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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Lessons from Popocatepetl Volcano (Mexico): Ancient Settlement Buried by Lavas, Mudflows, and Air-Fall Deposits

Popocatepetl volcano is 5452 m in altitude and capped by glaciers with a long Late Pleistocene-Holocene history. Volcanic activity has been intense during the last 10 000 years. Therefore, the valleys at the NE foothills of the volcano, covered by air-fall ejecta and drained by the runoff of the glaciers, became very attractive to ancient inhabitants of the Xalizintla Valley (XV) west of Puebla City, because of fertility of soils. The XV was occupied by humans about 2000 years ago who witnessed five events related to volcanic activity related to Popo. These events, described in this paper, are being taken into account for volcanic risk evaluation since several towns with a population of more than 23 000 people reoccupied again the Xalizintla Valley.

volcano Popocatepetl Mexico glacier mud flows huma↗
DOE OSTI2024

Identifying recharge sources and their impacts on a North Central New Mexico shallow aquifer using unsupervised machine learning

In this article, shallow aquifers are important but highly variable resources in arid to semi-arid regions. Limited shallow aquifer volume results in high sensitivity to recharge fluctuations, which can impact the local fauna and flora, and transport of contaminants in the aquifer or vadose zone. Aquifer response to external forcing (e.g., precipitation) is usually solved by estimating aquifer parameters and running physics-based models to match known fluctuations of hydraulic head. However, this technique is time and computationally expensive. Furthermore, high aquifer complexity decreases precision in physics-based models. Alternatively supervised machine learning is used to predict aquifer dynamics. However, these techniques rely on input data and struggle to interpret aquifer response for missing sources (i.e., snowpack data). To counter these problems, we propose an unsupervised machine learning technique (NMFk) to estimate the impact of different sources on aquifer recharge. NMFk is used to understand the influence of external forcing on shallow aquifer recharge in the Pajarito Plateau (Los Alamos, NM, USA). The results show how NMFk can be used to reduce the data dimension in a complex field dataset to three recharge signals that cause fluctuations within the field data. Here, the source signals are interpreted as rainfall, snowmelt, and a delayed aquifer response to the previous two signals. These results evidence how heterogeneous aquifers delimited by canyons incised into the Pajarito Plateau respond in similar ways to the source signals identified by NMFk. Furthermore, results show the importance of the local geology where faults act as sinks, and anthropogenic disturbances can facilitate infiltration amplifying the interpreted signal.

54 ENVIRONMENTAL SCIENCES↗