Research and Publications - Department of Civil, Construction & Environmental Engineering

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The 2009-2010 El Niño: Hydrologic Relief to U.S. Regions
(2009-12-15) Tootle, G. A.; Piechota, T. C.; Aziz, O.; Miller, W. P.; Lakshmi, V.; Dracup, J. A.; Jerla, C.; University of Alabama Tuscaloosa
Current forecasts by the U.S. National Oceanic and Atmospheric Administration (NOAA) are that the Pacific Ocean will experience El Niño conditions in late 2009 and into 2010. These forecasts are similar to past El Niño events in 1972–1973, 1982–1983, 1986–1987, and 2002–2003.Evaluating the hydrologic conditions for these past El Niño events reveals that during these times, surface water supply conditions improved in many parts of the United States, including the Southeast, Midwest, and Southwest. At the same time, the Pacific Northwest and other specific regions of the United States experienced below‐average water supply conditions. This is consistent with the long‐established linkages between oceanic‐atmospheric phenomena, El Niño, and streamflow [e.g., Kahya and Dracup, 1993; Tootle et al., 2005].
Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States
(Nature Portfolio, 2023) Yarveysi, Farnaz; Alipour, Atieh; Moftakhari, Hamed; Jafarzadegan, Keighobad; Moradkhani, Hamid; University of Alabama Tuscaloosa
The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks. Introduces a precise, machine-learning-based Socio-Economic-Infrastructure Vulnerability index for natural hazards that uncovers stark variations in vulnerability at the block level emphasizing crucial information for risk-informed decision making.
Cement substitution with secondary materials can reduce annual global CO2 emissions by up to 1.3 gigatons
(Nature Portfolio, 2022) Shah, Izhar Hussain; Miller, Sabbie A.; Jiang, Daqian; Myers, Rupert J.; Imperial College London; University of California Davis; University of Alabama Tuscaloosa
Population and development megatrends will drive growth in cement production, which is already one of the most challenging-to-mitigate sources of CO2 emissions. However, availabilities of conventional secondary cementitious materials (CMs) like fly ash are declining. Here, we present detailed generation rates of secondary CMs worldwide between 2002 and 2018, showing the potential for 3.5 Gt to be generated in 2018. Maximal substitution of Portland cement clinker with these materials could have avoided up to 1.3 Gt CO2-eq. emissions (similar to 44% of cement production and similar to 2.8% of anthropogenic CO2-eq. emissions) in 2018. We also show that nearly all of the highest cement producing nations can locally generate and use secondary CMs to substitute up to 50% domestic Portland cement clinker, with many countries able to potentially substitute 100% Portland cement clinker. Our results highlight the importance of pursuing regionally optimized CM mix designs and systemic approaches to decarbonizing the global CMs cycle.
Comparison of RUSLE and MMF Soil Loss Models and Evaluation of Catchment Scale Best Management Practices for a Mountainous Watershed in India
(MDPI, 2020) Das, Susanta; Deb, Proloy; Bora, Pradip Kumar; Katre, Prafull; Punjab Agricultural University; University of Alabama Tuscaloosa; Indira Gandhi Krishi Vishwavidyalaya (IGKV)
Soil erosion from arable lands removes the top fertile soil layer (comprised of humus/organic matter) and therefore requires fertilizer application which affects the overall sustainability. Hence, determination of soil erosion from arable lands is crucial to planning conservation measures. A modeling approach is a suitable alternative to estimate soil loss in ungauged catchments. Soil erosion primarily depends on soil texture, structure, infiltration, topography, land uses, and other erosive forces like water and wind. By analyzing these parameters, coupled with geospatial tools, models can estimate storm wise and annual average soil losses. In this study, a hilly watershed called Nongpoh was considered with the objective of prioritizing critical erosion hazard areas within the micro-catchment based on average annual soil loss and land use and land cover and making appropriate management plans for the prioritized areas. Two soil erosion models namely Revised Universal Soil Loss Equation (RUSLE) and Modified Morgan-Morgan-Finney (MMF) models were used to estimate soil loss with the input parameters extracted from satellite information and automatic weather stations. The RUSLE and MMF models showed similar results in estimating soil loss, except the MMF model estimated 7.74% less soil loss than the RUSLE model from the watershed. The results also indicated that the study area is under severe erosion class, whereas agricultural land, open forest area, and scrubland were prioritized most erosion prone areas within the watershed. Based on prioritization, best management plans were developed at catchment scale for reducing soil loss. These findings and the methodology employed can be widely used in mountainous to hilly watersheds around the world for identifying best management practices (BMP).
Coupled oceanic-atmospheric variability and U.S. streamflow
(American Geophysical Union, 2005-12-06) Tootle, GA; Piechota, TC; Singh, A; University of Wyoming; Nevada System of Higher Education (NSHE); University of Nevada Las Vegas; University of Alabama Tuscaloosa
[1] A study of the influence of interdecadal, decadal, and interannual oceanic-atmospheric influences on streamflow in the United States is presented. Unimpaired streamflow was identified for 639 stations in the United States for the period 1951 - 2002. The phases (cold/negative or warm/positive) of Pacific Ocean ( El Nino - Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)) and Atlantic Ocean ( Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation (NAO)) oceanic-atmospheric influences were identified for the year prior to the streamflow year (i.e., long lead time). Statistical significance testing of streamflow, based on the interdecadal, decadal, and interannual oceanic-atmospheric phase (warm/positive or cold/negative), was performed by applying the nonparametric rank-sum test. The results show that in addition to the well-established ENSO signal the PDO, AMO, and NAO influence streamflow variability in the United States. The warm phase of the PDO is associated with increased streamflow in the central and southwest United States, while the warm phase of the AMO is associated with reduced streamflow in these regions. The positive phase of the NAO and the cold phase of the AMO are associated with increased streamflow in the central United States. Additionally, the coupled effects of the oceanic-atmospheric influences were evaluated on the basis of the long-term phase (cold/negative or warm/ positive) of the interdecadal ( PDO and AMO) and decadal ( NAO) influences and ENSO. Streamflow regions in the United States were identified that respond to these climatic couplings. The results show that the AMO may influence La Nina impacts in the Southeast, while the NAO may influence La Nina impacts in the Midwest. By utilizing the streamflow water year and the long lead time for the oceanic-atmospheric variables, useful information can be provided to streamflow forecasters and water managers.
Development of a field testing protocol for identifying Deepwater Horizon oil spill residues trapped near Gulf of Mexico beaches
(PLOS, 2018) Han, Yuling; Clement, T. Prabhakar; Auburn University; University of Alabama Tuscaloosa
The Deepwater Horizon (DWH) accident, one of the largest oil spills in U.S. history, contaminated several beaches located along the Gulf of Mexico (GOM) shoreline. The residues from the spill still continue to be deposited on some of these beaches. Methods to track and monitor the fate of these residues require approaches that can differentiate the DWH residues from other types of petroleum residues. This is because, historically, the crude oil released from sources such as natural seeps and anthropogenic discharges have also deposited other types of petroleum residues on GOM beaches. Therefore, identifying the origin of these residues is critical for developing effective management strategies for monitoring the long-term environmental impacts of the DWH oil spill. Advanced fingerprinting methods that are currently used for identifying the source of oil spill residues require detailed laboratory studies, which can be cost-prohibitive. Also, most agencies typically use untrained workers or volunteers to conduct shoreline monitoring surveys and these worker will not have access to advanced laboratory facilities. Furthermore, it is impractical to routinely fingerprint large volumes of samples that are collected after a major oil spill event, such as the DWH spill. In this study, we propose a simple field testing protocol that can identify DWH oil spill residues based on their unique physical characteristics. The robustness of the method is demonstrated by testing a variety of oil spill samples, and the results are verified by characterizing the samples using advanced chemical fingerprinting methods. The verification data show that the method yields results that are consistent with the results derived from advanced fingerprinting methods. The proposed protocol is a reliable, cost-effective, practical field approach for differentiating DWH residues from other types of petroleum residues.
Does Global Progress on Sanitation Really Lag behind Water? An Analysis of Global Progress on Community- and Household-Level Access to Safe Water and Sanitation
(PLOS, 2014) Cumming, Oliver; Elliott, Mark; Overbo, Alycia; Bartram, Jamie; University of London; London School of Hygiene & Tropical Medicine; University of Alabama Tuscaloosa; University of North Carolina; University of North Carolina Chapel Hill
Safe drinking water and sanitation are important determinants of human health and wellbeing and have recently been declared human rights by the international community. Increased access to both were included in the Millennium Development Goals under a single dedicated target for 2015. This target was reached in 2010 for water but sanitation will fall short; however, there is an important difference in the benchmarks used for assessing global access. For drinking water the benchmark is community-level access whilst for sanitation it is household-level access, so a pit latrine shared between households does not count toward the Millennium Development Goal (MDG) target. We estimated global progress for water and sanitation under two scenarios: with equivalent household-and community-level benchmarks. Our results demonstrate that the "sanitation deficit" is apparent only when household-level sanitation access is contrasted with community-level water access. When equivalent benchmarks are used for water and sanitation, the global deficit is as great for water as it is for sanitation, and sanitation progress in the MDG-period (1990-2015) outstrips that in water. As both drinking water and sanitation access yield greater benefits at the household-level than at the community-level, we conclude that any post-2015 goals should consider a household-level benchmark for both.
Estimated Wind River Range (Wyoming, USA) Glacier Melt Water Contributions to Agriculture
(MDPI, 2009-10-28) Cheesbrough, Kyle; Edmunds, Jake; Tootle, Glenn; Kerr, Greg; Pochop, Larry; University of Tennessee System; University of Tennessee Knoxville; University of Wyoming; University of Alabama Tuscaloosa
In 2008, Wyoming was ranked 8th in barley production and 20th in hay production in the United States and these crops support Wyoming's $800 million cattle industry. However, with a mean elevation of 2,040 meters, much of Wyoming has a limited crop growing season (as little as 60 days) and relies on late-summer and early-fall streamflow for agricultural water supply. Wyoming is host to over 80 glaciers with the majority of these glaciers being located in the Wind River Range. These "frozen reservoirs" provide a stable source of streamflow (glacier meltwater) during this critical late-summer and early-fall growing season. Given the potential impacts of climate change (increased temperatures resulting in glacier recession), the quantification of glacier meltwater during the late-summer and early-fall growing seasons is needed. Glacier area changes in the Wind River Range were estimated for 42 glaciers using Landsat data from 1985 to 2005. The total surface area of the 42 glaciers was calculated to be 41.2 +/- 11.7 km(2) in 1985 and 30.8 +/- 8.2 km(2) in 2005, an average decrease of 25% over the 21 year period. Small glaciers experienced noticeably more area reduction than large glaciers. Of the 42 glaciers analyzed, 17 had an area of greater than 0.5 km(2) in 1985, while 25 were less than 0.5 km(2) in 1985. The glaciers with a surface area less than 0.5 km(2) experienced an average surface area loss (fraction of 1985 surface area) of 43%, while the larger glaciers (greater than 0.5 km(2)) experienced an average surface area loss of 22%. Applying area-volume scaling relationships for glaciers, volume loss was estimated to be 409 x 106 m(3) over the 21 year period, which results in an estimated 4% to 10% contribution to warm season (July-October) streamflow.
Five Hundred Years of Hydrological Drought in the Upper Colorado River Basin
(2007-06) Timilsena, Janak; Piechota, Thomas C.; Hidalgo, Hugo; Tootle, Glenn; University of Alabama Tuscaloosa
This article evaluates drought scenarios of the Upper Colorado River basin (UCRB) considering multiple drought variables for the past 500 years and positions the current drought in terms of the magnitude and frequency. Drought characteristics were developed considering water-year data of UCRB’s streamflow, and basin-wide averages of the Palmer Hydrological Drought Index (PHDI) and the Palmer Z Index. Streamflow and drought indices were reconstructed for the last 500 years using a principal component regression model based on tree-ring data. The reconstructed streamflow showed higher variability as compared with reconstructed PHDI and reconstructed Palmer Z Index. The magnitude and severity of all droughts were obtained for the last 500 years for historical and reconstructed drought variables and ranked accordingly. The frequency of the current drought was obtained by considering two different drought frequency statistical approaches and three different methods of determining the beginning and end of the drought period (annual, 5-year moving, and ten year moving average). It was concluded that the current drought is the worst in the observed record period (1923-2004), but 6th to 14th largest in terms of magnitude and 1st to 12th considering severity in the past 500 years. Similarly, the current drought has a return period ranging from 37 to 103 years based on how the drought period was determined. It was concluded that if the 10-year moving average is used for defining the drought period, the current drought appears less severe in terms of magnitude and severity in the last 500 years compared with the results using 1- and 5-year averages.
GIS-Enabled Culvert Design: A Case Study in Tuscaloosa, Alabama
(Hindawi, 2018) Greer, Ashton D.; Wilbanks, Zachary B.; Clifton, Leah D.; Wilson, Bradford; Graettinger, Andrew J.; University of Alabama Tuscaloosa
A GIS-enabled culvert design module is presented. This module employs Python programming to combine a proposed culvert location, topography, land use, and rainfall data to automatically design a culvert. The module is embedded within ESRI ArcGIS 10.4 software, providing a seamless single platform that eliminates error propagation associated with cross-platform data transfer as well as providing 95% time savings over traditional calculation methods. The module uses United States Geological Survey digital elevation data to analyze watershed topography. Runoff coefficients are determined from data available through the National Land Cover Database. Rainfall data are retrieved from the National Oceanic and Atmospheric Administration and combined with watershed and land use information to calculate peak discharge using the rational method. Peak discharge is then combined with culvert design parameters to design a single-barrel culvert. The module was used to redesign ten existing culverts along a highway in Tuscaloosa, Alabama, resulting in designs for updated land cover and rainfall conditions. Results from the techniques developed herein can be used for planning purposes and to highlight vulnerabilities in the existing infrastructure. The automation methods may be extended to other hydrologic objectives and runoff mitigation design such as open-channel design and detention or retention ponds.
Greening China Naturally
(Springer, 2011) Cao, Shixiong; Sun, Ge; Zhang, Zhiqiang; Chen, Liding; Feng, Qi; Fu, Bojie; McNulty, Steve; Shankman, David; Tang, Jianwu; Wang, Yanhui; Wei, Xiaohua; United States Department of Agriculture (USDA); United States Forest Service; Beijing Forestry University; Chinese Academy of Sciences; Research Center for Eco-Environmental Sciences (RCEES); Cold & Arid Regions Environmental & Engineering Research Institute, CAS; University of Alabama Tuscaloosa; Marine Biological Laboratory - Woods Hole; Chinese Academy of Forestry; Research Institute of Forest Ecology, Environment and Protection, CAF; University of British Columbia; University of British Columbia Okanagan
China leads the world in afforestation, and is one of the few countries whose forested area is increasing. However, this massive "greening" effort has been less effective than expected; afforestation has sometimes produced unintended environmental, ecological, and socioeconomic consequences, and has failed to achieve the desired ecological benefits. Where afforestation has succeeded, the approach was tailored to local environmental conditions. Using the right plant species or species composition for the site and considering alternatives such as grassland restoration have been important success factors. To expand this success, government policy should shift from a forest-based approach to a results-based approach. In addition, long-term monitoring must be implemented to provide the data needed to develop a cost-effective, scientifically informed restoration policy.
How can we reform the STEM tenure system for the 21st Century?
(National Academy of the Sciences, 2022) Clement, T. Prabhakar; University of Alabama Tuscaloosa
How did the COVID-19 pandemic affect road crashes and crash outcomes in Alabama?
(Pergamon, 2021) Adanu, Emmanuel Kofi; Brown, David; Jones, Steven; Parrish, Allen; University of Alabama Tuscaloosa
With the rising number of cases and deaths from the COVID-19 pandemic, nations and local governments, including many across the U.S., imposed travel restrictions on their citizens. This travel restriction order led to a significant reduction in traffic volumes and a generally lower exposure to crashes. However, recent preliminary statistics in the US suggest an increase in fatal crashes over the period of lockdown in comparison to the same period in previous years. This study sought to investigate how the pandemic affected road crashes and crash outcomes in Alabama. Daily vehicle miles traveled and crashes were obtained and explored. To understand the factors associated with crash outcomes, four crash-severity models were developed: (1) Single-vehicle (SV) crashes prior to lockdown order (Normal times SV); (2) multi-vehicle (MV) crashes prior to lockdown order (Normal times MV); (3) Single-vehicle crashes after lockdown order (COVID times SV); and (4) Multi-vehicle crashes after lockdown order (COVID times MV). The models were developed using the first 28 weeks of crashes recorded in 2020. The findings of the study reveal that although traffic volumes and vehicle miles traveled had significantly dropped during the lockdown, there was an increase in the total number of crashes and major injury crashes compared to the period prior to the lockdown order, with speeding, DUI, and weekends accounting for a significant proportion of these crashes. These observations provide useful lessons for road safety improvements during extreme events that may require statewide lockdown, as has been done with the COVID-19 pandemic. Traffic management around shopping areas and other areas that may experience increased traffic volumes provide opportunities for road safety stakeholders to reduce the occurrence of crashes in the weeks leading to an announcement of any future statewide or local lockdowns. Additionally, increased law enforcement efforts can help to reduce risky driving activities as traffic volumes decrease.
Identification of Pacific Ocean sea surface temperature influences of Upper Colorado River Basin snowpack
(American Geophysical Union, 2010-07-27) Aziz, Oubeidillah A.; Tootle, Glenn A.; Gray, Stephen T.; Piechota, Thomas C.; University of Tennessee System; University of Tennessee Knoxville; University of Wyoming; Nevada System of Higher Education (NSHE); University of Nevada Las Vegas; University of Alabama Tuscaloosa
Given the importance of Upper Colorado River Basin (UCRB) snowpack as the primary driver of streamflow (water supply) for the southwestern United States, the identification of Pacific Ocean climatic drivers (e. g., sea surface temperature (SST) variability) may prove valuable in long-lead-time forecasting of snowpack in this critical region. Previous research efforts have identified El Nino-Southern Oscillation (ENSO) and Pacific Decadel Oscillation (PDO) as the main drivers for western U. S. snowpack, but these drivers have limited influence on regional (Utah and Colorado) UCRB snowpack. The current research applies for the first time the Singular Value Decomposition (SVD) statistical method to Pacific Ocean SSTs and continental U. S. snowpack to identify the primary Pacific Ocean climatic driver of UCRB snowpack. The use of SSTs eliminates any "bias" as to specific climate signals. The second mode of SVD identified a UCRB snowpack region (Colorado and Utah) and a corresponding Pacific Ocean SST region. A "non-ENSO/non-PDO" Pacific Ocean SST region between 34 degrees N-24 degrees S and 150 degrees E-160 degrees W was identified as being the primary driver of UCRB snowpack. To confirm the UCRB snowpack results, data from 13 unimpaired (or naturalized) streamflow gages in Colorado and Utah were used to evaluate and support the snowpack findings. Finally, a new and beneficial data set (western U.S. 1 March, 1 April, and 1 May snow water equivalent) was developed, which may be used in future research efforts.
An in-depth analysis of head-on crash severity and fatalities in Ghana
(Cell Press, 2023) Adanu, Emmanuel Kofi; Agyemang, William; Lidbe, Abhay; Adarkwa, Offei; Jones, Steven; University of Alabama Tuscaloosa
Head-on collisions are often linked to more serious injuries compared to other types of crashes, due to the intense impact they cause. In low-and middle-income countries, these collisions frequently involve high occupancy public transportation vehicles, leading to higher fatality rates per crash. Given the high risk of injury and potential for multiple casualties, this study delves into the factors influencing the outcomes of head-on crashes and the number of fatalities in Ghana. The study analyzed six years of historical head-on collision data from Ghana and developed two models to address the issue. The injury-severity analysis was performed using a random param-eter multinomial logit with heterogeneity in means and variances approach and aimed to identify the factors that have a significant impact on the severity of injuries sustained in head-on colli-sions, while the random parameters negative binomial fatality count model was designed to examine the factors that contribute to the number of fatalities in these crashes in the country. Results showed that head-on collisions with drivers over 65, buses, motorcycles, and those be-tween 25 and 65 years of age were more likely to result in fatalities. Speeding and vehicle malfunctions were also found to be significant contributing factors to fatal head-on collisions. Head-on crashes involving minibuses and incidents where the driver was attempting to overtake another vehicle were found to be more likely to result in a higher number of fatalities. The results of this study uncover an intriguing interaction between human-related elements and socioeco-nomic factors, which pose obstacles to the Government's endeavor to upgrade the major high-ways in the country. Additionally, the increasing need for transportation has led to the presence of vehicles on the roads that may not meet safety standards. Consequently, it is no surprise that several of the study's findings align with expectations. Nevertheless, within the specific context of Ghana, these findings furnish compelling data-driven evidence supporting the adoption and implementation of the safe systems approach as a means to tackle fatal head-on collisions in the country.
Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches
(Nature Portfolio, 2022) Tanim, Ahad Hasan; Goharian, Erfan; Moradkhani, Hamid; University of South Carolina Columbia; University of Alabama Tuscaloosa
Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals' biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers' opinions.
A knowledge graph-based method for epidemic contact tracing in public transportation
(Pergamon, 2022) Chen, Tian; Zhang, Yimu; Qian, Xinwu; Li, Jian; Tongji University; University of Alabama Tuscaloosa
Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.
A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US
(Copernicus GmbH, 2014-01-07) Oubeidillah, A. A.; Kao, S. -C.; Ashfaq, M.; Naz, B. S.; Tootle, G.; United States Department of Energy (DOE); Oak Ridge National Laboratory; University of Alabama Tuscaloosa
To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation and data-intensive effort was conducted to organize a comprehensive hydrologic data set with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation - including meteorologic forcings, soil, land class, vegetation, and elevation - were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous US at refined 1/24 degrees (similar to 4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter data set was prepared for the macro-scale variable infiltration capacity (VIC) hydrologic model. The VIC simulation was driven by Daymet daily meteorological forcing and was calibrated against US Geological Survey (USGS) WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter data set may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous US. We anticipate that through this hydrologic parameter data set, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter data set will be provided to interested parties to support further hydro-climate impact assessment.
Mobile device use while crossing the street: Utilizing the theory of planned behavior
(Pergamon, 2019) Piazza, Andrew J.; Knowlden, Adam P.; Hibberd, Elizabeth; Leeper, James; Paschal, Angelia M.; Usdan, Stuart; University of Alabama Tuscaloosa; Worcester State University
Every year, thousands of pedestrians are killed and tens-of-thousands are nonfatally injured as a result of traffic crashes. The year 2016 holds the record for the most pedestrians killed in one year since 1990. Mobile device use while crossing the street has been associated with unsafe crossing behaviors and gait abnormalities, potentially increasing the risk of pedestrian injury or death. Expanding upon the small body of literature, the present study utilized the theory of planned behavior to guide the development of a questionnaire used to collect data from 480 adults on predictors of intentions to use a mobile device while crossing the street. Questionnaire development involved one round of expert panel review (N = 4), subsequent pilot testing of a revised questionnaire, and a test-retest reliability assessment. Results demonstrate that attitude toward the behavior, subjective norm, and perceived behavioral control significantly predicted the intention to use a mobile device while crossing the street in this population. Such a questionnaire can be used in the design and evaluation of TPB-based inter-ventions to decrease distracted mobile device use while crossing the street.
Oceanic-atmospheric variability and western US snowfall
(American Geophysical Union, 2006-07-08) Hunter, Thad; Tootle, Glenn; Piechota, Thomas; University of Wyoming; Nevada System of Higher Education (NSHE); University of Nevada Las Vegas; University of Alabama Tuscaloosa
A study of the influences of interdecadal and interannual oceanic-atmospheric influences on April 1 Snow-Water Equivalent (SWE) in the western U. S. is presented. SWE data was identified at 323 Natural Resources Conservation Service ( NRCS) SNOTEL (SNOwpack TELemetrysites) stations for the period of 1961 to 2004 and for 121 SNOTEL stations for the period 1941 to 2004. The phases (cold/negative or warm/positive) of Pacific Ocean [ El Nino-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)] and Atlantic Ocean [ Atlantic Multidecadal Oscillation ( AMO) and North Atlantic Oscillation (NAO)] oceanic-atmospheric influences were identified for the year prior to the SWE data set. Statistical significance testing of SWE data set, based on the interdecadal and interannual oceanic-atmospheric phase (warm/positive or cold/negative) was performed by applying the nonparametric rank-sum test. The results show that in addition to the well established ENSO signal in the northwest, the PDO and AMO influence SWE variability. Additionally, the coupled effects of the oceanic-atmospheric influences were evaluated on the basis of the long-term phase (cold/negative or warm/positive) of the interdecadal ( PDO, AMO, NAO) influences and the interannual ENSO. Finally, the coupled effects of the oceanic-atmospheric influences were evaluated on the basis of the long-term phase (cold/negative or warm/positive) of the interdecadal ( AMO, PDO, NAO) phenomena. Regions in the west were identified that responded to the interdecadal/ decadal climatic coupling. By utilizing the April 1 SWE and the long lead-time approach for the oceanic-atmospheric variables, useful information can be provided to snow forecasters and water managers.

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