Optimization methods for multi-scale sampling of soil moisture and snow in the Southern Sierra Nevada

Abstract:

Recent advancements in wireless sensing technologies are enabling real-time application of spatially representative point-scale measurements to model hydrologic processes at the basin scale. A major impediment to the large-scale deployment of these networks is the difficulty of finding representative sensor locations and resilient wireless network topologies in complex terrain. Currently, observatories are structured manually in the field, which provides no metric for the number of sensors required for extrapolation, does not guarantee that point measurements are representative of the basin as a whole, and often produces unreliable wireless networks. We present a methodology that combines LiDAR data, pattern recognition, and stochastic optimization to simultaneously identify representative sampling locations, optimal sensor number, and resilient network topologies prior to field deployment. We compare the results of the algorithm to an existing 55-node wireless snow and soil network at the Southern Sierra Critical Zone Observatory. Existing data show that the algorithm is able to capture a broader range of key attributes affecting snow and soil moisture, defined by a combination of terrain, vegetation and soil attributes, and thus is better suited to basin-wide monitoring. We believe that adopting this structured, analytical approach could improve data quality, increase reliability, and decrease the cost of deployment for future networks.