@article{fidan_gray_doll_nelson_2023, title={Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes}, volume={167}, ISSN={["1873-6726"]}, url={https://doi.org/10.1016/j.envsoft.2023.105758}, DOI={10.1016/j.envsoft.2023.105758}, abstractNote={Despite rural, agricultural landscapes being exposed to pluvial flooding, prior predictive flood modeling research has largely focused on urban areas. To improve and extend pluvial flood modeling approaches for use in agricultural regions, we built a machine learning model framework that uses remotely sensed imagery from Planet Labs, gridded rainfall data, and open-access geospatial landscape characteristics to produce a pluvial flood timeline. A Random Forest model was trained and daily flood timeline was generated for Hurricane Matthew (2016) at a 10-m resolution. The results show the model predicts pluvial flooding well, with overall accuracy of 0.97 and F1 score of 0.69. Further evaluation of model outputs highlighted that corn and soybean crops were most impacted by the pluvial flooding. The model may be used to identify agricultural areas susceptible to pluvial flooding, crops that may be potentially impacted, and characteristics of areas that experience pluvial flooding.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, author={Fidan, Emine and Gray, Josh and Doll, Barbara and Nelson, Natalie G.}, year={2023}, month={Sep} }
 @article{stablein_cruz_fidan_talbot_reed_walters_ogunyiola_frey_ramirez_casanova_et al._2022, title={Compound[ing] disasters in Puerto Rico: Pathways for virtual transdisciplinary collaboration to enhance community resilience}, volume={76}, ISSN={["1872-9495"]}, DOI={10.1016/j.gloenvcha.2022.102558}, abstractNote={• Novel virtual research is needed to address the complexity of compounding disasters. • Remote transdisciplinary team engages participatory action research in Puerto Rico. • Citizen science enhances NGO data pathways and socioenvironmental resilience. • Interactive mapping elucidates opportunities for community-based disaster solutions. • Lessons learned are offered on the iterative process and high-performance teaming. Puerto Rico has been subject to complex and compounding effects of multiple disasters, exacerbated by sociopolitical, climactic, and geographical challenges that complicate relief and resilience. Interdisciplinary teams are uniquely suited to traverse emerging challenges in post-disaster settings, but there are few studies that leverage transdisciplinary skill sets and virtual co-production of knowledge to build on local autonomous responses. Communities are key sources of information and innovation which can serve as a model for recovery amidst disaster. Thus, an interdisciplinary team of emerging scholars collaborated with Caras con Causa, a local organization in Cataño, Puerto Rico, to develop processes for enhancing autonomous responses to disaster events through participatory pathways, specifically highlighting local knowledge and preferences. The results of this collaboration include: (1) an iterative process model for transdisciplinary co-production in virtual settings and (2) key highlights from post engagement reflections including community-scale definitions of disaster, and limitations to virtual collaboration amidst disaster. Together, these results yielded critical insights and lessons learned, including recommendations for improved project communication methods within transdisciplinary and virtual collaborations. Collectively, the process, it’s resulting products, and the post-engagement reflections demonstrate a pathway for scholars and community members to engage disaster resilience challenges. These strategies are most effectively practiced through focused collaboration with community stakeholders and are paramount in solving real-world challenges related to the increasing complex of compounding disasters.}, journal={GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS}, author={Stablein, M. J. and Cruz, J. Gonzalez and Fidan, E. N. and Talbot, J. and Reed, S. P. and Walters, R. S. and Ogunyiola, A. J. and Frey, M. Fernandez and Ramirez, M. and Casanova, B. Rosado and et al.}, year={2022}, month={Sep} }
 @article{harris_fidan_nelson_emanuel_jass_kathariou_niedermeyer_sharara_reyes_riveros-iregui_et al._2021, title={Microbial Contamination in Environmental Waters of Rural and Agriculturally-Dominated Landscapes Following Hurricane Florence}, volume={1}, ISSN={["2690-0637"]}, url={https://doi.org/10.1021/acsestwater.1c00103}, DOI={10.1021/acsestwater.1c00103}, abstractNote={Hurricane Florence brought unprecedented rainfall and flooding to Eastern North Carolina in 2018. Extensive flooding had the potential to mobilize microbial contaminants from a variety of sources. Our study evaluated microbial contaminants in surface waters at 40 sites across Eastern North Carolina 1 week after the hurricane made landfall (Phase 1) and one month later (Phase 2). High concentrations of Escherichia coli were detected in flowing channel and floodwater samples across both phases; however, channel samples during Phase 2 had higher concentrations of E. coli compared to Phase 1. Human- and swine-associated fecal markers were detected in 26% and 9% of samples, respectively, with no trends related to phase of sampling. Arcobacter butzleri was previously shown to be recovered from most (73%) samples, and detection of this pathogen was not associated with any source-associated fecal marker. Detection of Listeria spp. was associated with the swine-associated fecal marker. These results suggest that improved swine and human feces management should be explored to prevent microbial contamination in surface water, especially in regions where extreme rainfall may increase due to climate change. Sampling at higher frequency surrounding rainfall events would provide more detailed characterization of the risks posed by floodwater at different time scales and under different antecedent conditions.}, number={9}, journal={ACS ES&T WATER}, publisher={American Chemical Society (ACS)}, author={Harris, Angela R. and Fidan, Emine N. and Nelson, Natalie G. and Emanuel, Ryan E. and Jass, Theo and Kathariou, Sophia and Niedermeyer, Jeffrey and Sharara, Mahmoud and Reyes, Francis Lajara, III and Riveros-Iregui, Diego A. and et al.}, year={2021}, month={Sep}, pages={2012–2019} }