HESS Hydrology and Earth System Sciences HESS Hydrol. Earth Syst. Sci. 1607-7938 Copernicus Publications Göttingen, Germany 10.5194/hess-14-1801-2010 Climatology-based regional modelling of potential vegetation and average annual long-term runoff for Mesoamerica Imbach P. 1 Molina L. 1 Locatelli B. 2 Roupsard O. 1 3 Ciais P. 4 Corrales L. 5 Mahé G. 6 CATIE 7170, Turrialba, Cartago 30501, Costa Rica CIRAD UPR Forest Ecosystem Services, Montpellier, France, CIFOR ENV Program, Bogor, Indonesia CIRAD, UPR80, Fonctionnement et Pilotage des Ecosystémes de Plantations, Montpellier, France IPSL – LSCE, CEA CNRS UVSQ, Centre d'Etudes Orme des Merisiers, 91191 Gif sur Yvette, France The Nature Conservancy, P.O. Box, 230–1225, San José, Costa Rica IRD/HydroSciences Montpellier, Case MSE, Université Montpellier 2, 34095 Montpellier, Cedex 5, France 11 10 2010 14 10 1801 1817 Copyright: © 2010 P. Imbach et al. 2010 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://hess.copernicus.org/articles/14/1801/2010/hess-14-1801-2010.html The full text article is available as a PDF file from https://hess.copernicus.org/articles/14/1801/2010/hess-14-1801-2010.pdf

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 (<i>R</i><sup>2</sup>=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 1500 mm of annual rainfall with the help of SVAT models such as MAPSS. We propose the first high-resolution (1 km<sup>2</sup> pixel) maps combining average long-term runoff, evapotranspiration, leaf area index and potential vegetation types for Mesoamerica.

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