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Lerback, Jory

Publications and source records attributed to Lerback, Jory.

Bonneville basin critical zones: Spring chemistry and gastropod ecology in playa-margin wetlands

Playa margin wetlands in the Bonneville basin are sustained by groundwater-fed brackish springs, which transport salts and other solutes into the playa basin. These wetlands are sensitive to changing water availability and quality, which are impacted by changing climate and land use, and whose sediments also provide important records of changing environmental conditions. Gastropods building their shells in these springs provide important recorders of water chemistry and may reflect changing aqueous conditions. In this paper, we analyze spring water chemistry, gastropod ecology and gastropod shell chemistry of Blue Lake (BL) and Horseshoe Springs (HRS), two groundwater-fed wetlands in the Great Salt Lake watershed. Here, we report the physical parameters including pH, temperature, and specific conductivity across the spring pond at Horseshoe springs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.

(ICON) principles to address↗