The color of environmental noise in river networks

Despite its far-reaching implications for conservation and natural resource management, little is known about the color of environmental noise, or the structure of temporal autocorrelation in random environmental variation, in streams and rivers. Here, we analyze the geography, drivers, and timescale-dependence of noise color in streamflow across the U.S. hydrography, using streamflow time series from 7504 gages. We find that daily and annual flows are dominated by red and white spectra respectively, and spatial variation in noise color is explained by a combination of geographic, hydroclimatic, and anthropogenic variables. Noise color at the daily scale is influenced by stream network position, and land use and water management explain around one third of the spatial variation in noise color irrespective of the timescale considered. Our results highlight the peculiarities of environmental variation regimes in riverine systems, and reveal a strong human fingerprint on the stochastic patterns of streamflow variation in river networks.


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All studies must disclose on these points even when the disclosure is negative. United States Geological Survey (USGS) flow gages (access at: https://waterdata.usgs.gov/nwis/). The precipitation and temperature datasets were retrieved from PRISM Climate Group at https://prism.oregonstate.edu. The water management datasets were retrieved from USGS Data Release at https://doi.org/10.5066/ F7XW4J1J. The gage properties were retrieved from USGS at https://doi.org/10.3133/70046617. The datasets of upstream catchment area and stream order were retrieved at https://figshare.com/articles/dataset/Mapping_the_world_s_free-flowing_rivers_data_set_and_technical_documentation/7688801. n/a n/a n/a n/a In this work, we explore the geography and drivers of flow noise color in river networks. We first examine the spatial patterns of flow noise color at different temporal scales (daily and annual) across streams and rivers in the conterminous United States by leveraging spectral methods on 7,504 gages with long-term high resolution discharge data. We then quantify the associations and relative importance of a suite of natural and anthropogenic variables (geography, hydroclimate, land-use, regulation by dams). Our analysis demonstrates that the characteristics of environmental fluctuations in riverine networks differ from those previously reported from terrestrial and marine ecosystems, and bear the signature of human activities. These results advance current understanding of stochastic variation in the environment-potentially assisting in the identification, management, and conservation of river ecosystems degraded by hydrologic alteration.
The streamflow datasets used in this study are daily streamflow time series from the United States Geological Survey (USGS) flow gages (access at: https://waterdata.usgs.gov/nwis/). Gages were selected from all the available gages based on the following criteria: (1) Recording period of at least 15 consecutive years, within 1960 to 2019; (2) missing records being less than 5% of the total length. Any missing data was estimated by linear interpolation. When available, sub-daily flow values were converted to mean daily discharge. Finally, we used daily records from 7,504 gages and annual records from 2,594 gages.
To investigate the impact of natural and anthropogenic factors, we used the following variables: The precipitation and temperature datasets were retrieved from PRISM Climate Group at https://prism.oregonstate.edu. The water management datasets were retrieved from USGS Data Release at https://doi.org/10.5066/F7XW4J1J. The gage properties were retrieved from USGS at https://doi.org/10.3133/70046617. The datasets of upstream catchment area and stream order were retrieved at https://figshare.com/articles/dataset/Mapping_the_world_s_free-flowing_rivers_data_set_and_technical_documentation/7688801.
The sampling frequency is daily (for daily noise color). The frequency of data collection is one of the standard procedures operated by the United States Geological Survey (USGS).
The United States Geological Survey (USGS) collected the raw data at streamgages by current meter and Acoustic Doppler Current Profiler when needed.
At daily and point scale (USGS streamgage locations), across the contiguous United States from 1960 to 2019.
Following similar methods used in previous studies in handling missing data, gages were selected based on the following criteria: (1) Recording period of at least 15 consecutive years, within 1960 to 2019; (2) missing records being less than 5% of the total length. Any missing data was estimated by linear interpolation. When available, sub-daily flow values were converted to mean daily discharge.
The results can be reproduced based on the code and data.
The randomization was implemented based on a random seed generated by R (version 4.0.5, we provided the random seed in the