From Standard Weather Stations to Virtual Micro-Meteorological Towers in Ungauged Sites: Modeling Tool for Surface Energy Fluxes, Evapotranspiration, Soil Temperature, and Soil Moisture Estimations

Abstract

One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available.

Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images

“…In a crop field, evapotranspiration comprises evaporation (E) from the soil and intercepted canopy water and transpiration (T) from plants [66,99]. During the crop growing season, transpiration usually exceeds evaporation [100][101][102].…”

“…Moreover, the number of good-quality observations from satellite optical sensors would decrease if frequent clouds occurred. In addition, EC flux tower estimates are influenced by the surrounding fields as the footprint changes with the season, weather conditions, vegetation height, and vegetation cover [66,106,148]. The sources of uncertainty from the EC tower increase the challenges of validating GPP estimates, and the sources of error in the system increase based on the condition of the instruments and sensors.…”

“…However, the practical application of T and ET data at the field level faces multiple challenges, including the course spatial resolution of current remote-sensing products [59,62], the high costs associated with field-scale ground T data, and the intricacies of ET partitioning methods [63]. Multiple models have successfully integrated the Penman-Monteith (PM) method [64,65] with successful results [66], but the scalability limitations and structural uncertainties in the vegetation phenology complicate its application in commercial crops [67,68].…”