Evaluating four gap-filling methods for eddy covariance measurements of evapotranspiration over hilly crop fields

Nissaf Boudhina,Nétij Ben Mechlia,Frédéric Jacob,Moncef Masmoudi,Rim Zitouna-Chebbi,Insaf Mekki,Laurent Prévot

doi:10.5194/gi-7-151-2018

Abstract

Abstract. Estimating evapotranspiration in hilly watersheds is paramount for managing water resources, especially in semiarid/subhumid regions. The eddy covariance (EC) technique allows continuous measurements of latent heat flux (LE). However, time series of EC measurements often experience large portions of missing data because of instrumental malfunctions or quality filtering. Existing gap-filling methods are questionable over hilly crop fields because of changes in airflow inclination and subsequent aerodynamic properties. We evaluated the performances of different gap-filling methods before and after tailoring to conditions of hilly crop fields. The tailoring consisted of splitting the LE time series beforehand on the basis of upslope and downslope winds. The experiment was setup within an agricultural hilly watershed in northeastern Tunisia. EC measurements were collected throughout the growth cycle of three wheat crops, two of them located in adjacent fields on opposite hillslopes, and the third one located in a flat field. We considered four gap-filling methods: the REddyProc method, the linear regression between LE and net radiation (Rn), the multi-linear regression of LE against the other energy fluxes, and the use of evaporative fraction (EF). Regardless of the method, the splitting of the LE time series did not impact the gap-filling rate, and it might improve the accuracies on LE retrievals in some cases. Regardless of the method, the obtained accuracies on LE estimates after gap filling were close to instrumental accuracies, and they were comparable to those reported in previous studies over flat and mountainous terrains. Overall, REddyProc was the most appropriate method, for both gap-filling rate and retrieval accuracy. Thus, it seems possible to conduct gap filling for LE time series collected over hilly crop fields, provided the LE time series are split beforehand on the basis of upslope–downslope winds. Future works should address consecutive vegetation growth cycles for a larger panel of conditions in terms of climate, vegetation, and water status.

Highlights

Watersheds are widespread within coastal areas around the Mediterranean basin, as well as in eastern Africa, India, and China
Amongst the existing latent heat flux (LE) gap-filling methods listed in the Introduction (Table 1), we selected the commonly used REddyProc method that relies on lookup tables (LUTs) and mean diurnal variation (MDV) to fill missing flux data with those collected under similar meteorological conditions or with averaged values over adjacent days
To REddyProc, the Linear regression method (LE–Rn), Multiple linear regression (MLR), and Evaporative fraction (EF) methods were not able to fill gaps induced by total shutdowns of the flux stations

Summary

Introduction

Watersheds are widespread within coastal areas around the Mediterranean basin, as well as in eastern Africa, India, and China They experience agricultural intensification since hilly topographies allow water-harvesting techniques that compensate for precipitation shortage (Mekki et al, 2006). Their fragility is likely to increase with climate change and human pressure, especially as water scarcity already limits crop production. In this context, understanding evapotranspiration processes within hilly watersheds is paramount for the design of decision support tools devoted to water resource management (McVicar et al, 2007). Evapotranspiration (or latent heat flux, LE) directly drives biomass production through intertwining with photosynthesis (Olioso et al, 2005), and it is a major term of water balance, up to twothirds of the annual water balance for semiarid/subhumid Mediterranean climates (Montes et al, 2014; Moussa et al, 2007; Yang et al, 2014).

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Discussion

Conclusion