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1. Mutual Information between Streamwater Microbiome and Hydrology

   https://doi.org/10.5281/zenodo.6835083  

   URycki, Dawn R; Bassiouni, Maoya (January 2022, Zenodo)

   This record includes software and data used to compute shared information between streamwater microbial taxa and hydrologic metrics for the article: URycki, D. R., Bassiouni, M., Good, S. P., Crump, B. C., & Li, B. (2022). The streamwater microbiome encodes hydrologic data across scales. Science of The Total Environment, 157911. https://doi.org/10.1016/j.scitotenv.2022.157911 Abstract: Many fundamental questions in hydrology remain unanswered due to the limited information that can be extracted from existing data sources. Microbial communities constitute a novel type of environmental data, as they are comprised of many thousands of taxonomically and functionally diverse groups known to respond to both biotic and abiotic environmental factors. As such, these microscale communities reflect a range of macroscale conditions and characteristics, some of which also drive hydrologic regimes. Here, we assess the extent to which streamwater microbial communities (as characterized by 16S gene amplicon sequence abundance) encode information about catchment hydrology across scales. We analyzed 64 summer streamwater DNA samples collected from subcatchments within the Willamette, Deschutes, and John Day river basins in Oregon, USA, which range 0.03–29,000 km2 in area and 343–2334 mm/year of precipitation. We applied information theory to quantify the breadth and depth of information about common hydrologic metrics encoded within microbial taxa. Of the 256 microbial taxa that spanned all three watersheds, we found 9.6 % (24.5/256) of taxa, on average, shared information with a given hydrologic metric, with a median 15.6 % (range = 12.4–49.2 %) reduction in uncertainty of that metric based on knowledge of the microbial biogeography. All of the hydrologic metrics we assessed, including daily discharge at different time lags, mean monthly discharge, and seasonal high and low flow durations were encoded within the microbial community. Summer microbial taxa shared the most information with winter mean flows. Our study demonstrates quantifiable relationships between streamwater microbial taxa and hydrologic metrics at different scales, likely resulting from the integration of multiple overlapping drivers of each. Streamwater microbial communities are rich sources of information that may contribute fresh insight to unresolved hydrologic questions. This record can also be found on Github: https://github.com/uryckid/Microbial-Mutual-Information 

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2. River network travel time is correlated with dissolved organic matter composition in rivers of the contiguous United States

   https://doi.org/10.1002/hyp.14124  

   Hosen, Jacob D.; Allen, George H.; Amatulli, Giuseppe; Breitmeyer, Sara; Cohen, Matthew J.; Crump, Byron C.; Lu, YueHan; Payet, Jérôme P.; Poulin, Brett A.; Stubbins, Aron; et al (May 2021, Hydrological Processes)

   Abstract Most terrestrial allochthonous organic matter enters river networks through headwater streams during high flow events. In headwaters, allochthonous inputs are substantial and variable, but become less important in streams and rivers with larger watersheds. As allochthonous dissolved organic matter (DOM) moves downstream, the proportion of less aromatic organic matter with autochthonous characteristics increases. How environmental factors converge to control this transformation of DOM at a continental scale is less certain. We hypothesized that the amount of time water has spent travelling through surface waters of inland systems (streams, rivers, lakes, and reservoirs) is correlated to DOM composition. To test this hypothesis, we used established river network scaling relationships to predict relative river network flow‐weighted travel time (FWTT) of water for 60 stream and river sites across the contiguous United States (3090 discrete samples over 10 water years). We estimated lentic contribution to travel times with upstream in‐network lake and reservoir volume. DOM composition was quantified using ultraviolet and visible absorption and fluorescence spectroscopy. A combination of FWTT and lake and reservoir volume was the best overall predictor of DOM composition among models that also incorporated discharge, specific discharge, watershed area, and upstream channel length. DOM spectral slope ratio (R² = 0.77) and Freshness Index (R² = 0.78) increased and specific ultraviolet absorbance at 254 nm (R² = 0.68) and Humification Index (R² = 0.44) decreased across sites as a function of FWTT and upstream lake volume. This indicates autochthonous‐like DOM becomes continually more dominant in waters with greater FWTT. We assert that river FWTT can be used as a metric of the continuum of DOM composition from headwaters to rivers. The nature of the changes to DOM composition detected suggest this continuum is driven by a combination of photo‐oxidation, biological processes, hydrologically varying terrestrial subsidies, and aged groundwater inputs. 

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3. The Lake Tahoe Basin Stream Catchment Database: A resource for water quality monitoring in the basin

   https://doi.org/10.17632/sn5dpnzc83.1  

   Dilts, Thomas; Blaszczak, Joanna (May 2025, Mendeley Data)

   Although the Lake Tahoe Basin and its receiving waterbody, Lake Tahoe, are intensively monitored, managed, and studied, there has been no centralized resource for evaluating variation in environmental characteristics among watersheds (i.e., catchments). To address this opportunity, we compiled and calculated 161 variables for 60 non-overlapping contiguous watersheds draining to Lake Tahoe . Watershed-scale variables include climatic, topographic, vegetation, edaphic, hydrologic, and anthropogenic characteristics. Data were downloaded from publicly-available sources including: the National Elevation Dataset, USDA SSURGO soils, Calfire FRAP dataset of fire perimeters, the National Land Cover Dataset, and the Rangeland Analysis Platform. We compiled data in a Geographic Information System at the scale of the watershed. Existing and custom scripts were used to process data and derive variables that could not be obtained from existing databases. These data will be useful for environmental managers and scientists who work in the Lake Tahoe Basin and can assist with future site selection intended to span environmental gradients. 

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4. Streambank and floodplain geomorphic change and contribution to watershed material budgets

   https://doi.org/10.1088/1748-9326/ac6e47  

   Noe, G B; Hopkins, K G; Claggett, P R; Schenk, E R; Metes, M J; Ahmed, L; Doody, T R; Hupp, C R (May 2022, Environmental Research Letters)

   Abstract Stream geomorphic change is highly spatially variable but critical to landform evolution, human infrastructure, habitat, and watershed pollutant transport. However, measurements and process models of streambank erosion and floodplain deposition and resulting sediment fluxes are currently insufficient to predict these rates in all perennial streams over large regions. Here we measured long-term lateral streambank and vertical floodplain change and sediment fluxes using dendrogeomorphology in streams around the U.S. Mid-Atlantic, and then statistically modeled and extrapolated these rates to all 74 133 perennial, nontidal streams in the region using watershed- and reach-scale predictors. Measured long-term rates of streambank erosion and floodplain deposition were highly spatially variable across the landscape from the mountains to the coast. Random Forest regression identified that geomorphic change and resulting fluxes of sediment and nutrients, for both streambank and floodplain, were most influenced by urban and agricultural land use and the drainage area of the upstream watershed. Modeled rates for headwater streams were net erosional whereas downstream reaches were on average net depositional, leading to regional cumulative sediment loads from streambank erosion (−5.1 Tg yr −1 ) being nearly balanced by floodplain deposition (+5.3 Tg yr −1 ). Geomorphic changes in stream valleys had substantial influence on watershed sediment, phosphorus, carbon, and nitrogen budgets in comparison to existing predictions of upland erosion and delivery to streams and of downstream sediment loading. The unprecedented scale of these novel findings provides important insights into the balance of erosion and deposition in streams within disturbed landscapes and the importance of geomorphic change to stream water quality and carbon sequestration, and provides vital understanding for targeting management actions to restore watersheds. 

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5. StreamStats Data Query

   https://doi.org/10.5281/zenodo.3902476  

   URycki, Dawn; Good, Stephen (January 2020, Zenodo)

   This Python script queries the USGS StreamStats Service API for a list of available basin characteristics, and the values for those characteristics, for each input site. The script takes as input a matrix of site identifiers and location coordinates and returns 1) a matrix of values for available basin characteristics obtained from StreamStats for each input location and 2) a matrix of basin characteristic variable names and definitions. To run this script exactly as written, create 3 columns of data in comma-separated format: 1) 'Site,' which are the study site identifiers, 2) 'lonSS,' the longitudinal coordinates, and 3) 'latSS,' the latitudinal coordinates (in decimal degrees). Name the input file 'ssLocs.csv' and store it in a subfolder named 'Data.' Otherwise, the pathnames for input and output files can be modified within the script. The output files 'ssDats.csv' and 'Descriptions.csv' will also be saved to the subfolder 'Data'. Multiple code runs may be necessary to obtain information for all sites; as long as the output file 'ssDats.csv' remains in the 'Data' folder, the script will only query for sites with missing information. If the program returns an error or is unable to obtain data for a site after several attempts, it may be that the input coordinates do not point to a cell defined as water in the StreamStats application. A solution is to check the coordinates manually in the StreamStats web application (http://streamstats.usgs.gov). This script was developed as part of the analysis described in: URycki DR, Good SP, Crump BC, Chadwick J and Jones GD (2020) River Microbiome Composition Reflects Macroscale Climatic and Geomorphic Differences in Headwater Streams. Front. Water 2:574728. doi: 10.3389/frwa.2020.574728 

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