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Publicação
Assessing the impact of LAI data assimilation on simulations of the soil water balance and maize development using MOHID-Land
Orientador(es)
Resumo(s)
Hydrological modeling at the catchment scale requires the upscaling of many input
parameters for better characterizing landscape heterogeneity, including soil, land use and climate
variability. In this sense, remote sensing is often considered as a practical solution. This study aimed
to access the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on
MOHID-Land’s simulations of the soil water balance and maize state variables (LAI, canopy height,
aboveground dry biomass and yield). Data assimilation impacts on final model results were first
assessed by comparing distinct modeling approaches to measured data. Then, the uncertainty
related to assimilated LAI values was quantified on final model results using a Monte Carlo method.
While LAI assimilation improved MOHID-Land’s estimates of the soil water balance and simulations
of crop state variables during early stages, it was never sufficient to overcome the absence of a
local calibrated crop dataset. Final model estimates further showed great uncertainty for LAI
assimilated values during earlier crop stages, decreasing then with season reaching its end. Thus,
while model simulations can be improved using LAI data assimilation, additional data sources should
be considered for complementing crop parameterization
Descrição
Palavras-chave
biomass crop transpiration direct forcing leaf area index soil evaporation
Contexto Educativo
Citação
Water 2018, 10, 1367
Editora
MDPI