Multi‐Objective Urban Observational Strategies: A Risk‐Based Framework for Expanding Flood Sensor Networks

In coupled human and natural systems, developing an observation strategy which maximizes insight into both the natural system and the human system is a challenging multi-objective optimization problem. In this article, we describe the expansion of a flood risk observation system in Southeast Texas designed to improve our understanding of both physical and socioeconomic exposure to hydrological hazards at fine spatial scales, in the context of a structured hazard-exposure-vulnerability risk framework. We describe a new approach for assessing the spatial extent through which a flood sensor's observations can be assumed to be relevant, and estimate the population served within each sensor's area of information using downscaled socio-demographic data. As hydrological observations and modeling move to ever finer scale, assessing the information they contain in the context of both social and natural systems becomes increasingly important for developing actionable scientific insights.. Authors: Brelsford, Christa Maria [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000234908020); Coon, Ethan T. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000181249622); Wang, Mark [Univ. of Texas, Austin, TX (United States)] (ORCID:0000000206637980); Rosenheim, Nathanael [Texas A & M Univ., College Station, TX (United States)] (ORCID:0000000156010126); Brake, Nicholas [Lamar Univ., Beaumont, TX (United States)] (ORCID:0000000243267800). DOE Contract: 89233218CNA000001; SC0023216. Subjects: 54 ENVIRONMENTAL SCIENCES; Flood risk; flood sensors; observation systems; sensor networks