Abstract
Mesoscale regional climate models (RCMs), the primary tool for climate predictions, have recently increased in sophistication and are being run at increasingly higher resolutions to be also used in climate impact studies on ecosystems, particularly in agricultural crops. As satellite remote sensing observations of the earth terrestrial surface become available for assimilation in RCMs, it is possible to incorporate complex land surface processes, such as dynamics of state variables for hydrologic, agricultural and ecologic systems at the smaller scales. This study focuses on parameterization of vegetation characteristics specifically designed for high resolution RCM applications using various remote sensing products, such as Advanced Very High Resolution Radiometer (AVHRR), Système Pour l’Observation de la Terre-VEGETATION (SPOT-VGT) and Moderate Resolution Imaging Spectroradiometer (MODIS). The primary vegetative parameters, such as land surface characteristics (LCC), fractional vegetation cover (FVC), leaf area index (LAI) and surface albedo localization factors (SALF), are currently presented over the Nakdong River Watershed domain, Korea, based on 1-km remote sensing satellite data by using the Geographic Information System (GIS) software application tools. For future high resolution RCM modeling efforts on climate-crop interactions, this study has constructed the deriving parameters, such as FVC and SALF, following the existing methods and proposed the new interpolation methods to fill missing data with combining the regression equation and the time series trend function for time-variant parameters, such as LAI and NDVI data at 1-km scale.
Highlights
Mesoscale regional climate models (RCMs) are recognized as an essential and powerful tool to address scientific information associated with climate variability, changes and impacts at local and regional scales [1,2,3]
Both global and regional climate models have recently increased in sophistication and are being run at the increasingly higher resolution, which is supported by increases in the availability of remote sensing observations, as well as computational power
The vegetative surface boundary conditions (SBCs) consist of the land cover category (LCC), the fractional vegetation cover (FVC), the leaf area index (LAI) and the surface albedo localization factors (SALF) to mainly determine contribution partitioning between bare soil and vegetation for fluxes crucial to land-atmosphere interactions
Summary
Mesoscale regional climate models (RCMs) are recognized as an essential and powerful tool to address scientific information associated with climate variability, changes and impacts at local and regional scales [1,2,3] Both global and regional climate models have recently increased in sophistication and are being run at the increasingly higher resolution, which is supported by increases in the availability of remote sensing observations, as well as computational power. The remote sensing observations provided in various map projections and different data formats often contain missing values or inconsistencies between variables It is, significant and required for labor-intensive efforts to convert the vast and various raw data sets onto the RCM-specific grid mesh and model input data format. To assess impacts of the new SBCs treatments, future studies are required to perform the RCM climate sensitivity to these SBCs constructed at 1-km scale
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