Preprints
https://doi.org/10.5194/hess-2020-343
https://doi.org/10.5194/hess-2020-343

  15 Jul 2020

15 Jul 2020

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

Statistical Characterization of Environmental Hot Spots and Hot Moments and Applications in Groundwater Hydrology

Jiancong Chen1, Bhavna Arora2, Alberto Bellin3, and Yoram Rubin1 Jiancong Chen et al.
  • 1Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA
  • 2Energy Geosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
  • 3Department of Civil, Environmental and Mechanical Engineering, University of Trento, Italy

Abstract. Environmental hot spots and hot moments (HSHMs) represent rare locations and events that exert disproportionate influence over the environment. While several mechanistic models have been used to characterize HSHMs behavior at specific sites, a critical missing component of research on HSHMs has been the development of clear, conventional statistical models. In this paper, we introduced a novel stochastic framework for analyzing HSHMs and the uncertainties. This framework can easily incorporate heterogeneous features in the spatiotemporal domain and can offer inexpensive solutions for testing future scenarios. The proposed approach utilizes indicator random variables (RVs) to construct a statistical model for HSHMs. The HSHMs indicator RVs are comprised of spatial and temporal components, which can be used to represent the unique characteristics of HSHMs. We identified three categories of HSHMs and demonstrated how our statistical framework are adjusted for each category. The three categories are (1) HSHMs defined only by spatial (static) components, (2) HSHMs defined by both spatial and temporal (dynamic) components, and (3) HSHMs defined by multiple dynamic components. The representation of an HSHM through its spatial and temporal components allows researchers to relate the HSHM’s uncertainty to the uncertainty of its components. We illustrated the proposed statistical framework through several HSHM case studies covering a variety of surface, subsurface, and coupled systems.

Jiancong Chen et al.

 
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for authors/editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Jiancong Chen et al.

Jiancong Chen et al.

Viewed

Total article views: 350 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
241 97 12 350 10 11
  • HTML: 241
  • PDF: 97
  • XML: 12
  • Total: 350
  • BibTeX: 10
  • EndNote: 11
Views and downloads (calculated since 15 Jul 2020)
Cumulative views and downloads (calculated since 15 Jul 2020)

Viewed (geographical distribution)

Total article views: 312 (including HTML, PDF, and XML) Thereof 312 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 11 Apr 2021
Download
Short summary
We developed a stochastic framework with indicator random variables to characterize the spatiotemporal distribution of environmental Hot Spots Hot Moments (HSHMs) that represent rare locations and events that exert disproportionate influence over the environment. Characteristics of HSHMs are characterized by static and dynamic indicators. This framework is advantageous as it allows us to calculate the uncertainty associated with HSHMs based on the uncertainty associated with its contributors.