Preprints
https://doi.org/10.5194/hess-2021-120
https://doi.org/10.5194/hess-2021-120

  31 Mar 2021

31 Mar 2021

Review status: this preprint is currently under review for the journal HESS.

Critical transitions in the hydrological system: Early-warning signals and network analysis

Xueli Yang1, Zhi-Hua Wang1, and Chenghao Wang2 Xueli Yang et al.
  • 1School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA
  • 2Department of Earth System Science, Stanford University, Stanford, CA 94305, USA

Abstract. In this study, we identified the critical transitions of hydrological processes including precipitation and potential evapotranspiration by analysing their early-warning signals and system-based network structures. The statistical early-warning signals are manifest in increasing trends of autocorrelation and variance in the hydrology system ranging from regional to global scales, prior to climate shifts in the 1970s and 1990s in agreement with observations. We further extended the conventional statistics-based measures of early-warning signals to system-based network analysis in urban areas across the contiguous United States. The topology of urban precipitation network features hub-periphery (clustering) and modular organization, with strong intra-regional connectivity and inter-regional gateways (teleconnection). We found that several network parameters (mean correlation coefficient, density, and clustering coefficient) gradually increased prior to the critical transition in the 1990s, signifying the enhanced synchronization among urban precipitation pattern. These topological parameters not only can serve as novel system-based early-warning signals to critical transitions in hydrological processes, but also shed new lights on structure-dynamic interactions in the complex hydrological system.

Xueli Yang et al.

Status: open (until 26 May 2021)

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Xueli Yang et al.

Xueli Yang et al.

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Short summary
In this study, we investigated potentially catastrophic transitions in hydrological processes by identifying the early-warning signals manifest as critical “slowing-down” in complex dynamic systems. We then analysed the precipitation network of cities in the contiguous United States and found that key network parameters, such as the nodal density and the clustering coefficient, exhibit similar dynamic behaviour, which can serve as novel early-warning signals of the hydrological system.