Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-4127-2021
© 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-25-4127-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Statistical characterization of environmental hot spots and hot moments and applications in groundwater hydrology
Jiancong Chen
Department of Civil and Environmental Engineering, University of
California, Berkeley, California, USA
Bhavna Arora
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
Berkeley, California, USA
Alberto Bellin
Department of Civil, Environmental and Mechanical Engineering,
University of Trento, Trento, Italy
Department of Civil and Environmental Engineering, University of
California, Berkeley, California, USA
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Short summary
We developed a stochastic framework with indicator random variables to characterize the spatiotemporal distribution of environmental hot spots and hot moments (HSHMs) that represent rare locations and events exerting a disproportionate influence over the environment. 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 uncertainty associated with its contributors.
We developed a stochastic framework with indicator random variables to characterize the...