Improving hyper resolution soil moisture estimation



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The need for improved accuracy of terrestrial hydrological variables across different landscapes is driven by the development of hyper-resolution land surface modeling. The goal of this work is to propose a new framework to estimate surface and root zone soil moisture at resolutions that are useful for decision making and water resources management. In order to achieve this goal, a hyper-resolution atmospheric forcing dataset (temperature, pressure, humidity, wind speed, incident longwave and shortwave radiation) is developed 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. Hourly NLDAS-2 (North America Land Data Assimilation System) atmospheric variables at 0.125° have been downscaled to 500 m 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-2 forcing dataset at their native resolution and ground observations. Results are therefore encouraging as they demonstrate that the 500 m forcing dataset has a good agreement with ground-based information and can be adapted to force a land surface model for soil moisture estimation. A land surface model is then forced with both the native resolution NLDAS-2 dataset and the downscaled one to simulate surface and root zone soil moisture. Model outputs are compared with in situ soil moisture observations at different spatial resolutions. Results show that the hyper-resolution simulation is able to bring modeled surface and root zone soil moisture closer to in situ observations. This is particularly evident in drier than usual cases, due to the improved ability of the downscaled precipitation to detect missed events and no-rain cases. In summary, finer resolution forcings have the potential to improve simulations of soil moisture, and the resolution of precipitation plays a critical role in improving the time series of soil moisture standard-normal deviates. Then, a land data assimilation system is adopted to merge the satellite soil moisture products into the land surface model. Satellite products offer a unique look at global soil moisture variability and have the potential to correct model biases. This work offers a radical improvement over current state-of-the-art forcing data and soil moisture estimates and will move us into the era of hyper-resolution land modeling.