Scholarly Works, Civil and Environmental Engineering

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Research articles, presentations, and other scholarship

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Effects of the Traditional and Flipped Classrooms on Undergraduate Student Opinions and Success
Hotle, Susan; Garrow, Laurie A. (ASCE, 2016-01)
The flipped classroom is becoming increasingly popular at universities because of its perceived benefits in promoting active learning and decreasing educational costs. Studies have found positive benefits associated with flipped classrooms; however, many have failed to control for confounding factors. Examples of confounding factors include comparing courses taught by different instructors or across courses taught in different semesters using different quizzes. The objective of this paper is to compare the traditional and flipped classrooms in an undergraduate civil engineering course while controlling for potential confounding factors. The quasi-experimental study incorporates students’ online behaviors, in-class performance, office hour attendance, and responses to both attitudinal and behavioral questions to assess student opinions and learning outcomes. It was found that student performance on quizzes was not significantly different across the traditional and flipped classrooms. A key shortcoming noted with the flipped classroom was students’ inability to ask questions during lectures. Students in flipped classrooms were more likely to attend office hours compared to traditional classroom students, but the difference was not statistically significant. Future research should explore whether students’ inability to ask questions when the material is presented in flipped classrooms affects learning outcomes.
The Role of Competitor Pricing in Multiairport Choice
Hotle, Susan; Garrow, Laurie A. (Sage, 2014-01)
This paper investigates how competitors’ low-fare offerings in multi-airport regions influence the online search behavior of customers at a major carrier's website. Clickstream data from a major U.S. airline are combined with detailed information about competitors’ low-fare offerings for 10 directional markets. The use of a truncated negative binomial model allows the prediction of the number of searches on the carrier's website as a function of low-fare offerings for the same airport pair as well as for competing airport pairs in the region. The study finds that the number of searches decreases as the difference between the carrier's lowest fare and competitors’ lowest fare increases. However, trip characteristics have more impact on search behavior than do the fare variables. Overall search on the carrier's website is limited, with less than 5% of customers searching for fares across multiple airports. The findings provide insight into the role of competitor pricing on multiairport choice as it relates to customers’ online search behavior.
Cannabis pollen dispersal across the United States
Nimmala, Manu; Ross, Shane D.; Foroutan, Hosein (Nature Portfolio, 2024-09-04)
For the recently legalized US hemp industry (Cannabis sativa), cross-pollination between neighboring fields has become a significant challenge, leading to contaminated seeds, reduced oil yields, and in some cases, mandated crop destruction. As a step towards assessing hemp cross-pollination risk, this study characterizes the seasonal and spatial patterns in windborne hemp pollen dispersal spanning the conterminous United States (CONUS). By leveraging meteorological data obtained through mesoscale model simulations, we have driven Lagrangian Stochastic models to simulate wind-borne hemp pollen dispersion across CONUS on a county-by-county basis for five months from July to November, encompassing the potential flowering season for industrial hemp. Our findings reveal that pollen deposition rates escalate from summer to autumn due to the reduction in convective activity during daytime and the increase in wind shear at night as the season progresses. We find diurnal variations in pollen dispersion: nighttime conditions favor deposition in proximity to the source, while daytime conditions facilitate broader dispersal albeit with reduced deposition rates. These shifting weather patterns give rise to specific regions of CONUS more vulnerable to hemp cross-pollination.
Toward Collaborative Adaptation: Assessing Impacts of Coastal Flooding at the Watershed Scale
Mitchell, Allison; Bukvic, Anamaria; Shao, Yang; Irish, Jennifer L.; McLaughlin, Daniel L. (Springer Nature, 2022-12)
The U.S. Mid-Atlantic coastal region is experiencing higher rates of SLR than the global average, especially in Hampton Roads, Virginia, where this acceleration is primarily driven by land subsidence. The adaptation plans for coastal flooding are generally developed at the municipal level, ignoring the broader spatial implications of flooding outside the individual administrative boundaries. Flood impact assessments at the watershed scale would provide a more holistic perspective on what is needed to synchronize the adaptation efforts between the neighboring administrative units. This paper evaluates flooding impacts from sea level rise (SLR) and storm surge among watersheds in Hampton Roads to identify those most at risk of coastal flooding over different time horizons. It also explores the implications of flooding on the municipalities, the land uses, and land covers throughout this region within the case study watershed. The 2% Annual Exceedance Probability (AEP) storm surge flood hazard data and NOAA’s intermediate SLR projections were used to develop flooding scenarios for 2030, 2060, and 2090 and delineate land areas at risk of combined flooding. Findings show that five out of 98 watersheds will substantially increase in inundation, with two intersecting multiple municipalities. They also indicate significant inundation of military, commercial, and industrial land uses and wetland land covers. Flooding will also impact residential land use in urban areas along the Elizabeth River and Hampton city, supporting the need for collaborative adaptation planning on hydrologically influenced spatial scales.
The Impact of advance purchase deadlines on airline consumers’ search and purchase behaviors
Hotle, Susan; Castillo, Marco; Garrow, Laurie A.; Higgins, Matthew J. (Elsevier, 2015-12)
Airlines frequently use advance purchase ticket deadlines to segment consumers. Few empirical studies have investigated how individuals respond to advance purchase deadlines and price uncertainties induced by these deadlines. We model the number of searches (and purchases) for specific search and departure dates using an instrumental variable approach that corrects for price endogeneity. Results show that search and purchase behaviors vary by search day of week, days from departure, lowest offered fares, variation in lowest offered fares across competitors, and market distance. After controlling for the presence of web bots, we find that the number of consumer searches increases just prior to an advance purchase deadline. This increase can be explained by consumers switching their desired departure dates by one or two days to avoid higher fares that occur immediately after an advance purchase deadline has passed. This reallocation of demand has significant practical implications for the airline industry because the majority of revenue management and scheduling decision support systems currently do not incorporate these behaviors.
Eco-Friendly Route Planning Algorithms: Taxonomies, Literature Review and Future Directions
Fahmin, Ahmed; Cheema, Muhammad Aamir; Eunus Ali, Mohammed; Nadjaran Toosi, Adel; Lu, Hua; Li, Huan; Taniar, David; Rakha, Hesham A.; Shen, Bojie (ACM, 2024)
Eco-friendly navigation (aka eco-routing) finds a route from A to B in a road network that minimizes the greenhouse gas (GHG) emission or fuel/energy consumption of the traveling vehicle. As road transport is a major contributor to GHG emissions, eco-routing has received considerable research attention in the past decade, mainly on two research themes: 1) developing models to estimate emissions or fuel/energy consumption of vehicles; and 2) developing algorithms to find eco-friendly routes for a vehicle. There are some excellent literature reviews that cover the existing estimation models. However, there is no literature review on eco-friendly route planning algorithms. This paper fills this gap and provides a systematic literature review in this area. From mainstream online databases, we obtained 2,494 articles and shortlisted 76 articles using our exclusion criteria. Accordingly, we establish a holistic view of eco-routing systems and define five taxonomies of estimation models, eco-routing problems and algorithms, vehicle types, traffic, and road network characteristics. Concerning the taxonomies, we categorize and review the shortlisted articles. Finally, we highlight research challenges and outline future directions in this important area.
Evaluation of Airport Size and Delay Causal Factor Effects on Delay Propagation Dissipation
Atallah, Stephanie; Hotle, Susan (Sage, 2021-11-21)
The International Civil Aviation Organization identifies departure and arrival punctuality as on-time key performance indicators. However, these metrics assume a flight’s delay is a result of either the origin or destination airport, providing limited information on where delay should be mitigated in the U.S. National Airspace System (NAS). This study evaluates the relationship between delay propagation magnitude, delay causal factor, airport size, and charged facility (airport or Air Route Traffic Control Center), to examine if certain delays take longer to dissipate. First, using flights from July 2018, results show that most delay propagation chains originate at large-hub airports. However, these delays were the quickest to recover. Second, this study presents a regression model, predicting total propagated delay using fixed effects based on the weather region where the original delay occurred. Each additional flight affected by downstream delay adds 18.7 min on average to total arrival delay in a propagation chain. Additionally, if weather was the original causal factor, total propagated delay increased by 11.6 min compared with non-weather delays. Lastly, this study compares delay propagation in July 2018 and July 2020. Results show uneven impacts of the coronavirus disease 2019 (COVID-19) across the large-hub airports. Some of the investigated airports did not witness large improvements in average delay per delayed flight, warranting further research in the future. While delay and delay propagation have not been completely eradicated in the NAS during the COVID-19 pandemic, findings suggest that both have significantly declined on average.
Modeling Arrival Flight Times within the Terminal Airspace
Alsalous, Osama; Hotle, Susan (Sage, 2021-05-10)
Air traffic management efficiency in the descent phase of flights is a key area of interest in aviation research for the United States, Europe, and recently other parts of the world. The efficiency of arrival travel times within the terminal airspace is one of nineteen key performance indicators defined by the Federal Aviation Administration (FAA) and the International Civil Aviation Organization, typically within 100 nmi of arrival airports. This study models the relationship between travel time within the terminal airspace and contributing factors using a multivariate log-linear model to quantify the impact that these factors have on the total travel time within the last 100 nmi. The results were compared with the baseline set of variables that are currently used for benchmarking at the FAA. The analyzed data included flight and weather data from January 1, 2018 to March 31, 2018 for five airports in the United States: Chicago O’Hare International Airport, Hartsfield-Jackson Atlanta International, San Francisco International Airport, John F. Kennedy International Airport, and LaGuardia Airport. The modeling results showed that there is a significant improvement in prediction accuracy of travel times compared with the baseline methodology when additional factors, such as wind, meteorological conditions, demand and capacity, ground delay programs, market distance, time of day, and day of week, are included. Root mean squared error values from out-of-sample testing were used to measure the accuracy of the estimated models.
Decision Support for Civil Engineering Students: Analysis of Alumni Career Paths
Hotle, Susan; Katz, Bryan J. (Sage, 2018-11-02)
Undergraduate students in engineering face many important decisions in the final 2 years of their degree program. These decisions can have an impact on long-term career choices, such as specialization area, career role of interest, and whether to apply to graduate school. Unfortunately, uninformed decisions can lead to missed opportunities, as well as the student potentially leaving science, technology, engineering, and mathematics education due to choosing a specialization that is not well aligned with their interests. This survey-based study assists students by analyzing the personality types, demographics, and career paths of 567 alumni that have earned an undergraduate degree in civil and environmental engineering (CEE) and are no longer enrolled in a university. Study findings include the fact that certain demographics, personality types, and job preferences are significant predictors of the final outcome of an alumni’s career when choosing between the different technical areas within CEE and professional roles. Family history of having an engineer in the immediate family did not prove to be a significant factor in these decisions. In addition, little significance was found between the data captured in the survey of whether or not someone would go on to earn a graduate degree in CEE. Given where significant relationships were found, it is recommended that future studies focus on testing additional personality types (e.g., is enthusiastic) and job traits (e.g., likes a desk job) to provide even greater distinctions between the technical areas and roles.
Assessment of Contributing Factors to Air Service Loss in Small Communities
Atallah, Stephanie; Hotle, Susan (National Academy of Sciences, 2019-04)
As indicated by current literature, service at small community airports was negatively affected by the Great Recession from 2007 to 2009 and recent changes in competition structure. Existing studies have looked at the recession’s lingering impact on small community airports (e.g., hub premiums, airport dominance, connectivity) and markets (e.g., market competition structure). However, to date it has been difficult to determine which factors contribute to a market’s potential future loss of service that serves a small community. In this study, we identified characteristics that could potentially contribute to a market’s loss or gain of service by incorporating different regional- and market-specific characteristics that have evolved over the years. This study used a fixed-effects conditional logistic regression and focused on region-to-region markets serving small communities that were in service at least once between 2007 and 2013. In total, the panel data included 1,367 markets departing from a small region and arriving at a small-, medium-, or large-sized region with 453 markets adding or losing service during that time. Fixed-effects were used to identify the impact of within-market variation on service loss over the years. Results showed that, first, markets affected by a merger were indeed at a higher risk of losing service. Second, markets operated by a fuel-intensive, small-aircraft fleet had a higher chance of being discontinued. Third, an increased number of competitors greatly reduced potential market service loss.
Urban Air Mobility: Airport Ground Access Demand Estimation
Rimjha, Mihir; Hotle, Susan; Trani, Antonio; Hinze, Nick; Smith, Jeremy; Dollyhigh, Samuel (AIAA, 2021-08)
This study aims to estimate passenger demand of Urban Air Mobility (UAM) for airport ground access trips while considering airspace restrictions in the Dallas-Fort Worth region. UAM is a concept mode of transportation designed to bypass ground congestion for time-sensitive, price-inelastic travelers using autonomous, electric aircraft with Vertical Takeoff and Landing (VTOL) capabilities. Airport ground access trips constitute a trip purpose that can utilize this mode. This study analyzes originating ground access trips for two major airports in the Dallas-Fort Worth region: Dallas-Fort Worth International Airport (DFW) and Dallas Love Field Airport (DAL). First, a mode choice model is calibrated on the existing airport ground access behavior. UAM demand is then estimated using the developed model, airspace restrictions, and the results from UAM demand stated-preference surveys in literature. Airspace restrictions consist of unusable pieces of airspaces based on current air traffic patterns, where the placement of UAM vertiports and overflying of UAM vehicles are prohibited. The demand model considers the trajectories of the UAM vehicles, which navigate on pre-defined routes inside Class-B airspace to prevent Air Traffic Control (ATC) involvement requirements. This study includes sensitivity analyses of UAM demand to the cost per passenger mile (CPM), number of vertiports placed in the region, and other secondary factors like vertiport location, intermodal cost, fixed cost, and average speed. Corridors with significant UAM demand are identified from the spatial distribution of demand and potential bottlenecks in the UAM network. The findings predict up to 4% market share of UAM for trips to the airport at the optimistically lower fare of $2 per passenger mile (in addition to the fixed cost of $23) and a 50-vertiport UAM network. Average Value of Times (VOTs) for business and non-business travelers are estimated to be around $57/hr and $36/hr, respectively. Business travelers comprise three-quarters of the total UAM demand because of relatively higher VOTs. Airport access trips in Dallas-Fort Worth region have considerable potential for UAM if the trip's price is below $4 per passenger mile (in addition to the fixed cost of $23).
Urban Air Mobility: Preliminary Noise Analysis of Commuter Operations
Rimjha, Mihir; Trani, Antonio; Hotle, Susan (AIAA, 2021-08)
This study aims to estimate potential noise levels generated due to Urban Air Mobility (UAM) commuter operations in the Northern California and the Dallas-Fort Worth regions. UAM is a concept aerial transportation mode designed to bypass ground congestion using an electric vehicle with Vertical Take-Off and Landing (VTOL) capabilities. UAM vehicles are expected to be significantly quieter than traditional helicopters, but operate on a much larger scale. Commuter travel demand will not be uniformly distributed with operations concentrated in a small geographical area such as Central Business Districts (CBD) and short time windows such as morning or evening peak periods. The objective of this study is to evaluate the aircraft noise annoyance generated by commuter UAM operations using flight trajectories developed in a previous study estimating UAM commuter demand. This study estimates the noise level from overflying UAM vehicles in a full day of operation (24 hours) and identifies areas where the noise levels may pose a challenge to future UAM operations. Noise estimation is performed at the Census Block group level using the Day-Night Level (DNL) metric. We run a parametric analysis considering two scenarios in each region: the UAM vehicle has a 10 dBA and 15 dBA noise reduction compared to the Robinson R-44 helicopter. The findings indicate a considerable difference between the 10 dBA and 15 dBA reduction scenarios. Although challenging, achieving a 15-dBA reduction compared to a 10-dBA reduction could reduce land area with DNL value above 50 dBA by 94% and highly-annoyed population by 91% in Northern California. Similarly, in Dallas-Fort Worth, achieving a 15-dBA reduction compared to a 10-dBA reduction could reduce the land area with DNL value above 50 dBA by 80% and a highly annoyed population by 85%. Lastly, we analyze the high-demand vertiport in the San Francisco Financial District in the Aviation Environmental Design Tool (AEDT) to observe the DNL contours for the varying noise performance scenarios.
The Evolution of low-cost Carrier operational strategies pre- and post-recession
Atallah, Stephanie; Hotle, Susan; Mumbower, Stacey (Elsevier, 2018-09)
This study presents an analysis of low-cost carrier (LCC) competition strategies for Continental US (CONUS) domestic markets. Using OAG schedule data from 2005 to 2015, pre- and post-recession trends in LCC flight offerings were analyzed and compared with their major carrier counterparts in terms of number of markets served, flight frequency, and competition structures of served markets. Results show that LCCs are increasing the number of markets served to/from large airports and are entering highly-competitive markets. The results further suggest that LCCs and major carrier strategies evolved differently during the study period, where LCCs outpaced major carriers in terms of markets entered while major carriers have gained a greater flight frequency share in the markets they already serve. Results clearly indicate that overall LCCs are still growing in terms of O-D markets served and increasing competition with major carriers. However, evidence suggests that each of the top four LCCs adopted different operating strategies as part of their business model during the study period.
Evaluation of Taxiing Behavior by Airport and Flight Characteristics
Li, Mia K.; Hotle, Susan (AIAA, 2020-06)
Taxiing efficiency is a critical measurement for airport surface performance. The purpose of this study is to evaluate the impact of airport and flight characteristics on taxiing behavior that is not included in preceding surface performance studies based on Aviation System Performance Metrics (ASPM) timestamps. Specifically, the influence of the airport, flight’s equipment type Taxiway Design Group (TDG), time of day (peak vs off-peak), and taxiing area (movement vs non-movement area) on taxiing speed and distance are included. This study evaluates the taxiing efficiency at six major U.S. airports using Airport Surface Detection Equipment – Model X (ASDE-X) Surveillance data, which provides the aircraft position with second-by-second timestamps for each recorded movement at the airport. The computer tool is created to overlay the ASDE-X flight tracks with the geospatial information of the airport layouts. This study presents the advantages of using surveillance flight data and the computer tool to extract valuable information for both landing and takeoff operations. Results suggest that the operation (i.e. arrival vs departure), airport and TDG are important to incorporate when estimating surface performance benchmark metrics.
Using ASDE-X Surveillance for Taxi-Out Time Benchmarking and Delay Estimation
Hotle, Susan; Baszczewski, Bryan; Gulding, John (AIAA, 2016-06)
Surface performance indicators for taxi-out delay depend on reference ideal unimpeded (nominal) times to identify areas in need of improvement. Current FAA practice derives unimpeded taxi-out times through a statistical analysis of ASPM-provided Out-Off-On-In (OOOI) sensor timestamps. This statistical technique uses a regression of taxi-times against the number of aircraft active on the surface to determine times of low congestion and presumably under conditions when times would be "unimpeded." These OOOI timestamps are rounded to the nearest minute and reported by a subset of carriers, leading to data concerns. Furthermore, the current unimpeded taxi-out method is only based on departure airport, operating carrier, season, and calendar year, which fails to include both start taxi-out location (gate/terminal) and wheels-off location (runway). Given these shortcomings, opportunities exist to estimate surface performance indicators through the use of surveillance data. Surveillance systems, such as the Airport Surface Detection Equipment-Model X (ASDE-X) system, provide aircraft temporal and spatial information by the second, enabling terminal area and runway information to be incorporated. The purpose of this paper is to utilize these new capabilities when identifying taxi-out routings that contribute the most to delay and inefficiency. The paper first assesses the current statistically derived unimpeded time against alternatives based on upper percentile benchmark methods. It then evaluates alternatives to current methodology by developing both an ASDE-X and hybrid ASDE-X/ASPM Key Surface Event Database. Techniques using the ASPM, ASDE-X, and hybrid surface databases are compared for five top U.S. airports during Fiscal Year 2014. Results show that ASDE-X provides better and more consistent data coverage when compared to the current ASPM process. An ASDE-X source also allows the taxi-start location to be identified, unlike ASPM. However, given existing data limitations, it is recommended that ASDE-X be supplemented with ASPM messages in order for an analysis to capture a more complete understanding of surface performance, specifically in the masked, non-movement area.
Taxi Event Extraction from Surveillance for Surface Performance Evaluation
Mirmohammadsadeghi, Navid; Hotle, Susan; Trani, Antonio; Gulding, John (AIAA, 2020-06)
Estimated unimpeded taxi times can be used to quantify a flight’s taxiing delay when compared with its actual taxi time. Currently, flight unimpeded taxi times are calculated by the Federal Aviation Administration using a regression method where flights in the Aviation System Performance Metrics data are clustered on the season of operation, airline, airport, and calendar year. The method uses the airline-reported gate-Out, wheels-Off, wheels-On, gate-In (OOOI) times rounded to the nearest minute. For nonreporting airlines, these times are estimated from similar flights. The purpose of this paper is to evaluate the unimpeded time using a surveillance-based approach by identifying the time a flight spent waiting in the system and comparing with the total taxi time. This study focuses on analyzing both arrivals and departures for six U.S. airports (Atlanta, Charlotte, Denver, Houston, New York Kennedy, and Chicago O'Hare) during themonth of July 2015. Thestudy results showhowthe non-surveillance-based andsurveillance-based metrics compare, with nonsurveillance methods including the current regression method and the 5th-to-15th clustering method described in current literature. The benefits of using surveillance information for surface performance are explored, as spatial analyses allow for quick identification of taxiway locations that were the most susceptible to delays.
Taxi Event Extraction from ASDE-X Surveillance for Surface Performance Evaluation
Mirmohammadsadeghi, Navid; Hotle, Susan; Trani, Antonio; Gulding, John (AIAA, 2018-06)
Unimpeded taxi times can be used to quantify a flight’s taxiing delay when compared with its actual taxi time. Currently, flight unimpeded taxi times are calculated by the Federal Aviation Administration using a regression method where flights in the Aviation System Performance Metrics (ASPM) data are clustered on the season of operation, airline, airport, and calendar year. It utilizes the airline-reported gate-Out, wheels-Off, wheels-On, gate-In (OOOI) times reported in ASPM, rounded to the nearest minute. For non-reporting airlines, these times are estimated from similar flights. The purpose of this paper is to evaluate the unimpeded time using a surveillance-based approach by identifying the time a flight spent waiting in the system (i.e. traveling slower than 3 m/s) and comparing it to the total taxi time. This study specifically focuses on analyzing both arrivals and departures for 6 top U.S. airports (ATL, CLT, DEN, IAH, JFK, ORD) during the month of July 2015. Airport Surface Detection Equipment-Model X (ASDE-X) surveillance data was matched with ASPM data in order to have a complete coverage of the taxiing phase of airplanes between the gate and runway for taxi out and in procedures. Results show the benefits of a spatial analysis, which allows for a quick identification of which locations on the taxiways were the most susceptible to cause. This study also evaluates changes in the unimpeded metric when compared to the current method and other proposed methods, such as the 5th-to-15th clustering, that is present in literature.
A Case Study: Educating Transportation Engineers with Simulation Software
Luken, Brittany Lynn; Hotle, Susan; Alemdar, Meltem; Garrow, Laurie A. (2011 ASEE Annual Conference & Exposition, 2011)
Airport Scheduling and Operational Performance: A Clustering Analysis of Airport Response to COVID-19
Alsalous, Osama; Hotle, Susan (American Institute of Aeronautics and Astronautics, 2023-06)
In early 2020, the Coronavirus disease 2019 (COVID-19) pandemic started and forced air travel demand to decrease sharply in most parts of the world due to travel restrictions that were put in place to limit the spread of the virus. The pandemic also impacted capacity due to reasons such as workforce social distancing, days when Air Traffic Control (ATC) facilities were shut down due to COVID cases, and financial challenges due to the decreased demand. The reduced demand created a unique challenge in the system since capacity exceeded demand by very large margins in the NAS, however, delays in the system did not fall to zero despite the sharp drop in demand. This study analyzed operations at 77 United States (US) airports to compare and contrast their responses to the COVID-19 pandemic in terms of capacity, throughput, and the resulting operational performance. We evaluate the response of airports to the initial shock event during 2020 in addition to the recovery period that followed in 2021. The data showed a 67% decline in total operations at the lowest point during the pandemic. The impact during the shock time period varied greatly across the airports, ranging from a reduction of 14.8% at MEM to 81.5% at LGA. We performed a clustering analysis to study airports’ response to the COVID-19 pandemic. There was a number of airport characteristics that were correlated to the changes in airport metrics. For example, the data showed that being located in a multi-airport city was significantly correlated to the decrease in operations during the shock, however, it was not significant in the recovery trends. Our analysis showed that delays in the system did not change proportionately to the change in operations. Similarly, there were only minor improvements in punctuality, on-time flights at the ASPM 77 airports increased by 9.5% while operations declined by 52% during the shock event time period compared to pre-COVID. Part of this phenomenon was a result of schedule peaking which caused delays due to creating busy hours at the airports. This analysis can inform airport management when responding to future disruptive events, it provides insight into airport operational resiliency, response to disruption, and demand recovery patterns based on airport characteristics.
PyCHAMP: A Crop-Hydrological-Agent Modeling Platform for Groundwater Management
Lin, Chung-Yi; Alegria, Maria Elena Orduna; Dhakal, Sameer; Zipper, Sam; Marston, Landon (Environmental Modelling and Software, 2024-08)
The Crop-Hydrological-Agent Modeling Platform (PyCHAMP) is a Python-based open-source package designed for modeling agro-hydrological systems. The modular design, incorporating aquifer, crop field, groundwater well, finance, and behavior components, enables users to simulate and analyze the interactions between human and natural systems, considering both environmental and socio-economic factors. This study demonstrates PyCHAMP’s capabilities by simulating the dynamics in the Sheridan 6 Local Enhanced Management Area, a groundwater conservation program in the High Plains Aquifer in Kansas. We highlight how a model, empowered by PyCHAMP, accurately captures human-water dynamics, including groundwater level, water withdrawal, and the fraction of cropland dedicated to each crop. We also show how farmer behavior, and its representation, drives system outcomes more strongly than environmental conditions. The results indicate PyCHAMP’s potential as a useful tool for human-water research and sustainable groundwater management, offering prospects for future integration with detailed sub-models and systematic evaluation of model structural uncertainty.

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