Reconstruction of the Upper Slope Conditions of an Extraordinary Hydro-Meteorological Event Along the Jamapa Glacier Drainage System, Citlaltépetl (Pico de Orizaba) Volcano, Mexico

A singular precipitation event on the summit glacial slopes of Mexico’s highest volcanic peak, Citlatépetl (also known as Pico de Orizaba), associated with the passage of Hurricane Ernesto across the southern Mexico mainland in August 2012, resulted in a debris flow at altitudes above 4,400 m asl, culminating in a hyperconcentrated flow downstream that had major impacts to a river valley’s channel morphology as well as to communities along a 25 km runout. The lahar originated at the terminal moraine and proglacial ramp of the Little Ice Age (LIA) extent of Citlaltépetl’s Jamapa glacier. Precipitation amounts were estimated based on nearby CONAGUA stations, but also on TRMM satellite images leading to an estimated 106 mm for a 3 day total, with 85 mm (80% of the total) falling on August 9th, the date when the lahar event occurred. The initial debris flow removed a minimum estimated 60,000 m3 of material from the proglacial ramp. A possible causative scenario is that the precipitation event overpressured the groundwater hydrology of an already unstable glacial-melt-saturated moraine. We demonstrate a methodology for the recreation of a pre-event landscape and the environmental conditions at the onset of the lahar, utilizing satellite products, in-situ geomorphological and geological evidence, and UAS technology.

Statistical characterization of environmental hot spots and hot moments and applications in groundwater hydrology

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.

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

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.