Abstract
Abstract. Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distributed hydrological model, while the energy-balance approach is often used with remotely sensed observations of, for example, the land surface temperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case of the MIKE SHE ET module). However, all three of them use the same ancillary data (meteorological measurements, land cover type and leaf area index, etc.) and produce output at similar spatial resolution (1 km for the TSEB models, 500 m for MIKE SHE). The comparison is performed on the spatial patterns of the fluxes present within the catchment area as well as on temporal patterns on the whole catchment scale in 8-year long time series. The results show that the spatial patterns of latent heat flux produced by the remote sensing models are more similar to each other than to the fluxes produced by MIKE SHE. The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r ≈ 0.8). This indicates potential benefits to the hydrological modelling community of integrating spatial information derived through remote sensing methodology (contained in the ET maps derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.
Highlights
Evapotranspiration (ET) acts as a coupling between two of the most important natural processes affecting the land surface: the water exchange and the energy exchange (Campbell and Norman, 1998)
The results of pixel-to-pixel comparisons of fluxes between the three model pairs are presented in Figs. 2 (MIKE SHE– DTD), 3 (MIKE SHE–two-source energy-balance (TSEB)-2ART), and 4 (TSEB-2ART– DTD) with statistics summarized in Table 2 and described for each model pair in the subsections below
The differences in the turbulent fluxes cannot be caused mainly by differences in the parametrization of the available energy since in that case the correlation reaches 0.97. This was expected since the two models use the same incoming solar radiation forcing and the same albedo maps so the majority of the 35 W m−2 root mean square difference (RMSD) (55 % of mean square difference (MSD)) is systematic and caused by the differences in the net longwave radiation estimation due to different land surface temperature (LST), with DTD using MODIS LST and MIKE SHE the modelled LST from the SW-ET module, and by the ground heat flux calculations
Summary
Evapotranspiration (ET) acts as a coupling between two of the most important natural processes affecting the land surface: the water (mass) exchange and the energy exchange (Campbell and Norman, 1998). At the same time the knowledge of both the magnitude of water loss from the ground through evapotranspiration and spatial distribution of this flux has many practical applications, such as in agri- and aqua-culture, water resource management or drought monitoring (Anderson et al, 2012). This has led to an active interest from the research community in the spatially distributed modelling of evapotranspiration and to the development of a number of different methodologies. Two of the most common approaches are (1) the modelling of land surface energy fluxes, mostly with the use of land surface temperature (LST) maps derived from remote sensing observations, and (2) distributed physically based hydrological models.
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