Introduction to a special section on ecohydrology of semiarid environments: Confronting mathematical models with ecosystem complexity


Journal Article

© 2015. American Geophysical Union. All Rights Reserved. Current literature provides large number of publications about ecohydrological processes and their effect on the biota in drylands. Given the limited laboratory and field experiments in such systems, many of these publications are based on mathematical models of varying complexity. The underlying implicit assumption is that the data set used to evaluate these models covers the parameter space of conditions that characterize drylands and that the models represent the actual processes with acceptable certainty. However, a question raised is to what extent these mathematical models are valid when confronted with observed ecosystem complexity? This Introduction reviews the 16 papers that comprise the Special Section on Eco-hydrology of Semiarid Environments: Confronting Mathematical Models with Ecosystem Complexity. The subjects studied in these papers include rainfall regime, infiltration and preferential flow, evaporation and evapotranspiration, annual net primary production, dispersal and invasion, and vegetation greening. The findings in the papers published in this Special Section show that innovative mathematical modeling approaches can represent actual field measurements. Hence, there are strong grounds for suggesting that mathematical models can contribute to greater understanding of ecosystem complexity through characterization of space-time dynamics of biomass and water storage as well as their multiscale interactions. However, the generality of the models and their low-dimensional representation of many processes may also be a "curse" that results in failures when particulars of an ecosystem are required. It is envisaged that the search for a unifying "general" model, while seductive, may remain elusive in the foreseeable future. It is for this reason that improving the merger between experiments and models of various degrees of complexity continues to shape the future research agenda.

Full Text

Duke Authors

Cited Authors

  • Svoray, T; Assouline, S; Katul, G

Published Date

  • November 1, 2015

Published In

Volume / Issue

  • 51 / 11

Start / End Page

  • 8677 - 8683

Electronic International Standard Serial Number (EISSN)

  • 1944-7973

International Standard Serial Number (ISSN)

  • 0043-1397

Digital Object Identifier (DOI)

  • 10.1002/2015WR018131

Citation Source

  • Scopus