Simulating Aerial Migrations through Use of Empirical Movement Models

Aerial migrations are historically difficult to observe and quantify, especially the environment in which these migration take place. However, with increasingly accurate tracking methods and international datasets containing remote sensing and weather renalyses, it is becoming easier to observe this environment and find the conditions that mostly affect the migrants. Track annotation is the method of combining the tracking data with the environmental data, and can be used to create models of the animals’ movement. I performed a track annotation of Swainson’s thrush and created an empirical model based on the environmental conditions that mostly affect the flight. A Swainson’s thrush (Catharus ustulatus) is a small songbird that migrates from northeastern North America to Central and South America in the winter. This annual migration involves a 1000-kilometer trip across the Gulf of Mexico. Little is known about the details surrounding this annual flight, including the variables that affect the flight itself. In a National Science Foundation (NSF) funded experiment, the thrushes are tracked by a radio transmitter which allows us to record arrival and departure timestamps of the transGulf flight. The Environmental-Data Automated Track Annotation (Env-DATA) system—a data exploration system developed through Movebank (www.movebank.org) and The Ohio State University allows us to link the movement track with data from global and regional weather reanalysis models and remote sensing. I annotated the movement tracks with several different environmental variables and followed a hierarchal process to build a series of empirical movement models. I concluded that the combination of boundary layer height and wind speed most strongly affect the flight.National Science Foundation (IOS Award #1147096)NASA (grant #NNX11AP61G)National Geographic Society Committee on Research and Exploration (Award # 8971-11)Eastern Illinois University (Research and Creative Activity Awards to J.L.D. and L.S.)University of Illinois Urbana-ChampaignNo embargoAcademic Major: Environmental Engineerin

Predicting Urban Reservoir Levels Using Statistical Learning Techniques

Urban water supplies are critical to the growth of the city and the wellbeing of its citizens. However, these supplies can be vulnerable to hydrological extremes, such as droughts and floods, especially if they are the main source of water for the city. Maintaining these supplies and preparing for future conditions is a crucial task for water managers, but predicting hydrological extremes is a challenge. This study tested the abilities of eight statistical learning techniques to predict reservoir levels, given the current hydroclimatic conditions, and provide inferences on the key predictors of reservoir levels. The results showed that random forest, an ensemble, tree-based method, was the best algorithm for predicting reservoir levels. We initially developed the models using Lake Sidney Lanier (Atlanta, Georgia) as the test site; however, further analysis demonstrated that the model based on the random forest algorithm was transferable to other reservoirs, specifically Eagle Creek (Indianapolis, Indiana) and Lake Travis (Austin, Texas). Additionally, we found that although each reservoir was impacted differently, streamflow, city population, and El Niño/Southern Oscillation (ENSO) index were repeatedly among the most important predictors. These are critical variables which can be used by water managers to recognize the potential for reservoir level changes

Assessing Global Environmental Sustainability Via an Unsupervised Clustering Framework

The importance of sustainable development has risen in recent years due to the significant number of people affected by lack of access to essential resources as well as the need to prepare for and adapt to intensifying climate change and rapid urbanization. Modeling frameworks capable of effectively assessing and tracking sustainability lie at the heart of creating effective policies to address these issues. Conventional frameworks, such as the Environmental Performance Index (EPI), that support such policies often involve ranking countries based on a weighted sum of a number of relevant environmental metrics. However, the selection and weighing processes are often biased. Moreover, the ranking process fails to provide policymakers with possible avenues to improve their country’s environmental sustainability. This study aimed to address these gaps by proposing a novel data-driven framework to assess the environmental sustainability of countries objectively by leveraging unsupervised learning theory. Specifically, this framework harnesses a clustering technique known as Self-Organized Maps to group countries based on their characteristic environmental performance metrics and track progression in terms of shifts within clusters over time. The results support the hypothesis that the inconsistencies in the EPI calculation can lead to misrepresentations of the relative sustainability of countries over time. The proposed framework, which does not rely on ranking or data transformations, enables countries to make more informed decisions by identifying effective and specific pathways towards improving their environmental sustainability

Contemporary climate analogs project north-south polarization of urban water-energy nexus across US cities under warming climate

Despite the coupled nature of water and electricity demand, the two utilities are often managed by different entities with minimal interaction. Neglecting the water-energy demand nexus leads to to suboptimal management decisions, particularly under climate change. Here, we leverage state-of-the-art machine learning and contemporary climate analogs to project the city-level coupled water and electricity demand of 46 major U.S. cities into the future. The results show that many U.S. cities may experience an increase in electricity (water) demand of up to 20% (15%) due to climate change under a high emissions scenario, with a clear north-south gradient. In the absence of appropriate mitigation strategies, these changes will likely stress current infrastructure, limiting the effectiveness of the ongoing grid decarbonization efforts. In the event that cities are unable to match the increasing demand, there may be increased occurrence of supply shortages, leading to blackouts with disproportionate impacts on vulnerable populations. As such, reliable projections of future water and electricity demand under climate change are critical not only for preventing further exacerbation of the existing environmental injustices but also for more effective design and execution of climate change mitigation and adaptation plans

Toward impact-based monitoring of drought and its cascading hazards

Growth in satellite observations and modelling capabilities has transformed drought monitoring, offering near-real-time information. However, current monitoring efforts focus on hazards rather than impacts, and are further disconnected from drought-related compound or cascading hazards such as heatwaves, wildfires, floods and debris flows. In this Perspective, we advocate for impact-based drought monitoring and integration with broader drought-related hazards. Impact-based monitoring will go beyond top-down hazard information, linking drought to physical or societal impacts such as crop yield, food availability, energy generation or unemployment. This approach, specifically forecasts of drought event impacts, would accordingly benefit multiple stakeholders involved in drought planning, and risk and response management, with clear benefits for food and water security. Yet adoption and implementation is hindered by the absence of consistent drought impact data, limited information on local factors affecting water availability (including water demand, transfer and withdrawal), and impact assessment models being disconnected from drought monitoring tools. Implementation of impact-based drought monitoring thus requires the use of newly available remote sensors, the availability of large volumes of standardized data across drought-related fields, and the adoption of artificial intelligence to extract and synthesize physical and societal drought impacts.</p

Climate Change Effects on Urban Water Resources: An Interdisciplinary Approach to Modeling Urban Water Supply and Demand

Urban populations are growing at unprecedented rates around the world, while simultaneously facing increasingly intense impacts of climate change, from sea level rise to extreme weather events. In the face of this concurrent urbanization and climate change, it is imperative that cities improve their resilience to a multitude of stressors. A key aspect of urban resilience to climate change is ensuring that there is enough drinking water available to service the city, especially given the projections of more frequent and intense droughts in some areas. However, the study of climate impacts on urban water resources is fairly nascent and many gaps remain. In this dissertation, I aim to begin to close some of those gaps by adopting an interdisciplinary approach to studying water availability. First, I focus on urban water supply, and in particular, reservoir operations. I employ a variety of methods, ranging from data science techniques to traditional hydrological models, to predict the reservoir levels under a variety of climate conditions. Following the analysis of water supply, I shift focus to urban water demand. Here, I include interconnected systems, such as electricity, to evaluate and characterize the impact of climate on water demand and the benefit of considering system interconnectivities. Additionally, I present an analysis on the projection of water and electricity demand into the future, based on representative concentration pathways of CO2. Finally, I focus on the human dimension to the demand studies. By studying the social norms surrounding water conservation in urban areas, as well as the demographics, I built a predictive model to estimate monthly water consumption at the census tract-level. Through these interdisciplinary studies,I have made progress in filling knowledge gaps related to the impact of climate change on urban water resources, as well as the impact of people on these water resources

Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet been tested in United States. In this study, we quantified the severity of 2012 drought which affected the agricultural output for much of the Midwestern US. We used several popular drought indices, including the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index with multiple time scales, Palmer Drought Severity Index, Palmer Z-index, VegDRI, and PADI by comparing the spatial distribution, temporal evolution, and crop impacts produced by each of these indices with the United States Drought Monitor. Results suggested this drought incubated around June 2011 and ended in May 2013. While different drought indices depicted drought severity variously. SPI outperformed SPEI and has decent correlation with yield loss especially at a 6 months scale and in the middle growth season, while VegDRI and PADI demonstrated the highest correlation especially in late growth season, indicating they are complementary and should be used together. These results are valuable for comparing and understanding the different performances of drought indices in the Midwestern US

The overlooked environmental footprint of increasing Internet use

often recognized too late, typically when changing the adopted technologies and behavioral norms is difficult. A similar story may unfold if society continues to blindly transition to an unregulated and environmentally unaudited digital world, a transition path that has been facilitated by the fourth industrial revolution and is now accelerated by the global COVID-19 crisis. The newly developed digital lifestyle has major environmental benefits, including the reduction of travel-related CO2 emissions. Yet, increased Internet use has some hidden environmental impacts that must be uncovered (Fig. 1a) to make the transition to a lowcarbon and green economy successful. The data centers’ electricity consumption accounts for 1% of the global energy demand (Masanet et al., 2020), more than the national energy consumption of many countries. Depending on the energy supply mix and use efficiency, Internet traffic contributes differently to negative environmental impacts and climate change. As the number of Internet users increases, the number of online services and applications they use grow. This trend exacerbates the environmental footprint of the Internet, despite the many successful and significant efforts to improve the efficiency of data centers (Masanet et al., 2020) and reduce their reliance on fossil energy. In order to build a sustainable digital world, it is imperative to carefully assess the environmental footprints of the Internet and identify the individual and collective actions that most affect its growth