Abstract
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 HSHM 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 into 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 HSHM 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 is 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.
Highlights
Environmental hot spots and hot moments (HSHMs) were originally defined as rare locations or events that support or induce disproportionately high activity levels compared to surrounding areas or preceding times (McClain et al, 2003). Vidon et al (2010) further classified HSHMs into either transport-driven or biogeochemically driven HSHMs, based on the mechanisms causing the HSHMs. Bernhardt et al (2017) derived the concept of ecological control points (CPs) related to HSHMs, defining CPs as areas of the landscape that exert a disproportionate influence on the biogeochemical behavior of an ecosystem under study
We developed a general stochastic framework for characterizing the spatiotemporal distribution of environmental hot spots and hot moments (HSHMs)
– The HSHMs are defined as random variables, and the goal is to derive their stochastic distribution in terms of the relevant processes and attributes
Summary
Environmental hot spots and hot moments (HSHMs) were originally defined as rare locations or events that support or induce disproportionately high activity levels (e.g., chemical reaction rates) compared to surrounding areas or preceding times (McClain et al, 2003). Vidon et al (2010) further classified HSHMs into either transport-driven or biogeochemically driven HSHMs, based on the mechanisms causing the HSHMs. Bernhardt et al (2017) derived the concept of ecological control points (CPs) related to HSHMs, defining CPs as areas of the landscape that exert a disproportionate influence on the biogeochemical behavior of an ecosystem under study. Bernhardt et al (2017) derived the concept of ecological control points (CPs) related to HSHMs, defining CPs as areas of the landscape that exert a disproportionate influence on the biogeochemical behavior of an ecosystem under study. Environmental hot spots and hot moments (HSHMs) were originally defined as rare locations or events that support or induce disproportionately high activity levels (e.g., chemical reaction rates) compared to surrounding areas or preceding times (McClain et al, 2003). These definitions have mainly focused on HSHMs related to elevated biogeochemical activities triggered by hydrological or biogeochemical processes or a confluence of both processes. We provide a unified treatment of both positive and negative impacts of HSHMs, which allows us to present an integrative analytical framework for understanding and modeling HSHMs in various fields
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