As the cost of individual sensors and chemical analysis goes down, synoptic sampling of stream water (taking many samples in a stream network in a short period of time) and catchment-scale sampling campaigns (monitoring many points in the landscape at the same time) become more attractive and easier to perform. This allows researchers to take ‘snap-shots’ of catchments and stream water systems with regard to both water quantity and quality. It may be possible to gain new insights into whole-catchment processes and the interconnections across various natural systems (i.e., geological, hydrological, pedological, ecological, biogeochemical) using such highly-distributed sampling approaches.

This session focuses on all aspects of such large-number-of-points-in-space datasets including the collection of data using both automatic and manual methods, the processing of data both spatially (i.e., geostatistics) and temporally, and the modeling and interpretation of such datasets. We specifically invite abstracts dealing with (1) the tradeoff between quantity and quality (higher prediction uncertainty) of samples, (2) methods for mixing high-spatial/low-temporal resolution data/sampling networks with low-spatial/high-temporal resolution data/sampling networks, (3) sampling strategy design (i.e., preferential selection of sampling locations versus random selection of sampling), and (4) scaling methods from isolated point observations to spatially continuous maps.