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| Titel |
Uncertainty analysis of a spatially explicit annual water-balance model: case study of the Cape Fear basin, North Carolina |
| VerfasserIn |
P. Hamel, A. J. Guswa |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 2 ; Nr. 19, no. 2 (2015-02-06), S.839-853 |
| Datensatznummer |
250120625
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| Publikation (Nr.) |
 copernicus.org/hess-19-839-2015.pdf |
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| Zusammenfassung |
| There is an increasing demand for assessment of water provisioning ecosystem
services. While simple models with low data and expertise requirements are
attractive, their use as decision-aid tools should be supported by
uncertainty characterization. We assessed the performance of the InVEST
annual water yield model, a popular tool for ecosystem service assessment
based on the Budyko hydrological framework. Our study involved the
comparison of 10 subcatchments ranging in size and land-use configuration,
in the Cape Fear basin, North Carolina. We analyzed the model sensitivity to
climate variables and input parameters, and the structural error associated
with the use of the Budyko framework, a lumped (catchment-scale) model
theory, in a spatially explicit way. Comparison of model predictions with
observations and with the lumped model predictions confirmed that the InVEST
model is able to represent differences in land uses and therefore in the
spatial distribution of water provisioning services. Our results emphasize
the effect of climate input errors, especially annual precipitation, and
errors in the ecohydrological parameter Z, which are both comparable to the
model structure uncertainties. Our case study supports the use of the model
for predicting land-use change effect on water provisioning, although its
use for identifying areas of high water yield will be influenced by
precipitation errors. While some results are context-specific, our study
provides general insights and methods to help identify the regions and
decision contexts where the model predictions may be used with confidence. |
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