Articles | Volume 23, issue 8
Research article
19 Aug 2019
Research article |  | 19 Aug 2019

Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

Zhengke Pan, Pan Liu, Shida Gao, Jun Xia, Jie Chen, and Lei Cheng

Related authors

Response of active catchment water storage capacity to a prolonged meteorological drought and asymptotic climate variation
Jing Tian, Zhengke Pan, Shenglian Guo, Jiabo Yin, Yanlai Zhou, and Jun Wang
Hydrol. Earth Syst. Sci., 26, 4853–4874,,, 2022
Short summary
The influence of a prolonged meteorological drought on catchment water storage capacity: a hydrological-model perspective
Zhengke Pan, Pan Liu, Chong-Yu Xu, Lei Cheng, Jing Tian, Shujie Cheng, and Kang Xie
Hydrol. Earth Syst. Sci., 24, 4369–4387,,, 2020
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114,,, 2023
Short summary
Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004,,, 2023
Short summary
Changes in Mediterranean flood processes and seasonality
Yves Tramblay, Patrick Arnaud, Guillaume Artigue, Michel Lang, Emmanuel Paquet, Luc Neppel, and Eric Sauquet
Hydrol. Earth Syst. Sci., 27, 2973–2987,,, 2023
Short summary
Can the combining of wetlands with reservoir operation reduce the risk of future floods and droughts?
Yanfeng Wu, Jingxuan Sun, Boting Hu, Y. Jun Xu, Alain N. Rousseau, and Guangxin Zhang
Hydrol. Earth Syst. Sci., 27, 2725–2745,,, 2023
Short summary
Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644,,, 2023
Short summary

Cited articles

Ajami, N. K., Duan, Q. Y., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403,, 2007. 
Bracken, C., Holman, K. D., Rajagopalan, B., and Moradkhani, H.: A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis, Water Resour. Res., 54, 243–255,, 2018. 
Brigode, P., Oudin, L., and Perrin, C.: Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?, J. Hydrol., 476, 410–425,, 2013. 
Broderick, C., Matthews, T., Wilby, R. L., Bastola, S., and Murphy, C.: Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods, Water Resour. Res., 52, 8343–8373,, 2016. 
Cha, Y., Park, S. S., Lee, H. W., and Stow, C. A.: A Bayesian hierarchical approach to model seasonal algal variability along an upstream to downstream river gradient, Water Resour. Res., 52, 348–357,, 2016. 
Short summary
Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.