Estimating Evapotranspiration of Mediterranean Oak Savanna at Multiple Temporal and Spatial Resolutions. Implications for Water Resources Management

Elisabet Carpintero,Feng Gao,William P Kustas,Christopher Hain,Martha C Anderson,María P González-Dugo,Ana Andreu

doi:10.3390/rs13183701

Abstract

Mediterranean oak savanna is composed of a mixture of scattered oak trees, crops, pasture, and shrubs. It is the most widespread agroforestry landscape in Europe, and its conservation faces multiple threats including water scarcity, which has been exacerbated by global warming and greater climate variability. Evapotranspiration (ET) can be used as a proxy of the vegetation water status and response to water shortage conditions, providing relevant information about the ecosystem stability and its hydrological dynamics. This study evaluates a framework to estimate ET at multiple spatial and temporal scales and applies it to the monitoring of the oak savanna vegetation water consumption for the years 2013–2015. We used a remote sensing-based energy balance model (ALEXI/DisALEXI approach), and the STARFM data fusion technique to provide daily ET estimates at 30 m resolution. The results showed that modeled energy balance components compared well to ground measurements collected by an eddy covariance system, with root mean square error (RMSE) values ranging between 0.60 and 2.18 MJ m−2 d−1, depending on the sensor dataset (MODIS or Landsat) and the flux. The daily 30 m ET series generated by STARFM presented an RMSE value of 0.67 mm d−1, which yielded a slight improvement compared to using MODIS resolution or more simple interpolation approaches with Landsat. However, the major advantage of the high spatio-temporal resolution was found in the analysis of ET dynamics over different vegetation patches that shape the landscape structure and create different microclimates. Fine-scale ET maps (30 m, daily) provide key information difficult to detect at a coarser spatial resolution over heterogeneous landscapes and may assist management decisions at the field and farm scale.

Highlights

The modeled fluxes compared reasonably well to field observations, with root mean square error (RMSE) values ranging between 0.60 and 2.18 MJ m−2 d−1 depending on the sensor resolution and the flux component, a similar range to those obtained by other authors over complex landscapes
30 m ET series from STARFM model obtained a RMSE value of 0.67 mm d−1, which is considered an acceptable error for dehesa management purposes
The ET time series generated by both approaches, DisALEXI and STARFM with different resolutions, showed an annual bimodal behavior of the vegetation water consumption over the dehesa structure, with two marked peaks of different magnitudes

Summary

Introduction

In water-controlled ecosystems with limited water resources, soil moisture dynamics play a central role in the existence and spatial distribution of the different vegetation functional types [1,2]. The discontinuities in the functioning of these ecosystems are related to an alternation in the dry and wet periods [3]. A feedback relationship is observed in these environments, where the vegetation water consumption strongly conditions the hydrological balance of the system, while plants are impacted by water stress situations. Higher variability in climate patterns coupled with an increase in water demand due to anthropogenic factors is expected to cause a considerable reduction in the quality 4.0/).

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Discussion

Conclusion