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Satellite-based evapotranspiration (ET) products inform decision-making regarding water availability and plant water use from sub-field to watershed scales. These products are validated using eddy covariance flux towers, where observations are subject to the influence of landscape heterogeneity due to a constantly shifting contributing area, or flux footprint, governed by surface and atmospheric drivers. In agricultural regions with multiple crop types, local heterogeneity may amplify the importance of appropriate grid cell selection for satellite ET comparisons. We evaluate the extent to which different satellite ET products capture both ET magnitudes and spatial variability due to landscape heterogeneity. We compare ECOSTRESS 70m instantaneous and daily ET products to flux tower observations in central Illinois with measurements at two heights, representing different but overlapping flux footprint areas. We estimate satellite ET based on a flux footprint weighting, gridded averages around the tower, and for crop-specific corn or soybean grid cells. In this region, the disALEXI daily ET product has better overall performance relative to the PT-JPL daily product, which is more sensitive to overpass times and tends to overestimate instantaneous ET. However, the PT-JPL model reflects more variability at high spatial resolution. Specifically, a footprint-derived ET improves the data-model comparison relative to disALEXI, and PT-JPL more closely replicates crop-specific and field-specific differences as inferred from the 2-height experimental setup. This study highlights differences in how models integrate spatial inputs, which lead to different representations of spatial variability for the same nominal resolution. This can also have important implications for understanding and predicting field-level differences in land-atmosphere fluxes. 

Abstract Environmental DNA (eDNA) has revolutionized ecological research, particularly for biodiversity assessment in various environments, most notably aquatic media. Environmental DNA analysis allows for non‐invasive and rapid species detection across multiple taxonomic groups within a single sample, making it especially useful for identifying rare or invasive species. Due to dynamic hydrological processes, eDNA samples from running waters may represent biodiversity from broad contributing areas, which is convenient from a biomonitoring perspective but also challenging, as hydrological knowledge is required for meaningful biological interpretation. Hydrologists could also benefit from eDNA to address unsolved questions, particularly concerning water movement through catchments. While naturally occurring abiotic tracers have advanced our understanding of water age distribution in catchments, for example, current geochemical tracers cannot fully elucidate the timing and flow paths of water through landscapes. Conversely, biological tracers, owing to their immense diversity and interactions with the environment, could offer more detailed information on the sources and flow paths of water to the stream. The informational capacity of eDNA as a tracer, however, is determined by the ability to interpret the complex biological heterogeneity at a study site, which arguably requires both biological and hydrological expertise. As eDNA data has become increasingly available as part of biomonitoring campaigns, we argue that accompanying eDNA surveys with hydrological observations could enhance our understanding of both biological and hydrological processes; we identify opportunities, challenges, and needs for further interdisciplinary collaboration; and we highlight eDNA's potential as a bridge between hydrology and biology, which could foster both domains. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > MethodsWater and Life > Nature of Freshwater Ecosystems 

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 

This entry contains the data and code for 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. Frontiers in Water 2:574728. doi: 10.3389/frwa.2020.574728 Abstract: Maintaining the quality and quantity of water resources in light of complex changes in climate, human land use, and ecosystem composition requires detailed understanding of ecohydrologic function within catchments, yet monitoring relevant upstream characteristics can be challenging. In this study, we investigate how variability in riverine microbial communities can be used to monitor the climate, geomorphology, land-cover, and human development of watersheds. We collected streamwater DNA fragments and used 16S rRNA sequencing to profile microbiomes from headwaters to outlets of the Willamette and Deschutes basins, two large watersheds prototypical of the U.S. Pacific Northwest region. In the temperate, north-south oriented Willamette basin, microbial community composition correlated most strongly with geomorphic characteristics (mean Mantel test statistic r = 0.19). Percentage of forest and shrublands (r = 0.34) and latitude (r = 0.41) were among the strongest correlates with microbial community composition. In the arid Deschutes basin, however, climatic characteristics were the most strongly correlated to microbial community composition (e.g., r = 0.11). In headwater sub-catchments of both watersheds, microbial community assemblages correlated with catchment-scale climate, geomorphology, and land-cover (r = 0.46, 0.38, and 0.28, respectively), but these relationships were weaker downstream. Development-related characteristics were not correlated with microbial community composition in either watershed or in small or large sub-catchments. Our results build on previous work relating streamwater microbiomes to hydrologic regime and demonstrate that microbial DNA in headwater streams additionally reflects the structural configuration of landscapes as well as other natural and anthropogenic processes upstream. Our results offer an encouraging indication that streamwater microbiomes not only carry information about microbial ecology, but also can be useful tools for monitoring multiple upstream watershed characteristics.