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Lorrayne Miralha’s Research Showcase: Environmental drivers of change and hydrological modelin

I come from a rural community in Rio de Janeiro, Brazil, where agriculture is the main economy. During most of my life, I lived on a small farm surrounded by other farms that nowadays are a mix of settlements and bare lands. By seeing the changes in my surrounding from childhood to adulthood, I questioned why the landscape in my town changed to a degraded condition and how it impacted my family and neighbors. In other words, what are the consequences of extensive intervention in the environment and how does it impact society? This question is the core of my research and what led me to develop interests in spatial modeling, geographic information systems, and water quality.

Presently, as a Ph.D. Candidate at Arizona State University and a member of Dr. Rebecca Muenich’s Lab Group, my research investigates how agriculture influences forest and wetland landscapes and surface water quality as well as how climate change may exacerbate environmental conditions [1]. For instance, part of my research involves spatiotemporal analysis of environmental variables such as land surface temperature and vegetation indexes surrounding animal farms in the US (Figure 1). Literature has shown that these farms are responsible for the production of large amounts of manure. Manure is a valuable resource, composed of nitrogen and phosphorus that are crucial nutrients for food production. However, in excess this resource may impact ecosystems and human health, principally when not well-managed. As an example, flood events in North Carolina have led to the dispersion of animal waste containing numerous hazards, potentially impacting community drinking water sources. Understanding how these farms and their waste are managed as well as investigating the changes they have caused in the environment will be crucial to protect their surrounding resources and community health [2]. With guidance from my advisor Dr. Rebecca Muenich and other collaborators, I have been working on temporal and spatial approaches to find sustainable solutions for these food production systems. Since knowing where these farms are located is key for this research, I am also working on developing algorithms that can detect the spatial distribution of these farms via remote sensing products and socio-economic variables.

Figure1: Land use change analysis around animal farms in North Carolina demonstrating the replacement of important ecosystems such as wetlands, savannas, and forests to croplands and shrublands. This study is based on the assumption of manure application within a certain distance from each farm.

Another part of my research involves modeling the drivers of change in aquatic ecosystem dynamics. In fellowship with the Cooperative Institute for Great Lakes Research (CIGLR) and the National Oceanic and Atmospheric Administration – Great Lakes Environmental Research Laboratory (NOAA-GLERL), I recently worked on investigating mechanisms that drive the emergence of harmful algal blooms (HABs) in the western basin of Lake Erie (WBLE). This basin is composed mostly of agricultural fields and animal facilities which are responsible for large amounts of nutrients entering the lake system (Figure 2). Being able to detect the environmental variable thresholds leading to HABs may inform agencies, guide policymakers, and alert society about unsuitable water conditions for consumption and recreational activities. Early findings of this research have suggested that water temperature and nitrogen concentration may lead to shifts in algae composition and dominance in the WBLE. This project’s presentation can be found here. My mentors, Dr. Regan Errera, Dr. Jim Hood, and other NOAA scientists, and I are currently working on developing a model based on these findings to support a forecasting system and NOAA’s water quality monitoring program.

Figure 2: Sentinel images from 2018 and 2019 in the bloom season in western Lake Erie demonstrating the fluxes and loads that entered in the system from major tributaries such as the Maumee and the Detroit Rivers which potentially influence the emergence of harmful algal blooms.

The opportunity to collaborate with CIGLR and NOAA was synergistic with one of my Ph.D. projects in the same basin (WBLE). The project involves climate and nutrient modeling in the Maumee basin using the Soil & Water Assessment Tool (SWAT) model. This research has shown that climate model choices as well as the methods applied to manipulate the climate model outputs influences hydrological processes as well as nitrogen and phosphorus load forecasting, principally in terms of load magnitude (Figure 3). These findings highlight the importance of decisions being made by policymakers in the region based on model simulations and may serve as a guide to modelers on the choice of appropriate methods for nutrient forecasting [3].

Figure 3: Preliminary results of the comparison between modeled total phosphorus loads in kg based on climate model outputs that were bias-corrected (Delta, Scaling, QDM, and EQM) and not bias-corrected (Historical).

Being part of these research projects and collaborating with high-level researchers during these past years in academia have assured me about my career path and the next steps I want to follow. In the coming years, I plan to search for opportunities in research-driven agencies or academic institutions where I can keep working on finding solutions to environmental problems.

References: 1. Muenich, R. L., Bell, M. L., Schaffer-Smith, D., Miralha, L., & Rauh, E. (2019). Advancing a regional assessment and management of large-scale animal operations. AGUFM, 2019, H42G-01.

2. Miralha, L., & Muenich, R. L. (2019). Climate change impact analysis on confined animal feeding operations: A case study in Iowa. AGUFM, 2019, GC41H-1257.

3. Muenich, R. L., Long, C. M., Miralha, L., Rauh, E., Wang, Y. C., Kalcic, M. M., … & Scholz, M. (2018). Innovative approaches to improving watershed models for management decisions and policy applications. AGUFM, 2018, H41D-01.

Arizona State University

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