Modeling Dynamic Landscapes in Open Source GIS

Associate Professor in Marine, Earth, & Atmospheric Sciences, North Carolina State UniversityPlatinum Sponsors KU Institute for Policy & Social Research Gold Sponsors Bartlett & West KU Department of Geography KU Libraries State of Kansas Data Access and Support Center (DASC) Silver Sponsors Kansas Biological Survey KU Center for Global & International Studies KU Environmental Studies Program Bronze Sponsors Global Information Systems KU Center for Remote Sensing of Ice Sheets (CReSIS) TREKK Design Group, LL

Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education

Open education materials are critical for the advancement of open science and the development of open-source soft-ware. These accessible and transparent materials provide an important pathway for sharing both standard geospa-tial analysis workflows and advanced research methods. Computational notebooks allow users to share live code with in-line visualizations and narrative text, making them a powerful interactive teaching tool for geospatial analyt-ics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we intro-duce a new GRASS GIS package, grass.jupyter, that enhances the existing GRASS Python API to allow Jupyter Notebook users to easily manage and visualize GRASS data including spatiotemporal datasets. While there are many Python-based geospatial libraries available for use in Jupyter Notebooks, GRASS GIS has extensive geospatial functionality including support for multi-temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. We discuss the devel-opment of grass.jupyter and demonstrate how the package facilitates teaching open-source geospatial mode-ling with a collection of Jupyter Notebooks designed for a graduate-level geospatial modeling course. The open educa-tion notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growthpublishedVersio

Quantifying the fate of agricultural nitrogen in an unconfined aquifer: Stream-based observations at three measurement scales

We compared three stream-based sampling methods to study the fate of nitrate in groundwater in a coastal plain watershed: point measurements beneath the streambed, seepage blankets (novel seepage-meter design), and reach mass-balance. The methods gave similar mean groundwater seepage rates into the stream (0.3–0.6 m/d) during two 3–4 day field campaigns despite an order of magnitude difference in stream discharge between the campaigns. At low flow, estimates of flowweighted mean nitrate concentrations in groundwater discharge ([NO-3 ]FWM) and nitrate flux from groundwater to the stream decreased with increasing degree of channel influence and measurement scale, i.e., [NO-3 ]FWM was 654, 561, and 451 mM for point, blanket, and reach mass-balance sampling, respectively. At high flow the trend was reversed, likely because reach mass-balance captured inputs from shallow transient high-nitrate flow paths while point and blanket measurements did not. Point sampling may be better suited to estimating aquifer discharge of nitrate, while reach mass-balance reflects full nitrate inputs into the channel (which at high flow may be more than aquifer discharge due to transient flow paths, and at low flow may be less than aquifer discharge due to channel-based nitrate removal). Modeling dissolved N2 from streambed samples suggested (1) about half of groundwater nitrate was denitrified prior to discharge from the aquifer, and (2) both extent of denitrification and initial nitrate concentration in groundwater (700–1300 mM) were related to land use, suggesting these forms of streambed sampling for groundwater can reveal watershed spatial relations relevant to nitrate contamination and fate in the aquifer

Using detrending to assess SARS-CoV-2 wastewater loads as a leading indicator of fluctuations in COVID-19 cases at fine temporal scales: Correlations across twenty sewersheds in North Carolina

Wastewater surveillance emerged during the COVID-19 pandemic as a novel strategy for tracking the burden of illness in communities. Previous work has shown that trends in wastewater SARS-CoV-2 viral loads correlate well with reported COVID-19 case trends over longer time periods (i.e., months). We used detrending time series to reveal shorter sub-trend patterns (i.e., weeks) to identify leads or lags in the temporal alignment of the wastewater/case relationship. Daily incident COVID-19 cases and twice-weekly wastewater SARS-CoV-2 viral loads measured at 20 North Carolina sewersheds in 2021 were detrended using smoothing ranges of ∞, 16, 8, 4 and 2 weeks, to produce detrended cases and wastewater viral loads at progressively finer time scales. For each sewershed and smoothing range, we calculated the Spearman correlation between the cases and the wastewater viral loads with offsets of -7 to +7 days. We identified a conclusive lead/lag relationship at 15 of 20 sewersheds, with detrended wastewater loads temporally leading detrended COVID-19 cases at 11 of these sites. For the 11 leading sites, the correlation between wastewater loads and cases was greatest for wastewater loads sampled at a median lead time of 6 days before the cases were reported. Distinct lead/lag relationships were the most pronounced after detrending with smoothing ranges of 4–8 weeks, suggesting that SARS-CoV-2 wastewater viral loads can track fluctuations in COVID-19 case incidence rates at fine time scales and may serve as a leading indicator in many settings. These results could help public health officials identify, and deploy timely responses in, areas where cases are increasing faster than the overall pandemic trend

Chapter 11 MULTISCALE SOIL EROSION SIMULATIONS FOR LAND USE MANAGEMENT

Increasing pressures on the land and an improved understanding of human impacts on the environment are leading to profound changes in land management, with emphasis on integration of local actions with watershed-scale approaches. This trend has a significant impact o

Tangible modeling with open source GIS

This book presents a new type of modeling environment where users interact with geospatial simulations using 3D physical models of studied landscapes. Multiple users can alter the physical model by hand during scanning, thereby providing input for simulation of geophysical processes in this setting. The authors have developed innovative techniques and software that couple this hardware with open source GRASS GIS, making the system instantly applicable to a wide range of modeling and design problems. Since no other literature on this topic is available, this Book fills a gap for this new technology that continues to grow. Tangible Modeling with Open Source GIS will appeal to advanced-level students studying geospatial science, computer science and earth science such as landscape architecture and natural resources. It will also benefit researchers and professionals working in geospatial modeling applications, computer graphics, hazard risk management, hydrology, solar energy, coastal and fluvial flooding, fire spread, landscape, park design and computer games

GIS-based environmental modeling with tangible interaction and dynamic visualization

We present a new, affordable version of TanGeoMS, a tangible geospatial modeling and visualization system designed for collaboratively exploring how terrain change impacts landscape processes. It couples a physical, three-dimensional model of a landscape with geospatial modeling and analysis through a cycle of scanning and projection. Multiple users can modify the physical model by hand while it is being scanned; by sculpting the model they generate input for modeling of geophysical processes. The modeling results are then visualized by projecting images or animations back on the physical model. This feedback loop is an intuitive way to evaluate the impacts of different scenarios including anthropogenic and natural landscape change. Integration with GRASS GIS, a free and open source geographic information system, provides TanGeoMS with a variety of easily accessible geospa-tial analysis and modeling tools. To demonstrate the environmental modeling applications of TanGeoMS, we will demonstrate how development can be planned based on feedback from landscape processes such as hydrologic simulation and wildfire modeling with variable fuel distribution