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EEWR Brown Bag Seminar with Graduate Students David Miller and Daniel Wright

Speaker: David Miller and Daniel Wright, Graduate Students
Series: EEWR Brown Bag Seminars
Location: Engineering Quad E225
Date/Time: Friday, April 19, 2013, 12:00 p.m. - 1:00 p.m.

Abstract:

Speaker: David Miller
Title: Characterizing spatial distributions of atmospheric trace gas emissions:
Long open-path and mobile measurements


The spatial distributions of air pollutant and greenhouse gas concentrations contain useful information for  understanding their emission sources and simulating their dynamics. Two novel sensor platforms are presented for characterizing the spatial distributions of atmospheric trace gas concentrations: (1) a long open-path methane sensor and (2) a multi-gas, open-path mobile vehicular measurement platform.

Methane is an important greenhouse gas with poorly quantified, highly variable emission sources in space and time. For instance, capturing transient methane emissions from sources such as ebullition (bubbling) events from Arctic thermokarst lake-bottom sediments is almost impossible by conventional methods. The field demonstration of a long open-path, quantum cascade laser-based methane sensor to characterize the spatial and temporal variability of methane concentrations across Toolik Lake, Alaska with high precision and stability is presented.

High temporal and spatial resolution, on-road mobile measurements of agricultural emissions in the Central Valley, California as part of the NASA DISCOVER-AQ field campaign are also presented. Six trace gases (NH3, N2O, CO, CH4, CO2 and H2O) were measured simultaneously with four open-path sensors. Emission ratio analyses will be used to characterize highly uncertain agricultural emissions of ammonia, methane and other trace gases. Future analyses will inform model simulations of agricultural emissions and aerosol processes.

Speaker: Daniel Wright
Title: Toward Robust Flood Risk Assessment: Stochastic Storm Transposition


Spatial and temporal variability of rainfall, and its interactions with surface, subsurface, and drainage network properties, is a key driver of flood response across a range of watershed scales. Conventional flood risk assessment neglects these interactions, relying on a number of simplifying assumptions. The impacts of these assumptions on flood risk estimates are poorly understood. I present an alternative framework for flood risk assessment based on stochastic storm transposition (SST). In this framework, “storm catalogs” are derived from a ten-year high-resolution (15-minute, 1-km2) bias-corrected radar rainfall dataset for the region surrounding Charlotte, North Carolina, USA. SST aims to synthesize the regional climatology of extreme rainfall. When coupled with a physics-based distributed hydrologic model, SST enables us to examine the full impact of spatial and temporal rainfall variability on flood risk at a range of scales. Results show that rainfall associated with tropical cyclones dominates the upper tail of flood risk in larger watersheds, while flood risk in small watersheds is driven by organized thunderstorm systems. These findings point to a useful way of understanding climate-driven nonstationary flood risk associated with specific hydrometeorological hazards. SST provides a framework for examining these nonstationarities.