Parameterization of the Surface Energy Balance of a Shallow Water Table Grassland

Qianguang Tu,Peng Qin,Chunmei Cheng

doi:10.3390/w12020523

Abstract

Extending instantaneous latent heat flux to daily, monthly, or even yearly evapotranspiration (ET) is a fundamental issue in using remote sensing to estimate ET at local and regional scales. In this study, the extending parameterizations of the surface energy balance of a mid-latitude grassland with shallow water table (SWT) at diurnal and seasonal time scales are examined based on data measured by the eddy covariance system and automated weather station from Wageningen University from June 2014 to October 2018. The results show that the ratio of turbulent heat flux to available surface energy (often called budget closure rate) ranges between 0.86 and 0.93 for warm times (March to October), and between 0.59 and 0.77 for cold times (November to February the following year). The parameterization models used to approximate the surface albedo and evaporative fraction (EF) are also evaluated. Although obvious variation under clear skies during daytime are observed, the constant EF and albedo method provided an acceptable estimation of the daily scale ET with an underestimation of about 6–8% for the grassland with SWT and parameterization of diurnal correction shows little improvement in both the bias and RMSE. The progression of daily ET shows a seasonal cycle, which follows the variation of the net radiation flux. These results will be helpful for estimating ET at daily and long temporal scales based on satellite remote sensing.

Highlights

There is increased interest in evapotranspiration (ET) or consumptive use
This exculpation is made empirically to avoid the dates in which the surface energy balance is complicated by precipitation
This study mainly investigated the parameterizations of evaporative fraction (EF) and albedo to account for the diurnal and seasonal variation of ET estimation

Summary

Introduction

ET is an important parameter in the process of surface water circulation and energy balance. Accurate estimation of surface ET is essential to better understand the global climate change, hydrometeorological processes, ecological environmental issues, agricultural irrigation, and watershed management [1]. ET can be estimated through field measurement using eddy covariance (EC), lysimeters, and the Bowen ratio [2,3,4]. The traditional field ET observations are usually limited in a few point locations and not sufficient to meet requirements of the regional scale applications. Remote sensing provides a unique opportunity to retrieve a variety of surface parameters, such as reflectance and temperature that can be used to estimate the global and regional ET [5]. A lot of remote sensing models have been developed to predict ET, and they can be grouped into four main categories: empirical direct, residual, inference, and deterministic methods [6]

Methods

Results

Conclusion