Student Research (Posters)

Application of Macro-scale Hydrologic Model for Drought Identification in Cold Climate Regions

MOHAMMAD HADI BAZRKAR & XUEFENG CHU, Ph.D., NDSU Department of Civil & Environmental Engineering

Sponsors: ND EPSCoR through NSF Award OIA-1355466

Drought indices are essential tools to identify drought. The standardized runoff index (SRI) has been suggested for snowmelt regions. The aim of this research is to identify drought for cold climate regions by estimating SRI using surface runoff simulated by the macro-scale grid-based hydrologic model (GHM). Particularly, the SRI results were also compared to those from other drought indices. The results help decision-makers manage drought, especially under cold climate conditions.

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Air pollution over the Central and Northern Great Plains: A WRF-Chem modeling study.

CARLOS BUCARAM & FRANK BOWMAN, UND Chemical Engineering

Sponsor: ND EPSCoR through NSF Grant OIA-1355466

A WRF-Chem modeling study of the Central and Northern Great Plains is being conducted to assess seasonal distributions of ground-level O3 and PM2.5. The CBMZ-MOSAIC coupled gas-phase chemistry-aerosol mechanisms are used on a 24 km resolution domain. The 2010 meteorological year is modeled, with anthropogenic emissions from EPA’s National Emission Inventory (NEI) 2011 together with MEGAN 2.04 biogenic emissions. Statistical analysis will be performed between observed and modeled ground level concentrations, for validation purposes.

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Evaluation of Organic Matter Decomposition Using Modified Litter Bags in Eastern Montana

BRITTANY J. DECKER & JOSHUA J. STEFFAN, Ph.D., Dickinson State University, Natural Sciences

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

Organic matter decomposition rates within different land-use management practices were determined using a simplified litter bag decomposition method utilizing different teas as a substrate. We identified that our site incorporating livestock grazing had the greatest rate of organic matter decomposition, Organic matter decomposition rates are an important factor in determining the biodiversity of soil microorganisms and the rate of nutrient cycling within the different ecosystems, micro-climactic conditions, and land-uses within our study.

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Measuring Threats to Honey Bees in North Dakota from Land Use Change and Pesticide Exposure.

DANIEL DIXON & HAOCHI ZHENG, Ph.D., UND Earth System Science and Policy & CLINT OTTO, Ph.D., USGS Northern Prairie Wildlife Research Center

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

We measure changes in landscape quality around 14,000 registered apiary locations in North Dakota from 2006-2014. Using InVEST’s Habitat Quality model, we apply weighted pesticide application values to cropland on the North Dakota landscape as potential sources of quality degradation. We observe decreasing quality per apiary site, but also as grassland quantity decreases, the quality may also be impacted due to increasing grassland/cropland margins where honey bees receive non-target exposures from pesticide applications.

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Exploring the Usefulness of Adding Auxiliary Preprocessed Image Layers with Convolutional Neural Networks

JORDAN GOETZE & ANNE DENTON, Ph.D., NDSU Department of Computer Science

Sponsors: ND EPSCoR through NSF Award OIA-1355466

For this study, we will use the SegNet CNN architecture, along with imagery comprised of Red, Green, Blue, Near-Infrared and Normalized Difference Vegetation Index image bands. We will also use image bands generated from the original 5 bands. The goal of this study is to discern whether the inclusion of several auxiliary image layers aids in the classification accuracy and quantitative quality of classifications in the realm of land use classification of orthoimagery.

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Taking Terrain Analysis to the Big Data Era for Understanding Soil Health in Depressions

RAHUL GOMES & ANNE DENTON, Ph.D. NDSU Computer Science

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

This study aims to evaluate GIS attributes such as slope, aspect, elevation and curvature using an iterative aggregation algorithm with variable window sizes. Since this algorithm uses values from previous iteration it scales logarithmically making it more efficient than modern GIS software that employs a fixed 3-by-3 cell size which is not effective for high resolution DEMs. The algorithm will be tested on DEMs to evaluate soil health in depressions that are prone to salinization.

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SWAT Modeling to Evaluate the Impacts of Geographically Isolated Wetlands on Hydrologic Processes

KENDALL GRIMM & XUEFENG CHU, NDSU Department of Civil & Environmental Engineering

Sponsor: ND EPSCoR through NSF Award OIA-1355466

The Prairie Pothole Region is dominated by geographically-isolated wetlands (GIWs) which rarely have any hydrologic connection to other surface waters. Few studies have been conducted to assess the impacts of GIW connectivity and anthropogenic disturbance on hydrologic processes. This study aims to propose a new way to simulate dynamic hydrologic connectivity and GIW hydrologic processes using the widely-used SWAT. The results from this study can help understand the hydrologic impacts of GIWs.

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Severe Weather Environments and Trends in the North American Atmospheric Reanalyses (NARR).

AUSTIN KING & AARON KENNEDY, Ph.D., UND Department of Atmospheric Sciences

Sponsors: ND EPSCoR through NSF Award OIA-1355466

Atmospheric parameters can be used to estimate the frequency of severe weather in past and future climates. These parameters are calculated for the North American Reanalysis (NARR) from 1979-2016. Trends and year-to-year variability of severe weather environments are investigated over the state of North Dakota. Details and uses of a soon to be published, publicly available dataset based on this work will be discussed.

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Effects of Varying Temperature on SOA Yields and Partitioning from North Dakota Crop Emissions

NICOLE LARSON & FRANK BOWMAN, UND, Department of Chemical Engineering

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

This laboratory study focuses on the effects of varying temperature on secondary organic aerosol (SOA) yields involving the reactions of beta-farnesene and alpha-humulene with ozone. Experiments are conducted in the UND aerosol chamber where gas-phase compounds are able to react, leading to the formation of semivolatile products that partition to the aerosol phase. The SOA yield is calculated by measuring the amount of organic material formed and the amount of the crop emission that reacted.

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Window Histogram for Characterizing Basins

SHUHANG LI & ANNE DENTON, Ph.D., NDSU, Computer Science

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

In this research, our goal is to identify the relationship between vegetation density and low-lying areas. We use a window-based approach to preprocess the data within Fair Mount area. The distribution of NDVI and near-infrared data inside each window will be summarized using histograms. Each column inside a histogram will then be treated as a separate attribute. Using the preprocessed data set, we build a decision tree classifier that has a sensitivity of 67% and a specificity of 81%.

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Land Management Changes and its Effects on Soil Microbial Communities.

BILLI JEAN PETERMANN & ERIC BREVIK, Ph.D, & JOSHUA STEFFAN, Ph.D, & PAUL BARNHART, Ph.D, Department of Natural Science, Dickinson State University

Sponsor: ND EPSCoR through NSF award # OIA-1355466

The soil microbial communities play an important role in keeping soils healthy and functional. This studies focus compares the effect three different land management practices have on soil microbial communities. Soil samples collected in the spring and fall of 2016 and 2017 underwent Phospholipid Fatty Acid analysis. Shown here are trends during the first two collection years. Throughout this three year study, we expect changes to the soil microbial communities between the different management practices.

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Initial Development for Simulating Land Surface Change Impacts on Climate in the Northern Plains.

AARON SCOTT & AARON KENNEDY, Ph.D, UND, Department of Atmospheric Sciences

Sponsors: ND EPSCoR through NSF award # OIA-1355466

Changes in crop type and coverage can have significant impact on local weather patterns. For this study, a weather model is used to downscale a climate model. Initial simulations are performed using standard model settings at 12 km grid spacing to simulate the period from 2001-2005 in the Northern Plains. Additional simulations are performed with a dynamic crop model incorporated with the goal of crop growth information tuned to the specifications of the NP region.

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Examining the Impact of Devils Lake Outlets on Flood Risk of the Sheyenne River.

AFSHIN SHABANI & XIAODONG ZHANG, Ph.D., UND Earth System Science & Policy

Sponsor: ND EPSCoR through NSF Grant OIA-1355466, ND WRRI

SWAT and HEC-RAS models were coupled to investigate the impact of discharging water from two Devils Lake outlets on the Sheyenne River streamflow and floodplain. Since 2005, operating the two outlets has lowered the lake water level by 0.94 m, with a maximum impact being to expand the floodplain of the Sheyenne River to the historical two-year flood zone and average overbank water depth of 0.2 m.

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Species Distribution Modelling (SDM) of Odonata to Investigate Climatic Habitat

ANUJ SHRESTHA & ANNE DENTON, Ph.D., North Dakota State University, Computer Science

Sponsors: ND EPSCoR through NSF Award OIA-1355466

Local climate could affect the abundance of species. In this study, we create SDM models for known occurrence of interested Odonata species (source: Odonata Central), using popular machine learning and regression methods by utilizing climate data as predictors (source: PRISM), to investigate past and present distributions. We perform this analysis on a “moving-decade” framework. The results allowed us to interpret the climatic variables of interest and understand the gradual change in their distributions over time.

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Adding Dynamic Social-Economic Factors to Crop Simulations to Improve Acreage and Yield Predictions.

JON STARR & HAOCHI ZHENG, Ph.D., JIANGLONG ZHANG, Ph.D., UND Atmospheric Science & Earth System Science and Policy

Sponsors: ND EPSCoR through NSF Award OIA-1355466

Crop simulation models are widely used to quickly simulate a variety of crops and management techniques which are typically run utilizing a predetermined, static set of scenarios for its land management. In contrast to this, in this study we established a dynamic two way link between an economic land use model with the ALMANAC crop model, improving land use prediction of maize and soybean by 13% and 11% respectively, and yield prediction for maize.

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Utilizing space-borne remote sensing data to improve model parameter calibration in crop simulations.

JON STARR & JIANGLONG ZHANG, Ph.D., DAVID ROBERTS, Ph.D., UND Atmospheric Science

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

One of the largest obstacles to accurate regional scale crop simulations is the lack of available calibration data for the wide spectrum of crops and varieties planted each season. In this study we overcome this issue by utilizing space-borne remote sensing data from platforms such as MODIS coupled with atmospheric reanalysis data. Calibrating the simulation model parameters over North Dakota by utilizing these datasets resulted in an overall increase in yield prediction accuracy.

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Joint Modeling and Cross-calibration of Different Scale Hydrologic Models

MOHSEN TAHMASEBI NASAB & LAN ZENG & KENDALL GRIMM & MOHAMMAD HADI BAZRKAR & NING WANG & XINGWEI LIU & ZHULU LIN & XIAODONG ZHANG, Ph.D., HAOCHI ZHENG, Ph.D., & XUEFENG CHU, Ph.D., NDSU Department of Civil & Environmental Engineering & UND Department of Earth System Science and Policy

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

Although macro-scale hydrologic models provide a holistic view of hydrologic processes, their calibration is a challenge. In this study, we develop a joint modeling framework that integrates the macro-scale Grid-based Hydrologic Model (GHM) and the meso-scale subbasin-based Soil and Water Assessment Tool (SWAT). The models provide detailed hydrologic processes at the two different scales. The modeling results can be used for agricultural water management and other water-related decisions.

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Northern Plains Blizzards in Past and Future Climates.

ALEX TRELLINGER & AARON KENNEDY, Ph.D., UND Department of Atmospheric Sciences

Sponsors: ND EPSCoR through NSF Award # OIA-1355466

Areas that reside in the high-latitudes such as the northern United States can experience hazardous conditions during the winter months due to blizzards. While the climatology for these extreme snowstorms is known, the frequency and intensity of how these events may change in a warming climate is not certain. The presented work identifies meteorological patterns associated with past blizzard events, and then compares the frequencies of these patterns to those under future emissions scenarios.

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Grasslands in a changing climate: How do we build resilient ecosystems?

YARI VILLANUEVA, Vally City State University, Biological Sciences, KATHRYN YURKONIS, Ph.D., University of North Dakota, Department of Biology, & LAUREN DENNHARDT, Ph.D., Vally City State University, Biological Sciences

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

To understand how grasslands may “keep up” with climate change, we need to understand the basis for their responses to changing environmental conditions. By planting material from a range of locations in the Great Plains, we have a setting to elucidate how different populations could potentially fill the expanding C3 niche in North Dakota. We planted 48 plots consisting of four treatments from regions across the great Plains and three cool-season grasses.

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A Novel Modeling Framework for Simulating Dynamic Water Release from Depressions

NING WANG & XUEFENG CHU, Ph.D., NDSU Department of Civil and Environmental Engineering

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

Surface depressions are important topographic characteristics. A novel modeling framework with hierarchical thresholds was developed and incorporated into the depression-oriented hydrologic model (HYDROL-D) to simulate dynamic overland flow processes under the influence of surface depressions. The new model was tested and compared with the traditional modeling method. The results demonstrated its unique capability and effectiveness in hydrologic modeling.

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Dragonfly Distribution Changes May Indicate Climate Change in North Dakota

HAYDEN ZANDER & ANDRE DELORME, Ph.D., Valley City State University, Science Department

Sponsors: ND EPSCoR through NSF Grant OIA-1355466

Climate change can be indicated by changes in aquatic organism distributions via habitat alteration. Dragonflies are excellent indicators of aquatic habitat quality because they live in both aquatic and terrestrial habitats throughout their life cycle. This study established a baseline distribution of dragonfly species across North Dakota in order to compare to past and future distributions. 637 specimens were identified, and eight new state records were documented.

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