Preprints
https://doi.org/10.5194/hess-2023-265
https://doi.org/10.5194/hess-2023-265
14 Dec 2023
 | 14 Dec 2023
Status: this preprint is currently under review for the journal HESS.

Variation and attribution of probable maximum precipitation of China using high-resolution dataset in a changing climate

Jinghua Xiong, Shenglian Guo, Abhishek, Jiabo Yin, Chongyu Xu, Jun Wang, and Jing Guo

Abstract. Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in the premise of existing ~100,000 reservoirs. Here, we use the finest spatiotemporal resolution (1d & 1 km) precipitation dataset to present the spatial distribution of 1d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in-situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find the national PMP generally decreases from Southeast to Northwest and is typically dominated by the high variability of precipitation extremes in North China and high intensity in South China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing with satellite and in-situ results due to differences in spatial scales and computation methods. Inter-annual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land-atmosphere coupling effects, leads to the widespread increase (>20 %) of PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emission. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

Jinghua Xiong, Shenglian Guo, Abhishek, Jiabo Yin, Chongyu Xu, Jun Wang, and Jing Guo

Status: open (until 29 Feb 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-265', Simon Michael Papalexiou, 20 Jan 2024 reply
    • AC1: 'Reply on RC1', Shenglian Guo, 20 Feb 2024 reply
  • RC2: 'Comment on hess-2023-265', Guoqiang Tang, 17 Feb 2024 reply
    • AC2: 'Reply on RC2', Shenglian Guo, 20 Feb 2024 reply
Jinghua Xiong, Shenglian Guo, Abhishek, Jiabo Yin, Chongyu Xu, Jun Wang, and Jing Guo
Jinghua Xiong, Shenglian Guo, Abhishek, Jiabo Yin, Chongyu Xu, Jun Wang, and Jing Guo

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Short summary
Temporal variability and spatial heterogeneity of climate systems challenge the 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 country, which is projected to amplify in future due to land-atmosphere coupling.