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This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Forecast uncertainty is derived from multiple sources, including model parameters and driver data, among others. Knowing the uncertainty associated with a forecast enables forecast users to evaluate the forecast and make more informed decisions. This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown version at https://zenodo.org/doi/10.5281/zenodo.10380339). Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module6.html). 

Near‐term freshwater forecasts, defined as sub‐daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near‐term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water‐related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near‐term forecasts of waterquantityand that near‐term waterqualityforecasts are fewer in number and in the early stages of development (i.e., non‐operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near‐term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end‐user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near‐term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

 

Depth profiles of fluorescence-based phytoplankton biomass were sampled using a bbe Moldaenke FluoroProbe during 2014 to 2022 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of depth profiles of fluorescence-based phytoplankton biomass measured at the deepest site of each reservoir adjacent to the dam, except in Falling Creek Reservoir, where depth profiles were also taken at four upstream sites ranging from the riverine to the lacustrine zone during 2016-2019. Casts were taken approximately weekly from May-October and monthly from November-April. Casts were collected at Beaverdam and Falling Creek Reservoirs during all years (2014-2022); casts were collected at Carvins Cove Reservoir during 2014-2016 and 2018-2022; casts were collected at Spring Hollow Reservoir during 2014-2016 and 2019; and casts were collected at Gatewood Reservoir in 2015-2016. 

Abstract Globally significant quantities of carbon (C), nitrogen (N), and phosphorus (P) enter freshwater reservoirs each year. These inputs can be buried in sediments, respired, taken up by organisms, emitted to the atmosphere, or exported downstream. While much is known about reservoir-scale biogeochemical processing, less is known about spatial and temporal variability of biogeochemistry within a reservoir along the continuum from inflowing streams to the dam. To address this gap, we examined longitudinal variability in surface water biogeochemistry (C, N, and P) in two small reservoirs throughout a thermally stratified season. We sampled total and dissolved fractions of C, N, and P, as well as chlorophyll-a from each reservoir’s major inflows to the dam. We found that heterogeneity in biogeochemical concentrations was greater over time than space. However, dissolved nutrient and organic carbon concentrations had high site-to-site variability within both reservoirs, potentially as a result of shifting biological activity or environmental conditions. When considering spatially explicit processing, we found that certain locations within the reservoir, most often the stream–reservoir interface, acted as “hotspots” of change in biogeochemical concentrations. Our study suggests that spatially explicit metrics of biogeochemical processing could help constrain the role of reservoirs in C, N, and P cycles in the landscape. Ultimately, our results highlight that biogeochemical heterogeneity in small reservoirs may be more variable over time than space, and that some sites within reservoirs play critically important roles in whole-ecosystem biogeochemical processing. 

This data publication includes code and results from a systematic literature review on the current state of near-term forecasting of freshwater quality. The review aimed to address the following questions: (1) Freshwater variables, scales, models, and skill: Which freshwater variables and temporal scales are most commonly targeted for near-term forecasts, and what modeling methods are most commonly employed to develop these forecasts? How is the accuracy of freshwater quality forecasts assessed, and how accurate are they? How is uncertainty typically incorporated into water quality forecast output? (2) Forecast infrastructure and workflows: Are iterative, automated workflows commonly employed in near-term freshwater quality forecasting? How are forecasts validated and archived? (3) Human dimensions: What is the stated motivation for development of most near-term freshwater quality forecasts, and who are the most common end users (if any)? How are end users engaged in forecast development? An initial search was conducted for published papers presenting freshwater quality forecasts from 1 January 2017 to 17 February 2022 in the Web of Science Core Collection. Results were subsequently analyzed in three stages. First, paper titles were screened for relevance. Second, an initial screen was conducted to assess whether each paper presented a near-term freshwater quality forecast. Third, papers that passed the initial screen were analyzed using a standardized matrix to assess the state of near-term freshwater quality forecasting and identify areas of recent progress and ongoing challenges. Additional details regarding the systematic literature search and review are presented in the Methods section of the metadata. 

Sediment traps were deployed to assess the mass and composition (iron, manganese, total organic carbon, and total nitrogen) of settling particulates in the water column of two drinking water reservoirs—Beaverdam Reservoir and Falling Creek Reservoir, both located in Vinton, Virginia, USA. Sediment traps were deployed at two depths in each reservoir to capture both epilimnetic and hypolimnetic (total) sediment flux. The particulates were collected from the traps approximately fortnightly from April to December from 2018 to 2022, then filtered, dried, and analyzed for either iron and manganese or total organic carbon and total nitrogen. Beaverdam and Falling Creek are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia. The sediment trap dataset consists of logs detailing the sample filtering process, the mass of dried particulates from each filter, and the raw concentration data for iron (Fe) and manganese (Mn), total organic carbon (TOC) and total nitrogen (TN). The final products are the calculated downward fluxes of solid Fe, Mn, TOC and TN during the deployment periods. 

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.

 

Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real‐world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision‐making, we developed a hands‐on teaching module within the Macrosystems Environmental Data‐Driven Inquiry and Exploration (EDDIE;MacrosystemsEDDIE.org) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre‐ and post‐module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision‐making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software‐based learning, which can increase students' ability to engage and understand complex ecological concepts.