Reliable estimation of evapotranspiration on agricultural fields predicted by the Priestley–Taylor model using soil moisture data from ground and remote sensing observations compared with the Common Land Model
Evapotranspiration (ET) is a crucial factor in understanding the hydrological cycle and is essential to many applications in hydrology, ecology and water resources management. However, reliable ET measurements and predictions for a range of temporal and spatial scales are difficult.
This study focused on the comparison of ET estimates using a relatively simple model, the Priestley–Taylor (P-T) approach, and the physically based Common Land Model (CLM) using ground and remotely sensed soil moisture data as input. The results from both models were compared directly
with hourly eddy covariance measurements at two agricultural field sites during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in the corn soybean production region in the Upper Midwest, USA. The P-T model showed a significant overestimation of the potential ET compared to
the measurements, with a root mean square error (RMSE) between 115 and 130 W m–2. Actual ET was better predicted by the CLM, with the RMSE ranging between 50 and 75 W m–2. However, actual ET from the P-T model constrained with a soil moisture dependency parameterization
showed improved results when compared to the measurements, with a significantly reduced bias and RMSE values between 60 and 65 W m–2. This study suggests that even with a simple semi-empirical ET model, similar performance in estimating actual ET for agricultural crops compared
to more complex land surface–atmosphere models (i.e. the CLM) can be achieved when constrained with the soil moisture function. This suggests that remote sensing soil moisture estimates from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and others
such as the Soil Moisture and Ocean Salinity (SMOS) mission may be effective alternatives under certain environmental conditions for estimating actual ET of agricultural crops using a fairly simple algorithm.
Document Type: Research Article
Affiliations: 1: Department of Civil and Environmental Engineering,Hanyang University, Seoul133-791, Korea 2: Department of Civil and Environmental Engineering,Hanyang University, Ansan426-791, Korea 3: U.S. Department of Agriculture, Agricultural Research Service,Hydrology and Remote Sensing Laboratory, BeltsvilleMD20705, USA
Publication date: 20 August 2011
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content