Seasonal Adaptation of the Thermal-Based Two-Source Energy Balance Model for Estimating Evapotranspiration in a Semiarid Tree-Grass Ecosystem

Vicente Burchard-Levine,M Pilar Martín,Tarek S El-Madany,Arnaud Carrara,Héctor Nieto,Mirco Migliavacca,Oscar Perez-Priego,David Riaño

doi:10.3390/rs12060904

Abstract

The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m−2 to −4 and 59 W m−2, respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m−2. The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models.

Highlights

Land surface models, mathematical representations of surface-atmospheric exchanges, are important tools to understand fluxes of energy and mass, which drive climatic and Earth system processes [1]
They are located relatively close to each other (< 650 m, Figure 1) as part of a large scale manipulation experiment, where nitrogen was added to the northern tower (NT), nitrogen and phosphorus were added to the southern tower (NPT) and the central tower kept as a control (CT) [28,43]
A local leaf area index (LAI) sensitivity analyses (SA) in Li et al [39] added +-20% deviation to LAI and investigated the associated relative H change in two-source energy balance (TSEB). Their associated ~3–8% bias in modeled H is similar to the results presented here, with a 20% change in LAI being associated with a median H bias of 6.1% with TSEB-DF

Summary

Introduction

Mathematical representations of surface-atmospheric exchanges, are important tools to understand fluxes of energy and mass, which drive climatic and Earth system processes [1]. These models provide vital information to understand the response of ecosystems to climate and environmental changes, and monitor Earth system dynamics [2,3]. Latent heat flux (LE), the aggregated water flux consisting of evaporation from the soil and other wet surfaces (LEs) and plant transpiration (LEc), has recently been the subject of extensive research [4] due to its importance in evaluating ecosystem functional properties. LE is highly variable and dynamic, making remote sensing techniques notably useful to predict it at different temporal and spatial scales

Methods

Results

Conclusion