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.

Spatial and Temporal Patterns in Precipitation and Dry-Fall Deposition of Nitrogen and Phosphorus in Iowa: Implications for Nutrient Transport and Water Quality

Atmospheric nutrient (nitrogen and phosphorus; N and P) loading and transport through precipitation and dry deposition is one of the least understood and may be one of the most important pathways of nutrient transport in agricultural landscapes. Atmospheric P deposition through precipitation on a lakes surface has recently been found to contribute >30% of the annual P load, single handedly preventing eventual remediation to attain projected federal nutrient standards. The purpose of this project is to fill three essential information gaps: (I) to characterize both nitrogen and phosphorus deposition, (II) through both wet- and dry-deposition to dry- and wet-surfaces, and (III) to characterize the spatial and temporal variation of this deposition across the state of Iowa. We will measure nutrient deposition from April 1st, 2003-March 31st, 2005 at seven sites representing a range of landscape characteristics common in Iowa. Upon collection, samples will be returned to the Limnology Lab at Iowa State University for analysis. Comparisons among types of deposition measures will be made using non-parametric equivalents of ANOVA. Temporal analyses will be made graphically as well as using multivariate methods to relate deposition to storm type, source and intensity. Spatial patterns will be characterized using kriging within geostatistical (GIS) packages. This project will allow a broader understanding of the process of atmospheric nutrient transport in agricultural landscapes and a means of evaluating the role of atmospheric deposition in water quality impairment and remediation.

Fate and Transfer of Antibiotic-Resistance Genes Excreted by Farm Animals

The use of antibiotic agents in fields other than human treatment has grown rapidly over the past few decades. Currently, the veterinary use of antibiotics accounts for approximately 40-50% of all antibiotics produced, and only 20% of that amount is employed in disease treatment, with the rest serving a sub-therapeutic role in growth promotion and disease prevention. Unfortunately, due to the poor uptake mechanisms of the treated animals, most of the antibiotics administered are excreted, unaltered, into the soil surrounding feed lots. Recently, it has been observed that microbial strains have developed antibiotic resistance as an effect of high dosages of antibiotic agents in the soil. Some effort has gone into identifying the phenomena of antibiotic resistance development around feed lots, specifically the detection of the identities of the microorganisms present and the tet-determinants responsible for the resistance. The interest of the current project lies in the detection and monitoring of the development of resistant strains as well as the specific genes responsible for the antibiotic resistance. Flow through columns (30 cm) have been packed with previously unexposed (to antibiotics) soil, as to mimic the natural conditions of the runoff water underlying feed lots. Currently, two columns are operating in a continuous manner, with one receiving a solution consisting of synthetic ground water and acetate, and the second, enriched with tetracycline. The concentrations of both acetate and tetracycline are monitored at both the entrance and exit of the columns, and profiles of these columns along their length are also monitored. Early results point out that tetracycline is being degraded upon contact with the column soil, and only about 5% of the original amount is detectable at the column exit. Most probable number (MPN) enumeration technique was used to monitor changes on the total heterotrophic population and antibiotic resistant strain populations of the two columns. PCR detection of tet-genes of the isolated resistant strains was performed but the targeted determinants were not detected in these microorganisms, suggesting the presence of tet-determinants coding for the efflux pump excretion mechanisms. Preliminary results suggest that sustained TC exposure decreases the concentrations of total heterotrophs and increases the fraction of the microorganisms that are resistant to the antibiotic. Whether discontinuing TC exposure results in the rapid loss of tet-resistance in the exposed microorganisms remains to be determined.

Water Quality, Nutrient Loadinig and Mosquito Production in Northeastern Iowa

Impaired surface water and West Nile Virus (WNV), two issues that are critically important to Iowans, may be interrelated. High levels of nutrients and other agricultural chemicals that contaminate surface water may increase production of mosquitoes and their potential for carrying disease. Because mosquito numbers and vector efficiencies are determined by food quality during larval development, higher levels of nutrients may increase mosquito numbers. Likewise, pesticides applied to crops and gardens may have unexpected consequences upon mosquito production. Biology students at the University of Northern Iowa and I will evaluate various bodies of surface water in northeastern Iowa for their potential as mosquito developmental sites. We will estimate the risk of mosquito production for each site through on-the-ground surveys of biological, physical, and chemical attributes. We will identify sources of nutrients through land-use surveys and through remote sensing imagery based upon predictive characteristics from our on-the-ground survey. We will generate a Geographic Information System model for high-nutrient water bodies that will estimate risk of mosquito production and disease transmission. This study will provide valuable information concerning environmental impacts on impaired water quality, will provide educational opportunities for student researchers, and should result in a publication in a refereed journal.