This page is intended for members of the FEWS NET science community. The goal here is to document NOAA's ongoing progress toward the following:

1. Development of the multi-generational global Reference Evapotranspiration (ET₀) dataset, including: <link>

• a) development of ET₀ from MERRA2 (native, coarse-resolution data), <link>
• b) verification of native-resolution ET₀ data, <link>
• c) downscaling to 0.125° (fine-resolution data), and <link>
• d) potential improvements to downscaling procedure. <link>

2. Distribution of the multi-generational global Reference Evapotranspiration (ET₀) dataset, at multiple resolutions: <link>

• fine scale (0.125°),
• coarse-scale (0.5° x 0.625°).

3. Distribution of the experimental global Evaporative Demand Drought Index (EDDI), in two formats: <link>

• raw data in flat ascii files,
• context-enhanced maps,

and at various timescales:

• dekadal (10-day) at the end of each dekad, and
• 1- to 12-monthly at the end of each month.

 

 

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1. Developing the multi-generational global reference evapotranspiration (ET₀) dataset:

1.a. Development of coarse-resolution ET₀ from MERRA2:

The data set currently employed by FEWS NET for their ET₀ estimations – based on GDAS drivers – was verified by Senay et.al. (2008), using weather station data from the California Irrigation Management Information System (CIMIS). In order to facilitate inter-study comparisons with FEWS NET’s current ET₀ estimates, by Senay et al. (2008) using GDAS in the ASCE hourly version of the Penman-Monteith equation, we use the daily version of ASCE’s formula, taking the form:

We assume G = 0 in Eqn 1. The constant values 900 [K.mm.s³/Mg/day] and 0.34 [s/m] reflect the ASCE values assuming the short reference crop (0.12-m grass), and 0.23 is the albedo [dimensionless].

 

 

 

Eqns 1 - 5 are driven by the following five variables, all provided by MERRA2: 

• T, 2-m air temperature [°C; from MERRA2 variable T2M],
• q, 2-m specific humidity [kg/kg; from QV2M],
• R_s, surface downward short wave radiation [W/m²; from SWGDN],
• U₂, 2-m wind speed at 2 m height [m/s; from 10-m orthogonal wind vectors U2M and V2M], and
• P, surface atmospheric pressure [kPa; from PS].

The equation for e_s deviates from ASCE convention, where it takes the form:

where T_max and T_min are the daily maximum and minimum temperatures, respectively. Since the MERRA2 data set only provides daily mean temperature T, we use the form in Eqn 4. On the other hand, our verification dataset (GSOD) provides daily T_max and T_min, so instead of using the ASCE (2005) convention, for consistency we estimated the daily average T by calculating the average of the two values:

and subsequently used Eqn 4.

The MERRA2 reanalysis provides all five required variables at a 0.5° lat x 0.625° long resolution.

 

1.b. Verification of native resolution ET₀ data:

So far, we have conducted a preliminary verification of the ET₀ reanalysis against station-based ET₀ "observations" across Africa. These are not true observations of ET₀; rather they are synthesized using the equations above (section 1a) driven by observations of T, U2, and q from Global Summary of the Data (GSOD) stations and R_s from the Global Land Data Assimilation System v 2.1 at a 1° lat/long resolution but linearly interpolated to the GSOD station locations. Spatially distributed and downscaled ET₀ estimates derived from MERRA2 are compared with these (mostly) station-based ET₀ estimates (hereafter referred to as GSOD) to test the performance of MERRA2 both regionally and seasonally. MERRA2 ET₀ values are extracted by linear interpolation to the daily GSOD station coordinates. The comparison uses moving-mean plots of spatially averaged daily ET₀ versus time, and scatter plots, the latter allowing for determination of r²-values and linear fitting parameters.

Although GSOD provides daily mean station pressure, we follow the ASCE (2005) method of estimating the atmospheric pressure P using station elevation z according to:

Furthermore, as q is not included in the GSOD dataset we derive it from the dew-point temperature T_dew, a quantity provided by the dataset, and surface atmospheric pressure P, using:

where e_a is from:

Using GLDASv2.1 limits our verification of MERRA2 ET₀ to the period 1980-2000.

Eqn 1 requires wind speeds at 2 m above the surface, U₂. Measurements performed at a different heights can be converted to approximate a 2-m wind speed using the following equation from ASCE (2005):

GSOD, however, does not specify any anemometer height, introducing an uncertainty in the value for our U₂ input to GSOD-derived ET₀. In order to constrain this uncertainty, we contacted the meteorological institutes of many African countries to try to ascertain anemometer heights used at their weather stations. Contact information is, however, very limited for many countries, and many times we did not get a response. The four countries that did respond--South Africa, Algeria, Kenya and Tanzania--all informed us that they use a 10-m anemometer height pursuant to WMO specifications. It seems therefore a fair assumption that most other countries also use the same convention. For stations not located in one of the four countries with known anemometer height, we calculate ET₀ twice: once with the assumption that wind speeds are measured at 2 m (i.e., we use the GSOD value as is), and another time with the assumption of 10-m anemometer heights. From Eqn 11, we know that U₂ ≈ 0.75 U₁₀.

Figure 1 shows the spatial and temporal characteristics of the number of GSOD weather stations in Africa. It can be seen that, although the number of reporting stations can vary strongly from one day to another, the number of stations has significantly increased since the beginning of the year 2000. Globally (not shown in the figure), the total amount of station is over 9000. For reasons unknown to the authors, the GSOD data set contains some days where the number of stations suddenly drops dramatically, with a lowest station count of 25 on September 9, 2013. The spatial map shows that South Africa and Algeria have the highest density of stations, whereas most of the Sahel region, Central Africa and the Horn of Africa are poorly represented. Throughout this work, Africa is divided into five regions, as shown in Figure 1, in order to compare the performance of MERRA2 on a regional basis.

 

1.c. Downscaling to fine-resolution (0.125°) ET₀ data:

Daily ET₀ surfaces are spatially downscaled to 0.125° x 0.125°, using climatological monthly potential evaporation (PET) estimates from the International Water Management Institute (IWMI). Fine-scale IWMI pixels are averaged spatially to the extent of the encompassing MERRA2-derived ET₀ pixel (a block of 4 x 5 0.125-degree IWMI pixels to each 0.5 x 0.625-degree MERRA2 pixel) to derive a coarse-scale IWMI estimate at the same spatial resolution of the MERRA2-derived ET₀. The ratio of the fine-scale :: coarse-scale IWMI PET is applied at the fine-scale 0.125-deg resolution to the MERRA2-derived ET₀ to provide a fine-scale (0.125-degree) MERRA2-derived ET₀.

This simple downscaling procedure is dissatisfying: it introduces no fine-scale temporal variability and so does nothing to enhance the performance of EDDI even going to finer scales.

 

1.d. Potential improvements to downscaling procedure:

As an improvement to the IWMI-based downscaling outlined above, an upcoming alternative is to downscale each driver in anomaly space, and then reconstruct the fine-scale ET₀ from the fine-scale drivers.

First, we define anomalies for each response or driver variable, i.e., X = {ET₀, T, q, U₂, R_d}, as:

where X^f is the value of variable X at the fine scale, and X^c is the value of X at the coarse scale. Clearly, each parent pixel at X^c will have multiple child pixels with a range of X^f values.

The anomaly in ET₀ is also defined as the sum of the four drivers' contributions, themselves the product of the sensitivity of ET₀ to the driver (∂ET₀/∂X) and the anomaly in the driver, or:

What we seek is fine-scale ET₀ (ET₀^f), so we need to solve:

or, in more detail:

where every term on the right-hand side of Eqn 15 is known, as follows:

• ET₀^c is derived from Penman-Monteith driven by {T^c, q^c, U₂^c, R_d^c} from MERRA2.
• T^f, q^f, U₂^f, R_d^f are derived by downscaling, as follows:
  • T^f is derived by applying a lapse rate (γ) to T^c according to the anomaly in elevation (z) from the coarse- and fine-scale grids:

• • q^f, U₂^f, R_d^f are derived by budget downscaling of q^c, U₂^c, R_d^c
• Sensitivity expressions (∂ET₀/∂X) are derived from partial differentiation of the Penman-Montieth equation with respect to each variable X, and are provided in Hobbins (2016). They can be evaluated for each day of the year from climatological means of either the fine-scale X^f or coarse-scale X^c.

Needs:

• elevation grid (from MERRA2) for z^c
• elevation grid (from a fine-scale source) for z^f
• budget downscaling software / access to LIS
• GSOD assimilation (pre or post-downscaling?)

Advantages:

• conservative
• temporal variability gained at fine scale
• physically based
• no latency introduced (beyond that of MERRA2)

Disadvantages:

• processing time?
• disk space?

 

 

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2. Access to the multi-generational global ET₀ dataset:

The archive of NOAA-Physical Science Division's global reference evapotranspiration (ET₀) reanalysis is freely available to all from here: https://downloads.psl.noaa.gov/Projects/RefET/global/. This directory structure contains ET₀ data and imagery for multiple generations of the reanalysis, from January 1, 1980 to the near-present across the globe. Data are daily depths of reference evapotranspiration (ET₀; mm) for the indicated date.

Generation-0 ET₀ reanalysis: this generation has been created as outline above in steps 1(a) through 1(c), and is located here: https://downloads.psl.noaa.gov/Projects/RefET/global/Gen-0/. This directory structure contains the following two subdirectories:

• /coarse_resolution/ for the coarse-resolution daily ET₀ data. Contains two subdirectories /data_v1/ and /data_v2/, each divided into annual subdirectories /1980/ … /2018/, each containing a year (complete prior to 2018) of daily, coarse-resolution ET₀ data for short and long reference crops, estimated using the ASCE Standardized Reference Evapotranspiration Equation forced by MERRA2 drivers. Briefly the reanalysis specs are as follows:
  • File format: NetCDF,
  • Filenames indicate date: e.g., ETos_19801215.nc contains ET_os data for December 15, 1980,
  • Units: mm,
  • Temporal resolution: daily,
  • Spatial resolution: 0.5° lat X 0.625° long,
  • ET_rs: long-crop ET₀, i.e., for 0.5-m alfalfa,
  • ET_os: short-crop ET₀, i.e., for 0.12-m grass.
• The difference between the data in /data_v1/ and /data_v2/ is an improved daily wind speed estimation in v2.

• /fine_resolution/ for the fine-resolution daily ET₀ data--downscaled from the coarse-resolution data and masked to land areas. The reanalysis specs are similar to those listed above for the coarse-resolution data, except: 
  • Filenames indicate fine resolution and date: e.g., ETos_fine_19801215.nc contains fine-scale ET_os data for December 15, 1980,
  • Spatial resolution: 0.125° lat X 0.125° long.

Generation-1 ET₀ reanalysis: this generation will have the GSOD/GLDASv2.1 station data (currently used as verification in step 1(b) above) assimilated into it, and have an improved downscaling procedure. It is yet to come.

 

 

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3. Access to the experimental global Evaporative Demand Drought Index (EDDI):

The archive of the global Evaporative Demand Drought Index (EDDI; Hobbins et al. 2016, McEvoy et al. 2016) service is freely available to all from here: https://downloads.psl.noaa.gov/Projects/EDDI/global_archive/. This directory structure contains EDDI imagery (/images/) and data (/data/) from January 1, 1980 to the near-present across the globe, based on the fine-scale (0.125°), Generation-0, tall-crop reference evapotranspiration (ET_rs) reanalysis listed above. Data are EDDI values (and images are mapped EDDI values) for the timescale prior to the indicated date.

• All files--data and images--are arranged in annual subdirectories /1980/ ... /2018/.
• Available timescales are as follows:
  • dekadal (~10-day) EDDI at the end of each dekad (1^st dekad = 10^th day of month, 2^nd = 20^th, 3^rd = last day),
  • 1- to 12-monthly EDDI at the end of each month.
• Filenames indicate timescale and date: e.g., EDDI_ETrs_02dk_19801220.asc is the dekadal EDDI data file for the 10-day period ending December 20, 1980.
• Data files are currently stored in ascii format, starting in the northwest corner, reading west to east (left to right across each row), and then southwards (downwards). The coordinated system is geographic. With the addition of the following six header lines preceding the data, these files would be directly importable into ArcGIS:

ncols 2880
nrows 1440
xllcorner -90 – this line is not correct
yllcorner -90
cellsize 0.125
nodata_value -9.

• Soon, all data files will be converted to NetCDF format.

 

 

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References:

• American Society of Civil Engineers (ASCE; 2005), The ASCE standardized reference evapotranspiration equation. Technical Committee report to the Environmental and Water Resources Institute of the American Society of Civil Engineers from the Task Committee on Standardization of Reference Evapotranspiration. Reston, Va.: ASCE Water Resources Institute, 173 pp. <link>
• Hobbins, M.T. (2016), The variability of ASCE Standardized Reference Evapotranspiration: a rigorous, CONUS-wide decomposition and attribution. Transactions of the ASABE, 59(2): 561-576, doi:10.13031/trans.59.10975. <link>
• Hobbins, M.T., A.W. Wood, D.J. McEvoy, J.L. Huntington, C.G. Morton, M.C. Anderson, and C.R. Hain (2016), The Evaporative Demand Drought Index: Part I - Linking drought evolution to variations in evaporative demand. Journal of Hydrometeorology, 17: 1745–1761, doi:10.1175/JHM-D-15-0121.1. <link>
• McEvoy, D.J., J.L. Huntington, M.T. Hobbins, A.W. Wood, C.G. Morton, M.C. Anderson, and C.R. Hain (2016), The Evaporative Demand Drought Index: Part II - CONUS-wide assessment against common drought indicators. Journal of Hydrometeorology, 17: 1763–1779, doi:10.1175/JHM-D-15-0122.1. <link>
• Senay, G.B., J.P. Verdin, R. Lietzow, and A.M. Melesse (2008), Global daily reference evapotranspiration modeling and evaluation. Journal of the American Water Resources Association, 44(4): 969–979, doi:10.1111⁄j.1752-1688.2008.00195.x. <link>

Check here for the latest developments regarding the status of the global ETo reanalysis and derived EDDI service.