Abstract
In this study, we have developed a hyper-resolution land-surface forcing
dataset (temperature, pressure, humidity, wind speed, incident longwave
and shortwave radiation) from coarse resolution products using a
physically-based downscaling approach. These downscaling techniques rely
on correlations with landscape variables, such as topography,
temperature lapse rate corrections, surface roughness and land cover. A
proof-of-concept has been implemented over the Oklahoma domain, where
high-resolution observations are available for validation purposes. The
hourly NLDAS (North America Land Data Assimilation System) forcing data
at 0.125° have been downscaled to 500m resolution over the study area
during 2015. Results show that correlation coefficients between the
downscaled forcing dataset and ground observations are consistently
higher and biases are lower than the ones between the NLDAS forcing
dataset at their native resolution and ground observations. Results are
therefore encouraging as they demonstrate that the 500m forcing dataset
has a good agreement with the ground information and can be adopted to
force the land surface model for land state estimation. The Noah-MP land
surface model is then forced with both the native resolution NLDAS
dataset and the downscaled one to simulate surface and root zone soil
moisture. Model outputs are compared with in situ soil moisture
observations and SMAP (Soil Moisture Active Passive Mission) products at
different spatial resolutions. This work will result in a radical
improvement over the current state-of-the-art forcing data and will move
into the era of hyper-resolution land modeling.