| dc.description.abstract | Mean annual cycles of runoff, evapotranspiration,
leaf area index (LAI) and potential vegetation were
modelled for Mesoamerica using the SVAT model MAPSS
with different climatology datasets. We calibrated and validated
the model after building a comprehensive database
of regional runoff, climate, soils and LAI. The performance
of several gridded precipitation climatology datasets (CRU,
FCLIM, WorldClim, TRMM, WindPPT and TCMF) was
evaluated and FCLIM produced the most realistic runoff.
Annual runoff was successfully predicted (R2=0.84) for a set
of 138 catchments, with a low runoff bias (12%) that might
originate from an underestimation of the precipitation over
cloud forests. The residuals were larger in small catchments
but remained homogeneous across elevation, precipitation,
and land-use gradients. Assuming a uniform distribution
of parameters around literature values, and using a Monte
Carlo-type approach, we estimated an average model uncertainty
of 42% of the annual runoff. The MAPSS model was
most sensitive to the parameterization of stomatal conductance.
Monthly runoff seasonality was mimicked "fairly" in
78% of the catchments. Predicted LAI was consistent with
MODIS collection 5 and GLOBCARBON remotely sensed
global products. The simulated evapotranspiration:runoff ratio
increased exponentially for low precipitation areas, highlighting
the importance of accurately modelling evapotranspiration
below 1500mm of annual rainfall with the help of
SVAT models such as MAPSS. We propose the first high-resolution (1 km2 pixel) maps combining average long-term
runoff, evapotranspiration, leaf area index and potential vegetation
types for Mesoamerica. | es_ES |
