Articles | Volume 29, issue 8
https://doi.org/10.5194/hess-29-2023-2025
© Author(s) 2025. 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-29-2023-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2025
Research article |
| 22 Apr 2025
| 22 Apr 2025
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Haoran Hao
CORRESPONDING AUTHOR
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin, China
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Mingxiang Yang
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Jianhui Wei
Institute of Meteorology and Climate Research (IMKIFU), Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Shiqin Xu
Hydrology, Agriculture and Land Observation (HALO) Laboratory, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Harald Kunstmann
Institute of Meteorology and Climate Research (IMKIFU), Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Institute of Geography, University of Augsburg, Augsburg, Germany
Centre for Climate Resilience, University of Augsburg, Augsburg, Germany
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This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
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Christof Lorenz, Tanja C. Portele, Patrick Laux, and Harald Kunstmann
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Cited articles
Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geogr., 36, 480–513, https://doi.org/10.1177/0309133312444943, 2012.
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and Mendoza, P. A.: Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges, Hydrol. Sci. J., 65, 712–725, https://doi.org/10.1080/02626667.2019.1683182, 2020.
Adnan, R. M., Liang, Z., Trajkovic, S., Zounemat-Kermani, M., Li, B., and Kisi, O.: Daily streamflow prediction using optimally pruned extreme learning machine, J. Hydrol., 577, 123981, https://doi.org/10.1016/j.jhydrol.2019.123981, 2019.
Balint, G., Csik, A., Bartha, P., Gauzer, B., and Bonta, I.: Application of meteorological ensembles for Danube flood forecasting and warning, in: Transboundary Floods: Reducing Risks through Flood Management, edited by: Marsalek, J., Stancalie, G., and Balint, G., NATO Sci. Ser., Springer, Dordrecht, 57–68, https://doi.org/10.1007/1-4020-4902-1_6, 2006.
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020.
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections, Clim. Dynam., 57, 2941–2951, https://doi.org/10.1007/s00382-021-05847-0, 2021.
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015.
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023.
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions, Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173, 2015.
Bonavita, M.: On some limitations of current machine learning weather prediction models, Geophys. Res. Lett., 51, e2023GL107377, https://doi.org/10.1029/2023GL107377, 2024.
Bremnes, J. B.: Ensemble postprocessing using quantile function regression based on neural networks and Bernstein polynomials, Mon. Weather Rev., 148, 403–414, https://doi.org/10.1175/MWR-D-19-0227.1, 2020.
Brotzge, J. A., Berchoff, D., Carlis, D. L., Carr, F. H., Carr, R. H., Gerth, J. J., Gross, B. D., Hamill, T. M., Haupt, S. E., Jacobs, N., McGovern, A., Stensrud, D. J., Szatkowski, G., Szunyogh, I., and Wang, X.: Challenges and opportunities in numerical weather prediction, B. Am. Meteorol. Soc., 104, E698–E705, https://doi.org/10.1175/BAMS-D-22-0172.1, 2023.
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?, J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1, 2015.
Chen, G. and Wang, W.-C.: Short-term precipitation prediction for contiguous United States using deep learning, Geophys. Res. Lett., 49, e2022GL097904, https://doi.org/10.1029/2022GL097904, 2022.
Cho, D., Yoo, C., Im, J., and Cha, D. H.: Comparative assessment of various machine learning-based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in urban areas, Earth Space Sci., 7, e2019EA000740, https://doi.org/10.1029/2019EA000740, 2020.
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009.
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016.
de Andrade, F. M., Young, M. P., MacLeod, D., Hirons, L. C., Woolnough, S. J., and Black, E.: Subseasonal precipitation prediction for Africa: Forecast evaluation and sources of predictability, Weather Forecast., 36, 265–284, https://doi.org/10.1175/WAF-D-20-0102.1, 2021.
Dehshiri, S. S. H. and Firoozabadi, B.: A multi-objective framework to select numerical options in air quality prediction models: A case study on dust storm modeling, Sci. Total Environ., 863, 160681, https://doi.org/10.1016/j.scitotenv.2022.160681, 2023.
Di Luca, A., de Elía, R., and Laprise, R.: Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations, Clim. Dynam., 44, 2519–2537, https://doi.org/10.1007/s00382-011-1068-3, 2015.
Dong, N., Wei, J., Yang, M., Yan, D., Yang, C., Gao, H., Arnault, J., Laux, P., Zhang, X., Liu, Y., and Niu, J.: Model estimates of China's terrestrial water storage variation due to reservoir operation, Water Resour. Res., 58, e2021WR031787, https://doi.org/10.1029/2021WR031787, 2022.
Dong, N., Yang, M., Wei, J., Arnault, J., Laux, P., Xu, S., Wang, H., Yu, Z., and Kunstmann, H.: Toward improved parameterizations of reservoir operation in ungauged basins: A synergistic framework coupling satellite remote sensing, hydrologic modeling, and conceptual operation schemes, Water Resour. Res., 59, e2022WR033026, https://doi.org/10.1029/2022WR033026, 2023.
Dong, N., Hao, H., Yang, M., Wei, J., Xu, S., and Kunstmann, H.: Model, Zenodo [code], https://doi.org/10.5281/zenodo.12664798, 2024a.
Dong, N., Hao, H., Yang, M., Wei, J., Xu, S., and Kunstmann, H.: Data, Zenodo [data set], https://doi.org/10.5281/zenodo.12664851, 2024b.
Ebert-Uphoff, I. and Hilburn, K.: Evaluation, tuning and interpretation of neural networks for working with images in meteorological applications, B. Am. Meteorol. Soc., 101, E1654–E1677, https://doi.org/10.1175/BAMS-D-19-0324.1, 2020.
Ferranti, L., Corti, S., and Janousek, M.: Flow-dependent verification of the ECMWF ensemble over the Euro-Atlantic sector, Q. J. Roy. Meteor. Soc., 144, 317–326, https://doi.org/10.1002/qj.3204, 2018.
Gao, S., Huang, D., Du, N., Ren, C., and Yu, H.: WRF ensemble dynamical downscaling of precipitation over China using different cumulus convective schemes, Atmos. Res., 271, 106116, https://doi.org/10.1016/j.atmosres.2022.106116, 2022.
Gassman, P. W., Reyes, M. R., Green, C. H., and Arnold, J. G.: The Soil and Water Assessment Tool: Historical development, applications, and future research directions, Trans. ASABE, 57, 1211–1250, https://doi.org/10.13031/2013.23637, 2014.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
Ham, Y.-G., Kim, J.-H., and Luo, J.-J.: Deep learning for multi-year ENSO forecasts, Nature, 573, 568–572, https://doi.org/10.1038/s41586-019-1559-7, 2019.
Han, L., Chen, M., Chen, K., Chen, H., Zhang, Y., Lu, B., Song, L., and Qin, R.: A deep learning method for bias correction of ECMWF 24–240 h forecasts, Adv. Atmos. Sci., 38, 1444–1459, https://doi.org/10.1007/s00376-021-0434-2, 2021.
Hao, H., Dong, N., Yang, M., Wei, J., Zhang, X., Xu, S., Yan, D., Ren, L., Leng, G., Chen, L., and Zhou, X.: The changing hydrology of an irrigated and dammed Yangtze River: Streamflow, extremes, and lake hydrodynamics, Water Resour. Res., 60, e2024WR037841, https://doi.org/10.1029/2024WR037841, 2024.
Horat, N. and Lerch, S.: Deep Learning for Postprocessing Global Probabilistic Forecasts on Subseasonal Time Scales, Mon. Weather Rev., 152, 667–687, https://doi.org/10.1175/MWR-D-23-0112.1, 2024.
Hu, C. H., Guo, S. L., Xiong, L. H., and Peng, D. Z.: A modified Xin'anjiang model and its application in northern China, Hydrol. Res., 36, 175–192, https://doi.org/10.2166/nh.2005.0013, 2005.
Huang, Z., Zhao, T., Xu, W., Cai, H., Wang, J., Zhang, Y., Liu, Z., Tian, Y., Yan, D., and Chen, X.: A Seven-Parameter Bernoulli-Gamma-Gaussian Model to Calibrate Subseasonal to Seasonal Precipitation Forecasts, J. Hydrol., 610, 127896, https://doi.org/10.1016/j.jhydrol.2022.127896, 2022.
Humphrey, G. B., Gibbs, M. S., Dandy, G. C., and Maier, H. R.: A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network, J. Hydrol., 540, 623–640, https://doi.org/10.1016/j.jhydrol.2016.06.026, 2016.
Jaun, S., Ahrens, B., Walser, A., Ewen, T., and Schär, C.: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Nat. Hazards Earth Syst. Sci., 8, 281–291, https://doi.org/10.5194/nhess-8-281-2008, 2008.
Jiang, M., Weng, B., Chen, J., Huang, T., Ye, F., and You, L.: Transformer-enhanced spatiotemporal neural network for post-processing of precipitation forecasts, J. Hydrol., 630, 130720, https://doi.org/10.1016/j.jhydrol.2024.130720, 2024.
Jiang, Z., Yang, S., Liu, Z., Xu, Y., Xiong, Y., Qi, S., Pang, Q., Xu, J., Liu, F., and Xu, T.: Coupling machine learning and weather forecast to predict farmland flood disaster: A case study in Yangtze River basin, Environ. Model. Softw., 155, 105436, https://doi.org/10.1016/j.envsoft.2022.105436, 2022.
Jin, W., Zhang, W., Hu, J., Weng, B., Huang, T., and Chen, J.: Using the residual network module to correct the sub-seasonal high temperature forecast, Front. Earth Sci., 9, 760766, https://doi.org/10.3389/feart.2021.760766, 2022.
Kim, H., Ham, Y.-G., Joo, Y.-S., and Son, S.-W.: Deep learning for bias correction of MJO prediction, Nat. Commun., 12, 3087, https://doi.org/10.1038/s41467-021-23406-3, 2021.
Kim, T., Yang, T., Zhang, L., and Hong, Y.: Near real-time hurricane rainfall forecasting using convolutional neural network models with Integrated Multi-satellitE Retrievals for GPM (IMERG) product, Atmos. Res., 270, 106037, https://doi.org/10.1016/j.atmosres.2022.106037, 2022.
Kisi, O.: Streamflow forecasting using different artificial neural network algorithms, J. Hydrol. Eng., 12, 532–539, https://doi.org/10.1061/(ASCE)1084-0699(2007)12:5(532), 2007.
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., Herrnegger, M., Sampson, A. K., Hochreiter, S., and Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning, Water Resour. Res., 55, 11344–11354, https://doi.org/10.1029/2019WR026065, 2019.
Kratzert, F., Gauch, M., Nearing, G., and Klotz, D.: NeuralHydrology – A Python library for Deep Learning research in hydrology, J. Open Source Softw., 7, 4050, https://doi.org/10.21105/joss.04050, 2022 (code available at: https://neuralhydrology.readthedocs.io/en/latest/index.html, last access: 1 July 2024).
Lagerquist, R., McGovern, A., and Gagne, D. J., II: Deep learning for spatially explicit prediction of synoptic-scale Fronts, Weather Forecast., 34, 1137–1160, https://doi.org/10.1175/WAF-D-18-0183.1, 2019.
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P.: Learning skillful medium-range global weather forecasting, Science, 382, 1416–1421, https://doi.org/10.1126/science.adi2336, 2023.
Larraondo, P. R., Renzullo, L. J., Van Dijk, A. I., Inza, I., and Lozano, J. A.: Optimization of deep learning precipitation models using categorical binary metrics, J. Adv. Model. Earth Sy., 12, e2019MS001909, https://doi.org/10.1029/2019MS001909, 2020.
Li, J., Li, L., Zhang, T., Xing, H., Shi, Y., Li, Z., Wang, C., and Liu, J.: Flood forecasting based on radar precipitation nowcasting using U-net and its improved models, J. Hydrol., 632, 130871, https://doi.org/10.1016/j.jhydrol.2024.130871, 2024a.
Li, L., Yun, Z., Liu, Y., Wang, Y., Zhao, W., Kang, Y., and Gao, R.: Improving Categorical and Continuous Accuracy of Precipitation Forecasts by Integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian Distribution, Atmos. Res., 298, 107133, https://doi.org/10.1016/j.atmosres.2023.107133, 2024b.
Li, W., Pan, B., Xia, J., and Duan, Q.: Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts, J. Hydrol., 605, 127301, https://doi.org/10.1016/j.jhydrol.2021.127301, 2022.
Li, X., Wu, H., Nanding, N., Chen, S., Hu, Y., and Li, L.: Statistical Bias Correction of Precipitation Forecasts Based on Quantile Mapping on the Sub-Seasonal to Seasonal Scale, Remote Sens., 15, 1743, https://doi.org/10.3390/rs15071743, 2023.
Liang, P., Lin, H., and Ding, Y.: Dominant modes of subseasonal variability of East Asian summertime surface air temperature and their predictions, J. Climate, 31, 2729–2743, https://doi.org/10.1175/JCLI-D-17-0368.1, 2018.
Ling, F., Li, Y., Luo, J.-J., Zhong, X., and Wang, Z.: Two deep learning-based bias-correction pathways improve summer precipitation prediction over China, Environ. Res. Lett., 17, 124025, https://doi.org/10.1088/1748-9326/aca68a, 2022a.
Ling, F., Luo, J.-J., Li, Y., Tang, T., Bai, L., Ouyang, W., and Yamagata, T.: Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole, Nat. Commun., 13, 7681, https://doi.org/10.1038/s41467-022-35412-0, 2022b.
Liu, D., Jiang, W., Mu, L., and Wang, S.: Streamflow prediction using deep learning neural network: case study of Yangtze River, IEEE Access, 8, 90069–90086, https://doi.org/10.1109/ACCESS.2020.2993874, 2020.
Liu, J., Yuan, X., Zeng, J., Jiao, Y., Li, Y., Zhong, L., and Yao, L.: Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning, Hydrol. Earth Syst. Sci., 26, 265–278, https://doi.org/10.5194/hess-26-265-2022, 2022.
Lyu, Y., Zhu, S., Zhi, X., Ji, Y., Fan, Y., and Dong, F.: Improving subseasonal-to-seasonal prediction of summer extreme precipitation over southern China based on a deep learning method, Geophys. Res. Lett., 50, e2023GL106245, https://doi.org/10.1029/2023GL106245, 2023.
Manzanas, R., Lucero, A., Weisheimer, A., and Gutiérrez, J. M.: Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?, Clim. Dynam., 50, 1161–1176, https://doi.org/10.1007/s00382-017-3669-y, 2018.
Mao, G., Vogl, S., Laux, P., Wagner, S., and Kunstmann, H.: Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data, Hydrol. Earth Syst. Sci., 19, 1787–1806, https://doi.org/10.5194/hess-19-1787-2015, 2015.
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010.
Merino, A., García-Ortega, E., Navarro, A., Sánchez, J. L., and Tapiador, F. J.: WRF hourly evaluation for extreme precipitation events, Atmos. Res., 274, 106215, https://doi.org/10.1016/j.atmosres.2022.106215, 2022.
Michalek, A. T., Villarini, G., and Kim, T.: Understanding the impact of precipitation bias-correction and statistical downscaling methods on projected changes in flood extremes, Earth's Future, 12, e2023EF004179, https://doi.org/10.1029/2023EF004179, 2024.
Ni, L., Wang, D., Singh, V. P., Wu, J., Chen, X., Tao, Y., Zhu, X., Jiang, J., and Zeng, X.: Monthly precipitation prediction at regional scale using deep convolutional neural networks, Hydrol. Process., 37, e14954, https://doi.org/10.1002/hyp.14954, 2023.
Nie, Y. and Sun, J.: Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China, Environ. Res. Lett., 19, 044032, https://doi.org/10.1088/1748-9326/ad5370, 2024.
Nooni, I. K., Tan, G., Hongming, Y., Chaibou, A. A. S., Habtemicheal, B. A., Gnitou, G. T., and Lim Kam Sian, K. T. C.: Assessing the performance of WRF Model in simulating heavy precipitation events over East Africa using satellite-based precipitation product, Remote Sens., 14, 1964, https://doi.org/10.3390/rs14091964, 2022.
Peng, T., Zhi, X., Ji, Y., Ji, L., and Tian, Y.: Prediction skill of extended range 2-m maximum air temperature probabilistic forecasts using machine learning postprocessing methods, Atmosphere, 11, 805, https://doi.org/10.3390/atmos11080805, 2020.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005.
Rasp, S. and Lerch, S.: Neural networks for postprocessing ensemble weather forecasts, Mon. Weather Rev., 146, 3885–3900, https://doi.org/10.1175/MWR-D-18-0187.1, 2018.
Robertson, D. E. and Wang, Q. J.: Seasonal forecasts of unregulated inflows into the Murray River, Australia, Water Resour. Manag., 27, 2747–2769, https://doi.org/10.1007/s11269-013-0313-4, 2013.
Sachindra, D. A., Ahmed, K., Rashid, M. M., Shahid, S., and Perera, B. J. C.: Statistical downscaling of precipitation using machine learning techniques, Atmos. Res., 212, 240–258, https://doi.org/10.1016/j.atmosres.2018.05.022, 2018.
Scheuerer, M. and Hamill, T. M.: Statistical post-processing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions, Mon. Weather Rev., 143, 4578–4596, https://doi.org/10.1175/MWR-D-15-0061.1, 2015.
Shi, X.: Enabling smart dynamical downscaling of extreme precipitation events with machine learning, Geophys. Res. Lett., 47, e2020GL090309, https://doi.org/10.1029/2020GL090309, 2020.
Singhal, A., Jaseem, M., and Jha, S. K.: Spatial connections in extreme precipitation events obtained from NWP forecasts: A complex network approach, Atmos. Res., 282, 106538, https://doi.org/10.1016/j.atmosres.2022.106538, 2023.
Srivastava, A. K., Ullrich, P. A., Rastogi, D., Vahmani, P., Jones, A., and Grotjahn, R.: Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS, Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, 2023.
Sun, L. and Lan, Y.: Statistical downscaling of daily temperature and precipitation over China using deep learning neural models: Localization and comparison with other methods, Int. J. Climatol., 41, 1128–1147, https://doi.org/10.1002/joc.6769, 2021.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K. L.: A review of global precipitation data sets: Data sources, estimation, and intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2014RG000477, 2016.
Tabari, H., Paz, S. M., Buekenhout, D., and Willems, P.: Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought, Hydrol. Earth Syst. Sci., 25, 3493–3517, https://doi.org/10.5194/hess-25-3493-2021, 2021.
Taillardat, M., Mestre, O., Zamo, M., and Naveau, P.: Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics, Mon. Weather Rev., 144, 2375–2393, https://doi.org/10.1175/MWR-D-15-0260.1, 2016.
Tan, Y., Dong, N., Hou, A., and Yan, W.: An improved Xin'anjiang hydrological model for flood simulation coupling snowmelt runoff module in Northwestern China, Water, 15, 3401, https://doi.org/10.3390/w15193401, 2023.
Valdez, E. S., Anctil, F., and Ramos, M.-H.: Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems, Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, 2022.
Vandal, T., Kodra, E., and Ganguly, A. R.: Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation, Theor. Appl. Climatol., 137, 557–570, https://doi.org/10.1007/s00704-018-2613-3, 2019.
Vigaud, N., Tippett, M. K., and Robertson, A. W.: Deterministic skill of subseasonal precipitation forecasts for the East Africa-West Asia sector from September to May, J. Geophys. Res.-Atmos., 124, 11887–11896, https://doi.org/10.1029/2019JD030747, 2019.
Vrac, M. and Friederichs, P.: Multivariate-intervariable, spatial, and temporal bias correction, J. Climate, 28, 218–237, https://doi.org/10.1175/JCLI-D-14-00059.1, 2015.
Wang, R., Zhang, J., Guo, E., Zhao, C., and Cao, T.: Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China, Atmos. Res., 222, 62–73, https://doi.org/10.1016/j.atmosres.2019.02.008, 2019.
Wei, L., Hu, K.-H., and Hu, X.-D.: Rainfall occurrence and its relation to flood damage in China from 2000 to 2015, J. Mt. Sci., 15, 2492–2504, https://doi.org/10.1007/s11629-018-4931-4, 2018.
Weyn, J. A., Durran, D. R., Caruana, R., and Cresswell-Clay, N.: Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models, J. Adv. Model. Earth Sy., 13, e2021MS002502, https://doi.org/10.1029/2021MS002502, 2021.
Xie, J., Hsu, P.-C., Hu, Y., Ye, M., and Yu, J.: Skillful extended-range forecast of rainfall and extreme events in East China based on deep learning, Weather Forecast., 38, 467–486, https://doi.org/10.1175/WAF-D-22-0132.1, 2023.
Xu, Y. P., Gao, X., Zhu, Q., and Zhang, Y.: Coupling a regional climate model and distributed hydrological model to assess future water resources in Jinhua River Basin, East China, J. Hydrol. Eng., 20, 04014054, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001007, 2015.
Yang, S., Yang, D., Chen, J., Santisirisomboon, J., 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.
You, X. X., Liang, Z. M., Wang, Y. Q., and Zhang, H.: A study on loss function against data imbalance in deep learning correction of precipitation forecasts, Atmos. Res., 281, 106500, https://doi.org/10.1016/j.atmosres.2022.106500, 2023.
Yuan, X., Wood, E. F., Luo, L., and Pan, M.: A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction, Geophys. Res. Lett., 38, L13401, https://doi.org/10.1029/2011GL047792, 2011.
Yuan, X., Ma, F., Wang, L., Zheng, Z., Ma, Z., Ye, A., and Peng, S.: An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions, Hydrol. Earth Syst. Sci., 20, 2437–2451, https://doi.org/10.5194/hess-20-2437-2016, 2016.
Yuan, X., Wang, S., and Hu, Z.-Z.: Do climate change and El Niño increase likelihood of Yangtze River extreme rainfall?, B. Am. Meteorol. Soc., 99, S113–S117, https://doi.org/10.1175/BAMS-D-17-0089.1, 2018.
Zhang, Q., Li, Y. P., Huang, G. H., Wang, H., Li, Y. F., Liu, Y. R., and Shen, Z. Y.: A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region, J. Hydrol., 615, 128730, https://doi.org/10.1016/j.jhydrol.2022.128730, 2022a.
Zhang, T., Liang, Z., Li, W., Wang, J., Hu, Y., and Li, B.: Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks, Hydrol. Earth Syst. Sci., 27, 1945–1960, https://doi.org/10.5194/hess-27-1945-2023, 2023.
Zhang, Y., Ragettli, S., Molnar, P., Fink, O., and Peleg, N.: Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments, J. Hydrol., 614, 128577, https://doi.org/10.1016/j.jhydrol.2022.128577, 2022b.
Zhao, R. J.: The Xin'anjiang model applied in China, J. Hydrol., 135, 371–381, https://doi.org/10.1016/0022-1694(92)90096-E, 1992.
Zhu, E., Yuan, X., and Wood, A.: Benchmark decadal forecast skill for terrestrial water storage estimated by an elasticity framework, Nat. Commun., 10, 1237, https://doi.org/10.1038/s41467-019-09245-3, 2019.
Zhu, S., Remedio, A. R. C., Sein, D. V., Sielmann, F., Ge, F., Xu, J., Peng, T., Jacob, D., Zhi, X., and Fraedrich, K.: Added value of the regionally coupled model ROM in the East Asian summer monsoon modeling, Theor. Appl. Climatol., 140, 375–387, https://doi.org/10.1007/s00704-020-03093-8, 2020.
Short summary
Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.
Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme...
