Читать книгу Global Drought and Flood - Группа авторов - Страница 31

Recognition of the natural evaporation processes might have started about 500 BCE according to the chronological sketch in Brutsaert (1982), but the search for understanding the evaporation and plant transpiration processes may not have begun until a couple of centuries ago. Early efforts of estimating evaporation or ET rates include empirically relating potential evaporation rate to factors such as near‐surface vapor pressure deficit and a ratio of the monthly daily air temperature over a heat index that depends on the 12‐month mean air temperature (Thornthwaite, 1948). For open water or surfaces without water limitation, Penman (1948) derived the famous Penman equation for estimating potential or optimal evaporation rate from shortwave radiation, vapor pressure deficit, daily mean temperature, and wind speed. Introducing a vegetation canopy or surface conductance into the derivation of the Penman equation, Monteith (1965) developed the widely used Penman–Monteith equation to estimate actual surface ET that depends on net shortwave radiation, ground heat flux, vapor pressure deficit, daily air temperature, wind speed, aerodynamic conductance and canopy conductance. By removing the aerodynamic term and adding an empirical surface related factor alpha to the Penman–Monteith equation, Priestley and Taylor (1972) developed the well‐known Priestley–Taylor equation for approximating ET rate. Many variations of the above equations and other ET observation and estimation approaches have been developed and used for ET rates of different land surface at different spatial and temporal scales (see review in Wang & Dickinson, 2012).

With the emergence of remote sensing technology, many studies have started to estimate ET rates from the remotely sensed, spatially distributed observations of land surface properties in recent decades. Land surface temperature data indicate the state of the land surface and the partitioning of the available energy (the net radiation minus the soil heat flux) into sensible heat and latent heat. Satellite remote sensing is the only technology able to provide radiometric surface temperature observations at the global scale (Kustas & Norman, 1996). Optical satellite sensors can provide information on land surface type and vegetation dynamics, which is required by some advanced algorithms used to estimate ET, such as the Penman–Monteith equation (Monteith, 1965). The effectiveness, efficiency, and economic advantage of obtaining globally spatial distributed input data from satellite remote sensing have led to active international research activities for ET estimation (Sellers et al., 1990). Price (1980) started to use NOAA Advanced Very High Resolution Radiometer (AVHRR) day–night pairs of land surface temperature and daily average climate data with the daily integrated land surface energy balance equation to estimate daily fluxes including ET. Price (1982) further refined his method and obtained ET with reasonable accuracy when comparing estimates based on meteorological data and pan evaporation data. Assuming that the instantaneous differences between radiometric land surface temperature and air temperature is directly related to daily ET, R. D. Jackson et al. (1977) derived a simple relationship for field scale ET estimation. Similar approaches are then widely used for mapping daily ET over large areas from observations of land surface temperature (Allen et al., 2005; Carlson & Ripley, 1997; Courault et al., 1994; Lagouarde, 1991). To estimate actual ET, satellite observed vegetation indices are widely used to compute a reduction factor (e.g., the Priestley–Taylor parameter alpha) from potential ET based on ground meteorological forcing data (e.g., Allen et al., 2005; Kalluri et al., 1998; Neale et al., 2005).

Kustas and Norman (1996) reviewed these approaches and classified them into three general categories: empirical or semiempirical/statistical, physical/analytical, and numerical modeling approaches. More than a dozen ET models and studies of these categories were evaluated in Kustas and Norman (1996). Courault et al. (2005) reviewed four categories of ET estimation approaches developed from local to regional spatial scales primarily for water management and agricultural applications: (a) direct empirical relationship, (b) energy budget residual, (c) deterministic complex model, and (d) vegetation index approaches.

Vinukollu et al. (2011) have evaluated three process‐based approaches for global estimation of ET for climate research: Surface Energy Balance System (SEBS), Penman–Monteith algorithm (PM), and the Priestley–Taylor algorithm (PT). Using only remote‐sensing‐based data for inputs and forcing, they generated ET data products from each of these three approaches for the years 2003–2006 and compared them with measurements in situ over various watersheds around the world. The SEBS type model they used is the single source energy balance model evaluated in Su et al. (2005). In their evaluation the PM type algorithm they used is that by Mu et al. (2007) to generate a time series of global ET data product based on Moderate‐Resolution Imaging Spectroradiometer (MODIS) observations. The PT type algorithm they evaluated is that by Fisher et al. (2008). With these data products, Vinukollu et al. (2011)showed that the average of the three model ET estimates for 2003–2006 well represented the seasonal cycle over the continents, and the ET suppression during major droughts in Europe, Australia, and the Amazon were identified.

Among the various satellite‐based ET estimation techniques, the Surface Energy Balance Algorithms for Land (SEBAL; Bastiaanssen, Menenti, et al., 1998; Bastiaanssen, Pelgrum, et al., 1998)_and its enhancement, Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC; Allen et al., 2007, 2011), have been widely adopted and routinely used to monitor irrigation, determine drought, and estimate consumptive use in agricultural areas around the world. The SEBAL and METRIC techniques are widely used with Landsat data to estimate ET at the field level in agricultural areas. Both these techniques determine ET as a residual of sensible fluxes in net radiation. The novelty of these techniques is that several key parameters that drive energy fluxes, such as the gradient between surface temperature and air temperature and surface resistance to sensible and latent heat transfer, are determined directly from the imagery itself while compensating for uncertainties in measurements, such as satellite estimates of surface temperature, the determination of surface albedo, and ground‐based measurements of windspeed fields. These two approaches have been widely validated and routinely used with Landsat data, which has 30 m spatial resolution over agricultural areas where the assumptions of micrometeorology incorporated in to the SEBAL and METRIC techniques are valid. Nevertheless, extending these techniques to coarse resolution satellite imagery at continental scales is yet to be demonstrated, as the land cover types are seldom homogeneous within a pixel and parametrization schemes for such heterogeneous environments, where both the surface types and the atmosphere vary substantially within a single satellite image that extends thousands of kilometers, are difficult to establish.

Using ground flux tower measurements of ET from irrigated cropland in Oregon, Tang et al. (2009) found that a variation of the MODIS ET product algorithm, based on near real time MODIS and NOAA GOES data products of land cover, vegetation indices, land surface temperature, albedo, and surface radiation budget, may estimate instantaneous and daily ET with biases less than 10% and 15%, respectively. For seasonal ET, they found the MODIS‐based ET could underestimate while the METRIC‐based algorithm (Allen et al., 2007) may overestimate.

Based on the above historical sketch of ET remote sensing studies, it may be concluded that satellite observations of land surface temperature, solar radiation, and vegetation status are the input of most ET algorithms or models (Courault et al., 2005; Kustas & Norman, 1996; Vinukollu et al., 2011, Wang & Dickinson, 2012). The science of these ET remote sensing models has significantly advanced with the recognition of the difference between radiometric temperature observed from thermal infrared sensors and the aerodynamic temperature of the land surface required in the calculation of sensible heat flux using the energy balance equation (Kustas, 1990; Norman & Becker, 1995). Zhan et al. (1996) showed that the models containing the least empiricism to account for the differences between the two temperatures gave the best results of the sensible and latent heat flux estimations. Among the dozens of ET remote sensing models, the two‐source energy balance (TSEB) model, known as the Atmosphere–Land Exchange Inversion (ALEXI) model with the least empiricism (Anderson et al., 1997, 2007, 2011; Kustas & Norman, 1997), has addressed the most issues associated with remote sensing of ET (Kustas & Norman, 1996). Many intercomparison studies have shown that the ET estimates from the ALEXI model demonstrated its reliability and robustness (Anderson et al., 2013; Fang et al., 2016; Hain et al., 2011). Based on the demonstrated advantages, we selected the ALEXI model to develop an operational GET‐D product system in NOAA NESDIS to provide users with daily ET estimates and multiweekly drought maps for North America. The following sections introduce how the satellite remote sensing observations can be used to estimate daily ET with the ALEXI model and how the ET estimates can be used for drought monitoring. Global applications of the ALEXI model will be discussed in the final session of this chapter.