Scholarly Works, Forest Resources and Environmental Conservation

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Identifying Barriers and Bridging Gaps Between Researchers and Decision Makers in Water Quality Modeling
Chowdhury, Mahabub; Carey, Cayelan C.; Figueiredo, Renato; Gramacy, Robert; Hoffman, Kathryn; Lofton, Mary; Patil, Parul; Schreiber, Madeline; Thomas, R. Quinn; Calder, Ryan S. D. (2024-12-12)
Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models
Prior, Elizabeth M.; Michaelson, Nathan; Czuba, Jonathan A.; Pingel, Thomas J.; Thomas, Valerie A.; Hession, W. Cully (American Geophysical Union, 2024-07-07)
Topography and the computational mesh grid are fundamental inputs to all two-dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter-scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC-RAS model with varying mesh grid resolutions (1 and 2 m) and drone-based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel-floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.
Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir
Wander, Heather L.; Thomas, R. Quinn; Moore, Tadhg N.; Lofton, Mary E.; Breef-Pilz, Adrienne; Carey, Cayelan C. (Wiley, 2024-02-13)
Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near-term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near-term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost-prohibitive or impossible for forecasting ecological variables that lack high-frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1- to 35-day-ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1-day-ahead forecast root mean square error (RMSE) of 0.81°C, mean 7-day RMSE of 1.15°C, and mean 35-day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1- to 7-day-ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8- to 35-day-ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8-day forecast horizon during mixed spring/autumn periods and 5- to 14-day-ahead horizons during the summer-stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high-frequency sensor data.
Translational Edge and Cloud Computing to Advance Lake Water Quality Forecasting
Figueiredo, Renato J.; Carey, Cayelan C.; Thomas, R. Quinn (IEEE, 2024-11-20)
In this article, we report on our experiences with interdisciplinary projects at the intersection of freshwater ecology, data science, and computer science. The translational research process has progressively led to the development of distributed systems that apply both edge computing and function-as-a-service (FaaS) cloud computing to support end-to-end water quality forecasting workflows across the edge-to-cloud continuum.
Can you predict the future? A tutorial for the National Ecological Observatory Network Ecological Forecasting Challenge
Olsson, Freya; Boettiger, Carl; Carey, Cayelan C.; Lofton, Mary E.; Thomas, R. Quinn (The Open Journal, 2024-12)
This tutorial introduces participants to key concepts in ecological forecasting and provides hands-on materials for submitting forecasts to the National Ecological Observatory Network (NEON) Forecasting Challenge (hereafter, Challenge), hosted by the Ecological Forecasting Initiative Research Coordination Network. The tutorial has been developed and used with >300 participants and provides the ecological understanding, workflows, and tools to enable ecologists with minimal forecasting experience to participate in the Challenge via a hands-on R-based tutorial. This tutorial introduces participants to a near-term, iterative forecasting workflow that includes obtaining observations from NEON, developing a simple forecasting model, generating a forecast, and submitting the forecast to the Challenge, as well as evaluating forecast performance once new observations become available. The overarching aim of this tutorial is to lower the barrier to ecological forecasting and empower participants to develop their own ecological forecasts.
Regional variation in growth and survival responses to atmospheric nitrogen and sulfur deposition for 140 tree species across the United States
Dalton, Rebecca M.; Miller, Jesse N.; Greaver, Tara; Sabo, Robert D.; Austin, Kemen G.; Phelan, Jennifer N.; Thomas, R. Quinn; Clark, Christopher M. (Frontiers, 2024-11-11)
Atmospheric deposition of nitrogen (N) and sulfur (S) alter tree demographic processes via changes in nutrient pools, soil acidification, and biotic interactions. Previous work established tree growth and survival response to atmospheric N and S deposition in the conterminous United States (CONUS) data by species; however, it was not possible to evaluate regional variation in response using that approach. In this study, we develop species- and region-specific relationships for growth and survival responses to N and S deposition for roughly 140 species within spatially demarcated regions of the U.S. We calculated responses to N and S deposition separately for 11 United States Forest Service (USFS) Divisions resulting in a total of 241 and 268 species × Division combinations for growth and survival, respectively. We then assigned these relationships into broad categories of vulnerability and used ordinal logistic regressions to explore the covariates associated with vulnerability in growth and survival to N and S deposition. As with earlier studies, we found growth and survival responses to air pollution differed by species; but new to this study, we found 45%−70% of species responses also varied spatially across regions. The regional variation in species responses was not simply related to atmospheric N and S deposition, but was also associated with regional effects from precipitation, soil pH, mycorrhizal association, and deciduousness. A large amount of the variance remained unexplained (total variation explained ranged from 6.8%−13.8%), suggesting that these or additional factors may operate at finer spatial scales. Taken together, our results demonstrate that regional variation in tree species' response has significant implications for setting critical load targets, as critical loads can now be tailored for specific species at management-relevant scales.
Describing and Modelling Stem Form of Tropical Tree Species with Form Factor: A Comprehensive Review
Oluwajuwon, Tomiwa V.; Ogbuka, Chioma E.; Ogana, Friday N.; Hossain, Md. Sazzad; Israel, Rebecca; Lee, David J. (MDPI, 2024-12-27)
The concept of tree or stem form has been central to forest research for over a century, playing a vital role in accurately assessing tree growth, volume, and biomass. The form factor is an essential component for expressing the shape of a tree, enabling more accurate volume estimation, which is vital for sustainable forest management and planning. Despite its simplicity, flexibility, and advantages in volume estimation, the form factor has received less attention compared to other measures like taper equations and form quotient. This review summarizes the concept, theories, and measures of stem form, and describes the factors influencing its variation. It focuses on the form factor, exploring its types, parameterization, and models in the context of various tropical species and geographic conditions. The review also discusses the use of the form factor in volume estimation and the issues with using default or generic values. The reviewed studies show that tree stem form and form factor variations are influenced by multiple site, tree, and stand characteristics, including site quality, soil type, climate conditions, tree species, age, crown metrics, genetic factors, stand density, and silviculture. The breast height form factor is the most adopted among the three common types of form factors due to its comparative benefits. Of the five most tested form factor functions for predicting tree form factors, Pollanschütz’s function is generally considered the best. However, its performance is often not significantly different from other models. This review identifies the “Hohenadl” method and mixed-effects modelling as overlooked yet potentially valuable approaches for form factor modelling. Using the form factor, especially by diameter or age classes, can enhance tree volume estimation, surpassing volume equations. However, relying on default or generic form factors can lead to volume and biomass estimation errors of up to 17–35%, underscoring the need to limit variation sources in form factor modelling and application. Further recommendations are provided for improving the statistical techniques involved in developing form factor functions.
Assessing Methods to Measure Stem Diameter at Breast Height with High Pulse Density Helicopter Laser Scanning
Sumnall, Matthew J.; Raigosa-Garcia, Ivan; Carter, David R.; Albaugh, Timothy J.; Campoe, Otávio C.; Rubilar, Rafael A.; Alexander, Bart; Cohrs, Christopher W.; Cook, Rachel L. (MDPI, 2025-01-10)
Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal size was estimated every 25 cm below the living crown, and a cubic spline was used to estimate where there were gaps. Individual stem diameter at breast height (DBH) was estimated for 77% of field-measured trees. The root mean square error (RMSE) of DBH estimates was 7–12 cm using stem circle fitting. Adapting the approach to use an existing stem taper model reduced the RMSE of estimates (<1 cm). In contrast, estimates that were produced from a previously existing DBH estimation method (PREV) could be achieved for 100% of stems (DBH RMSE 6 cm), but only after location-specific error was corrected. The stem classification method required comparatively little development of statistical models to provide estimates, which ultimately had a similar level of accuracy (RMSE < 1 cm) to PREV. HALS datasets can measure broad-scale forest plantations and reduce field efforts and should be considered an important tool for aiding in inventory creation and decision-making within forest management.
Winter cover cropping increases albedo and latent heat flux in a Texas High Plains agroecosystem
McNellis, Risa; van Gestel, Natasja; Thomas, R. Quinn; Smith, Nicholas G. (2024-02-22)
Winter cover crops represent a land cover change that may sequester carbon in the soil and improve agricultural sustainability. Their adoption may also change the Earth’s radiative balance and result in biophysical feedbacks to climate through alterations in albedo and latent heat fluxes. Understanding the mechanisms underlying these alterations to the radiative balance is important for making reliable future climate projections. However, data on cover crop biophysics are limited, requiring models to rely on data from summer plants for parameterization, likely biasing predictions. To address this gap, we measured the albedo and stomatal conductance of two summer crops and three winter crops on farms in the High Plains region of Texas. We also established a winter cover crop field experiment with two cover crops and fallow fields to estimate the change in albedo and latent heat flux that results from a switch to winter cover cropping. We found that albedo was significantly higher in winter-like conditions than in summer-like conditions due to an increase in plant albedo and a reduction in leaf area index. The albedo of winter cover crops was higher than the soil albedo, resulting in an increase in top-of-atmosphere reflected radiation of 7%–14% when converting from fallow fields to winter cover cropped fields. There was an additional cooling effect through doubling of the estimated latent heat flux caused by the presence of cover crops. The combined changes in albedo and latent heat resulted in a change in the surface energy balance that is associated with an overall cooling effect of winter cover crops on surface atmospheric temperatures. While this effect is likely to be region-specific, these results strongly indicate that winter cover crops alter the surface albedo and latent heat flux of agricultural fields and provide a direct cooling effect in the High Plains region of Texas.
Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth
Woelmer, Whitney M.; Thomas, R. Quinn; Olsson, Freya; Steele, Bethel G.; Weathers, Kathleen C.; Carey, Cayelan C. (Elsevier, 2024-09-17)
Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. To explore how forecast performance varies for water temperature and dissolved oxygen, two freshwater variables important for lake ecosystem functioning, we produced probabilistic forecasts at multiple depths over two open-water seasons in Lake Sunapee, NH, USA. Our forecasting system, FLARE (Forecasting Lake And Reservoir Ecosystems), uses a 1-D coupled hydrodynamic-biogeochemical process model, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over null models across varying environmental conditions. We found that FLARE water temperature forecasts were always more skillful than FLARE oxygen forecasts. Specifically, temperature forecasts outperformed both null models up to 11 days into the future, as compared to only two days for oxygen. Across different years, we observed variable forecast skill, with performance generally decreasing with depth for both variables. Overall, all temperature forecasts and surface oxygen, but not deep oxygen, forecasts were more skillful than at least one null model >80 % of the forecasted period, indicating that our process-based model was able to reproduce the dynamics of these two variables with greater reliability than the null models. However, process-based oxygen forecasts from deeper waters were less skillful than both null models during a majority of the forecasted period, which suggests that deep-water oxygen dynamics are dominated by autocorrelation and seasonal change, which are inherently captured by the null forecasts. Our results highlight that forecast performance varies among lake water quality metrics and that process-based forecasts can provide important information in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change.
Climate Change Could Negate U.S. Forest Ecosystem Services Benefits Gained Through Reductions in Nitrogen and Sulfur Deposition
Phelan, Jennifer N.; Van Houtven, George; Clark, Christopher M.; Buckley, John; Cajka, James; Hargrave, Ashton; Horn, Kevin; Thomas, R. Quinn; Sabo, Robert D. (Nature Portfolio, 2024-05-10)
Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) impact the health and productivity of forests. Here, we explored the potential impacts of these environmental stressors on ecosystem services provided by future forests in the contiguous U.S. We found that all stand-level services benefitted (+ 2.6 to 8.1%) from reductions in N+S deposition, largely attributable to positive responses to reduced S that offset the net negative effects of lower N levels. Sawtimber responded positively (+ 0.5 to 0.6%) to some climate change, but negatively (− 2.4 to − 3.8%) to the most extreme scenarios. Aboveground carbon (C) sequestration and forest diversity were negatively impacted by all modelled changes in climate. Notably, the most extreme climate scenario eliminated gains in all three services achieved through reduced deposition. As individual tree species responded differently to climate change and atmospheric deposition, associated services unique to each species increased or decreased under future scenarios. Our results suggest that climate change should be considered when evaluating the benefits of N and S air pollution policies on the services provided by U.S. forests.
FaaSr: R Package for Function-as-a-Service Cloud Computing
Park, Sungjae; Ku, Yun-Jung; Mu, Nan; Daneshmand, Vahid; Thomas, R. Quinn; Carey, Cayelan C.; Figueiredo, Renato J. (The Open Journal, 2024-11)
The FaaSr software makes it easy for scientists to execute computational workflows developed natively using the R programming language in Function-as-a-Service (FaaS) serverless cloud infrastructures and using S3 cloud object storage (Amazon, 2024b; MinIO, 2024). A key objective of the software is to reduce barriers to entry to cloud computing for scientists in domains such as environmental sciences, where R is widely used (Lai et al., 2019). To this end, FaaSr is designed to hide complexities associated with using cloud Application Programming Interfaces (APIs) for different FaaS and S3 providers, and exposes to the end user a set of simple function interfaces to: 1) register and invoke FaaS functions, 2) compose functions to create workflow execution graphs, and 3) access cloud storage at run time. The software supports encapsulation of execution environments in Docker images that can be deployed reproducibly across multiple providers: AWS Lambda (Amazon, 2024a), GitHub Actions (Github, 2024), and OpenWhisk (Apache, 2024), where users are able to leverage a baseline image with the widely-used Rocker/Tidyverse runtime (Nüst et al., 2020), as well as customize their execution environment if needed. FaaSr is available as a CRAN package to facilitate its installation in R environments.
A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts
Olsson, Freya; Moore, Tadhg N.; Carey, Cayelan C.; Breef-Pilz, Adrienne; Thomas, R. Quinn (2024-03)
Water temperature forecasting in lakes and reservoirs is a valuable tool to manage crucial freshwater resources in a changing and more variable climate, but previous efforts have yet to identify an optimal modeling approach. Here, we demonstrate the first multi‐model ensemble (MME) reservoir water temperature forecast, a forecasting method that combines individual model strengths in a single forecasting framework. We developed two MMEs: a three‐model process‐based MMEand a five‐modelMMEthat includes process‐based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. We found that the five‐model MME improved forecast performance by 8%–30% relative to individual models and the process‐based MME, as quantified using an aggregated probabilistic skill score. This increase in performance was due to large improvements in forecast bias in the five‐model MME, despite increases in forecast uncertainty. High correlation among the process‐based models resulted in little improvement in forecast performance in the process‐based MME relative to the individual process‐based models. The utility of MMEs is highlighted by two results: (a) no individual model performed best at every depth and horizon (days in the future), and (b) MMEs avoided poor performances by rarely producing the worst forecast for any single forecasted period (<6% of the worst ranked forecasts over time). This work presents an example of how existing models can be combined to improve water temperature forecasting in lakes and reservoirs and discusses the value of utilizing MMEs, rather than individual models, in operational forecasts.
Risk Perception in the Nigua River Basin: Key Determinants and Policy Implications
Maldonado-Santana, Casimiro; Torres-Valle, Antonio; Franco-Billini, Carol; Jauregui-Haza, Ulises Javier (MDPI, 2024-12-27)
The Nigua River basin in the Dominican Republic is a critical hydrographic area facing significant environmental challenges, including deforestation, soil erosion and pollution from mining and agricultural activities. This study explores the role of risk perception among local residents in shaping policies for the basin’s sustainable management. The research aims to identify the factors influencing risk perception and propose actionable strategies to improve environmental governance in the region. A “perceived risk profile” methodology was applied, using survey data from 1223 basin residents. The analysis identified key variables that influence risk perception, including demographic factors such as education, gender, and place of residence. The findings reveal that risk underestimation correlates with low awareness of risks, uncertainty about the origins of disasters, fatalism toward natural events, and low trust in institutions. In contrast, risk over-estimation is linked to infrequent risk communication, heightened catastrophism and a strong emphasis on the benefits of environmental protection. The study also highlights significant regional differences in risk perception, with residents of the lower basin exhibiting higher perceptions of risk due to cumulative pollution and frequent disaster impacts. Based on these insights, the study recommends targeted strategies to bridge risk perception gaps, including tailored risk communication, community-based environmental education and stronger institutional trust-building initiatives, all aimed at fostering more effective and inclusive environmental governance in the Nigua basin.
FaaSr: Cross-Platform Function-as-a-Service Serverless Scientific Workflows in R
Park, Sungjae; Thomas, R. Quinn; Carey, Cayelan C.; Delany, Austin D.; Ku, Yun-Jung; Lofton, Mary E.; Figueiredo, Renato J. (IEEE, 2024-09)
Modern Function-as-a-Service (FaaS) cloud platforms offer great potential for supporting event-driven scientific workflows. Nonetheless, there remain barriers to adoption by the scientific community in domains such as environmental sciences, where R is the focal language used for the development of applications and where users are typically not well-versed with FaaS APIs. This paper describes the design and implementation of FaaSr, a novel middleware system that supports event-driven scientific workflows in R. A key novelty in FaaSr is the ability to deploy workflows across FaaS providers without the need for any managed servers for coordination. With FaaSr: 1) functions are written in R; 2) the runtime environments for their execution are customizable containers; 3) functions access data in cloud storage (S3) with a familiar file-based abstraction supporting both full file put/get primitives and subsetting using the Parquet format; and 4) function invocation and workflow coordination only requires S3 cloud object storage, without relying on any dedicated, active workflow engine server or cloud-specific queues/databases. The paper reports on the functionality and performance of FaaSr for micro-benchmarks and two case studies: event-driven forecast and batch job workflows. These demonstrate the ability to deploy workflows across multiple platforms (GitHub Actions, Amazon Web Services Lambda, and the open-source OpenWhisk), without the need for dedicated coordination servers, across both cloud and edge resources. FaaSr is open-source and available as a CRAN package.
Near-term ecological forecasting for climate change action
Dietze, Michael; White, Ethan P.; Abeyta, Antoinette; Boettiger, Carl; Bueno Watts, Nievita; Carey, Cayelan C.; Chaplin-Kramer, Rebecca; Emanuel, Ryan E.; Ernest, S. K. Morgan; Figueiredo, Renato J.; Gerst, Michael D.; Johnson, Leah R.; Kenney, Melissa A.; McLachlan, Jason S.; Paschalidis, Ioannis Ch.; Peters, Jody A.; Rollinson, Christine R.; Simonis, Juniper; Sullivan-Wiley, Kira; Thomas, R. Quinn; Wardle, Glenda M.; Willson, Alyssa M.; Zwart, Jacob (Springer Nature, 2024-11-08)
A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.
Interflow, subsurface stormflow and throughflow: A synthesis of field work and modelling
McGuire, Kevin J.; Klaus, Julian; Jackson, C. Rhett (Wiley, 2024-09-03)
Interflow, throughflow and subsurface stormflow are interchangeable terms that refer to the lateral subsurface flow above a restricting layer of lower hydraulic con- ductivity that occurs during and following storm events. Interflow (used here) is a more dominant process in steeper catchments with high infiltration capacity soils overlying a more impermeable soil or geologic layer. Interflow as a runoff process was first recognised in the early 1900s, yet hydrologists still struggle to predict its occurrence, persistence, importance, interaction with other streamflow generation processes, and potential to connect to valleys and streams during and following storms. We review the history of interflow research and address some of the chal- lenges in understanding its role in runoff production. We argue that characterising the controls on interflow initiation and occurrence relies on detailed field observa- tions of subsurface properties, which exist only in limited experimental settings. This data shortcoming contributes to our inability to predict interflow or determine its contribution to streamflow more broadly. There remain many opportunities to advance our understanding of interflow that include both modelling and experimental or observational approaches in hydrology.
Forest catchment structure mediates shallow subsurface flow and soil base cation fluxes
Pennino, Amanda; Strahm, Brian D.; McGuire, Kevin J.; Bower, Jennifer A.; Bailey, Scott W.; Schreiber, Madeline E.; Ross, Donald S.; Duston, Stephanie A.; Benton, Joshua R. (Elsevier, 2024-10)
Hydrologic behavior and soil properties across forested landscapes with complex topography exhibit high variability. The interaction of groundwater with spatially distinct soils produces and transports solutes across catchments, however, the spatiotemporal relationships between groundwater dynamics and soil solute fluxes are difficult to directly evaluate. While whole-catchment export of solutes by shallow subsurface flow represents an integration of soil environments and conditions but many studies compartmentalize soil solute fluxes as hillslope vs. riparian, deep vs. shallow, or as individual soil horizon contributions. This potentially obscures and underestimates the hillslope variation and magnitude of solute fluxes and soil development across the landscape. This study determined the spatial variation and of shallow soil base cation fluxes associated with weathering reactions (Ca, Mg, and Na), soil elemental depletion, and soil saturation dynamics in upland soils within a small, forested watershed at the Hubbard Brook Experimental Forest, NH. Base cation fluxes were calculated using a combination of ion-exchange resins placed in shallow groundwater wells (0.3 – 1 m depth) located across hillslope transects (ridges to lower backslopes) and measurements of groundwater levels. Groundwater levels were also used to create metrics of annual soil saturation. Base cation fluxes were positively correlated with soil saturation frequency and were greatest in soil profiles where primary minerals were most depleted of base cations (i.e., highly weathered). Spatial differences in soil saturation across the catchment were strongly related to topographic properties of the upslope drainage area and are interpreted to result from spatial variations in transient groundwater dynamics. Results from this work suggest that the structure of a catchment defines the spatial architecture of base cation fluxes, likely reflecting the mediation of subsurface stormflow dynamics on soil development. Furthermore, this work highlights the importance of further compartmentalizing solute fluxes along hillslopes, where certain areas may disproportionately contribute solutes to the whole catchment. Refining catchment controls on base cation generation and transport could be an important tool for opening the black box of catchment elemental cycling.
Toward Collaborative Adaptation: Assessing Impacts of Coastal Flooding at the Watershed Scale
Mitchell, Allison; Bukvic, Anamaria; Shao, Yang; Irish, Jennifer L.; McLaughlin, Daniel L. (Springer Nature, 2022-12)
The U.S. Mid-Atlantic coastal region is experiencing higher rates of SLR than the global average, especially in Hampton Roads, Virginia, where this acceleration is primarily driven by land subsidence. The adaptation plans for coastal flooding are generally developed at the municipal level, ignoring the broader spatial implications of flooding outside the individual administrative boundaries. Flood impact assessments at the watershed scale would provide a more holistic perspective on what is needed to synchronize the adaptation efforts between the neighboring administrative units. This paper evaluates flooding impacts from sea level rise (SLR) and storm surge among watersheds in Hampton Roads to identify those most at risk of coastal flooding over different time horizons. It also explores the implications of flooding on the municipalities, the land uses, and land covers throughout this region within the case study watershed. The 2% Annual Exceedance Probability (AEP) storm surge flood hazard data and NOAA’s intermediate SLR projections were used to develop flooding scenarios for 2030, 2060, and 2090 and delineate land areas at risk of combined flooding. Findings show that five out of 98 watersheds will substantially increase in inundation, with two intersecting multiple municipalities. They also indicate significant inundation of military, commercial, and industrial land uses and wetland land covers. Flooding will also impact residential land use in urban areas along the Elizabeth River and Hampton city, supporting the need for collaborative adaptation planning on hydrologically influenced spatial scales.
A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills
Lofton, Mary E.; Moore, Tadhg N.; Woelmer, Whitney M.; Thomas, R. Quinn; Carey, Cayelan C. (Oxford University Press, 2024-10-10)
Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates.

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