Potential evaporation estimation method and data sets 70

Evaporation is one of the most important fluxes in the hydrological cycle. Recently, there has been a wide interest in estimating evaporation fluxes, on both continental and global scales, for a variety of purposes(Mueller et al., 2011; 2013; Marshall et al., 2013). According to Yates and Strzepe (1994), potential evapotranspiration (PET) is a process by which water is

extracted from the soil column. The data represents the atmospheric demand for water from the Earth’s surface as a function of: solar radiation; air temperature; wind; humidity and atmospheric pressure and factors like soil moisture availability and vegetation type. Actual evapotranspiration (AET) depends on the water present in the soil column. Literature reviews of regional to global scale PET modelling techniques can be found in Diak et al. (2004), Glenn

et al. (2007), Jimenez et al. (2011) and Mueller et al. (2011). Among these techniques, remote

sensing methods have been gaining popularity, because they do not suffer from the same scale dependencies and can readily be driven by global scale data (Oudin et al., 2005; Miralles

et al., 2011a; b; Jimenez et al., 2011).

Remote sensing based modelling techniques use near real-time visible, near infrared, and/or thermal sensor and, typically, certain amounts/types of meteorological data to estimate PET as a residual of the energy balance. The vegetation fraction, the primary control factor for PET, can be updated with readily available remote sensing data; meteorological forcing can be estimated from ground, meteorological satellite, and surface climate re-analysis data. Operational Land Surface Models (LSMs), like remote sensing based methods, provide near real-time continuous and global estimates of PET using process based techniques driven by assimilated ground, satellite and surface climate reanalysis data (Rodell et al., 2004).

Because of the above, PET data (produced by remote sensing based modelling techniques) were used as input for Geospatial Stream Flow Modelling (GeoSFM) (Verdin and Klaver, 2002). The method consists of solving the Penman–Monteith model (Verdin and Klaver, 2002), which uses GIS routines to ingest grids of input variables – produced by NOAA’s Global Data Assimilation System on a 11 grid for the entire globe. The dataset is compiled by using output fields from NOAA’s Global Data Assimilation System (GDAS) as inputs into the Penman-Monteith equation (Equation 3.1); (Asante et al., 2008; Shrestha et al., 2008). NOAA’s output parameters include: air temperature; fluxes of long-/short-wave radiation; atmospheric pressure; relative humidity and wind speed. The approach assumes: a hypothetical reference crop with an assumed crop height of 0.12 m; a fixed surface resistance of 70 s m-1 _{and an albedo of 0.23 (Asante et al., 2008). The computation is performed at an}

hourly interval and aggregated to obtain a daily value of PET. However, the input fields remain constant for at least 6 hours during the computation when applying the Penman-Monteith equation (Equation 3.1) because GDAS data are updated every 6 hours.

∑

∆ . ∗ 0.408∆ Equation 3.1

where PET is the potential evapotranspiration [mm day-1_{]; ∆ is the slope of the saturation}

vapour pressure [kPa o_C-1_{]; γ is the psychrometric constant [kPa} o_C-1_{], µ2 is the wind speed at}

flux [MJ m-2 _dyr-1_{]; T is the air temperature [}o_{C]; es is the saturation vapour pressure [kPa}o_C-1_]

and ea is the actual vapour pressure [kPaoC-1].

The radiation and heat fluxes are generated by the GDAS model; the vapour pressure terms (γ, es and ea) are computed from GDAS temperature and humidity fields. Wind fields computed

by GDAS at 10 m heights are downscaled to obtain 2 m wind fields for use in the evapotranspiration computations. The resulting product has a spatial resolution of 1◦ _{by 1}◦ _and

has a global spatial extent in Geographic projection (and is available for downloading by users from: http://earlywarning.usgs.gov/fews/pet/).

However, the accurate estimation of these parameters on large scales has always been a difficult (Verdin and Klaver, 2002). Direct measurements of evaporation are only possible over small regions – e.g. using flux towers – and are limited to only a few sites, particularly in certain developed regions (Oudin et al., 2005; Marshall et al., 2013). Conversely, most of the existing global products are verified only in those particular regions with available (and reliable) data – generally in North America and Europe (Alton et al., 2009; Zhang et al., 2010; Miralles et al., 2011b).

In developing countries, taking for example the Zambezi Basin, the availability and accessibility of the potential evapotranspiration data is far worse than that for the rainfall data – even recognising that PET is the second most important component of the water balance in the Zambezi River Basin after rainfall (Mazvimavi, 2003). Surface runoff (a dependent variable) from a river basin can be regarded as the by-product of two large processes: precipitation and evapotranspiration (independent variables). Using the USGS PET data, the Mean Annual Evaporation (MAE) in the Zambezi River Basin increases generally from West to South and ranges from 1 200 mm yr-1_{, to 1 700 mm yr}-1 _{(Figure 3.11).}

Figure 3.11: Spatial distribution of mean annual evaporation in the study area (accessed from: http://earlywarning.usgs.gov/fews/africa/)

3.4.3.2. Potential evaporation data sets

Potential Evaporation (PET) is a measure of the ability of the atmosphere to remove water through Evapo-Transpiration (ET) processes. The FAO introduced the definition of PET as the ET of a reference crop under optimal conditions, having the characteristics of: well-watered grass, with an assumed height of 12 centimetres; a fixed surface resistance of 70 seconds per meter and an albedo of 0.23 (Allen et al., 1998). Among several equations to estimate PET, a FAO application of the Penman-Monteith equation (Allen et al., 1998), is currently widely considered as the standard method (Walter et al., 2000). The Penman-Monteith method is predominately a physically based approach, which can be used globally because it does not require estimations of additional site-specific parameters. However, a major drawback of the FAO-PM method is its relatively high need for specific data for a variety of parameters (i.e. wind speed, relative humidity, solar radiation, etc.). These parameters are reliably observed by a limited number of meteorological stations around the globe, and are especially lacking in developing countries (Verdin and Klaver, 2002, Droogers and Allen, 2002). Table 3.3 summarises the sources of evaporation data available globally. All these global products can be extracted for Africa at a daily temporal resolution, with the exception of the MOD16 product, (a monthly product) (Mu et al., 2011).

Table 3.3: An example of available global evaporation products, listing the providers of the datasets and their spatial coverage and lengths of data

Product Provider Input precipitation data Potential evaporation- method Spatial resolution Time periods of data Reference PCR- GlobWB PCR-

GLOBWB ERAI + GPCP Hargreaves 0.50°

1 Jan 1979-31 Dec 2010

van Beek and Bierkens, 2009 PCR-PM PCR-_GLOBWB ERAI + GPCP Penman-_Monteith 0.50° 1 Jan 1979-31 _{Dec 2010} Hargreaves and _{Allen, 2003} PCR-

TRMM

PCR- GLOBWB

TRMM 3B42

v6 Hargreaves 0.50° Since 1 Jan 1998 Thiemig et al., 2012 PCR-Irrig PCR-_GLOBWB ERAI + GPCP Hargreaves 0.50° 1 Jan 1979-31 _{Dec 2010} Van Beek , 2011 ERAI ECMWF ERAI No PE Input 0.70° 1 Jan 1979-near-_real-time Dee et al., 2011 ERAL ECMWF ERAI+GPCP No PE Input 0.70° 1 Jan 1979-31 _{Dec 2010} Balsamo et al., 2012 MOD16 University _{of Montana} NASA's GMO Penman-_Monteith 1.00° Since 1 Jan 2000 Mu et al., 2011, ₂₀₀₇ GLEAM VU _Amsterdam PERSIANN Priestley and _Taylor 0.25° Since 1 Jan 1998 Miralles et al., _2011a,b USGS-

PET USGS CPC-RFE Penman-

Monteith 1° Since 1 Jan 1998

Verdin and Klaver, 2002

From this information, it can be seen that the potential evaporation data is primarily derived using the Penman–Monteith and Hargreaves equations. A study conducted by Trambauer et

al. (2014), aiming at comparing different evaporation estimates over the African continent,

equations resulted in very similar values throughout the continent. The small differences between the two methods are because of their different formulations and the greater number of input parameters that the Penman-Monteith requires (Section 3.4.3.1) when compared with the more simplified Hargreaves method (Oudin et al., 2005; Senay et al., 2007; Trambauer et

al., 2014). Furthermore, Trambauer et al. (2014) reported that, in general, the differences

between evaporation products is much smaller in humid areas than in arid areas – where the MOD16 product is commonly 20–30% higher. This difference is probably primarily because of the difference in temperature data sets used in the estimates.