, 2004.
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D., and Veith, T. L.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., and Gupta, H. V.: What role does hydrological science play in the age of machine learning?, Water Resour. Res., 57, e2020WR028091, https://doi.org/10.1029/2020WR028091, 2021.
Nijssen, B., O'Donnell, G. M., Hamlet, A. F., and Lettenmaier, D. P.: Hydrologic sensitivity of global rivers to climate change, Climatic Change, 50, 143–175, https://doi.org/10.1023/A:1010616428763, 2001.
Peterson, B. J., Holmes, R. M., McClelland, J. W., Vörösmarty, C. J., Lammers, R. B., Shiklomanov, A. I., Shiklomanov, I. A., and Rahmstorf, S.: Increasing river discharge to the Arctic Ocean, Science, 298, 2171–2173, https://doi.org/10.1126/science.1077445, 2002.
Pölz, A., Blaschke, A. P., Komma, J., Farnleitner, A. H., and Derx, J.: Transformer versus LSTM: a comparison of deep learning models for karst spring discharge forecasting, Water Resour. Res., 60, e2022WR032602, https://doi.org/10.1029/2022WR032602, 2024.
Prowse, T., Alfredsen, K., Beltaos, S., Bonsal, B. R., Bowden, W. B., Duguay, C. R., Korhola, A., McNamara, J., Vincent, W. F., Vuglinsky, V., Walter Anthony, K. M., and Weyhenmeyer, G. A.: Effects of changes in arctic lake and river ice, Ambio, 40, 63–74, https://doi.org/10.1007/s13280-011-0217-6, 2011.
Rawlins, M. A. and Karmalkar, A. V.: Regime shifts in Arctic terrestrial hydrology manifested from impacts of climate warming, The Cryosphere, 18, 1033–1052, https://doi.org/10.5194/tc-18-1033-2024, 2024.
Schneider, U., Hänsel, S., Finger, P., Rustemeier, E., and Ziese, M.: GPCC full data monthly version 2022 at 2.5°: monthly land–surface precipitation from rain-gauges built on GTS-based and historic data: globally gridded monthly totals (2022), https://doi.org/10.5676/DWD_GPCC/FD_M_V2022_250, 2022.
Sergeev, A., Baglaeva, E., and Subbotina, I.: Hybrid model combining LSTM with discrete wavelet transformation to predict surface methane concentration in the arctic island belyy, Atmos. Environ., 317, 120210, https://doi.org/10.1016/j.atmosenv.2023.120210, 2024.
Singh, A., Kalke, H., Loewen, M., and Ray, N.: River ice segmentation with deep learning, IEEE T. Geosci. Remote, 58, 7570–7579, https://doi.org/10.1109/TGRS.2020.2981082, 2020.
Snieder, E. and Khan, U. T.: A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting, Hydrol. Earth Syst. Sci., 29, 785–798, https://doi.org/10.5194/hess-29-785-2025, 2025.
Spencer, R. G. M., Mann, P. J., Dittmar, T., Eglinton, T. I., McIntyre, C., Holmes, R. M., Zimov, N., and Stubbins, A.: Detecting the signature of permafrost thaw in arctic rivers, Geophys. Res. Lett., 42, 2830–2835, https://doi.org/10.1002/2015GL063498, 2015.
Suzuki, K., Liston, G. E., and Matsuo, K.: Estimation of continental-basin-scale sublimation in the lena river basin, siberia, Adv. Meteorol., 2015, 1–14, https://doi.org/10.1155/2015/286206, 2015.
Tank, S. E., McClelland, J. W., Spencer, R. G. M., Shiklomanov, A. I., Suslova, A., Moatar, F., Amon, R. M. W., Cooper, L. W., Elias, G., Gordeev, V. V., Guay, C., Gurtovaya, T. Yu., Kosmenko, L. S., Mutter, E. A., Peterson, B. J., Peucker-Ehrenbrink, B., Raymond, P. A., Schuster, P. F., Scott, L., Staples, R., Striegl, R. G., Tretiakov, M., Zhulidov, A. V., Zimov, N., Zimov, S., and Holmes, R. M.: Recent trends in the chemistry of major northern rivers signal widespread arctic change, Nat. Geosci., 16, 789–796, https://doi.org/10.1038/s41561-023-01247-7, 2023.
Towner, J., Cloke, H. L., Zsoter, E., Flamig, Z., Hoch, J. M., Bazo, J., Coughlan de Perez, E., and Stephens, E. M.: Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin, Hydrol. Earth Syst. Sci., 23, 3057–3080, https://doi.org/10.5194/hess-23-3057-2019, 2019.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I.: Attention is all you need, in: Advances in Neural Information Processing Systems, arXiv [preprint], https://doi.org/10.48550/arXiv.1706.03762, 2017.
Vonk, J. E., Fritz, M., Speetjens, N. J., Babin, M., Bartsch, A., Basso, L. S., Bröder, L., Göckede, M., Gustafsson, Ö., Hugelius, G., Irrgang, A. M., Juhls, B., Kuhn, M. A., Lantuit, H., Manizza, M., Martens, J., O'Regan, M., Suslova, A., Tank, S. E., Terhaar, J., and Zolkos, S.: The land–ocean arctic carbon cycle, Nat. Rev. Earth Environ., 6, 86–105, https://doi.org/10.1038/s43017-024-00627-w, 2025.
Walvoord, M. A. and Kurylyk, B. L.: Hydrologic impacts of thawing permafrost – a review, Vadose Zone J., 15, 1–20, https://doi.org/10.2136/vzj2016.01.0010, 2016.
Wang, P., Huang, Q., Pozdniakov, S. P., Liu, S., Ma, N., Wang, T., Zhang, Y., Yu, J., Xie, J., Fu, G., Frolova, N. L., and Liu, C.: Potential role of permafrost thaw on increasing siberian river discharge, Environ. Res. Lett., 16, 34046, https://doi.org/10.1088/1748-9326/abe326, 2021.
Woo, M.-K., Kane, D. L., Carey, S. K., and Yang, D.: Progress in permafrost hydrology in the new millennium, Permafrost Periglac., 19, 237–254, https://doi.org/10.1002/ppp.613, 2008.
Xie, K., Liu, P., Zhang, J., Han, D., Wang, G., and Shen, C.: Physics-guided deep learning for rainfall–runoff modeling by considering extreme events and monotonic relationships, J. Hydrol., 603, 127043, https://doi.org/10.1016/j.jhydrol.2021.127043, 2021.
Yang, D., Kane, D. L., Hinzman, L. D., Zhang, X., Zhang, T., and Ye, H.: Siberian lena river hydrologic regime and recent change, J. Geophys. Res.-Atmos., 107, https://doi.org/10.1029/2002JD002542, 2002.
Yang, S., Yang, D., Chen, J., Santisirisomboon, J., Lu, W., and Zhao, B.: A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data, J. Hydrol., 590, 125206, https://doi.org/10.1016/j.jhydrol.2020.125206, 2020.
Ye, B., Yang, D., and Kane, D. L.: Changes in lena river streamflow hydrology: human impacts versus natural variations, Water Resour. Res., 39, 2003WR001991, https://doi.org/10.1029/2003WR001991, 2003.
Zhang, S., Gan, T. Y., Bush, A. B. G., and Zhang, G.: Evaluation of the impact of climate change on the streamflow of major pan-arctic river basins through machine learning models, J. Hydrol., 619, 129295, https://doi.org/10.1016/j.jhydrol.2023.129295, 2023.
Zhi, W., Ouyang, W., Shen, C., and Li, L.: Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers, Nat. Water, 1, 249–260, https://doi.org/10.1038/s44221-023-00038-z, 2023.
Zhong, L., Lei, H., and Yang, J.: Development of a distributed physics-informed deep learning hydrological model for data-scarce regions, Water Resour. Res., 60, e2023WR036333, https://doi.org/10.1029/2023WR036333, 2024.
Zhou, R.: Multi-scale dynamic spatiotemporal graph attention network for forecasting karst spring discharge, J. Hydrol., 133289, https://doi.org/10.1016/j.jhydrol.2025.133289, 2025.
Zhou, R.: Zhou-R/HESS_KAN: v0.01 (v0.0.1), Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.19862397, 2026.
Zhou, R. and Zhang, Y.: On the role of the architecture for spring discharge prediction with deep learning approaches, Hydrol. Process., 36, https://doi.org/10.1002/hyp.14737, 2022a.
Zhou, R. and Zhang, Y.: Reconstruction of missing spring discharge by using deep learning models with ensemble empirical mode decomposition of precipitation, Environ. Sci. Pollut. R., https://doi.org/10.1007/s11356-022-21597-w, 2022b.
Zhou, R. and Zhang, Y.: Linear and nonlinear ensemble deep learning models for karst spring discharge forecasting, J. Hydrol., 627, 130394, https://doi.org/10.1016/j.jhydrol.2023.130394, 2023a.
Zhou, R. and Zhang, Y.: Predicting and explaining karst spring dissolved oxygen using interpretable deep learning approach, Hydrol. Process., 37, e14948, https://doi.org/10.1002/hyp.14948, 2023b.
Zhou, R., Zhang, Y., Wang, Q., Jin, A., and Shi, W.: A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting, J. Hydrol., 634, 131128, https://doi.org/10.1016/j.jhydrol.2024.131128, 2024a.
Zhou, R., Wang, Q., Jin, A., Shi, W., and Liu, S.: Interpretable multi-step hybrid deep learning model for karst spring discharge prediction: integrating temporal fusion transformers with ensemble empirical mode decomposition, J. Hydrol., 132235, https://doi.org/10.1016/j.jhydrol.2024.132235, 2024b.
Short summary
Arctic rivers move enormous amounts of water and carbon into the ocean, influencing global climate, but their flow is hard to predict because the region is remote and the frozen ground behaves in unusual ways. This research combines artificial intelligence with the physics of snow and permafrost to forecast river flow more accurately. Demonstrated on the Kolyma River, the new model outperforms existing approaches and provides a robust framework for understanding Arctic hydrological systems.
Arctic rivers move enormous amounts of water and carbon into the ocean, influencing global...