@Article{RosolemGupShuGonZen:2013:ToCoAp,
               author = "Rosolem, Rafael and Gupta, Hoshin V. and Shuttleworth, W. James 
                         and Gon{\c{c}}alves, Luis Gustavo Gon{\c{c}}alves de and Zeng, 
                         Xubin",
          affiliation = "{University of Arizona} and {University of Arizona} and 
                         {University of Arizona} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {University of Arizona}",
                title = "Towards a comprehensive approach to parameter estimation in land 
                         surface parameterization schemes",
              journal = "Hydrological Processes",
                 year = "2013",
               volume = "27",
                pages = "2075–2097",
             keywords = "parameter estimation, model diagnostics, mean squared error 
                         decomposition, land surface modelling, simple biosphere model, 
                         Amazon biomes.",
             abstract = "In climate models, the landatmosphere interactions are described 
                         numerically by land surface parameterization (LSP) schemes. The 
                         continuing improvement in realism in these schemes comes at the 
                         expense of the need to specify a large number of parameters that 
                         are either directly measured or estimated. Also, an emerging 
                         problem is whether the relationships used in LSPs are universal 
                         and globally applicable. One plausible approach to evaluate this 
                         is to first minimize uncertainty in model parameters by 
                         calibration. In this paper, we conduct a comprehensive analysis of 
                         some model diagnostics using a slightly modified version of the 
                         Simple Biosphere 3 model for a variety of biomes located mainly in 
                         the Amazon. First, the degree of influence of each individual 
                         parameter in simulating surface fluxes is identified. Next, we 
                         estimate parameters using a multi-operator genetic algorithm 
                         applied in a multi-objective context and evaluate simulations of 
                         energy and carbon fluxes against observations. Compared with the 
                         default parameter sets, these parameter estimates improve the 
                         partitioning of energy fluxes in forest and cropland sites and 
                         provide better simulations of daytime increases in assimilation of 
                         net carbon during the dry season at forest sites. Finally, a 
                         detailed assessment of the parameter estimation problem was 
                         performed by accounting for the decomposition of the mean squared 
                         error to the total model uncertainty. Analysis of the total 
                         prediction uncertainty reveals that the parameter adjustments 
                         significantly improve reproduction of the mean and variability of 
                         the flux time series at all sites and generally remove seasonality 
                         of the errors but do not improve dynamical properties. Our results 
                         demonstrate that error decomposition provides a meaningful and 
                         intuitive way to understand differences in model performance. To 
                         make further advancements in the knowledge of these models, we 
                         encourage the LSP community to adopt similar approaches in the 
                         future.",
                  doi = "10.1002/hyp.9362",
                  url = "http://dx.doi.org/10.1002/hyp.9362",
                 issn = "0885-6087",
                label = "lattes: 6072354470541631 4 RosolemGupShuGonZen:2012:ToCoAp",
             language = "en",
           targetfile = "hyp9362.pdf",
        urlaccessdate = "30 abr. 2024"
}