Albuquerque Evapotranspiration in Albuquerque's South Valley


Research Report 787
Aquiles Saz, A. Salim Bawazir, Zohrab Samani, and Rhonda Skaggs
College of Agricultural, Consumer and Environmental Sciences, New Mexico State University.


Authors: Respectively, Former Research Assistant, Associate Professor, and Professor (MSC 3CE, PO Box 30001, Las Cruces, NM 88003; (575) 646-3801; abawazir@nmsu.edu and zsamani@nmsu.edu), Department of Civil Engineering; and Professor (MSC 3169, PO Box 30003, Las Cruces, NM 88003; (575) 646-3215; rskaggs@nmsu.edu), Department of Agricultural Economics and Agricultural Business, New Mexico State University. (Print-friendly PDF)

Summary

The Albuquerque, NM, South Valley region is undergoing rapid urbanization and population growth. This growth has created pressure on farmers to reduce agricultural irrigation water consumption in order to release water for industrial, municipal, and environmental uses. In order to help South Valley farmers better understand and manage their water resources, water use or evapotranspiration (ET) of alfalfa in the South Valley was measured using the eddy covariance and energy budget methods during years 2010 and 2011. Measured alfalfa ET was 1191 mm or 3.91 acre-ft/acre during 2010 (growing season = 274 days) and 1187 mm or 3.9 acre-ft/acre during 2011 (growing season = 294 days). Using weather station data, daily ET referenced to grass (ETsz) was also determined for the same growing seasons. Daily crop coefficients were developed for the periods of measurement. The alfalfa crop coefficient was determined as the ratio of measured ET to ETsz.

Introduction

The South Valley is located in the Middle Rio Grande Basin south of the central Albuquerque, NM, metropolitan area. The South Valley has experienced intense and rapid urbanization and population growth in recent years. This growth threatens the existence of South Valley peri-urban1 agriculture and the quality-of-life factors arising from the traditional agricultural landscape. Economic and land use changes are creating pressure on farmers to reduce their agricultural water use in order to release water for industrial, municipal, and environmental uses. Alfalfa is the primary crop grown in the South Valley, and is thus the region’s principle water-using crop. The objectives of this research were to estimate South Valley alfalfa water use or evapotranspiration (ET) and develop alfalfa crop coefficients for the South Valley. The larger goal of the research is to provide information that will contribute to improved South Valley irrigation water management and agricultural sustainability in the region.

The South Valley

The study site was located in the South Valley portion of the Middle Rio Grande Basin, south of the central Albuquerque, NM, metropolitan area. The South Valley is historically an agricultural region; however, it is now a peri-urban region and significantly influenced by the Albuquerque metropolitan area. Agricultural fields are intermingled with residences, commercial buildings, and an extensive transportation network. Although alfalfa is the region’s major crop, South Valley agriculture is a complex mix of fruit, vegetable, grain, forage, and livestock production for both commercial markets and home consumption.

The primary method of irrigation in the South Valley is surface flooding via furrows or wide basins using water from the Rio Grande. The area is crisscrossed with a system of irrigation canals and drains that are managed by the Middle Rio Grande Conservancy District (MRGCD).

Two South Valley farms participated in this alfalfa water-use study. The two research sites were located about two miles apart (Figure 1). Both farms grow alfalfa as their major crop.

Fig. 1: An aerial image showing the location of two research sites at Farms A and C in the South Valley, Albuquerque, NM.
Figure 1. An aerial image showing the location of two research sites at Farms A and C in the South Valley, Albuquerque, NM.

Methodology

Energy Budget and Eddy Covariance

Evapotranspiration (ET) of alfalfa in the South Valley was measured using energy budget and eddy covariance methods. Both methods are well described in literature (Jensen et al., 1990; Bawazir, 2000; Dingman, 2002; Brutseart, 2005). The accuracy of the eddy covariance technique for measuring ET was best summarized by Allen et al. (2011a). The recommendation for reporting ET measurements is discussed in Allen et al. (2011b).

The energy budget method is based on conservation of energy. The energy coming into the system and leaving the system must be equal. The system, in this case the crop surface, is the interface between the earth and the atmosphere. The energy budget equation can be written as

(1) Rn + G + H + LE = 0

where Rn is the net radiation flux, G is the soil heat flux, H is the sensible heat flux, and LE is the latent heat flux. All the units are expressed as W/m². There are other sources of energy that could be added to equation (1), such as energy stored within the crop canopy and energy used in photosynthesis. However, these energies are considered very small on a daily basis for crops such as alfalfa and have a minor contribution to the energy budget; they are therefore ignored in the energy budget equation. The sign convention for the energy fluxes described in equation (1) is such that fluxes are positive when moving toward the hypothetical surface and negative when moving away from the surface.

Reference Evapotranspiration

Evapotranspiration referenced to grass (ETsz) was determined using American Society of Civil Engineers (ASCE) s tandardized equation (Allen et al., 2005). The details of the ETsz equation to estimate reference evapotranspiration are described in detail in Allen et al. (2005). The equation for daily time steps of ETsz (mm/d) following Allen et al. (2005) is

Equation 2

where

Δ = slope of the saturation vapor pressure–temperature curve (kPa/°C)
Rn = net radiation at the crop surface (MJ/m2 d)
G = soil heat flux (MJ/m2 d)
γ = psychrometric constant (kPa/°C)
Cn = numerator constant that depends on the reference vegetation type and calculation time step; Cn for short reference surface = 900
T = mean daily air temperature (°C)
u2 = mean daily wind speed at 2 m above the ground (m/s)
es = mean saturation vapor pressure at 1.5 to 2.5 m above the ground (kPa)
ea = mean actual vapor pressure at 1.5 to 2.5 m above the ground (kPa)
Cd = denominator constant that depends on the reference vegetation type and calculation time step; Cd

The coefficient of 0.408 has units of m² mm/MJ.

Crop Coefficient

The crop coefficient was determined from measured ET and calculated ETsz referenced to grass (short crop) from weather station climate data. The crop coefficient (Kc) was determined as

Equation 3

Instrumentation

Evapotranspiration Flux Measurement

Instrumentation to measure the flux components in the energy budget were installed on a 10-ft triangulated tower at Farm C. Figure 2 shows the flux tower with instrumentation. The instrumentation for measuring the components of the energy budget, including accuracy of measurement and installation heights, is listed in Table 1. The Farm C alfalfa field had plenty of fetch distance in all directions. The flux tower was surrounded by alfalfa fields (total of 50 acres) managed by the same farmer.

Fig. 2: Photograph of flux tower and instrumentation located in alfalfa field at Farm C in the South Valley.

Figure 2. Flux tower and instrumentation located in alfalfa field at Farm C in the South Valley.

Table 1. Flux Tower Instrumentation, Sensor Accuracy, and Sensor Heights in the South Valley

Measurement

Sensor Model

Accuracy

Sensor Height (m)

Barometric pressure

CS105

±6 millibars from -40°C to 60°C

1.20

Atmospheric water vapor

KH2O open path krypton hygrometer

N/A

2.00

Wind speed and direction, and virtual temperature

CSAT-3D three-dimensional sonic anemometer

±4 cm/s

2.00

Air temperature

FW05 type E fine wire thermocouple

±0.005°C from -150°C to 206°C

2.00

Net radiation

NR-LITE net radiometer

-5% with an angle of incidence between 0° and 60°

1.71

Relative humidity

HMP45C - Humicap 180

±2% RH (0–90% RH) and ±3% RH (90–100% RH)

2.00

Air temperature

HMP45C - 1000 Ω PRT, IEC 751 1/3 Class B

±0.5°C from -40°C to 60°C

2.00

Soil heat flux

HFT3 soil heat flux plates SHF1 and SHF2

Better than ±5% of reading

-0.08

Soil temperature

TCAV averaging soil thermocouple probes

±0.005°C from -150°C to 206°C

-0.02 to 0.08

Vertical wind speed (OPEC)

2706T Gill propeller anemometer

±0.3 m/s

4.42

Air (OPEC) temperature

FW05 type E fine wire thermocouple

±0.005°C from -150°C to 206°C

4.42

Sensible heat (H) was measured using a three-dimensional sonic anemometer (CSAT-3D) from Campbell Scientific Inc. (CSI), Logan, UT. An additional one-propeller eddy covariance (OPEC) system was installed to measure sensible heat for comparison purposes and as a backup in case CSAT-3D did not work. Latent heat (LE) was measured using a CSI krypton hygrometer (KH2O), but LE was also calculated as a residual from the energy budget (LE = Rn – G – H). Net radiation was measured using an NR-LITE net radiometer (CSI), soil heat (G) was measured using soil heat flux plates (REBS Inc.), soil moisture was measured using a CS 616 water content reflectometer (CSI), and TCAV averaging probes (CSI) were used to measure soil temperature. Additional sensors were installed to measure relative humidity and ambient temperature as shown in Table 1. Data were collected using a CR5000 model data logger by CSI on a 10-hertz basis with the exception of OPEC, which was collected at about 8 hertz. The collected data were averaged to 30 minutes using the on-board datalogger. The 30-minute flux data were later totaled to daily (24-hour) values. The datalogger was powered by a solar panel and battery. Data were retrieved using a compact flash card on a bi-weekly basis.

Weather Stations for Reference ETsz

Weather stations were installed on Farm A and Farm C (Figure 3). The stations measured solar radiation, air temperature, soil moisture and temperature, wind speed, relative humidity, precipitation, and ground surface temperature. Data from the weather stations were used to determine reference ETsz. The sensor accuracy and installation heights for the weather stations at Farms A and C are listed in Table 2. Data were collected on an hourly basis and converted to daily basis on a CR1000 datalogger (CSI). The datalogger was powered by a solar panel and battery.

Fig. 3: Photograph of weather stations at Farm A (left) and Farm C (right).

Figure 3. Weather stations at Farm A (left) and Farm C (right).

Table 2. Weather Station Sensors, Accuracy, and Sensor Heights Located at South Valley Farms A and C

Measurement

Sensor Model

Accuracy

Farm A Sensor Height (m)

Farm C Sensor
Height (m)

Barometric pressure

CS106

±1.5 millibits from -40°C to 60°C

1.20

1.20

Relative humidity

CS215 - Sensirion SHT75

±2% (10–90% RH) and ±4% (0–100% RH)

2.00

2.00

Air temperature

CS215 - Sensirion SHT75

±0.9°C from -40°C to 70°C

2.00

2.00

Soil temperature

107-L temperature probe

±0.01°C from -35°C to 50°C

-0.08

-0.08

Solar radiation

LiCor LI200X pyranometer

±5%

2.54

2.57

Wind speed and direction

05103-L Young wind monitor

±0.3 m/s or 1% of reading

3.05

3.17

Surface temperature

Infrared radiometer SI-111

±0.5°C from -40°C to 70°C

2.70

2.74

Soil moisture content

CS616-L soil moisture reflectometer

±5%

-0.04

-0.04

Precipitation

TB4-L rain gauge

Better than ±2% at 500 mm/hr

1.00

0.72

Results and Discussion

South Valley alfalfa ET was estimated as a residual of the energy budget method for years 2010 and 2011. Seasonal and annual ET were determined for both years. ETsz was determined using the ASCE standardized equations. Daily crop coefficient (Kc) was determined as the ratio of measured ET divided by ETsz. Data were scrutinized for sensor malfunction during maintenance, sensor error, sensor malfunction during rainfall events, etc. Data were corrected as recommended in literature and by instrument manufacturers.

Sensible heat fluxes measured by the CSAT-3D sonic anemometer were corrected for humidity effect according to Schotanus et al. (1983), as applied by Massman and Lee (2002), for frequency response, including line averaging and block averaging according to Massman (2000, 2001). Latent heat measured by CSAT-3D and krypton hygrometer (KH2O) was corrected for oxygen (Tanner et al., 1993; Van Dijk et al., 2003) and for frequency response, including line averaging, block averaging, and sensor separation (Massman, 2000, 2001). In addition, latent heat fluxes were corrected for water vapor density effect (Webb et al., 1980; Massman and Lee, 2002). The daily average energy budget closure for 2010 was 0.83 with standard deviation of 0.16 (n = 259 days) and for 2011 was 0.82 with standard deviation of 0.13 (n = 250 days). Latent heat fluxes for the entire growing season were not measured due to frequent KH2O malfunction; therefore, they were calculated as a residual in the energy budget, assuming the energy budget closed as recommended by Blanford and Gay (1992). Latent heat was converted to equivalent depth of water (ET) by dividing the latent heat fluxes by latent heat of vaporization of water (2.45 MJ/kg). The data collected during the growing season included a few days of missing data. For those instances when data were missing for one day or two consecutive days, data were linearly interpolated by averaging data from the days before and after to fill in the gaps. This procedure allowed for alfalfa ET estimation during the entire growing seasons of 2010 and 2011. The ET measured for South Valley alfalfa during 2010 and 2011 is presented in Figures 4 and 5, respectively.

Fig. 4: Graph of evapotranspiration of alfalfa measured in 2010 at South Valley Farm C.

Figure 4. Evapotranspiration of alfalfa measured in 2010 at South Valley Farm C.

Fig. 5: Graph of evapotranspiration of alfalfa measured in 2011 at South Valley Farm C.

Figure 5. Evapotranspiration of alfalfa measured in 2011 at South Valley Farm C.

Weather data collected at two weather stations (Farm A and Farm C) were checked for quality. Weather measurements at the two stations in 2010 and 2011 were very similar. Comparison using a linear regression resulted in a coefficient of determination (R²) greater than 0.96 (n = 730 days) and with very low standard errors of estimate (SEE; Table 3). Examples of weather data for Farm C are presented in Figures 6 through 10. The solar radiation (Rs) was checked using the clear sky solar radiation (Rso) following Allen et al. (1998). The ETsz was calculated for 2010 and 2011 (Figure 11).

Fig. 6: Graph of ambient temperature measured at South Valley Farm C.

Figure 6. Ambient temperature measured at South Valley Farm C.

Fig. 7: Graph of relative humidity measured at South Valley Farm C.

Figure 7. Relative humidity measured at South Valley Farm C.

Fig. 8: Graph of solar radiation, Rs (global), measured at South Valley Farm C.

Figure 8. Solar radiation, Rs (global), measured at South Valley Farm C. Clear sky solar radiation (Rso) was used as a check (Allen et al., 1998).

Fig. 9: Graph of precipitation measured at South Valley Farm C.

Figure 9. Precipitation measured at South Valley Farm C.

Fig. 10: Graph of daily average wind speed measured at South Valley Farm C.

Figure 10. Daily average wind speed measured at South Valley Farm C.

Fig. 11: Graph of daily South Valley evapotranspiration referenced to grass (ETsz).

Figure 11. Daily South Valley evapotranspiration referenced to grass (ETsz) using standardized equation (Allen et al., 2005); ETsz was calculated using Farm C weather data.

Table 3. Comparison Between Farm A and C Weather Data (Farm C was assumed as the independent variable and Farm A as the dependent variable in the regression analysis)

Slope

Intercept

Sample, n

SEE*

Daily air temperature (°C)

1.00

0.89

730

0.9972

1.97E–03

Daily mean relative humidity (%)

1.07

-5.00

730

0.9626

2.44E+00

Daily total solar radiation (MJ/m²)

1.00

0.72

730

0.9842

9.55E–01

Daily mean wind speed (m/s)

0.86

0.03

730

0.9788

1.25E–01

*SEE is the standard error for estimate of variability y (weather station A) with respect to x (weather station C) or the measure of dispersion of the observed values about the regression line.

The daily crop coefficient during the 2010 and 2011 growing seasons was determined using the ETsz calculated from the weather station at Farm C where ET was also measured (Figures 12 and 13). The crop coefficients in Figures 12 and 13 reflect the period when alfalfa was at peak growth (high Kc) and when alfalfa was harvested (lower Kc). Alfalfa was harvested four times in each year; field visual inspection indicated the yield of the fourth cutting was approximately one-half the volume of the earlier cuttings. The peak crop coefficients averaged about 1.20; this occurred during the peak ET period of the summer season as shown in Figures 4 and 5.

Fig. 12: Graph of alfalfa daily crop coefficient (Kc) during the 2010 South Valley growing season (February 24–November 24, 2010).

Figure 12. Alfalfa daily crop coefficient (Kc) during the 2010 South Valley growing season (February 24–November 24, 2010).

Fig. 13: Graph of Alfalfa daily crop coefficient (Kc) during the 2011 South Valley growing season (February 12–December 2, 2011).

Figure 13. Alfalfa daily crop coefficient (Kc) during the 2011 South Valley growing season (February 12–December 2, 2011).

The ETsz calculated for the 2010 South Valley growing season (February 24–November 24; 274 days) was 1244 mm, with a measured ET of 1191 mm. For 2011 (February 12–December 2; 294 days), ETsz was 1356 mm and measured ET was 1187 mm. Total annual (365 days) alfalfa ET measured during 2010 was 1231 mm and during 2011 was 1224 mm. Higher ET in 2010 was probably due to higher precipitation. There are no previous measurements of alfalfa ET in the South Valley against which to compare these 2010 and 2011 results. However, alfalfa ET measured in Las Cruces, NM, using non-weighing lysimeters (Sammis, 1981) was reported as 1565 mm during a growing season from February 9 through December 6, 1976. In the 2008 growing season (April 1–November 6), Kirksey (2009) measured 1173 mm of alfalfa ET in Doña Ana County, NM, using the OPEC eddy covariance system and energy budget methods. For the same location, Kirksey (2009) measured total annual alfalfa ET of 1374 mm.

Total annual precipitation measured at South Valley Farm A was 202 mm (7.95 in.) in 2010 and 148 mm (5.83 in.) in 2011; at South Valley Farm C it was 528 mm (20.79 in.) in 2010 and 140 mm (5.51 in.) in 2011. There was out-of-norm precipitation measured at Farm C of 69.34 mm (2.73 in.) on July 26, 2010, and 167.4 mm (6.59 in.) measured on July 27, 2010. The same out-of-norm precipitation was not measured during the same period at South Valley Farm A (located 2 miles away). Total 2011 measured annual precipitation amounts of 5.83 in. at Farm A and 5.51 in. at Farm C were low when compared to the annual average South Valley precipitation of 8.12 in. (Williams, 1986) and the average Albuquerque airport precipitation of 8.56 in. (Langman and Nolan, 2005; 1914–2002 precipitation record).

Conclusion

Water use or evapotranspiration (ET) of alfalfa was measured in the Albuquerque-region South Valley, NM, using eddy covariance and energy budget methods. Flux of net radiation, sensible heat, latent heat, and soil heat were collected at 10 Hz and averaged every 30 minutes during 2010 and 2011. Thirty-minute values were totaled to determine daily (24-hr) values. The ET was determined as a residual in the energy budget for the growing season. Daily ET was observed to increase until peaking when alfalfa was mature and dense, but then when alfalfa was harvested the ET declined following alfalfa growth pattern. This pattern was more pronounced in 2011—a dry year when precipitation was below normal (<8.12 in). Evapotranspiration measured during the 2010 and 2011 growing seasons was 1191 mm or 3.91 acre-ft/acre (274 days) and 1187 mm or 3.9 acre-ft/acre (294 days), respectively. Evapotranspiration referenced to grass (ETsz) was determined using the ASCE standardized equation. The ETsz for 2010 was 1244 mm and for 2011 was 1356 mm. Daily crop coefficients (Kc) were determined as the ratio of measured ET over ETsz; an average Kc of 1.2 was determined during the season when alfalfa was at maturity.

Acknowledgments

The project under which this research was conducted was supported by USDA-CSREES Small and Medium Size Farm Prosperity Agreement No. 2009-55618-05096, “Improving Economic Returns and Long-Run Sustainability in a Rapidly Growing Peri-Urban, Multicultural, Traditional Farming Community.”

Thanks to the Middle Rio Grande Conservancy District (MRGCD) and especially David Gensler and Matthew Martinez for their help with this project. Thanks also to the farmers who provided access and permission to work on their fields: James Head and family, Robert Gherardi, and Manuel Ojal.

References

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Allen, R.G., L.S. Pereira, T.A. Howell, and M.E. Jensen. 2011b. Evapotranspiration information reporting: II. Recommended documentation. Agricultural Water Management, 98, 921–929.

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Footnote

1 According to Peri-Urban Environmental Change (PUECH), peri-urban defines “the transition or interaction zone, where urban and rural activities are juxtaposed, and landscape features are subject to rapid modifications, induced by anthropogenic activity. These critical areas of land cover change, leading to transformations in the hydrological, ecological, geomorphological and socio-economic systems, are often neglected by both rural and urban administrations. As cities develop, much of their growth is located in such areas.” (back to top)

For more on this topic, see the following publications:

RR-786: The South Valley—A Look at Small Farm Practices and Objectives Near Albuquerque, New Mexico’s Largest City
http://aces.nmsu.edu/pubs/research/economics/RR786/welcome.html

RR-766: Furrow-Irrigated Alfalfa Dry Matter Yield is Not Affected by Different Seeding Rates in the Southern High Plains, USA
http://aces.nmsu.edu/pubs/research/agronomy/rr-766/welcome.html


Photo of Rhonda Skaggs.

Rhonda Skaggs is a Professor in the Agricultural Economics and Agricultural Business Department at New Mexico State University. She earned her B.S. and M.S. at Colorado State University and Ph.D. at Utah State University. She teaches and conducts research in the areas of food and agricultural policy, agricultural structure, agricultural ethics, and the future of the food and agricultural system


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December 2014 Las Cruces, NM