FUTURE HYDROLOGICAL FAILURE PROBABILITY OF DAMS IN NEW ENGLAND UNDER LAND USE AND CLIMATE CHANGE

Floods lead to the overtopping of dams which is the main cause of dam failures and can result in significant loss of lives and property. This study investigates how the hydrological failure probability of dams in New England may change with future changes in climate and land use. Non-stationarity of future precipitation caused by the anthropogenic climate change and altered watershed concentration times caused by anthropogenic alterations such as urbanization, industrialization or deforestation can impact the mechanisms of runoff production and transfer. This can potentially change the frequency, magnitude, or duration of floods. Therefore, due to different flood patterns and consequently different hydrological failure probability, dams in New England likely have very different future risk levels. As hydrological failure probability indicators, the magnitude and frequency, and duration of floods exceeding a threshold are used to determine the variability of hydrological failure probability. Aside from the historical measured and gridded climate and land use data, this study uses one high temporal- and spatial-resolution, dynamically downscaled climate change projection and 29 statistically downscaled climate change projections as well as four land use projections from “The New England Landscape Futures Project”. Results show that basin response in New England during high-flow events has not significantly changed during recent decades in spite of recent changes in climate and runoff generation mechanisms. Also, dammed basins with higher storage capacity are found to have a decrease in basin response and flood peaks while there is not enough evidence the significance of urban development on high-flow events in New England. It is likely that dams in New England experience higher levels of hydrological failure probability. This is because compared to historical data, future floods are likely to increase in magnitude and frequency, but they are not likely to last longer. Also, the results show more accentuated increase in the frequency compared to the magnitude of future floods. This study will help dam owners and state regulators plan for more resilient dam operations and more rigorous dam maintenance and account for the future risk associated with the approximately 15,000 dams in New England

Biogenic Organic Carbon Compounds in Air and Rain

Rainwater quality has not been an issue of concern until recent decades of increasing urbanization and industrialization. Therefore, the role of biogenic contamination sources has been always underestimated as generally, anthropogenic contaminants are thought to be responsible for rainwater quality deterioration. This study aims to find the sources and reasons of biogenic VOC emission into the air and their changes in the air. Also, transfer of biogenic VOCs into the rainwater and their abundance have been investigated. The effects of these biogenic VOCs on rainwater quality have been studied by sampling of two rain events in the University of Massachusetts in Amherst. Key water quality parameters such as UV/VIS absorbance, DOC, SUVA, chlorine residual and DBPs formation potentials have been measured and analyzed. The results show a number of high concentrations of DOC and DBP formation potentials in rainwater samples suggesting that although rainwater is still the highest quality of drinking water, but it may have some quality issues especially in terms of DBPs formation potentials that can be caused mainly by the emission of biogenic VOCs

Runoff Coefficients of High-flow Events in Undisturbed New England Basins

The New England region in the Northeast U.S. receives high annual precipitation as rain and snow, which results in floods that endanger people and infrastructure. Owing to the complexity of hydrologic systems, increases in the frequency and intensity of large precipitation events do not always translate into increases in surface runoff measured as event flow at the basin outlet. However, recent studies have recognized positive trends in the frequency and magnitude of high-flow events in New England. For high-flow events of equal or greater than 2-year daily runoff, the runoff coefficients, or the fraction of precipitation converted into surface runoff during an event, were determined for 28 undisturbed New England basins with natural flow conditions. Results indicated that runoff coefficients increase in magnitude and variability with distance from the Atlantic coast toward the north and west. The average runoff coefficient of high-flow events across all basins is 0.90, while there exist many high-flow events with runoff coefficients greater than one. Also, runoff coefficients were generally stationary showing that flood events in undisturbed basins have remained proportional to precipitation inputs, despite increases in extreme precipitation, possibly due to shifts in evapotranspiration, snowpack, and soil moisture. Flood management efforts should continue to focus on large springtime precipitation events, which generate the highest runoff coefficients. Finally, this study can serve as a reference point for future exploration of the flood susceptibility of basins with anthropogenic alterations like dam construction or land use change

New Hampshire Coastal Flood Risk Summary Part 1: Science

The New Hampshire Coastal Flood Risk Summary – Part 1: Science provides a synthesis of the state of the science relevant to coastal flood risks in New Hampshire. Specifically, this document provides updated projections of sea-level rise, coastal storms, groundwater rise, precipitation, and freshwater flooding for coastal New Hampshire. This information is intended to serve as the scientific foundation for the companion New Hampshire Coastal Flood Risk Summary - Part II: Guidance for Using Scientific Projections and is intended to inform coastal land use planning and decision-making

Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections

Flood hazard model calibration using multiresolution model output

Riverine floods pose a considerable risk to many communities. Improving flood hazard projections has the potential to inform the design and implementation of flood risk management strategies. Current flood hazard projections are uncertain, especially due to uncertain model parameters. Calibration methods use observations to quantify model parameter uncertainty. With limited computational resources, researchers typically calibrate models using either relatively few expensive model runs at high spatial resolutions or many cheaper runs at lower spatial resolutions. This leads to an open question: Is it possible to effectively combine information from the high and low resolution model runs? We propose a Bayesian emulation-calibration approach that assimilates model outputs and observations at multiple resolutions. As a case study for a riverine community in Pennsylvania, we demonstrate our approach using the LISFLOOD-FP flood hazard model. The multiresolution approach results in improved parameter inference over the single resolution approach in multiple scenarios. Results vary based on the parameter values and the number of available models runs. Our method is general and can be used to calibrate other high dimensional computer models to improve projections