Environmental Justice for All: Nutrient Impacts on Lake-Based Recreation and Tourism by Rural and Socially Disadvantaged Iowans

Over the past 18 years, nitrogen pollution flowing out of Iowa to the Gulf of Mexico has grown by close to 50% and contributed 29% of the total nitrogen headed to the Gulf from the Mississippi-Atchafalaya River basin. Increased nutrients in Iowa’s water deteriorates water quality and can lead to toxic algal blooms that can both decrease the oxygen fish and other aquatic life need to survive and can cause illness in humans through contact during recreational activities. In addition, in the presence of excess nutrients, potable water is more likely to be contaminated and cause disproportionate burdens and health risks for low-income and minority communities. An increase in the population impacted by nutrient issues in Iowa sparks concerns about broader socioeconomic disparities in nutrient pollution exposure. More studies are needed to understand the nutrient impacts on social wellbeing for all Iowans.

This 12-month project aims to understand nutrient impacts via the lens of local recreation and tourism as well as the economic impact of water quality improvement on rural and lower-income communities. Specifically, we have three objectives. First, focusing on lower-income and underrepresented households and building on nearly two decades of historical Iowa Lakes Survey work (2002–2019), we will examine how water quality influences participation in lake recreation activities and the roles of water quality perception and recreation equipment ownership on households’ recreation decisions. Second, we will supplement the forthcoming Iowa DNR 2020 Iowa Lakes Survey to include more household samples from rural and lower-income communities to counter the underrepresentation of these communities in previous surveys. The COVID-19 pandemic also provides us an opportunity to investigate if rural and lower-income communities respond differently to this change in 2020. Third, we will conduct a series of IMPLAN analyses to quantify the economic impacts of current recreation and tourism activities on local economies and project how these economic impacts change under different nutrient-driven water quality scenarios.


Understanding the Impacts of Coronavirus-related Reduction in Aerosols and Pollution on Precipitation and Discharge across Iowa

Among the many impacts of the COVID-19, the pandemic has led to improved air quality conditions in the countries under quarantine. Satellite-based maps highlight a remarkable reduction in aerosol and air pollution due to the shutdown of industries and very limited traffic in China and other Asian countries between late January and March 2020. Similarly, many areas across the United States have also experienced a large reduction in air pollution due to the lockdown in response to COVID-19. Meanwhile, large areas of the central United States, particularly within the Missouri River Basin, received less precipitation than normal during February-April 2020. Is it possible that the observed reduction in precipitation was driven by the reduced aerosols due to the coronavirus? If so, how much of these changes can be attributed to the pandemic and what does it mean in terms of discharge?

Our hypothesis is that the local aerosol reduction led to a detectable change in precipitation and discharge across the central United State and Iowa, and that these effects were either magnified or reduced depending on the magnitude of the long-range transport of particles. Therefore, the goal of this proposal is to evaluate how much of an impact this abrupt reduction in local and remote aerosols played during the past winter/spring in terms of precipitation and discharge, and to determine the physical mechanisms at play.

The research methodologies build on analytical tools and data sets with which the research team has extensive experience, and will be developed further to address the role of the reduction in aerosols due to COVID-19 in relation to the reduced precipitation and discharge across the central United States broadly, and Iowa in particular.

We will use the Weather Research and Forecasting (WRF) model coupled with Chemistry version 3.8.1 (WRF-Chem), and force it with the North American Regional Reanalysis (NARR) data and the near real-time Whole Atmosphere Community Climate Model (WACCM) outputs. We propose to design four experiments to isolate the relative contribution of the aerosols: Control (CTRL), Business-as-usual (BAU), NoTrans, and NoLocal experiments. In these runs, the circulation (e.g., zonal and meridional winds, geopotential height) is the same but they have different settings for transported and local aerosols. By comparing the four experiments, we can assess the relative roles of aerosols in forcing the observed changes in precipitation and discharge during February-April 2020. In addition to precipitation and discharge, the proposed experiments will produce outputs (e.g., temperature, humidity, ultraviolet radiation, ozone) which are important to diagnose the environmental conditions for the survival and spread of COVID-19.

The proposed research will enhance our knowledge of the role of local and remote aerosols on precipitation and discharge across the central United States, with a special focus on Iowa. Given the extreme reduction in aerosols due to the pandemic, our findings will have both short- and long-term impacts. In the short term, insights gained from this research can provide information in terms of preparation for the upcoming fall and winter from the perspective of water resources and emergency management. In the long term, our work will provide basic information on the potential impacts of different mitigation efforts aimed at reducing anthropogenic aerosols on precipitation across the central U.S.

The results and data from this project will be made available to federal and state agencies. Moreover, this information will be readily available to local stakeholders and users for their own use. The research team will leverage the tools and expertise provided by the Iowa Flood Center (IFC) to make the results of the proposed work relevant and immediately and directly available to agencies and to the general public. Moreover, the dissemination of the results will be facilitated by means of outreach activities through the IFC and the Iowa Water Center.

Catchment-scale hydrologic modeling of urban residential stormwater best management practices (BMPs)

The combined effects of urbanization and projected climate changes are expected to increase the already negative effects of urban areas on surface water resources. Increased runoff volumes, flow rates, and pollutant loads due to impervious surfaces will likely be exacerbated by increased intensity and duration of storm events, leading to degraded aquatic ecosystems and impaired water quality. In Cedar Falls, Iowa (the location of our project study sites) segments of Dry Run Creek which feed into the Cedar River have both biological and bacterial impairments. New approaches to stormwater management known as green infrastructure and stormwater best management practices (BMPs) are designed to restore the natural hydrologic cycle, increasing infiltration, reducing runoff volumes and rates, and trapping/filtering pollutants in soil. These practices have the potential to improve water quality and the ecological integrity of aquatic systems, but additional research is needed to quantify these effects. Extensive monitoring and modeling of distributed small-scale BMPs is crucial to understanding how these practices can reduce the impacts of stormwater on surface water resources, as well as to improving the long-term resiliency of stormwater management infrastructure to adapt to future climate changes. Additional empirical data for stormwater runoff quantity and quality are also crucial to the development of realistic and reliable urban hydrologic simulations. Several parameters within hydrological models, especially those used to assess and predict water quality, require extensive monitoring for robust model calibration. Subsequent model validation is also best performed using data independent of that used in the calibration process. Due to the time- and cost-intensive nature of long-term monitoring and sampling studies, many hydrologic models are not based on site-specific flow or water quality data, leading to high levels of uncertainty about whether they represent “real world” scenarios. This proposal seeks support to extend efforts to monitor stormwater runoff quantity and quality from two “paired” residential neighborhoods in Cedar Falls. The extended period of monitoring proposed here (from two years of previous monitoring to three years) will provide the data needed for parameterization and robust calibration and validation of hydrologic and water quality models. This will also allow for comparison to and closer replication of a previous five-year study conducted in the Easter Lake Watershed in Des Moines. The specific objectives of this study of two residential watersheds in the Dry Run Creek Watershed are to: 1) Monitor stormwater quantity and quality during storm events for one additional year; 2) Collate independent data sets to parameterize, calibrate and validate water quantity and quality models for both watersheds; 3) Characterize stormwater BMP effects on runoff volume and water quality parameters; and 4) Predict possible hydrologic outcomes of varying levels of BMP adoption across spatial and temporal scales, including the effects of climate variability over time. The results of this study will allow a range of hydrologic and water quality outcomes to be quantified and simulated, and will ascertain the levels of BMP adoption necessary to achieve water quality goals. Additionally, results of climate change simulations can be used to provide long-term hydrologic predictions and to support urban resiliency planning.

Coupling Physical and Societal Objectives for Decision Making in Water Resources Management

Sensor networks, built on the backs of the latest digital communication technologies, are increasingly being deployed in urban sewer networks and at regional scales to monitor flooding and water quality of rivers (Habibi et al. 2017; Mullapudi et al. 2017; Jones et al. 2018; Yildirim and Demir 2019). Concurrently, control technologies are being deployed alongside sensors which allow for water resources operators to actively manipulate these systems in places and in a manner that was previously inconceivable (Kerkez et al. 2016). Together, sensing and control deployments mean massively more complex systems to address persistent water resources challenges such as flooding and water quality. Recent research studies demonstrate the potential for coordinated and increasingly automated management and control approaches to improve performance considering both water quality and quantity objectives (Muschalla et al. 2014; Sadler et al. 2019; Sharior et al. 2019; Mullapudi et al. 2020; Sun et al. 2020; Troutman et al. 2020).

However, decision-making methodology based solely on underlying physical and technical characteristics ignore the social and political structures which overlay and interact with them. Considering these structures as part of a singular, sociotechnical whole exposes normative questions of “right” and “wrong,” or more profound questions of societal ethics and values. Take for example, if some flooding is an inevitable outcome within a catchment with dynamic control capabilities, how ought a controller act to consider the societal implications of such flooding? Or, if a control scheme consistently recommends distributing harm in poor neighborhoods and benefits in richer neighborhoods, ought its directives be followed? To address this gap in knowledge, we previously developed a framework to construct data-driven ethical preference models in relation to water resources issues, such as flooding, using a combination of voting-based practices and machine learning methods (Ewing and Demir, 2020). Ethical preference models captured group “wisdom” and demonstrate that the framework is a candidate for use in decision support toolchains to support water resources decisions.

In this study, we propose to explore a novel methodology to incorporate concepts of ethics and justice into decision support toolchains for water quantity and water quality objectives by building on the preliminary work from our research lab (Ewing and Demir, 2020). To do so we will utilize a modeling framework, the Python programming module pystorms, which allows hydrologic and hydraulic simulation of water conveyance networks with dynamic control of storage assets (Mullapudi and Troutman 2020). Our extension will allow dynamic operation of storage assets while considering social and geographic data – such as census data of social vulnerability, land use type, and landowner type (i.e. public or private) – and voting-based ethical preference models. Through this experimental setup we will investigate how different dynamic control strategies affect the distribution of benefits and harms across communities and landscapes.

The findings from this research will provide greater insight into how to manage our next generation sociotechnical systems such that they preempt injustices, inequities, and inefficiencies. The findings will also inform how novel decision support toolchains for dynamic control may integrate into our water resources management in both catastrophic and quotidian management scenarios.

A Serious Game on Flood Mitigation for K-12 and Public Education

This project aims to create a web-based serious game geared towards educating K-12 and college students, and public on flood prevention and mitigation strategies, such that they are more informed about the implications of future flooding events. A web-based interactive gaming environment will be designed and implemented that allows players to experiment with different flood mitigation strategies for a real-world location of their choice. As part of the gameplay, the user will be presented with a community under risk of flooding, and various mitigation and preparedness actions. The users will face challenges in decision-making and will evaluate trade-offs in terms of assignment of monetary resources with respect to societal gain. Once decisions are finalized, a realistic flood event will be generated and visualized so that the player can examine impacts on the community. In addition to visual inspection, the proposed game will allow interactive analysis of the economic damage and casualties. This immersive, repeatable, and engaging experience will allow K-12 students and the public to comprehend the consequences of individual measures, build a conceptual understanding of the needs and benefits of mitigation actions, and examine how floods may occur in their communities.

Does food web structure modify the resilience of shallow lakes to harmful algal blooms?

Problem: Shallow lakes are the most common inland waterbody and are highly susceptible to eutrophication which often leads to frequent and severe harmful algal blooms (HABs). HABs contain toxin-producing cyanobacteria, cause poor drinking water quality, and contribute to fish kills. Furthermore, more frequent HABs have been linked to increased greenhouse gas (GHG) emissions. Therefore, reducing HABs in shallow lakes is a priority for lake managers. However, restoration is often slow or unsuccessful due to a host of physical and biological barriers. Food web manipulation has had success improving water quality, but it is often only a short-term solution. Food web structure, and its underlying stability, has been linked to ecosystem resilience to perturbation in a large body of theoretical literature. Additionally, there is evidence that food web structure can also regulate GHG flux. Therefore, greater ecosystem resilience can reduce the frequency of HABs, decrease GHG flux, sustain more nutrient loading, and allow faster recovery from dangerous algal blooms.

Objectives: The objectives of our proposed research are two-fold: (1) Determine the empirical relationship between food web structure and ecosystem resilience and (2) assess how food web structure affects GHG flux.

Methods: To meet our objectives we propose a manipulation experiment using six large ponds to test the effect three food web structures have on ecosystem resilience to a one-time nutrient pulse, similar to a large storm event. We will establish three different food webs with varying levels of complexity and interaction strength, replicated in two ponds each. This will allow us to mechanistically assess how food web structure influences ecosystem resilience and GHG flux.

Outcomes: The outcomes of this project will further our understanding of how food web structure mediates disturbances in shallow lakes. This knowledge is crucial for managers in order to implement more successful lake restoration programs. This is because understanding the influence of food web structure on ecosystem resilience can inform strategies that could engineer more resilient ecosystems, thereby increasing restoration success.

Read the research findings here.

Strengthening the Foundation of Agroecosystem Models for Water Research: Precision Land Surface Analysis and Machine Learning for Enhanced Soil Maps

The state of Iowa is under increasing pressure to improve agronomic efficiency while mitigating soil and water degradation. Predictive agroecosystem process models are used to target solutions for soil and water quality issues in Iowa by quantifying degradation and monitoring the efficacy of sustainable land management practices. The problem is that these models are sensitive to soil input data. Currently available soil maps are outdated and lack the spatial resolution required to meet the needs of precision agroecosystem modeling. The Soil Survey Geographic (SSURGO) database is considered the ‘best available’ source for spatial soil information, however, these maps were built from data over 30years old. Moreover, technological advancements since the production of SSURGO maps greatly exceed those used to make them. We propose to make “next generation” soil maps via Digital Soil Mapping (DSM) techniques for soil properties pertinent to agroecosystem model predictions to support monitoring and mitigating water quality.Specifically, these soil properties are thickness of A horizon, 2) depth to permanent saturation (water table), 3) particle size fractions (sand, silt, and clay) and 4)Soil Organic Carbon(SOC)content. In support of our current DSM project in Story and Boone Counties, we propose a comparative “quad-county” study region in Osceola, Clay, Emmet, and Dickinson Counties. The quad-county region encompasses different soil and drainage characteristics on sub-regions of the Des Moines Lobe (DML) that are frequently overlooked.

The objectives of this study are to: 1) produce digital soil maps for the target soil properties have greater accuracy and spatial resolution than the currently available SSURGO maps, and 2) test the transferability of the spatial models used to produce the digital soil maps for their ability to predict soil variation on the greater DML region. Preliminary analysis on the two study regions with a limited pool of environmental covariates and existing soil datasets found that predictive maps created by DSM methods outperformed SSURGO predictions. For example, the RMSE for A horizonthickness was reduced from 25.7 cm for SSURGO to 13.7 cm for the digital soilmap. Similar reductions in uncertainty from SSURGO to the digital soil map were achieved for depth to water table (30.3 reduced to 17.6 cm), sand content (12.3 reduced to 7.8%), and silt content (9.5 reduced to 7.1%). Project deliverables will include 3-meter resolution maps of predicted target soil properties and their estimated error for the two study regions. Maps for particle size fractions and SOC content will be generated at six depth intervals down to two meters. Comparative performance against equivalent SSURGO maps and model transferability to the greater DML region will be evaluated and reported.

Next-Generation Stage Measurement at Ungauged Locations using IoT

The growing availability of smart devices with advanced sensors has increased opportunities for the Internet of Things (IoT)applications in environmental monitoring. Accurate and widespread monitoring of river stage is vital for modeling water resources. Reliable data points are required for model calibration and validation in forecast studies. While current embedded monitoring systems provide accurate measurements, the cost to replicate these systems on a large domain is prohibitively expensive, limiting the quantity of data available. This projectproposes a novel methodology for water level measurement by introducing geometric solutions that utilize prevalent smartphone sensors. The introduced approach creates a distinct opportunity for a low-cost camera-based embedded system that will measure water levels and share surveys to power environmental research and decision making. The framework includes communication protocols on the embedded systems as well as a web application as a centralized information system to view and analyze the vast amount ofmeasurements. In addition to the water level measurement, the presence of the camera enables further usage scenarios such as recognizing objects (e.g. debris, tree, human, boat) on the water surface using deep learning, and supplying annotated data for hydrological processes including surface water modeling, and streamflow estimation

Predicting Sorption, Mobility, Accumulation, and Degradation Potential of Antibiotics in Iowa’s Soil/Water Environment

In this project we propose to evaluate three common farm-animal antibiotics, chlortetracycline (Aureomycin), oxytetracycline (Terramycin) and erythromycin (a macrolide) with respect to their fate in Iowa’s soil/water environment. The expected results of this study will involve three aspects concerning fate of antibiotics. One aspect will involve elucidation of surface sorption of the selected antibiotics by organic and inorganic soil components. A second aspect will involve elucidation of the role of sorption on antibiotic mobility in soil, and a third aspect will involve half-life quantification of the selected antibiotics and cooperative or anti-cooperative contributions of the organic and inorganic surface-antibiotic interactions to the abiotic/biotic degradation process. The results from this project will allow us to elucidate and predict the potential long-term consequences of antibiotics with respect to their potential distribution and persistence in Iowa’s soil/water system. This type of information is of paramount importance in understanding the fate of antibiotics in the soil/water environment and proposing potential solutions to the problem.

Modeling, GIS, and Technology Transfer in Support of TMDL Development and Implementation in Iowa

This project integrates the research base on nonpoint source pollution control and watershed modeling and Geographic Information Systems (GIS) developed at Iowa State University’s Department of Agricultural and Biosystems Engineering with research capabilities at other centers and departments on campus to focus on key issues which will be faced in Iowa in implementing TMDLs on impaired watersheds and water bodies. The long-term goal of this project is to focus on developing critically needed decision support framework for enhancing the TMDL decision-making and implementation process by combining relevant information on in-stream water quality, pollutant sources, and alternative best management scenarios with issues related to costs and equity. The overarching aim is to develop tools and information resources that support and enlighten stakeholder input and participation and lead to better informed, science-based, and publicly acceptable management decisions regarding TMDLs. This project, in combination with other ongoing water quality monitoring and bio-economic modeling efforts, will result in a more practical approach to the development and implementation of TMDLs in Iowa. While the initial focus of the project is on waterbodies and watersheds impacted by agriculture, the approach and methods will be applicable to other TMDL issues in the state. Through the interaction with the Iowa Department of Natural Resources (IDNR), the Iowa State Water Resources Research Institute (ISWRRI) and other agencies and organizations, this project will provide valuable information needed for TMDL development and implementation both in Iowa and nationwide. Given the highly controversial nature of TMDL, research is needed now than ever to provide the tools needed to make tough and costly decisions mandated for effective watershed management.