Polymedia in interaction

This Special Issue on “Polymedia in interaction” theorizes and empirically investigates practices and ideologies of digitally mediated interaction under conditions of polymedia. We argue that the proliferation of mobile interpersonal communication in the 2010s calls for, and is reflected in, conceptual and methodological shifts in empirical research on digital language and communication in pragmatics and sociocultural linguistics. In this introduction, these shifts are crystallized in five interrelated themes: (1) a turn from ‘computer-mediated communication’ to ‘digitally mediated interaction’ as a bracket category; (2) a move beyond the on/offline divide and focus on the integration of mediated interaction in everyday communication on micro-units of social structure (e.g. transnational families, business or academic communication); (3) an empirical downscaling towards private and small-scale public data; publicness; (4) a shift from the study of single modes of digital communication to polymedia; and (5) a focus on semiotic repertoires and registers of digital mediation. Research that orients to (some or all of) these focal points is compared with other trends in digital language research, including computational methods. The papers in this issue flesh out these five dimensions with findings from qualitative research, based on multi-sited linguistic and digital ethnographies in various sociolinguistic settings.

Satellite-Based Water Consumption Dynamics Monitoring in an Extremely Arid Area

Evapotranspiration (ET) involves actual water consumption directly from the land surface; however, regional ET maps are usually neglected during water management and allocation. In this study, an integrated satellite-based ET monitoring approach with two spatial resolutions is proposed over an extremely arid basin in China that has experienced crop area expansion and has been the focus of a water-saving project since 2012. The proposed ETWatch approach combined with an empirical downscaling strategy based on vegetation condition was employed to produce monthly ET maps. This method achieves satisfactory accuracy and is validated by its reasonable spatial and temporal pattern results. Yearly results exhibit an increasing ET trend before 2012, which subsequently gradually decrease. This trend fits well with the dynamics of the basin-wide vegetation condition, indicating that there is a stronger correlation between water consumption and vegetation than between other environmental indicators. The average ET over three main crop types in the region (grape, cotton, and melon) decreased by approximately 5% due to optimizations of the irrigation timeline during the project, while 13% of the water savings can be attributed to the fallowing of crop areas. Based on the irrigation distribution in 2012, a comparison between drip and border irrigation that achieves water savings of 3.6% from grape and 5.8% from cotton is conducted. However, an afforestation project that involved planting young trees led to an approximate 25% increase in water consumption. Overall, since 2012, the water-saving project has achieved satisfactory performance regarding excessive groundwater withdrawal, showing a reduction trend of 3 million m3/year and an increase in Lake Aiding water levels since 2011. The results reveal the potential of the ET monitoring strategy as a basis for basin-scale water management.

Empirical Downscaling of High-Resolution Regional Precipitation from Large-Scale Reanalysis Fields

This study describes an EOF-based technique for statistical downscaling of high-spatial-resolution monthly-mean precipitation from large-scale reanalysis circulation fields. The method is demonstrated and evaluated for four widely separated locations: the southeastern United States, the upper Colorado River basin, China’s Jiangxi Province, and central Europe. For each location, the EOF-based downscaling models successfully reproduce the observed annual cycle while eliminating the biases seen in NCEP–NCAR reanalysis precipitation. They also frequently reproduce the monthly precipitation anomalies with greater fidelity than is seen in the precipitation field derived directly from reanalysis, and they outperform a suite of regional climate models over the two U.S. locations. With the relatively high skill achieved over a range of climate regimes, this technique may be a viable alternative to numerical downscaling of monthly-mean precipitation for many locations.

Stationarity of Regression Relationships: Application to Empirical Downscaling

The performance of a statistical downscaling model is usually evaluated for its ability to explain a large fraction of predictand variance. In this note, it is shown that although this fraction may be high, the longest time scales, including trends, may not be explained by the model. This implies that the model is nonstationary over the training period of the model, and it questions the basic stationarity assumption of statistical downscaling. This is exemplified by using a simple regression model for downscaling European precipitation and surface temperature where appropriate Monte Carlo–based field significance tests are developed, taking into account the intercorrelation between predictand series. Based on this test, it is concluded that care is needed in selecting predictors to avoid this form of nonstationarity. Even though this is illustrated for a simple regression-type statistical downscaling model, the main conclusions may also be valid for more complicated models.