『Summary
This paper investigates the link between vegetation types and
long-term water balance in catchment areas. We focus on the most
widely used water balance formulas - or models - that relate long-term
annual streamflow to long-term annual rainfall and long-term potential
evapotranspiration estimates. Our investigation seeks to assess
whether long-term streamflow can be explained by land cover attributes.
As all but one of these formulas do not use land cover information,
we develop a methodology to introduce land cover information into
the models' formulations. Then, the modified formulas are compared
to the original ones in terms of performance and a sensitivity
analysis is performed, with a special focus on the parameters
representing vegetation characteristics. In line with the global
coverage of long-term water balance models, we base our work on
as many basins as possible (1508) representing as large a hydroclimatic
variety as possible.
Results show that introducing additional degrees of freedom within
the original formulas improves overall model efficiency, and that
land cover information makes only a small but nonetheless significant
contribution to this improvement.
Keywords: Rainfall-runoff modelling; Land cover; Long-term water
balance; Overparametarization; Sensitivity analysis; Budyko formula』
Introduction
Are we able to predict the effects of land-use changes on
streamflow?
How should land cover information be integrated into water balance
formulas?
Scope of the paper
Material
Simple water balance models
Dataset
A systematic approach to determining the relevance of land cover
in water balance models
Modification of original water balance formulas
Assessing the sensitivity of the modified water balance formulas
to land cover
Assessing the water balance models' efficiency
Results
Performance of the water balance models
Sensitivity of the water balance models to land cover data
Model parameter interpretation
Resampling of the catchment set to assess possible dataset bias
Impact of the dataset's climate and catchment characteristics
Impact of the geographic origin of catchment data
Conclusion
Acknowledgments
References