Evaluation of the Operational Simplified Surface Energy Balance Model for Pastureland Evapotranspiration Mapping and Drought Monitoring in North Central Kentucky

Maheteme Gebremedhin,Ibukun Amusan,Jeremy Sandifer,Buddhi Gyawali,Ian Ries,Gabriel B Senay,Martin Matisoff

doi:10.1155/2020/1386468

Abstract

The use of remotely sensed evapotranspiration (ET) for field applications in drought monitoring and assessment is gaining momentum, but meeting this need has been hampered by the absence of extensive ground-based measurement stations for ground validation across agricultural zones and natural landscapes. This is particularly crucial for regions more prone to recurring droughts with limited ground monitoring stations. A three-year (2016–2018) flux ET dataset from a pastureland in north central Kentucky was used to validate the Operational Simplified Surface Energy Balance (SSEBop) model at monthly and annual scales. Flux and SSEBop ET track each other in a consistent manner in response to seasonal changes. The mean bias error (MBE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were 5.47, 21.49 mm mon−1, 30.94%, and 0.87, respectively. The model consistently underestimated ET values during winter months and overestimated them during summer months. SSEBop’s monthly ET anomaly maps show spatial ET distribution and its accurate representation. This is particularly important in areas where detailed surface meteorological and hydrological data are limited. Overall, the model estimated monthly ET magnitude satisfactorily and captured it seasonally. The SSEBop’s functionality for remote ET estimation and anomaly detection, if properly coupled with ground measurements, can significantly enhance SSEBop’s ability to monitor drought occurrence and prevalence quickly and accurately.

Highlights

Evapotranspiration (ET), the turbulent transfer of water from the ground and plant surfaces, is a key component of the hydrologic cycle, which is responsible for returning 60–90% of precipitation (P) to the atmosphere[1,2,3]
We found month-to-month bias (∼5 mm/month) that led to a pattern of overestimation in the warmer months or underestimation in the cooler months
Noted differences between the observed and estimated values during the wet and dry months may stem from the key assumption the model makes in that differences in land surface temperature (LST) over a homogeneous landscape are primarily due to differences in vegetation and functional differences in water use. e model ignores the contribution of albedo and ground heat flux as part of the ET estimation

Summary

Introduction

Evapotranspiration (ET), the turbulent transfer of water from the ground (evaporation; E) and plant surfaces (transpiration; T), is a key component of the hydrologic cycle, which is responsible for returning 60–90% of precipitation (P) to the atmosphere[1,2,3]. ET is the second largest component in the terrestrial water balance (ET = P-R-S-I, where P = precipitation, R = runoff, S = soil storage, and I = infiltration) and is the single most important predictor of seasonal crop water consumptive use drought prevalence. ET estimation and drought monitoring are determined by the availability and proliferation of remotely sensed data. The need for improved methods for monitoring and modeling the water cycle has fueled interest in the rapid, widespread use of remote-based ET data. Traditional land-based ET measurements have largely relied on in situ ground observation methods, such as Advances in Meteorology lysimtery, energy balance, Bowen ratio (FR), eddy covariance (EC), scintillometry, and soil water balance [8,9,10,11,12]

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