Projecting water withdrawal and supply for future decades in the U.S. under climate change scenarios.

The sustainability of water resources in future decades is likely to be affected by increases in water demand due to population growth, increases in power generation, and climate change. This study presents water withdrawal projections in the United States (U.S.) in 2050 as a result of projected population increases and power generation at the county level as well as the availability of local renewable water supplies. The growth scenario assumes the per capita water use rate for municipal withdrawals to remain at 2005 levels and the water use rates for new thermoelectric plants at levels in modern closed-loop cooling systems. In projecting renewable water supply in future years, median projected monthly precipitation and temperature by sixteen climate models were used to derive available precipitation in 2050 (averaged over 2040-2059). Withdrawals and available precipitation were compared to identify regions that use a large fraction of their renewable local water supply. A water supply sustainability risk index that takes into account additional attributes such as susceptibility to drought, growth in water withdrawal, increased need for storage, and groundwater use was developed to evaluate areas at greater risk. Based on the ranking by the index, high risk areas can be assessed in more mechanistic detail in future work.

Time-variable simulation of soil vapor intrusion into a building with a combined crawl space and basement.

A time-variable one-dimensional model (called ViM for Vapor Intrusion Model)to predict indoor vapor concentrations in a dwelling with a combined basement and crawl space has been developed. ViM predicts vapor concentrations in each of the three compartments. Volatile chemicals that intrude into the dwelling are assumed to originate from soil, groundwater (where an attenuating plume is simulated), or ambient air. Processes included in the model are advection, diffusion, biodecay, and adsorption in the soil column; transport by diffusion and advection into individual crawl space and basement compartments; advection from each compartment into an overlying dwelling space; and exchange of ambient air and indoor air. The time-variable concentration fields are solved by first transforming the partial and ordinary differential equations into Laplace space, solving the resulting ordinary differential equations or algebraic equations, and numerically inverting those equations. This approach was an expedient way of handling the coupling between the subsurface and the dwelling. ViM was applied to a building (Building 20) located at the former Moffett Field Naval Air Station, in Mountain View, CA. The building is a former bachelor officer's quarters. The shallow groundwater beneath the building is contaminated with a number of volatile chemicals, including trichloroethene, cis-1,2-dichloroethene, and trans-1,2-dichloroethene, all of which were simulated. Using indoor air data collected in 2003-2004, and other field data collected prior to that time, the accuracy of the model's predictions was demonstrated. ViM's results were also compared against a version of the steady-state Johnson and Ettinger model (1) that was modified to accommodate a dwelling with a combined crawl space and basement (called the JEM model in this paper). The predictions from the JEM model were consistently higher than the predictions from ViM, but still near the upper range of the observed data.

SPATS: Speech Perception Assessment and Training System.

A software system, SPATS (patent pending), that tests and trains important bottom-up and combined bottom-up/top-down speech-perception skills is described. Bottom-up skills are the abilities to identify the constituents of syllables: onsets, nuclei, and codas in quiet and noise as produced by eight talkers. Top-down skills are the abilities to use knowledge of linguistic context to identify words in spoken sentences. The sentence module in SPATS emphasizes combined bottom-up/top-down abilities in perceiving sentences in noise. The word-initial onsets, stressed nuclei, and word-final codas are ranked in importance and grouped into subsets based on their importance. Testing utilizes random presentation of all the items included in a subset. Training in Quiet (SNR = 40 dB) or in Noise (SNR = 5 dB), is adaptively focused on individual listener's learnable items of intermediate difficulty. Alternatively, SNR-adaption training uses Kaernbach's algorithm to find the SNR required for a target percent correct. The unique sentence module trains the combination of bottom-up (hearing) with top-down (use of linguistic context) abilities to identify words in meaningful sentences in noise. Scoring in the sentence module is objective and automatic.