Three‐Dimensional Neurointerpolation of Annual Mean Precipitation and Temperature Surfaces for China

Climatic variables such as annual mean precipitation and temperature display complex and nonlinear variation with latitude, longitude, and elevation. Neural networks are universal approximators and very good at detecting and representing nonlinear relationships between dependent and independent variables. In this paper we use resilient backpropagation (Rprop) neural networks to interpolate annual mean precipitation and temperature surfaces for China. Climate surfaces are interpolated from a total of 288 long‐term climate station data points using latitude, longitude, and elevation derived from a 5‐kilometer resolution digital elevation model. Initial trials of Rprop suggested very fast learning, insensitivity to selection of learning parameters, and a tendency not to overtrain. Cross‐validation was used to determine the best network structure and assess the error inherent in climate interpolation. With the error explicit, the final neurointerpolations of annual mean precipitation and temperature were constructed using all 288 climate station data points. Maps of residuals are also presented. The neurointerpolation of temperature was very successful and captures most of the regional trends found in established climate maps of China as well as significant topographically defined detail. For annual mean temperature the Rprop neural network was found to be an accurate and robust global spatial interpolator. However, the precipitation surface captures only the major latitudinally and continentally defined trends and misses many subregional rainfall features probably because of the influence of other nonparameterized atmospheric and topographic factors.     

Proto-Urban Establishments in Inner Asia: Surveys of an Iron Age Walled Site in Eastern Mongolia

Walled sites attributed to the Xiongnu steppe empire (2nd century b.c.–1st century a.d.) constitute the earliest investments in explicitly non-mortuary built environments among pastoralists of the Mongolian Steppe. These permanent constructions are important precedents to the medieval towns of subsequent steppe empires, yet very little of the complete layouts and surrounding remains of the large enclosures are understood. Our investigations of the walled site at Kherlen-Bars in eastern Mongolia are among the first surveys to fully document all aspects above and below ground across an entire Iron Age walled site area. In addition to detailed documentation of the large structures, our geomagnetic prospections and digital elevation modeling brought to light numerous small sub-surface and subtle-surface features that show such early walled sites, often deemed virtually empty, to be far more complex and diachronic in their formation processes and possible functions.     

Stone Age midden deposition assessed by bivalve sclerochronology and radiocarbon wiggle-matching of Arctica islandica shell increments

Sclerochronology, the study of the skeletal diaries of mollusks and corals, is a high-resolution geochronological tool of versatile usage in archaeology and paleontology with increasingly growing opportunities. Much of the recent efforts have concentrated on building multi-centennial bivalve growth records using annually deposited increments in the Holocene shells, comparable to tree-ring chronologies. In the context of geoarchaeology, the hitherto unachieved potential includes the application of sclerochronology to reconstruct long-term settlement histories. Here we contribute to both of these critical issues by presenting the first multi-shell sclerochronology constrained by methods originally developed in tree-ring research, using anthropogenically deposited bivalve shells of Arctica islandica excavated from a Stone Age midden in North Norway. Our systematic chronological approach to shell growth histories lays the foundation for a multi-dimensional dating framework that interacts between the incremental, radioisotopic and stratigraphic evidence. We show how the crossdating within and between the single-shell records yields a 155-year multi-shell sclerochronology, supported by the ¹⁴C AMS dates, that demonstrates minimum midden accumulation of 82 years and a depositional rate of 0.3 cm/yr. Sclerochronology paves the way for radiocarbon wiggle-matching, which narrows the probabilistic 2-σ uncertainty range for the oldest and youngest ¹⁴C AMS dates to 3150–2980 and 3060–2890 BC, respectively. We attribute the spectral characteristics of the chronology primarily to the North Atlantic Oscillation, suggesting essentially similar influences of climate variability on the Stone Age culture and our own society.     

Engaging children to voice their sense of place through location-based story making with photo-story maps

ABSTRACT

Sense of place is neither linear nor rooted in time. One way for children to voice their sense of place is through location-based stories with plots structured in space, rather than time. Since mobile devices are already ingrained in the everyday lives of many children in the U.S., a mobile application offers a familiar medium to engage children in location-based story making. Here, we present photo-story maps, our approach to leveraging an existing story-map mobile application as a research tool to collect and analyze children’s stories about sense of place. We found that photo-story maps facilitate the organization of nonlinear location-based stories, promote an inclusive story-making process through a mobile application, support triangulation of varied digital story elements, and provide dynamic interview material. We suggest photo-story maps demonstrate the value of location-based story making and the potential of familiar mobile applications for reducing the barriers to including children in research.