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Assessing placement bias of the global river gauge network.
Krabbenhoft, Corey A; Allen, George H; Lin, Peirong; Godsey, Sarah E; Allen, Daniel C; Burrows, Ryan M; DelVecchia, Amanda G; Fritz, Ken M; Shanafield, Margaret; Burgin, Amy J; Zimmer, Margaret A; Datry, Thibault; Dodds, Walter K; Jones, C Nathan; Mims, Meryl C; Franklin, Catherin; Hammond, John C; Zipper, Sam; Ward, Adam S; Costigan, Katie H; Beck, Hylke E; Olden, Julian D.
Afiliación
  • Krabbenhoft CA; Department of Biological Sciences and Research and Education in Energy, Environment and Water (RENEW) Institute, University at Buffalo, Buffalo, NY, USA.
  • Allen GH; Department of Geography, Texas A&M University, College Station, TX, USA.
  • Lin P; Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, China.
  • Godsey SE; Department of Geosciences, Idaho State University, Pocatello, ID, USA.
  • Allen DC; Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, PA, USA.
  • Burrows RM; School of Ecosystem and Forest Sciences, The University of Melbourne, Burnley, Victoria, Australia.
  • DelVecchia AG; Department of Biology, Duke University, Durham, NC, USA.
  • Fritz KM; Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH, USA.
  • Shanafield M; National Centre for Groundwater Research and Training, College of Science and Engineering, Flinders University, Bedford Park, South Australia, Australia.
  • Burgin AJ; Kansas Biological Survey-Center for Ecological Research, Environmental Studies Program, and Department of Ecology & Evolutionary Biology, University of Kansas, Lawrence, KS, USA.
  • Zimmer MA; Department of Earth and Planetary Sciences, University of California, Santa Cruz, CA, USA.
  • Datry T; INRAE, UR Riverly, Centre Lyon-Grenoble Auvergne-Rhône-Alpes, Villeurbanne, France.
  • Dodds WK; Division of Biology, Kansas State University, Manhattan, KS, USA.
  • Jones CN; Biological Sciences, University of Alabama, Tuscaloosa, AL, USA.
  • Mims MC; Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA.
  • Franklin C; Department of Geography, Texas A&M University, College Station, TX, USA.
  • Hammond JC; US Geological Survey MD-DE-DC Water Science Center, Catonsville, MD, USA.
  • Zipper S; Kansas Geological Survey, University of Kansas, Lawrence, KS, USA.
  • Ward AS; O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN, USA.
  • Costigan KH; Biological Sciences, University of Alabama, Tuscaloosa, AL, USA.
  • Beck HE; Joint Research Centre of the European Commission, Ispra, Italy.
  • Olden JD; School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA.
Nat Sustain ; 5: 586-592, 2022 Apr 25.
Article en En | MEDLINE | ID: mdl-36213515
Knowing where and when rivers flow is paramount to managing freshwater ecosystems. Yet stream gauging stations are distributed sparsely across rivers globally and may not capture the diversity of fluvial network properties and anthropogenic influences. Here we evaluate the placement bias of a global stream gauge dataset on its representation of socioecological, hydrologic, climatic and physiographic diversity of rivers. We find that gauges are located disproportionally in large, perennial rivers draining more human-occupied watersheds. Gauges are sparsely distributed in protected areas and rivers characterized by non-perennial flow regimes, both of which are critical to freshwater conservation and water security concerns. Disparities between the geography of the global gauging network and the broad diversity of streams and rivers weakens our ability to understand critical hydrologic processes and make informed water-management and policy decisions. Our findings underscore the need to address current gauge placement biases by investing in and prioritizing the installation of new gauging stations, embracing alternative water-monitoring strategies, advancing innovation in hydrologic modelling, and increasing accessibility of local and regional gauging data to support human responses to water challenges, both today and in the future.

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Sustain Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Sustain Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos