Forecasting hydrologic controls on juvenile anadromous fish out-migration with process-based modeling and machine learning.

River herring (Alosa sp.) are ecologically and economically foundational species in freshwater streams, estuaries, and oceanic ecosystems. The migration between fresh and saltwater is a key life stage of river herring, where the timing and magnitude of out-migration by juveniles can be limited when streams dry and hydrologic connectivity is lost. Operational decisions by water managers (e.g., restricting community water use) can impact out-migration success; however, these decisions are largely made without reliable predictions of outmigration potential across the migration season. This research presents a model to generate short-term forecasts of the probability of herring out-migration loss. We monitored streamflow and herring out-migration for 2 years at three critical runs along Long Island Sound (CT, USA) to develop empirical understandings of the hydrologic controls on out-migration. We used calibrated Soil and Water Assessment Tool hydrologic models of each site to generate 10,000 years of daily synthetic meteorological and streamflow records. These synthetic meteorological and streamflow data were used to train random forest models to provide rapid within-season forecasts of out-migration loss from two simple predictors: current spawning reservoir depth and the previous 30-day precipitation total. The resulting models were approximately 60%-80% accurate with a 1.5-month lead time and 70-90% accurate within 2 weeks. We anticipate that this tool will support regional decisions on spawning reservoir operations and community water withdrawals. The architecture of this tool provides a framework to facilitate broader predictions of the ecological consequences of streamflow connectivity loss in human-impacted watersheds.

Flood risk behaviors of United States riverine metropolitan areas are driven by local hydrology and shaped by race.

Flooding risk results from complex interactions between hydrological hazards (e.g., riverine inundation during periods of heavy rainfall), exposure, vulnerability (e.g., the potential for structural damage or loss of life), and resilience (how well we recover, learn from, and adapt to past floods). Building on recent coupled conceptualizations of these complex interactions, we characterize human-flood interactions (collective memory and risk-enduring attitude) at a more comprehensive scale than has been attempted to date across 50 US metropolitan statistical areas with a sociohydrologic (SH) model calibrated with accessible local data (historical records of annual peak streamflow, flood insurance loss claims, active insurance policy records, and population density). A cluster analysis on calibrated SH model parameter sets for metropolitan areas identified two dominant behaviors: 1) "risk-enduring" cities with lower flooding defenses and longer memory of past flood loss events and 2) "risk-averse" cities with higher flooding defenses and reduced memory of past flooding. These divergent behaviors correlated with differences in local stream flashiness indices (i.e., the frequency and rapidity of daily changes in streamflow), maximum dam heights, and the proportion of White to non-White residents in US metropolitan areas. Risk-averse cities tended to exist within regions characterized by flashier streamflow conditions, larger dams, and larger proportions of White residents. Our research supports the development of SH models in urban metropolitan areas and the design of risk management strategies that consider both demographically heterogeneous populations, changing flood defenses, and temporal changes in community risk perceptions and tolerance.

Predicting flood insurance claims with hydrologic and socioeconomic demographics via machine learning: Exploring the roles of topography, minority populations, and political dissimilarity.

Current research on flooding risk often focuses on understanding hazards, de-emphasizing the complex pathways of exposure and vulnerability. We investigated the use of both hydrologic and social demographic data for flood exposure mapping with Random Forest (RF) regression and classification algorithms trained to predict both parcel- and tract-level flood insurance claims within New York State, US. Topographic characteristics best described flood claim frequency, but RF prediction skill was improved at both spatial scales when socioeconomic data was incorporated. Substantial improvements occurred at the tract-level when the percentage of minority residents, housing stock value and age, and the political dissimilarity index of voting precincts were used to predict insurance claims. Census tracts with higher numbers of claims and greater densities of low-lying tax parcels tended to have low proportions of minority residents, newer houses, and less political similarity to state level government. We compared this data-driven approach and a physically-based pluvial flood routing model for prediction of the spatial extents of flooding claims in two nearby catchments of differing land use. The floodplain we defined with physically based modeling agreed well with existing federal flood insurance rate maps, but underestimated the spatial extents of historical claim generating areas. In contrast, RF classification incorporating hydrologic and socioeconomic demographic data likely overestimated the flood-exposed areas. Our research indicates that quantitative incorporation of social data can improve flooding exposure estimates.

Radiation Tolerance Testing Methodology of Robotic Manipulator Prior to Nuclear Waste Handling.

Dramatic cost savings, safety improvements and accelerated nuclear decommissioning are all possible through the application of robotic solutions. Remotely-controlled systems with modern sensing capabilities, actuators and cutting tools have the potential for use in extremely hazardous environments, but operation in facilities used for handling radioactive material presents complex challenges for electronic components. We present a methodology and results obtained from testing in a radiation cell in which we demonstrate the operation of a robotic arm controlled using modern electronics exposed at 10 Gy/h to simulate radioactive conditions in the most hazardous nuclear waste handling facilities.