 |
| Titel |
Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene |
| VerfasserIn |
S. E. Thompson, M. Sivapalan  , C. J. Harman, V. Srinivasan, M. R. Hipsey, P. Reed, A. Montanari, G. Blöschl |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 12 ; Nr. 17, no. 12 (2013-12-12), S.5013-5039 |
| Datensatznummer |
250086028
|
| Publikation (Nr.) |
 copernicus.org/hess-17-5013-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| Globally, many different kinds of water resources management issues call for
policy- and infrastructure-based responses. Yet responsible decision-making
about water resources management raises a fundamental challenge for
hydrologists: making predictions about water resources on decadal- to century-long timescales. Obtaining insight into hydrologic futures over 100 yr
timescales forces researchers to address internal and exogenous changes in
the properties of hydrologic systems. To do this, new hydrologic research
must identify, describe and model feedbacks between water and other
changing, coupled environmental subsystems. These models must be constrained
to yield useful insights, despite the many likely sources of uncertainty in
their predictions. Chief among these uncertainties are the impacts of the
increasing role of human intervention in the global water cycle – a
defining challenge for hydrology in the Anthropocene. Here we present a
research agenda that proposes a suite of strategies to address these
challenges from the perspectives of hydrologic science research. The
research agenda focuses on the development of co-evolutionary hydrologic
modeling to explore coupling across systems, and to address the implications
of this coupling on the long-time behavior of the coupled systems. Three
research directions support the development of these models: hydrologic
reconstruction, comparative hydrology and model-data learning. These
strategies focus on understanding hydrologic processes and feedbacks over
long timescales, across many locations, and through strategic coupling of
observational and model data in specific systems. We highlight the value of
use-inspired and team-based science that is motivated by real-world
hydrologic problems but targets improvements in fundamental understanding to
support decision-making and management. Fully realizing the potential of
this approach will ultimately require detailed integration of social science
and physical science understanding of water systems, and is a priority for
the developing field of sociohydrology. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|