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This resource contains source code and select data products behind the following Master's Thesis: Platt, L. (2024). Basins modulate signatures of river salinization (Master's thesis). University of Wisconsin-Madison, Freshwater and Marine Sciences. The source code represents an R-based data processing and modeling pipeline written using the R package "targets". Some of the folders in the source code zipfile are intentionally left empty (except for a hidden file ".placeholder") in order for the code repository to be setup with the required folder structure. To execute this code, download the zip folder, unzip, and open the salt-modeling-data.Rproj file. Then, reference the instructions in the README.md file for installing packages, building the pipeline, and examining the results. Newer versions of this repository may be updated in GitHub at github.com/lindsayplatt/salt-modeling-data. In addition to the source code, this resource contains three data files containing intermediate products of the pipeline. The first two represent data prepared for the random forest modeling. Data download and processing were completed in pipeline phases 1 - 5, and the random forest modeling was completed in phase 6 (see source code).  site_attributes.csv which contains the USGS gage site numbers and their associated basin attributes site_classifications.csv which contains the classification of a site for both episodic signatures ("Episodic" or "Not episodic") and baseflow salinization signatures ("positive", "none", "negative", or NA). Note that an NA in the baseflow classification column means that the site did not meet minimum data requirements for calculating a trend and was not used in the random forest model for baseflow salinization. site_attribute_details.csv contains a table of each attribute shorthand used as column names in site_attributes.csv and their names, units, description, and data source. 

Stormwater ponds are common features in urbanized landscapes and can suffer from rapid oxygen depletion when thermally stratified or ice-covered. To investigate under-ice oxygen dynamics and drivers of bottom water oxygen saturation, we sampled 20 stormwater ponds in Madison, Wisconsin, USA during the summer of 2021 and winter 2022. The urban ponds ranged in age, shape, size, and depth. We repeatedly took YSI profiles of water temperature, oxygen, and specific conductance 7 times in the summer and 3 times in the winter. Water chemistry variables were collected in the surface waters, habitat surveys were conducted in the summer, and ice/snow thickness was recorded in the winter. We also measured the concentration of greenhouse gases in the surface waters as a consequence to oxygen depletion using the headspace equilibrium method. 

Climate change is reducing winter ice cover on lakes; yet, the full societal and environmental consequences of this ice loss are poorly understood. The socioeconomic implications of declining ice include diminished access to ice-based cultural activities, safety concerns in traversing ice, changes in fisheries, increases in shoreline erosion, and declines in water storage. Longer ice-free seasons allow more time and capacity for water to warm, threatening water quality and biodiversity. Food webs likely will reorganize, with constrained availability of ice-associated and cold-water niches, and ice loss will affect the nature, magnitude, and timing of greenhouse gas emissions. Examining these rapidly emerging changes will generate more-complete models of lake dynamics, and transdisciplinary collaborations will facilitate translation to effective management and sustainability. 

Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents chlorophyll, light, and high frequency buoy data collected from this project. Related datasets are: https://doi.org/10.6073/pasta/962fa57959ff9828eb6f1cbda79b82c0 https://doi.org/10.6073/pasta/f6e271634a04819e25bc7c913cd67155 https://doi.org/10.6073/pasta/9a26e819522152e878d802df76cf90d7 

Freshwater salinization from anthropogenic activities threatens water quality and habitat suitability for many lakes and rivers in North America. Recognizing that salinization is a stress on freshwater environments globally, research on watershed salt transport is necessary for informed management strategies. Prior to this research, there were few studies that examined salt export regimes along a river–lake continuum to investigate the drivers, temporal dynamics, and modulators of freshwater salinization. Here, we use high-frequency in situ monitoring to assess specific conductance–discharge (cQ) relationships, chloride concentrations and fluxes, and the role of lakes in downstream salt transport. The Upper Yahara River Watershed in southern Wisconsin, USA, is a mixed urban and agricultural watershed where the lakes' chloride concentrations have risen from < 5 mg L−1 in the 1940s to > 50–80 mg L−1 in 2021. Our results suggest cQ behavior depends on land use, with urban areas exhibiting more frequent mobilization events during stormflow and agricultural areas exhibiting predominantly dilution dynamics. In addition, chloride loading is driven by hydrology and watershed size whereas concentrations and yields are a function of anthropogenic drivers like urbanization. We demonstrate how an in-network lake attenuates downstream salinity, dampening the hydrologic, anthropogenic, and seasonal patterns observed in rivers upstream of the lake. Importantly, biogeochemical processes in lakes overlay a seasonal signal on salinity that must be considered when investigating temporal dynamics of anthropogenic salinization. This research contributes to understanding of temporal dynamics of salt export through watersheds and can be used to inform management strategies for habitat protection. 

Creating separable representations via representation learning and clustering is critical in analyzing large unstructured datasets with only a few labels. Separable representations can lead to supervised models with better classification capabilities and additionally aid in generating new labeled samples. Most unsupervised and semisupervised methods to analyze large datasets do not leverage the existing small amounts of labels to get better representations. In this paper, we propose a spatiotemporal clustering paradigm that uses spatial and temporal features combined with a constrained loss to produce separable representations. We show the working of this method on the newly published dataset ReaLSAT, a dataset of surface water dynamics for over 680,000 lakes across the world, making it an essential dataset in terms of ecology and sustainability. Using this large unlabelled dataset, we first show how a spatiotemporal representation is better compared to just spatial or temporal representation. We then show how we can learn even better representations using a constrained loss with few labels. We conclude by showing how our method, using few labels, can pick out new labeled samples from the unlabeled data, which can be used to augment supervised methods leading to better classification. 

This repository includes the setup and output from the analysis ran on Lake Mendota to explore the trophic cascade caused by invasion of spiny water flea in 2010. Scripts to run the model are located under /src, and the processed results for the discussion of the paper are located under /data_processed.</p>