Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-5259-2021
© Author(s) 2021. 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-25-5259-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Bridging the scale gap: obtaining high-resolution stochastic simulations of gridded daily precipitation in a future climate
Qifen Yuan
CORRESPONDING AUTHOR
Norwegian Water Resources and Energy Directorate, Oslo, Norway
Department of Geosciences, University of Oslo, Oslo, Norway
Thordis L. Thorarinsdottir
Norwegian Computing Center, Oslo, Norway
Stein Beldring
Norwegian Water Resources and Energy Directorate, Oslo, Norway
Wai Kwok Wong
Norwegian Water Resources and Energy Directorate, Oslo, Norway
Chong-Yu Xu
Department of Geosciences, University of Oslo, Oslo, Norway
Related authors
No articles found.
Zitong Jia, Shouzhi Chen, Yongshuo H. Fu, David Martín Belda, David Wårlind, Stefan Olin, Chongyu Xu, and Jing Tang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4064, https://doi.org/10.5194/egusphere-2025-4064, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Groundwater sustains vegetation and regulates land-atmosphere exchanges, but most Earth system models oversimplify its movement. Our study develops an integrated framework coupling LPJ-GUESS with the 3D hydrological model ParFlow to explicitly represent groundwater-vegetation interactions. Our results add to the evidence that three-dimensional groundwater flow strongly regulates water exchanges, and provides a powerful tool to improve simulations of water cycles in Earth system models.
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025, https://doi.org/10.5194/hess-29-2133-2025, 2025
Short summary
Short summary
We compared hourly and daily extreme precipitation across Norway from HARMONIE Climate models at convection-permitting 3 km (HCLIM3) and 12 km (HCLIM12) resolutions. HCLIM3 more accurately captures the extremes in most regions and seasons (except in summer). Its advantages are more pronounced for hourly extremes than for daily extremes. The results highlight the value of convection-permitting models in improving extreme-precipitation predictions and in helping the local society brace for extreme weather.
Qiumei Ma, Chengyu Xie, Zheng Duan, Yanke Zhang, Lihua Xiong, and Chong-Yu Xu
EGUsphere, https://doi.org/10.5194/egusphere-2025-679, https://doi.org/10.5194/egusphere-2025-679, 2025
Short summary
Short summary
We propose a method to estimate the reservoir WLS curve based on the capacity loss induced by sediment accumulation and further assess the potential negative impact caused by outdated design WLS curve on flood regulation risks. The findings highlight that when storage capacity is considerably reduced, continued use of the existing design WLS curve may significantly underestimate, thus posing potential safety hazards to the reservoir itself and downstream flood protection objects.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025, https://doi.org/10.5194/hess-29-903-2025, 2025
Short summary
Short summary
This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Tian Lan, Xiao Wang, Hongbo Zhang, Xinghui Gong, Xue Xie, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-384, https://doi.org/10.5194/hess-2024-384, 2025
Preprint under review for HESS
Short summary
Short summary
Hydrological models are vital for water management but often fail to predict water flow in dynamic catchments due to model simplification. This study tackles it by developing an optimized calibration framework that considers dynamic catchment characteristics. To overcome potential difficulties, multiple schemes were tested on over 200 U.S. catchments. The results enhanced our understanding of simulation in dynamic catchments and provided a practical solution for improving future forecasting.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
Short summary
Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Jinghua Xiong, Shenglian Guo, Abhishek, Jiabo Yin, Chongyu Xu, Jun Wang, and Jing Guo
Hydrol. Earth Syst. Sci., 28, 1873–1895, https://doi.org/10.5194/hess-28-1873-2024, https://doi.org/10.5194/hess-28-1873-2024, 2024
Short summary
Short summary
Temporal variability and spatial heterogeneity of climate systems challenge accurate estimation of probable maximum precipitation (PMP) in China. We use high-resolution precipitation data and climate models to explore the variability, trends, and shifts of PMP under climate change. Validated with multi-source estimations, our observations and simulations show significant spatiotemporal divergence of PMP over the country, which is projected to amplify in future due to land–atmosphere coupling.
Danielle M. Barna, Kolbjørn Engeland, Thomas Kneib, Thordis L. Thorarinsdottir, and Chong-Yu Xu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2335, https://doi.org/10.5194/egusphere-2023-2335, 2023
Preprint archived
Short summary
Short summary
Estimating flood quantiles at data-scarce sites often involves single-duration regression models. However, floodplain management and reservoir design, for example, need estimates at several durations, posing challenges. Our flexible generalized additive model (GAM) enhances accuracy and explanation, revealing that single-duration models may underperform elsewhere, emphasizing the need for adaptable approaches.
Pengxiang Wang, Zuhao Zhou, Jiajia Liu, Chongyu Xu, Kang Wang, Yangli Liu, Jia Li, Yuqing Li, Yangwen Jia, and Hao Wang
Hydrol. Earth Syst. Sci., 27, 2681–2701, https://doi.org/10.5194/hess-27-2681-2023, https://doi.org/10.5194/hess-27-2681-2023, 2023
Short summary
Short summary
Considering the impact of the special geological and climatic conditions of the Qinghai–Tibet Plateau on the hydrological cycle, this study established the WEP-QTP hydrological model. The snow cover and gravel layers affected the temporal and spatial changes in frozen soil and improved the regulation of groundwater on the flow process. Ignoring he influence of special underlying surface conditions has a great impact on the hydrological forecast and water resource utilization in this area.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
Short summary
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Pengxiang Wang, Zuhao Zhou, Jiajia Liu, Chongyu Xu, Kang Wang, Yangli Liu, Jia Li, Yuqing Li, Yangwen Jia, and Hao Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-538, https://doi.org/10.5194/hess-2021-538, 2021
Manuscript not accepted for further review
Short summary
Short summary
Combining the geological characteristics of the thin soil layer on the thick gravel layer and the climate characteristics of the long-term snow cover of the Qinghai-Tibet Plateau, the WEP-QTP hydrological model was constructed by dividing a single soil structure into soil and gravel. In contrast to the general cold area, the special environment of the Qinghai–Tibet Plateau affects the hydrothermal transport process, which can not be ignored in hydrological forecast and water resource assessment.
Tian Lan, Kairong Lin, Chong-Yu Xu, Zhiyong Liu, and Huayang Cai
Hydrol. Earth Syst. Sci., 24, 5859–5874, https://doi.org/10.5194/hess-24-5859-2020, https://doi.org/10.5194/hess-24-5859-2020, 2020
Cited articles
Akima, H. and Gebhardt, A.:
akima: Interpolation of Irregularly and Regularly Spaced Data, R package version 0.6-2,
available at: https://CRAN.R-project.org/package=akima (last access: 27 March 2019), 2016. a
Andreoli, R. V. and Kayano, M. T.:
ENSO-related rainfall anomalies in South America and associated circulation features during warm and cold Pacific decadal oscillation regimes,
Int. J. Climatol.,
25, 2017–2030, https://doi.org/10.1002/joc.1222, 2005. a
Bengtsson, L.:
The global atmospheric water cycle,
Environ. Res. Lett.,
5, 025202, https://doi.org/10.1088/1748-9326/5/2/025002, 2010. a
Burton, A., Kilsby, C., Fowler, H., Cowpertwait, P., and O'Connell, P.:
RainSim: A spatial–temporal stochastic rainfall modelling system,
Environ. Modell. Softw.,
23, 1356–1369, https://doi.org/10.1016/j.envsoft.2008.04.003, 2008. a
Burton, A., Fowler, H. J., Kilsby, C. G., and O'Connell, P. E.:
A stochastic model for the spatial-temporal simulation of nonhomogeneous rainfall occurrence and amounts,
Water Resour. Res.,
46, W11501, https://doi.org/10.1029/2009wr008884, 2010. a
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. a
Chandler, R. E. and Wheater, H. S.:
Analysis of rainfall variability using generalized linear models: A case study from the west of Ireland,
Water Resour. Res.,
38, 10–1–10–11, https://doi.org/10.1029/2001wr000906, 2002. a, b, c
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A. J., Wehner, M. F., Allen, M. R., Andrews, T., Beyerle, U., Bitz, C. M., Bony, S., and Booth, B. B. B.:
Long-term climate change: projections, commitments and irreversibility,
in: Climate Change 2013 – The Physical Science Basis: Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, New York, NY, USA, 1029–1136, 2013. a
Cowpertwait, P. S. P., Kilsby, C. G., and O'Connell, P. E.:
A space-time Neyman-Scott model of rainfall: Empirical analysis of extremes,
Water Resour. Res.,
38, 6-1–6-14, https://doi.org/10.1029/2001wr000709, 2002. a
Cressie, N. and Wikle, C. K.:
Statistics for spatio-temporal data, e-book version,
John Wiley & Sons, New York, USA, 2015. a
Evin, G., Favre, A.-C., and Hingray, B.: Stochastic generation of multi-site daily precipitation focusing on extreme events, Hydrol. Earth Syst. Sci., 22, 655–672, https://doi.org/10.5194/hess-22-655-2018, 2018. a
Frei, C., Christensen, J. H., Déqué, M., Jacob, D., Jones, R. G., and Vidale, P. L.:
Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps,
J. Geophys. Res.-Atmos.,
108, D3, https://doi.org/10.1029/2002jd002287, 2003. a
Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader, J., Böttinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak,
K., Gayler, V., Haak, H., Hollweg, H.-D., Ilyina, T., Kinne, S., Kornblueh, L., Matei, D., Mauritsen, T., Mikolajewicz, U., Mueller, W., Notz, D., Pithan, F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur, R., Segschneider, J., Six, K. D., Stockhause, M., Timmreck, C., Wegner, J., Widmann, H., Wieners, K.-H., Claussen, M., Marotzke, J., and Stevens, B.:
Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5,
J. Adv. Model. Earth Sy.,
5, 572–597, https://doi.org/10.1002/jame.20038, 2013. a
Gneiting, T. and Raftery, A. E.:
Strictly proper scoring rules, prediction, and estimation,
J. Am. Stat. Assoc.,
102, 359–378, https://doi.org/10.1198/016214506000001437, 2007. a
Gräler, B., Pebesma, E., and Heuvelink, G.:
Spatio-Temporal Interpolation using gstat,
R J.,
8, 204–218, available at: https://journal.r-project.org/archive/2016-1/na-pebesma-heuvelink.pdf (last access: 27 March 2019), 2016. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.:
EURO-CORDEX: new high-resolution climate change projections for European impact research,
Reg. Environ. Change,
14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014. a, b
Kendall, M. G.:
The treatment of ties in ranking problems,
Biometrika,
33, 239–251, 1945. a
Lind, P., Belušić, D., Christensen, O. B., Dobler, A., Kjellström, E., Landgren, O., Lindstedt, D., Matte, D., Pedersen, R. A., Toivonen, E., and Wang, F.:
Benefits and added value of convection-permitting climate modeling over Fenno-Scandinavia,
Clim. Dynam.,
55, 1893–1912, https://doi.org/10.1007/s00382-020-05359-3, 2020. a
Luca, D. L. D., Petroselli, A., and Galasso, L.:
A Transient Stochastic Rainfall Generator for Climate Changes Analysis at Hydrological Scales in Central Italy,
Atmosphere,
11, 1292, https://doi.org/10.3390/atmos11121292, 2020. a
Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Tveito, O. E., and Andersen, J.: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018, 2018. a
Lussana, C., Tveito, O. E., Dobler, A., and Tunheim, K.: seNorge_2018, daily precipitation, and temperature datasets over Norway, Earth Syst. Sci. Data, 11, 1531–1551, https://doi.org/10.5194/essd-11-1531-2019, 2019. a
Maraun, D., Wetterhall, F., Ireson, A., Chandler, R., Kendon, E., Widmann, M., Brienen, S., Rust, H., Sauter, T., Themeßl, M., Venema, V., chun, K., Goodess, C., Jones, R., 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. a
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M., Hall, A., and Mearns, L. O.:
Towards process-informed bias correction of climate change simulations,
Nat. Clim. Change,
7, 764–773, https://doi.org/10.1038/nclimate3418, 2017. a, b
Mohr, M.:
Comparison of versions 1.1 and 1.0 of gridded temperature and precipitation data for Norway, met no note, 19,
Norwegian Meteorological Institute, Oslo, Norway, 2009. a
Pebesma, E. J.:
Multivariable geostatistics in S: the gstat package,
Comput. Geosci.,
30, 683–691, 2004. a
Prein, A. F., Rasmussen, R., Castro, C. L., Dai, A., and Minder, J.:
Special issue: Advances in convection-permitting climate modeling,
Clim. Dynam.,
55, 1–2, https://doi.org/10.1007/s00382-020-05240-3, 2020. a
R Core Team:
R: A Language and Environment for Statistical Computing,
R Foundation for Statistical Computing, Vienna, Austria, available at: https://www.R-project.org/ (last access: 27 March 2019), 2019. a
Rockel, B., Will, A., and Hense, A.:
The regional climate model COSMO-CLM (CCLM),
Meteorol. Z.,
17, 347–348, https://doi.org/10.1127/0941-2948/2008/0309, 2008. a
Serinaldi, F.:
Analysis of inter-gauge dependence by Kendall's τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields,
Stoch. Env. Res. Risk A.,
22, 671–688, https://doi.org/10.1007/s00477-007-0176-4, 2007. a
Smith, D. M., Eade, R., Scaife, A. A., Caron, L.-P., Danabasoglu, G., DelSole, T. M., Delworth, T., Doblas-Reyes, F. J., Dunstone, N. J.,
Hermanson, L., Kharin, V., Kimoto, M., Merryfield, W. J., Mochizuki, T., Müller, W. A., Pohlmann, H., Yeager, S., and Yang, X.:
Robust skill of decadal climate predictions,
npj Climate and Atmospheric Science,
2, 1–10, https://doi.org/10.1038/s41612-019-0071-y, 2019. a
Thorarinsdottir, T. L., Gneiting, T., and Gissibl, N.:
Using Proper Divergence Functions to Evaluate Climate Models,
SIAM/ASA Journal on Uncertainty Quantification,
1, 522–534, https://doi.org/10.1137/130907550, 2013. a, b
Vautard, R., Gobiet, A., Sobolowski, S., Kjellström, E., Stegehuis, A., Watkiss, P., Mendlik, T., Landgren, O., Nikulin, G., Teichmann, C., and Jocob, D.:
The European climate under a 2 ∘C global warming,
Environ. Res. Lett.,
9, 034006, https://doi.org/10.1088/1748-9326/9/3/034006, 2014. a
Voldoire, A., Sanchez-Gomez, E., y Mélia, D. S., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.:
The CNRM-CM5.1 global climate model: description and basic evaluation,
Clim. Dynam.,
40, 2091–2121, https://doi.org/10.1007/s00382-011-1259-y, 2013.
a
Volosciuk, C., Maraun, D., Vrac, M., and Widmann, M.: A combined statistical bias correction and stochastic downscaling method for precipitation, Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, 2017. a
Von Storch, H., Omstedt, A., Pawlak, J., and Reckermann, M.:
chap. Introduction and summary,
in: Second Assessment of Climate Change for the Baltic Sea Basin,
edited by: The BACC II Author Team,
Springer, Geesthacht, Germany, 1–22, 2015. a
Wilks, D.:
Multisite generalization of a daily stochastic precipitation generation model,
J. Hydrol.,
210, 178–191, https://doi.org/10.1016/s0022-1694(98)00186-3, 1998. a, b, c, d
Wong, G., Maraun, D., Vrac, M., Widmann, M., Eden, J. M., and Kent, T.:
Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes,
J. Climate,
27, 6940–6959, https://doi.org/10.1175/jcli-d-13-00604.1, 2014. a
Wood, S.:
Generalized Additive Models: An Introduction with R, 2 edn.,
Chapman and Hall/CRC, Boca Raton, 2017. a
Wood, S. N.:
Thin plate regression splines,
J. R. Stat. Soc. B,
65, 95–114, https://doi.org/10.1111/1467-9868.00374, 2003. a, b
Wu, R., Hu, Z.-Z., and Kirtman, B. P.:
Evolution of ENSO-related rainfall anomalies in East Asia,
J. Climate,
16, 3742–3758, 2003. a
Yang, C., Chandler, R. E., Isham, V. S., and Wheater, H. S.:
Spatial-temporal rainfall simulation using generalized linear models,
Water Resour. Res.,
41, W11415, https://doi.org/10.1029/2004wr003739, 2005. a, b, c
Yuan, Q., Thorarinsdottir, T. L., Beldring, S., Wong, W. K., Huang, S., and Xu, C.-Y.:
New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid,
J. Appl. Meteorol. Clim.,
58, 2617–2632, https://doi.org/10.1175/jamc-d-19-0086.1, 2019. a
Short summary
Localized impacts of changing precipitation patterns on surface hydrology are often assessed at a high spatial resolution. Here we introduce a stochastic method that efficiently generates gridded daily precipitation in a future climate. The method works out a stochastic model that can describe a high-resolution data product in a reference period and form a realistic precipitation generator under a projected future climate. A case study of nine catchments in Norway shows that it works well.
Localized impacts of changing precipitation patterns on surface hydrology are often assessed at...