Large-sample hydrology: recent progress,guidelines for new datasets and grand challenges |
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| Authors: | Nans Addor Hong X Do Camila Alvarez-Garreton Gemma Coxon Keirnan Fowler Pablo A Mendoza |
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| Institution: | 1. Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UKN.Addor@uea.ac.uk https://orcid.org/0000-0002-6057-3930;3. School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australia;4. Faculty of Environment and Natural Resources, Nong Lam University, Ho Chi Minh City, Vietnam;5. School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA;6. Instituto de Conservación, Biodiversidad y Territorio, Universidad Austral de Chile, Valdivia, Chile;7. Center for Climate and Resilience Research, Santiago, Chile;8. School of Geographical Sciences, University of Bristol, Bristol, UK;9. Department of Infrastructure Engineering, University of Melbourne, Parkville, Australia;10. Department of Civil Engineering, Universidad de Chile, Santiago, Chile;11. Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile |
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| Abstract: | ABSTRACTLarge-sample hydrology (LSH) relies on data from large sets (tens to thousands) of catchments to go beyond individual case studies and derive robust conclusions on hydrological processes and models. Numerous LSH datasets have recently been released, covering a wide range of regions and relying on increasingly diverse data sources to characterize catchment behaviour. These datasets offer novel opportunities, yet they are also limited by their lack of comparability, uncertainty estimates and characterization of human impacts. This article (i) underscores the key role of LSH datasets in hydrological studies, (ii) provides a review of currently available LSH datasets, (iii) highlights current limitations of LSH datasets and (iv) proposes guidelines and coordinated actions to overcome these limitations. These guidelines and actions aim to standardize and automatize the creation of LSH datasets worldwide, and to enhance the reproducibility and comparability of hydrological studies. |
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| Keywords: | streamflow records data standardization reproducibility of hydrological experiments data uncertainties human interventions cloud computing |
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![ORCID Icon]({"type":"image","src":"/templates/jsp/images/orcid.png"} "ORCID Icon")
https://orcid.org/0000-0002-6057-3930;3. School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australia;4. Faculty of Environment and Natural Resources, Nong Lam University, Ho Chi Minh City, Vietnam;5. School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA;6. Instituto de Conservación, Biodiversidad y Territorio, Universidad Austral de Chile, Valdivia, Chile;7. Center for Climate and Resilience Research, Santiago, Chile;8. School of Geographical Sciences, University of Bristol, Bristol, UK;9. Department of Infrastructure Engineering, University of Melbourne, Parkville, Australia;10. Department of Civil Engineering, Universidad de Chile, Santiago, Chile;11. Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile