Modeling Regional-Scale Vulnerabilities to Drought through Least Cost Analyses: An Archaeological Case Study from the Jemez Mountains, New Mexico
Author(s): Michael Aiuvalasit; Ian Jorgeson
This is an abstract from the "Novel Statistical Techniques in Archaeology I (QUANTARCH I)" session, at the 84th annual meeting of the Society for American Archaeology.
We present a new approach for identifying archaeological proxies for community vulnerabilities to climate change: least cost analyses of water acquisition costs from archaeological sites to water. By automating the least cost analysis through a custom Python script in ArcGIS Pro, we modeled the 1-way cost for water acquisition pairwise between 136 water sources and 5,480 archaeological sites across the Jemez and Pajarito Plateaus of the Jemez Mountains, New Mexico. We then compared travel times from water sources with different drought sensitivities to archaeological sites. This allowed us to explore diachronic regional-scale vulnerabilities in Ancestral Pueblo settlement patterns to hydrological droughts. Our research found that while hydrological droughts would not have made water acquisition costs prohibitive, they significantly increased costs. This problem was exacerbated on the Pajarito Plateau due to greater sensitivities in the geohydrological system to reduced surface water during droughts, and the decline of the dual residence pattern among Ancestral Pueblo communities. Therefore, hydrological droughts in concert with the socio-economic consequences of village aggregation in the 15th century cannot be ruled out as factors in the depopulation of Pajarito Plateau.
Cite this Record
Modeling Regional-Scale Vulnerabilities to Drought through Least Cost Analyses: An Archaeological Case Study from the Jemez Mountains, New Mexico. Michael Aiuvalasit, Ian Jorgeson. Presented at The 84th Annual Meeting of the Society for American Archaeology, Albuquerque, NM. 2019 ( tDAR id: 451183)
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North America: Southwest United States
min long: -124.365; min lat: 25.958 ; max long: -93.428; max lat: 41.902 ;
Abstract Id(s): 24989