Uncertainty attribution in global hydrological models
- Publication type:
- Other
- Metadata:
-
- Abstract
- <jats:p>&lt;div&gt; &lt;p&gt;Global hydrologic models have become an important research tool in assessing global water resources and hydrologic hazards in a changing environment, and for improving our understanding of how the water cycle is affected by climatic changes worldwide. These complex models have been developed over more than 20 years by multiple research groups, and valuable efforts like ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) contribute to our growing understanding of model uncertainties and differences. However, due to their complexity and vast data outputs, they remain a Blackbox to certain extents. Especially for processes that are poorly constrained by available observations &amp;#8211; like groundwater recharge &amp;#8211; model results vary largely, and it is unclear what processes dominate where and when. With the inclusion of even more sophisticated implementations e.g., coupled global gradient-based groundwater simulations, it is getting more and more challenging to understand and attribute these models' results.&amp;#160;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;In this talk, we argue that we need to intensify the efforts in investigating uncertainties within these models, including where they originate and how they propagate. We need to carefully and extensively examine where different processes drive the model results by applying state of the art sensitivity analysis methods. To this end, we discuss development needs and describe pathways to foster the application of sensitivity analysis methods to global hydrological models.&amp;#160;&amp;#160;&amp;#160; &amp;#160;&lt;/p&gt; &lt;/div&gt;</jats:p>
- Autoren
- Robert Reinecke
- Francesca Pianosi
- Thorsten Wagener
- DOI
- 10.5194/egusphere-egu21-2386
- Buchtitel
- Copernicus GmbH
- Datum der Veröffentlichung
- 2021
- Status
- Published
- Herausgeber URL
- http://dx.doi.org/10.5194/egusphere-egu21-2386
- Datum der Datenerfassung
- 2021
- Titel
- Uncertainty attribution in global hydrological models
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