Juan Sebastian Hernandez-Suarez

All Publications

• Performance of Sentinel-1 and 2 imagery in detecting aquaculture waterbodies in Bangladesh ENVIRONMENTAL MODELLING & SOFTWARE Hernandez-Suarez, J., Nejadhashemi, A., Ferriby, H., Moore, N., Belton, B., Haque, M. 2022; 157

  View details for DOI 10.1016/j.envsoft.2022.105534

  View details for Web of Science ID 000862376400001

• Probabilistic Predictions of Ecologically Relevant Hydrologic Indices Using a Hydrological Model WATER RESOURCES RESEARCH Hernandez-Suarez, J., Nejadhashemi, A. 2022; 58 (9)

  View details for DOI 10.1029/2021WR031104

  View details for Web of Science ID 000851118800001

• Agricultural Innovization: An Optimization-Driven solution for sustainable agricultural intensification in Michigan COMPUTERS AND ELECTRONICS IN AGRICULTURE Kropp, I., Nejadhashemi, A., Jha, P., Hernandez-Suarez, J. 2022; 199

  View details for DOI 10.1016/j.compag.2022.107143

  View details for Web of Science ID 000818640800006

• Connecting microbial, nutrient, physiochemical, and land use variables for the evaluation of water quality within mixed use watersheds. Water research Flood, M. T., Hernandez-Suarez, J. S., Nejadhashemi, A. P., Martin, S. L., Hyndman, D., Rose, J. B. 2022; 219: 118526

  Abstract

  As non-point sources of pollution begin to overtake point sources in watersheds, source identification and complicating variables such as rainfall are growing in importance. Microbial source tracking (MST) allows for identification of fecal contamination sources in watersheds; when combined with data on land use and co-occuring variables (e.g., nutrients, sediment runoff) MST can provide a basis for understanding how to effectively remediate water quality. To determine spatial and temporal trends in microbial contamination and correlations between MST and nutrients, water samples (n = 136) were collected between April 2017 and May of 2018 during eight sampling events from 17 sites in 5 mixed-use watersheds. These samples were analyzed for three MST markers (human - B. theta; bovine - CowM2; porcine - Pig2Bac) along with E. coli, nutrients (nitrogen and phosphorus species), and physiochemical paramaters. These water quality variables were then paired with data on land use, streamflow, precipitation and management practices (e.g., tile drainage, septic tank density, tillage practices) to determine if any significant relationships existed between the observed microbial contamination and these variables. The porcine marker was the only marker that was highly correlated (p value <0.05) with nitrogen and phosphorus species in multiple clustering schemes. Significant relationships were also identified between MST markers and variables that demonstrated temporal trends driven by precipitation and spatial trends driven by septic tanks and management practices (tillage and drainage) when spatial clustering was employed.

  View details for DOI 10.1016/j.watres.2022.118526

  View details for PubMedID 35598465

• Large-scale Multi-objective Optimization for Water Quality in Chesapeake Bay Watershed Toscano, G., Hernandez-Suarez, J., Blank, J., Nejadhashemi, P., Deb, K., Linker, L., IEEE IEEE. 2022

  View details for DOI 10.1109/CEC55065.2022.9870286

  View details for Web of Science ID 000859282000071

• Harnessing Machine Learning Techniques for Mapping Aquaculture Waterbodies in Bangladesh REMOTE SENSING Ferriby, H., Nejadhashemi, A., Hernandez-Suarez, J., Moore, N., Kpodo, J., Kropp, I., Eeswaran, R., Belton, B., Haque, M. 2021; 13 (23)

  View details for DOI 10.3390/rs13234890

  View details for Web of Science ID 000734638500001

• Quantification of resilience metrics as affected by conservation agriculture at a watershed scale AGRICULTURE ECOSYSTEMS & ENVIRONMENT Eeswaran, R., Nejadhashemi, A., Kpodo, J., Curtis, Z. K., Adhikari, U., Liao, H., Li, S., Hernandez-Suarez, J., Alves, F., Raschke, A., Jha, P. 2021; 320

  View details for DOI 10.1016/j.agee.2021.107612

  View details for Web of Science ID 000691679400006

• A novel multi-objective model calibration method for ecohydrological applications ENVIRONMENTAL MODELLING & SOFTWARE Hernandez-Suarez, J., Nejadhashemi, A., Deb, K. 2021; 144

  View details for DOI 10.1016/j.envsoft.2021.105161

  View details for Web of Science ID 000696696700002

• Multidimensional Aspects of Sustainable Biofuel Feedstock Production SUSTAINABILITY Raschke, A., Hernandez-Suarez, J., Nejadhashemi, A., Deb, K. 2021; 13 (3)

  View details for DOI 10.3390/su13031424

  View details for Web of Science ID 000615606000001

• Modeling the persistence of viruses in untreated groundwater SCIENCE OF THE TOTAL ENVIRONMENT Dean, K., Wissler, A., Hernandez-Suarez, J., Nejadhashemi, A., Mitchell, J. 2020; 717: 134599

  Abstract

  Several factors can affect virus behavior and persistence in water sources. Historically linear models have been used to describe persistence over time; however, these models do not consider all of the factors that can affect inactivation kinetics or the observed patterns of decay. Meanwhile, applying the appropriate persistence model is critical for ensuring that decision makers are minimizing human health risk in the event of contamination and exposure to contaminated groundwater. Therefore, to address uncertainty in predictions of decay or virus concentrations over time, this study fit seventeen different linear and nonlinear mathematical models to persistence data from a previously conducted sampling study on drinking water wells throughout the United States. The models were fit using Maximum Likelihood Estimation and the best fitting models were determined by the Bayesian Information Criterion. The purpose of the study was to identify the best model for estimating decay of viruses in groundwater and to determine if model uncertainty contributes to erroneous predictions of viral contamination when only conventional models are considered. For the datasets analyzed in this study, the Juneja and Marks models and the exponential damped model were more representative of the persistence of viruses in groundwater than the traditionally used linear models. The results from this study were then evaluated with classification trees in order to identify more relevant modeling methodology for future research. The classification trees aid in narrowing the scope of appropriate persistence models based on characteristics of the experimental conditions and water sampled.

  View details for DOI 10.1016/j.scitotenv.2019.134599

  View details for Web of Science ID 000519994800082

  View details for PubMedID 31836219

• Analyzing the Variability of Remote Sensing and Hydrologic Model Evapotranspiration Products in a Watershed in Michigan JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION Herman, M. R., Nejadhashemi, A., Hernandez-Suarez, J., Sadeghi, A. M. 2020; 56 (4): 738-755

  View details for DOI 10.1111/1752-1688.12849

  View details for Web of Science ID 000531899500001

• Evaluation of Multi- and Many-Objective Optimization Techniques to Improve the Performance of a Hydrologic Model Using Evapotranspiration Remote-Sensing Data JOURNAL OF HYDROLOGIC ENGINEERING Herman, M. R., Hernandez-Suarez, J., Nejadhashemi, A., Kropp, I., Sadeghi, A. M. 2020; 25 (4)

  View details for DOI 10.1061/(ASCE)HE.1943-5584.0001896

  View details for Web of Science ID 000515515900009

• An investigation of spatial and temporal drinking water quality variation in green residential plumbing BUILDING AND ENVIRONMENT Salehi, M., Odimayomi, T., Ra, K., Ley, C., Julien, R., Nejadhashemi, A., Hernandez-Suarez, J., Mitchell, J., Shah, A. D., Whelton, A. 2020; 169

  View details for DOI 10.1016/j.buildenv.2019.106566

  View details for Web of Science ID 000532293100015

• Multi-site watershed model calibration for evaluating best management practice effectiveness in reducing fecal pollution HUMAN AND ECOLOGICAL RISK ASSESSMENT Hernandez-Suarez, J., Woznicki, S. A., Nejadhashemi, A. 2020; 26 (10): 2690-2715

  View details for DOI 10.1080/10807039.2019.1680526

  View details for Web of Science ID 000492304800001

• A review of macroinvertebrate- and fish-based stream health modelling techniques ECOHYDROLOGY Hernandez-Suarez, J., Nejadhashemi, A. 2018; 11 (8)

  View details for DOI 10.1002/eco.2022

  View details for Web of Science ID 000451861100011

• Food Footprint as a Measure of Sustainability for Grazing Dairy Farms ENVIRONMENTAL MANAGEMENT Rojas-Downing, M., Nejadhashemi, A., Elahi, B., Cassida, K. A., Daneshvar, F., Hernandez-Suarez, J., Abouali, M., Herman, M. R., Al Masraf, S., Harrigan, T. 2018; 62 (6): 1073-1088

  Abstract

  Livestock productions require significant resources allocation in the form of land, water, energy, air, and capital. Meanwhile, owing to increase in the global demand for livestock products, it is wise to consider sustainable livestock practices. In the past few decades, footprints have emerged as indicators for sustainability assessment. In this study, we are introducing a new footprint measure to assess sustainability of a grazing dairy farm while considering carbon, water, energy, and economic impacts of milk production. To achieve this goal, a representative farm was developed based on grazing dairy practices surveys in the State of Michigan, USA. This information was incorporated into the Integrated Farm System Model (IFSM) to estimate the farm carbon, water, energy, and economic impacts and associated footprints for ten different regions in Michigan. A multi-criterion decision-making method called VIKOR was used to determine the overall impacts of the representative farms. This new measure is called the food footprint. Using this new indicator, the most sustainable milk production level (8618 kg/cow/year) was identified that is 19.4% higher than the average milk production (7215 kg/cow/year) in the area of interest. In addition, the most sustainable pasture composition was identified as 90% tall fescue with 10% white clover. The methodology introduced here can be adopted in other regions to improve sustainability by reducing water, energy, and environmental impacts of grazing dairy farms, while maximizing the farm profit and productions.

  View details for DOI 10.1007/s00267-018-1101-y

  View details for Web of Science ID 000450496000007

  View details for PubMedID 30310973

• Evaluation of the impacts of hydrologic model calibration methods on predictability of ecologically-relevant hydrologic indices JOURNAL OF HYDROLOGY Hernandez-Suarez, J., Nejadhashemi, A., Kropp, I. M., Abouali, M., Zhang, Z., Deb, K. 2018; 564: 758-772

  View details for DOI 10.1016/j.jhydrol.2018.07.056

  View details for Web of Science ID 000445316200059

• Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability JOURNAL OF HYDROLOGY Herman, M. R., Nejadhashemi, A., Abouali, M., Hernandez-Suarez, J., Daneshvar, F., Zhang, Z., Anderson, M. C., Sadeghi, A. M., Hain, C. R., Sharifi, A. 2018; 556: 39-49

  View details for DOI 10.1016/j.jhydrol.2017.11.009

  View details for Web of Science ID 000423641300004