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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). 

Discharge rates and water temperature of the primary inflow tributary into Falling Creek Reservoir (Vinton, Virginia, USA) were measured at a gauged weir on a 15-minute temporal resolution from May 2013 to December 2022. Falling Creek Reservoir is a drinking water supply reservoir owned and managed by the Western Virginia Water Authority (WVWA). The dataset consists of water temperatures and discharge rates calculated from a pressure transducer deployed by the WVWA in a rectangular weir (15 May 2013 - 06 June 2019) and in a v-notched weir (07 June 2019 - 31 December 2022) at the same site. From 07 June 2019 to 31 December 2022, water temperature and discharge data were also collected from a Virginia Tech-deployed (VT) pressure transducer installed in the same weir. 

Because of increased variability in populations, communities, and ecosystems due to land use and climate change, there is a pressing need to know the future state of ecological systems across space and time. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to preemptively prepare for fluctuations in important ecosystem services. However, forecasts must be effectively designed and communicated to those who need them to make decisions in order to realize their potential for protecting natural resources. In this module, students will explore real ecological forecast visualizations, identify ways to represent uncertainty, make management decisions using forecast visualizations, and learn decision support techniques. Lastly, students customize a forecast visualization for a specific stakeholder's decision needs. The overarching goal of this module is for students to understand how forecasts are connected to decision-making of stakeholders, or the managers, policy-makers, and other members of society who use forecasts to inform decision-making. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials and the files necessary to teach the module. Readers are referred to the Zenodo data package (Woelmer et al. 2022; DOI: 10.5281/zenodo.7074674) for the R Shiny application code needed to run the module locally. 

This dataset consists of meteorological variables measured by a research-grade Campbell Scientific meteorological station deployed on the dam of Falling Creek Reservoir. Falling Creek Reservoir (Vinton, Virginia, USA), is owned and operated by the Western Virginia Water Authority as a primary water source. The meteorological variables include photosynthetic active radiation, barometric pressure, ambient air temperature, relative humidity, rainfall, wind speed and direction, shortwave radiation, infrared radiation, and albedo. All variables were measured every 5 minutes from 2015-07-07 16:45:00 to 2015-07-13 12:20:00 (YYYY-MM-DD hh:mm:ss) and every minute thereafter to the end of the dataset at 2022-12-31 23:59:00. We applied substantial quality assurance/quality control protocols to the raw observations, as described in the methods. 

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 temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, and turbidity were collected with a CTD (Conductivity, Temperature, and Depth) profiler fitted with a SBE 43 Dissolved Oxygen sensor and an ECO Triplet Fluorometer and Backscattering Sensor from 2013 to 2022. From 2017-2022, pH and oxidation-reduction potential (ORP) were also collected with a SBE 27 pH and O.R.P. (redox) sensor. CTD profiles were collected in five drinking water reservoirs in southwestern Virginia, USA. All variables were measured every 0.25 seconds, resulting in depth profiles at approximately ten centimeter resolution. The five study 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 CTD depth profiles measured at the deepest site of each reservoir adjacent to the dam as well as well as other upstream reservoir sites. The profiles were collected approximately fortnightly in the spring months, weekly in the summer and early autumn, and monthly in the late autumn and winter. Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir were sampled every year in the dataset (2013-2022); Spring Hollow Reservoir was not in sampled in 2018 or 2020–2022; and Gatewood Reservoir was only sampled in 2016. 

Depth profiles of dissolved organic carbon and total and dissolved nitrogen and phosphorus were sampled from 2013 to 2022 in five drinking water reservoirs in southwestern Virginia, USA. Some additional dissolved nitrogen and phosphorus samples from January to March 2023 are included in this data product. The five drinking water 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 water chemistry samples measured at the deepest site of each reservoir adjacent to the dam. Additional water chemistry samples were collected at a gauged weir on Falling Creek Reservoir's primary inflow tributary, as well as surface samples at multiple upstream and inflow sites in Falling Creek Reservoir 2014-2022 and Beaverdam Reservoir in 2019 and 2020. One upstream site at BVR was sampled at depth in 2022. Inflow sites at Carvins Cove Reservoir were sampled from 2020 - 2022. The water column samples were collected approximately fortnightly from March-April, weekly from May-October, and monthly from November-February at Falling Creek Reservoir and Beaverdam Reservoir, approximately fortnightly from May-August in most years at Carvins Cove Reservoir, and approximately fortnightly from 2014-2016 in Gatewood and Spring Hollow Reservoirs, though sampling frequency and duration varied among reservoirs and years. Depth profiles of dissolved inorganic carbon were also collected from 2018-2022, but the analytical method for this analyte is still in development and these concentrations should be considered as preliminary data only. 

Depth profiles of water temperature on 1m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, and pressure at ~1.6 m depth were collected with a suite of high-frequency sensors at Falling Creek Reservoir (Vinton, Virginia, USA) on the 10-minute scale in 2018-2022. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled from water temperature data measured at multiple depths by thermistors, two dissolved oxygen sensors at multiple depths, pressure measured at one depth, and a YSI EXO2 sonde that measures temperature, dissolved oxygen, pressure, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, and fluorescent dissolved organic matter, at one depth, all measured at the deepest site of the reservoir adjacent to the dam.