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Mangel, Adam

Publications and source records attributed to Mangel, Adam.

Managed aquifer recharge as a strategy to redistribute excess surface flow to baseflow in snowmelt hydrologic regimes

Water management in snowmelt hydrologic regimes, characterized by large annual fluctuations in stream flow driven by seasonal snow melt, faces the challenge of highly variable supply that often does not align with timing of demand. Climate change may exacerbate management challenges by significantly reducing snowpack or shifting snow melt earlier. Here, managed aquifer recharge (MAR) is evaluated as a potential strategy to reallocate excess early-season stream flow to time periods when less surface water is available. This strategy differs from traditional MAR, where the goal is to minimize loss to surface water. We assess how to site MAR operations such that groundwater recharge flows back to the surface water system in a lagged manor to benefit water management objectives, which we term “enhanced baseflow.” We use a regional groundwater model for the Treasure Valley aquifer located in southwestern Idaho, United States to demonstrate a generalizable approach using regional groundwater models as tools to identify favorable baseflow enhancement locations. Hypothetical MAR is simulated at 197 candidate locations, which are then evaluated for how effectively they meet potential management objectives. In addition to demonstrating the modeling and evaluation approach, we discuss lessons learned from applying a pre-existing regional groundwater model to MAR for enhanced baseflow and also describe important considerations, such as the physical and institutional availability of surface flows and specific management objectives, when assessing regional and site-specific suitability of MAR for enhanced baseflow as a potential management strategy.

54 ENVIRONMENTAL SCIENCES↗

Emergent Concepts from a Community Ideation on AI4ESP

This AI4ESP white paper is the outcome of an open ideation session led by PNNL that numerous institutions participated in. The leadership team included Amy Goldman, Huiying Ren, Tim Scheibe, and James Stegen. The white paper is broken into two sections: (1) A summary of the ideation session, and (2) a short summary of a ‘meta idea’ that emerged from combining multiple ideas spanning the three primary focal area themes articulated in BER’s AI4ESP guidance: A - Data Acquisition and Assimilation, B - Predictive Modeling, and C - Insight from Complex Data. The white paper, therefore, has two goals. The first section is meant to be a resource for innovating around how to pursue open, community-based ideation in virtual environments. The second section is meant as an example of the kinds of ‘meta ideas’ that can emerge from bringing diverse ideas together in a single shared space. We consider the second section to be a submission to BER’s request for white papers around AI4ESP.

54 ENVIRONMENTAL SCIENCES↗