Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼  200 km) to 1∕8° ( ∼  12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼  50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis

Perceptions of Disabilities Among Native Americans Within the State of Utah

Native Americans are diagnosed with disabilities at a significantly higher rate than the general American population. Due to cultural differences, other factors are likely impacting this difference in diagnosis rates. One possible factor is that Native Americans may have a different definition for ‘disability’ than the general American population. This study aimed to identify whether there is a difference in the definition for ‘disability’ and to learn about the current services available and what changes should be made to better serve Native Americans with disabilities. Native American participants were asked to share their experiences and thoughts about disabilities in sharing circles. Four major themes were identified following these sharing circles. These themes included a culturally based definition of “disability”, barriers to services, acceptance, and needed action. These themes highlight the cultural strengths among Native communities surrounding disabilities and bring attention to what changes could be made to better serve the needs of Native Americans with disabilities

CHARITABLE DONATIONS: AN ANALYSIS OF THE DIFFERENCES IN DONATION PATTERNS BY INCOME LEVEL

This thesis examines the possibility of grouping charitable donors by income level to develop a set of models that can more accurately predict charitable donations. Previous work is inconsistent in predicting charitable donations. This work helps to determine if these inconsistencies are a result of methodological differences between researchers, or if group membership is an important factor in predicting charitable donations as suggested by some researchers. This research only found four variables that were common to all three income groups, frequency of church attendance, family income, age, and years of education. Results show that additional variables can serve as predictors of relative donations, but only when samples are grouped by income. This should be considered as evidence that group membership is an important factor to consider in future charitable donations research. These groupings should not be limited to income; other socio-demographic indicators should also be explored in more depth

PREDICTING COMPLEX PHENOTYPE-GENOTYPE RELATIONSHIPS IN GRASSES: A SYSTEMS GENETICS APPROACH

It is becoming increasingly urgent to identify and understand the mechanisms underlying complex traits. Expected increases in the human population coupled with climate change make this especially urgent for grasses in the Poaceae family because these serve as major staples of the human and livestock diets worldwide. In particular, Oryza sativa (rice), Triticum spp. (wheat), Zea mays (maize), and Saccharum spp. (sugarcane) are among the top agricultural commodities. Molecular marker tools such as linkage-based Quantitative Trait Loci (QTL) mapping, Genome-Wide Association Studies (GWAS), Multiple Marker Assisted Selection (MMAS), and Genome Selection (GS) techniques offer promise for understanding the mechanisms behind complex traits and to improve breeding programs. These methods have shown some success. Often, however, they cannot identify the causal genes underlying traits nor the biological context in which those genes function. To improve our understanding of complex traits as well improve breeding techniques, additional tools are needed to augment existing methods. This work proposes a knowledge-independent systems-genetic paradigm that integrates results from genetic studies such as QTL mapping, GWAS and mutational insertion lines such as Tos17 with gene co-expression networks for grasses--in particular for rice. The techniques described herein attempt to overcome the bias of limited human knowledge by relying solely on the underlying signals within the data to capture a holistic representation of gene interactions for a species. Through integration of gene co-expression networks with genetic signal, modules of genes can be identified with potential effect for a given trait, and the biological function of those interacting genes can be determined

Characterizing the Variability of Physical and Chemical Properties across the Soil Individuals Mapped as Amy Silt Loam Soils in Southeastern Arkansas

Knowledge of physical and chemical properties of soil is relevant for landowners, researchers, and foresters, so that appropriate crop species and management practices to maximize site productivity can be selected. In addition to issues of plant productivity, the need for assessing soil properties has been expanded due to public interest in determining the consequences of management practices on soil quality relative to sustainability of crop ecosystem functions. The USDA-Natural Resources Conservation Service (NRCS) delineated soil mapping units to provide information about physical and chemical properties of soil in each soil series. However, soil mapping units do not provide details about the variability of soil properties within a single soil series. To determine the variability of physical and chemical properties within Amy soil series, 200 soil samples were collected to a depth of 0–15cm and 15–30cm from soil individuals mapped as the Amy silt loam soils in five different locations in southeastern Arkansas. Comparisons of soil texture, bulk density, carbon, nitrogen, Mehlich III extractable macronutrients, and micronutrients revealed significant differences among soil individuals/ locations for both depth increments. Additionally, all nutrients except potassium, magnesium, and copper differed between the two soil depths. The results suggest inherent variation in biogeochemical and geochemical cycling in the surface horizons of soils mapped as the Amy series

Effects of Light Regime and Season of Clipping on the Growthof Cherrybark Oak, White Oak, Persimmon, and Sweetgum Sprouts

A mixture of cherrybark oak (Quercus pagoda Raf.), white oak (Q. alba L.), persimmon (Diospyros virginiana L.), and sweetgum (Liquidambar styraciflua L.) seedlings was grown in shadehouses to simulate light conditions beneath a canopy. After the first growing season, two release treatments were implemented (released and not released), and treatments were conducted during two seasons (winter and spring). All seedlings were clipped at 2.5 em from the groundline in height when treatments were imposed. Survival of persimmon and sweetgum was 100% following clipping. There appeared to be a weak seasonal effect on oak survival, especially for white oak; survival was 100% for winter clipping and 93% for spring clipping. The oaks were considerably smaller in height, diameter, and above-ground biomass than their competitors, and the competitors also produced more stems per rootstock than the oaks. Cherrybark oak was more productive than white oak especially in the released treatment. The oaks tended to have a higher percentage of their total biomass in foliage when compared with their competitors. Stem wood density of the oaks was considerably greater than that of their competitors. Leaf characteristics of all species were very responsive to the treatments; specific leaf area was consistently greater for the no-release treatment for all species. Results of this study suggest that for oak sprouts to grow faster than their competitors they must begin with an initial size advantage

Evaluation of experimental epoxy monomers

Future generation aircraft need higher performance polymer matrices to fully achieve the weight savings possible with composite materials. New resins are being formulated in an effort to understand basic polymer behavior and to develop improved resins. Some polymer/curing agent combinations that could be useful are difficult to process. In the area of epoxies, a major problem is that some components have physical properties which make them difficult to utilize as matrix resins. A previous study showed that the use of ultrasonic energy can be advantageous in the mixing of curing agents into a standard epoxy resin, such as MY 720 (Ciba-Geigy designation). This work is expanded to include three novel epoxides

Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼  200 km) to 1∕8° ( ∼  12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼  50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis

Climate Change Impacts on Streamflow and Subbasin- Scale Hydrology in the Upper Colorado River Basin

In the Upper Colorado River Basin (UCRB), the principal source of water in the southwestern U.S., demand exceeds supply in most years, and will likely continue to rise. While General Circulation Models (GCMs) project surface temperature warming by 3.5 to 5.6uC for the area, precipitation projections are variable, with no wetter or drier consensus. We assess the impacts of projected 21st century climatic changes on subbasins in the UCRB using the Soil and Water Assessment Tool, for all hydrologic components (snowmelt, evapotranspiration, surface runoff, subsurface runoff, and streamflow), and for 16 GCMs under the A2 emission scenario. Over the GCM ensemble, our simulations project median Spring streamflow declines of 36% by the end of the 21st century, with increases more likely at higher elevations, and an overall range of 2100 to +68%. Additionally, our results indicated Summer streamflow declines with median decreases of 46%, and an overall range of 2100 to +22%. Analysis of hydrologic components indicates large spatial and temporal changes throughout the UCRB, with large snowmelt declines and temporal shifts in most hydrologic components. Warmer temperatures increase averageannual evapotranspiration by ,23%, with shifting seasonal soil moisture availability driving these increases in late Winter and early Spring. For the high-elevation water-generating regions, modest precipitation decreases result in an even greater water yield decrease with less available snowmelt. Precipitation increases with modest warming do not translate into the same magnitude of water-yield increases due to slight decreases in snowmelt and increases in evapotranspiration. For these basins, whether modest warming is associated with precipitation decreases or increases, continued rising temperatures may make drier futures. Subsequently, many subbasins are projected to turn from semi-arid to arid conditions by the 2080 s. In conclusion, water availability in the UCRB could significantly decline with adverse consequences for water supplies, agriculture, and ecosystem health

Integrating Groundwater Observations with Models of Soil-Water Dynamics to Examine Recharge Patterns through Glacial Sediments in a Humid Continental Climate

Poster presented at American Geophysical Union meeting in 2015.Understanding the timing and magnitude of shallow groundwater recharge is critical for determining water balance and analyzing aquifer sensitivity for water resource planning. We analyzed data from six hydrometeorological monitoring stations using HYDRUS 1D to achieve physically based estimates of water-table recharge in various glaciated terrains in Indiana (USA). The models simulated runoff, root-water uptake, and flow through heterogeneous soil profiles to quantify water flux at the water table. Calibration by inverse modeling of data collected in 2013 yielded optimized hydraulic parameters that allowed accurate simulation of observed soil moisture (RMSE generally within 3%). The model validation period confirmed accurate simulation of soil moisture as well as correspondence between modeled recharge and observed water-table fluctuations. Additional modelling over a three-year study period indicated that diffuse water-table recharge in the region can be reasonably approximated as 35% of precipitation, but interannual and monthly variability can be significant depending on the glacial setting and pedological development. Soil parent material and horizon characteristics have a strong influence on average annual recharge primarily through their control on Ks, with clay-rich till parent materials producing values as low as 16% and coarse-grained outwash parent materials producing values as high as 58% of precipitation. The combined modelling and monitoring data reveal distinct seasonality of recharge, with most recharge occurring in the winter (seasonal mean of all sites was 66% of precipitation) and lesser but interannually stable amounts in the spring (44%), summer (13%), and autumn (16%). This ongoing research underscores the value of combining vadose zone characterization with hydrometeorological monitoring to more effectively represent how surface energy and moisture budgets influence the dynamics of surface water-groundwater interactions