A mobile system for quantifying the spatial variability of the surface energy balance: design and application
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We present a mobile device for the quantification of the small-scale (a few square meters) spatial variability in the surface energy balance components and several auxiliary variables of short-statured (<1 m) canopies. The key element of the mobile device is a handheld four-component net radiometer for the quantification of net radiation, albedo and infrared surface temperature, which is complemented with measurements of air temperature, wind speed, soil temperature and soil water content. Data are acquired by a battery-powered data logger, which is mounted on a backpack together with the auxiliary sensors. The proposed device was developed to bridge between the spatial scales of satellite/airborne remote sensing and fixed, stationary tower-based measurements with an emphasis on micrometeorological, catchment hydrological and landscape–ecological research questions. The potential of the new device is demonstrated through four selected case studies, which cover the issues of net radiation heterogeneity within the footprint of eddy covariance flux measurements due to (1) land use and (2) slope and aspect of the underlying surface, (3) controls on landscape-scale variability in soil temperature and albedo and (4) the estimation of evapotranspiration based exclusively on measurements with the mobile device.
KeywordsNet radiation Albedo Evapotranspiration Grassland Land use Topography
Different types of land surfaces differ in their R n which, through Eq. 1, determines how much energy is available for λE, H and S, which in turn critically affects the near-surface climate (e.g. Stegehuis et al. 2013; Seneviratne et al. 2006). For example, it was shown by Bonan (2008) and Bala et al. (2007) that grasslands and croplands, as opposed to forests, have a cooling effect at higher latitudes because the albedo of grasslands and croplands is typically higher, in particular when covered by snow, compared with forests, which absorb more solar energy. In contrast, in tropical regions, the difference in albedo between forests and grasslands is compensated by the cooling through the large amount of water transpired by (tropical) forests. In order to understand how past (e.g. Brovkin et al. 2006) and potential future (e.g. Bala et al. 2007; Brovkin et al. 2009) changes in land use affect the Earth’s climate, it is crucial to understand how changes in land surface properties affect R n and the partitioning into λE, H and S. For example, it was shown by Chapin et al. (2005) that warming-induced shorter periods of snow cover in the Arctic and associated trends of shrub/tree expansion are likely to cause local warming similar in magnitude to the warming expected from a doubling of atmospheric carbon dioxide concentrations.
The surface energy balance and its components can be quantified by a hierarchy of methods across spatial scales: At the largest scale, merging several satellite data streams with models allows estimating all four components of the energy balance (e.g. Diak et al. 2004; Kalma et al. 2008; Glenn et al. 2007) on a global scale. At the scale of catchments, evapotranspiration may be deduced on an annual basis by difference between precipitation and discharge (e.g. Peel et al. 2010). At the ecosystem-scale, i.e. typically a few hectares characterised by similar vegetation and soil, micrometeorological methods, such as the eddy covariance technique (Baldocchi et al. 1988; Aubinet et al. 2000), allow the direct quantification of both H and λE, with R n and S typically being estimated on/from the tower which supports the turbulence equipment (fast-response sonic anemometer and hygrometer). Within the FLUXNET network, the four terms of Eq. 1 are presently measured continuously at >400 sites globally (Baldocchi et al. 2001; Williams et al. 2012). Finally, at the plot, single plant and leaf scale, sap flux (Wilson et al. 2001), various types of chambers and lysimeters (Wohlfahrt et al. 2010a) can be used to quantify (evapo)transpiration.
In this comprehensive hierarchy of methods, it is the lower end of the microscale (Orlanski 1975), that is, spatial variability at the scale of square meters, which is presently poorly represented (e.g. Ahrends et al. 2012). Landscape variability at this spatial scale is much smaller than the typical pixel size of remote sensing-based approaches and also considerably smaller than the typical footprint of micrometeorological measurements. The only approaches suited for this spatial scale, lysimeters and ecosystem chambers, on the other hand, are generally impractical for surveying a large number of distributed samples within the footprint of eddy covariance flux measurements or in a landscape context.
We thus argue that, in micrometeorological, catchment hydrological and landscape ecological studies, there is the need for the development of approaches for spatially distributed energy balance measurements which can be applied at the lower end of the microscale and yet are portable enough to allow making a large number of spatially distributed measurements within short periods of time. To this end, we propose a mobile device which allows quantifying the small-scale (a few square meters) spatial heterogeneity of the energy balance over short-statured (<1 m) canopies. In the following, we first present the design of the mobile device, followed by four case studies which are meant to illustrate its potential and conclude with a discussion of its strengths and weaknesses, as well as an outlook on potential future developments.
Material and methods
In addition to the four components of R n (up- and down-welling short- and longwave radiation; W m–2), the data logger outputs the net radiometer body and infrared surface temperature (°C), air and soil temperature (°C), soil moisture (% volumetric soil moisture based on general calibration for mineral soil and raw mV output), wind speed (m s–1) and direction (°) as well as a digital sonic data quality flag.
The protocol at each measurement point is the following: First, soil temperature and moisture sensors are put into place. Then the operator gets into position by pointing the net radiometer horizontally (or slope-parallel; see below) towards South and waits 2 min before taking three (pseudo-)replicate measurements at the same spot. The 2-min delay accounts for the time response of the various sensors. The soil moisture sensor and the sonic anemometer have no quoted time response, while the air temperature/humidity sensor and the net radiometer have a quoted time response of <20 s (90 % response). The soil temperature sensor has a quoted response time of <80 s in air at a wind speed of 1 m s−1 (63 % response), no indications are given for response times in soil, which are likely to be longer. Following data acquisition, time and place of the measurement and environmental conditions (e.g. cloud cover) are noted in a field book, and any additional measurements are made on the plot (see case study 3 below for an example).
Results and discussion
In the following, we illustrate the potential of the EcoBot by reference to four selected case studies:
Case study 1: within eddy covariance footprint heterogeneity of R n
Case study 2: estimating slope-parallel R n
Measurements of R n are typically made horizontally, assuming a horizontal underlying surface. However, in case of measurements above sloping terrain, slope-parallel measurements are required to be able to relate R n to latent and sensible heat fluxes (Whiteman et al. 1989). Algorithms for correcting horizontal R n measurements for slope and aspect of the underlying non-horizontal surface exist, but, however, usually account only for differences between the angle of incident direct solar radiation and the surface (e.g. Matzinger et al. 2003) and, similar to case study 1, do not account for heterogeneity in slope and aspect within the flux footprint (but see Hammerle et al. 2007).
Case study 3: drivers of landscape-scale variability in soil temperature and albedo
Case study 3 is meant to illustrate the potential of EcoBot and concurrent auxiliary measurements to study landscape-scale variability in R n and its components and drivers. Briefly, the study was conducted between June and October 2011 and June 2013 in the Stubai Valley (Western Austria), in the Matscher/Mazia Valley and in the Tauferer-Ahrntal Valley (both in Northern Italy), at 51 different grassland and shrub ecosystems. The study sites covered an altitudinal range from 850 to 2,500 m asl and included abandoned areas and differently managed hay meadows and pastures. At each site, two to five replicate EcoBot measurements were taken as described above. At the same sites, the above-ground plant area index (PAI) was estimated directly based on harvesting and plant area determination and/or indirectly based on canopy light transmission measurements using a line quantum sensor as described in Wohlfahrt et al. (2001). The total above-ground biomass was quantified by harvesting the vegetation in a 0.3 × 0.3 m area. Species composition and dominance were estimated in a 2 × 2 m area based on Braun-Blanquet (1964) and the vegetation association according to Tasser et al. (2010).
Case study 4: using the EcoBot for inferring evapotranspiration
Clearly, the assumptions involved in and uncertainties associated with this approach, in particular, the crude estimation of the soil heat flux (Sauer and Horton 2005), the replacement of the aerodynamic surface temperature with the infrared surface temperature (e.g. Kustas and Norman 1996) and the calculation of the aerodynamic resistance to heat transfer (Liu et al. 2007), are likely to be significant. It is well-known that the difference between the aerodynamic and infrared surface temperature (RMSE = 1.9 K for the data shown in Fig. 7) may become substantial in situations with partial canopy cover, necessitating semi-empirical corrections of Eq. 4 (e.g. Kalma et al. 2008). The encouraging results shown in Fig. 7 may thus partially be owed to the relatively ideal circumstances, such as the high leaf area index of ca. 4 m2 m−2, within which the comparison with the eddy covariance fluxes was conducted. In addition, atmospheric conditions need to be steady and/or appropriate temporal averaging be applied to the EcoBot data for deriving meaningful energy fluxes. With these caveats in mind, we conclude that the estimation of G, H and λE based solely on EcoBot data requires further testing across a larger number of different ecosystems. At the same time, we stress that the preliminary evidence presented in Fig. 7 suggests the EcoBot to offer exciting potential for estimating the small-scale spatial variability in evapotranspiration in a landscape context, which is difficult to realise with other approaches. For example, the EcoBot may provide critical data for interpreting streamflow data and for the calibration/validation of evapotranspiration simulated by distributed hydrological models (e.g. Rigon et al. 2006) in catchment hydrological studies or for the ground validation of satellite products (Pasolli et al. 2011). In particular for ecosystems where microtopography strongly governs vegetation distribution, such as in Arctic or Alpine ecosystems (e.g. Scherrer and Körner 2011; Gamon et al. 2013), the EcoBot may offer considerable advantage over other approaches.
Conclusions and outlook
We have presented a mobile device, termed EcoBot, which allows quantifying the small-scale (a few square meters) spatial variability in the surface energy balance, its components (in particular evapotranspiration, net radiation and albedo) and several auxiliary variables (e.g. soil temperature and water content) of short-statured canopies. The proposed device was developed to bridge between the spatial scales of satellite/airborne remote sensing and fixed single-tower net radiation measurements with an emphasis on micrometeorological, catchment hydrological and landscape–ecological research questions. Due to the one-point-in-time nature of the measurements, the EcoBot will be most useful during intensive campaigns when small-scale spatial coverage is more important than long-term measurements. As illustrated in four selected case studies, the proposed device appears to offer potential for the interpretation of within-footprint heterogeneity effects on eddy covariance energy flux measurements (Figs. 2, 3, and 4), for questions related to landscape-scale spatial variability of the surface energy balance, its components and drivers (Figs. 5, 6, and 7) and thus more generally for validation of energy balance satellite products and distributed hydrological models. In particular during satellite/aerial overpasses, the EcoBot may provide an efficient means to acquire, complementary to stationary measurements, spatially distributed ground truth data.
Provided the proposed measurement protocol is followed, the EcoBot offers a reliable approach to measure a larger number of spatially distributed sampling points (possibly with the exception of soil temperature due to the relatively long time constant of the sensor). In combination with additional plant- (e.g. amount and composition of above-ground phytomass) and soil-related (e.g. soil type, colour) parameters, these measurements offer new avenues for research into the role of small-scale spatial variability of vegetation and soil for land–atmosphere coupling. The inferred distribution of R n into G, H and λE represents an even more exciting possible application of the EcoBot; however, due to the assumptions involved, it requires further testing. The preliminary comparison with direct eddy covariance flux measurements presented in Fig. 7 is however encouraging.
The EcoBot was designed for short-statured canopies, less than approximately 1 m tall, which allow a convenient operation of the net radiometer. Using a ladder, we anticipate that it would be possible to use the EcoBot for canopy heights up to around 2 m, such as larger bushes, agricultural crops or young trees. For taller canopies, such as adult forests, airborne measurements are likely to remain the only alternative. The EcoBot may however be used to quantify the spatial variability of R n in the forest understory.
The capabilities of the EcoBot may be easily augmented by adding additional sensors. One promising option would be to include a pair of down- and upward looking multi-spectral or photosynthetically active radiation (PAR) sensors. Multi-spectral sensors are available in configurations that allow calculation of frequently used vegetation indices such as photochemical reflectance index (Gamon et al. 1992) or normalised difference vegetation index (NDVI; Tucker 1979). By difference with the up- and down-welling shortwave radiation measurements, PAR sensors allow calculating a so-called broadband NDVI (Huemmrich et al. 1999). Acquisition of these additional data would further strengthen the link between Ecobot data and satellite/airborne remote sensing and provides proxies for the amount of vegetation and its photosynthetic activity (Wohlfahrt et al. 2010b; Huemmrich et al. 1999; Richardson et al. 2007; Eklundh et al. 2011).
We thank a large number of students for collecting EcoBot and auxiliary data. M. Deutschmann is thanked for assembling the EcoBot, and S. Käferböck, I. Stiperski and R. Diewald for logistical support. This work received financial support through the Austrian National Science Fund (FWF) under grant agreement P23267-B16 and the Autonomous Province of Bolzano/Bozen-South Tyrol (Division for the Promotion of Education, Universities and Research) within the frame of the HydroAlp and HiResAlp projects.
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