Andrew J. Guswa, Smith CollegeFollow
Doerthe Tetzlaff, Leibniz-Institute of Freshwater Ecology and Inland Fisheries
John S. Selker, Oregon State University
Darryl E. Carlyle-Moses, Thompson Rivers University
Elizabeth W. Boyer, Pennsylvania State University
Michael Bruen, University College Dublin
Carles Cayuela, CSIC - Instituto de Diagnostico Ambiental y Estudios del Agua (IDAEA)
Irena F. Creed, University of Saskatchewan
Nick van de Giesen, Faculteit Civiele Techniek en Geowetenschappen, TU Delft
Domenico Grasso, University of Michigan-Dearborn
David M. Hannah, University of Birmingham
Janice E. Hudson, University of Delaware
Sean A. Hudson, University of Delaware
Shin'ichi Iida, Forestry and Forest Products Research Institute
Robert B. Jackson, Stanford University
Gabriel G. Katul, Duke University
Tomo'omi Kumagai, The University of Tokyo
Pilar Llorens, CSIC - Instituto de Diagnostico Ambiental y Estudios del Agua (IDAEA)
Flavio Lopes Ribeiro, University of Delaware
Beate Michalzik, Friedrich Schiller Universität Jena
Kazuki Nanko, Forestry and Forest Products Research Institute
Christopher Oster, University of Delaware
Diane E. Pataki, The University of Utah
Catherine A. Peters, Princeton University
Andrea Rinaldo, Ecole Polytechnique Fédérale de Lausanne
Daniel Sanchez Carretero, University of Delaware
Branimir Trifunovic, University of Delaware
Maciej Zalewski, University of Lodz
Marja Haagsma, Oregon State University
Delphis F. Levia, University of Delaware

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Nature-based solutions for water-resource challenges require advances in the science of ecohydrology. Current understanding is limited by a shortage of observations and theories that can further our capability to synthesize complex processes across scales ranging from submillimetres to tens of kilometres. Recent developments in environmental sensing, data, and modelling have the potential to drive rapid improvements in ecohydrological understanding. After briefly reviewing advances in sensor technologies, this paper highlights how improved measurements and modelling can be applied to enhance understanding of the following ecohydrological examples: interception and canopy processes, root uptake and critical zone processes, and up-scaled effects of land use on streamflow. Novel and improved sensors will enable new questions and experiments, while machine learning and empirical methods provide additional opportunities to advance science. The synergy resulting from the convergence of these parallel developments will provide new insight into ecohydrological processes and thereby help identify nature-based solutions to address water-resource challenges in the 21st century.


critical zone processes, environmental sensing, interception, land use, machine learning, measurement, modelling, streamflow










© 2020 The Authors.


Archived as published.

Ecohydrology published by John Wiley & Sons Ltd

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