**Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in the Asian Monsoon Region**

Hanbo Yang and Dawen Yang

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/53067

### **1. Introduction**

The concept of the complementary relationship (CR) between actual and potential evapora‐ tions was first proposed by Bouchet [1]. The underlying argument of the CR can be devel‐ oped as follows. For one reason independent of energy considerations, actual evaporation *LE* decrease below wet environment evaporation *LE*w, a certain amount of energy not con‐ sumed in evaporation becomes sensible heat flux*ΔH* , which can be expressed as

$$LE\_w - LE = \Delta H \tag{1}$$

At the regional scale, this residual energy *ΔH* affects temperature, humidity, and other vari‐ ables of air near the ground surface, which lead to an increase in potential evaporation *LE*p, and one will have

$$LE\_p = LE\_w + \Delta H \tag{2}$$

Combination of equations (1) and (2) yields the complementary relationship

$$LE + LE\_p = 2LE\_w \tag{3}$$

Theoretically, the CR has been heuristically proven based on a series of restrictive assump‐ tions [2, 3]. Also, it has been proved in many applications, such as interpreting the evapora‐ tion paradox and estimating actual evaporation. The evaporation paradox was referred as that an increase in actual evaporation estimated by water balance methods over large areas, and a decrease in pan evaporation from measurements in many regions have been recently reported [4, 5], which can be interpreted based on the CR [6]. Using the CR, Brutsaert [7] estimated actual evaporation increase at about 0.44 mm/a2, according to typical values of global trends of net radiation, temperature and pan evaporation. Direct measurement of ac‐ tual evaporation over large areas is still difficult [8]. Consequently, the CR in which the feedback of potential evaporation with actual evaporation is considered suggests an attrac‐ tive method for estimating *LE* over a large region, without knowing underlying surface con‐ ditions such as soil moisture. This has been widely applied for actual evaporation estimation over different time scales, such as monthly [9-13], daily [14, 15], and hourly [16].

Nevertheless, it was found that the Bouchet hypothesis (Equation 3) was only partially ful‐ filled [17,18]. In fact, Bouchet [1] documented that Equation (3) was generally modified with consideration of changes to water vapor and energy exchanges of the system with its sur‐ roundings, so that*LE* + *L E*<sup>p</sup> ≤2*L E*w. Whereupon the expression was modified [19, 20] as *LE* + *L E*<sup>p</sup> =*mL E*w, where *m* is a constant of proportionality. Based on 192 data pairs from 25 basins over the United States, Ramirez et al. [19] determined a mean m of 1.97, but with high observed variability.

In the CR, wet environment evaporation (*LE*w) was suggested [14] to be given by the Priest‐ ley-Taylor equation [21]:

$$LE\_{\rm w} = \alpha \frac{A}{A+\gamma} \left( R\_{\rm n} - G \right) \tag{4}$$

On calculating *LE*w in the CR, Brutsaert and Stricker [14] suggested an average *α* on the or‐ der of 1.26–1.28. The value *α* =1.32 was predicted by Morton [11]. Hobbins et al. [9] obtained a value of *α* =1.3177 using data from 92 basins across the conterminous United States. Xu and Singh [24] determined *α* values in the advection-aridity (AA) model of Brutsaert and Stricker [14] for three study regions at 1.18, 1.04, and 1.00. Yang et al. [25] furnished an aver‐ age *α* =1.17 with range 0.87–1.48 from 108 catchments in the Yellow River and Hai River ba‐ sins of China, whereas Gao et al. [26] suggested an *α* of 1–1.23 for nine sub-basins of the Hai River basin. Using data from flux measurement stations #40 and #944 from the First Interna‐ tional Land Surface Climatology Field Experiment (FIFE) but not in the same period, Petti‐ john and Salvucci [27] and Szilagyi [20] obtained different values of*α*, 1.10 and 1.18 (or 1.15), respectively. According to data from Weishan flux measurement station, Yang et al. [28] in‐ dicated an *α* range of 1–1.5 for a daytime hourly average. These variable values of the Priest‐

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in

Under the condition without water limitation, *LE* equals *LE*p, and thus Equation (3) trans‐

This provides a simplified condition to study CR variability. According to analysis of satu‐ rated surface evaporation, Priestley and Taylor [21] gave an *α* range from 1.08 to 1.34, and took 1.26 as the average. Numerous papers report an average *α* of 1.26 [29-32]. Nevertheless, some details about *α* in these studies are noteworthy. Means in June, July and September were 1.27, 1.20 and 1.31, respectively [29], and *α* was less than 1.26 when *LE* was large, maybe in June or July [31]. Additionally, data in these studies were obtained only in particu‐ lar months of the year, such as September and October [30], June to September [29], July [31], and June, July and September [32]. Using observations from April to October over a large, shallow lake in the Netherlands, [33] found *α* had a seasonal variation from 1.20 in August to 1.50 in April. Seasonal variation of *α* in the Priestley-Taylor equation for calculat‐

This chapter tried to examine quantitatively the seasonal and regional variability of the CR on the basis of observation data from 6 flux experiment sites and 108 catchments in the Asian mon‐

A flux observation data set was collected from six flux experiment sites (the information was shown in Table 1 and Figure 1). These sites covered a wide range of climate and vegetation conditions from low latitude to high latitude in Asia. Therein five sites belong to the GE‐ WEX (Global Energy and Water cycle Experiment) Asian Monsoon Experiment (GAME), in‐

soon region, and then to find an explanation for CR seasonal and regional variability.

*<sup>w</sup> LE LE* = (5)

the Asian Monsoon Region http://dx.doi.org/10.5772/53067 63

ley-Taylor parameter *α* may imply the variability of the CR.

ing *LE*w can be considered an indicator of CR variability.

**2. Study area and data available**

**2.1. Flux experiment sites**

forms into

where *α* is a parameter,.*Δ*. (kPa/oC) is the slope of saturated vapor pressure at the air tempera‐ ture, *γ*(kPa/oC) is a psychometric constant, *R*n(mm/day) is net radiation, and *G* (mm/day) is soil heat flux. Central to wet environment evaporation is the concept of equilibrium evaporation. According to a theory for surface energy exchange in partly open systems, embracing a fully open system and fully closed system as limits, Raupach [22] asserted that a steady state with a steady-state *LE*w could be attained; the time to reach steady state (a steady proportion of availa‐ ble energy transforming into latent heat*α Δ <sup>Δ</sup>* <sup>+</sup> *<sup>γ</sup>* ) was 1–10 hours for a shallow convective boun‐ dary layer. Because of water vapor and energy exchanges between the system and surroundings, the proportion of available energy transforming into latent heat is usually modi‐ fied. Raupach [23] parameterized the effect of air exchange between system and surroundings on equilibrium evaporation, and suggested conservation equations for entropy and water va‐ por in an open system. This revealed that advection was likely to modify air temperature and

entropy at the system reference height, causing change in the proportion*α Δ <sup>Δ</sup>* <sup>+</sup> *<sup>γ</sup>* . On calculating *LE*w in the CR, Brutsaert and Stricker [14] suggested an average *α* on the or‐ der of 1.26–1.28. The value *α* =1.32 was predicted by Morton [11]. Hobbins et al. [9] obtained a value of *α* =1.3177 using data from 92 basins across the conterminous United States. Xu and Singh [24] determined *α* values in the advection-aridity (AA) model of Brutsaert and Stricker [14] for three study regions at 1.18, 1.04, and 1.00. Yang et al. [25] furnished an aver‐ age *α* =1.17 with range 0.87–1.48 from 108 catchments in the Yellow River and Hai River ba‐ sins of China, whereas Gao et al. [26] suggested an *α* of 1–1.23 for nine sub-basins of the Hai River basin. Using data from flux measurement stations #40 and #944 from the First Interna‐ tional Land Surface Climatology Field Experiment (FIFE) but not in the same period, Petti‐ john and Salvucci [27] and Szilagyi [20] obtained different values of*α*, 1.10 and 1.18 (or 1.15), respectively. According to data from Weishan flux measurement station, Yang et al. [28] in‐ dicated an *α* range of 1–1.5 for a daytime hourly average. These variable values of the Priest‐ ley-Taylor parameter *α* may imply the variability of the CR.

Under the condition without water limitation, *LE* equals *LE*p, and thus Equation (3) trans‐ forms into

$$LE = LE\_w \tag{5}$$

This provides a simplified condition to study CR variability. According to analysis of satu‐ rated surface evaporation, Priestley and Taylor [21] gave an *α* range from 1.08 to 1.34, and took 1.26 as the average. Numerous papers report an average *α* of 1.26 [29-32]. Nevertheless, some details about *α* in these studies are noteworthy. Means in June, July and September were 1.27, 1.20 and 1.31, respectively [29], and *α* was less than 1.26 when *LE* was large, maybe in June or July [31]. Additionally, data in these studies were obtained only in particu‐ lar months of the year, such as September and October [30], June to September [29], July [31], and June, July and September [32]. Using observations from April to October over a large, shallow lake in the Netherlands, [33] found *α* had a seasonal variation from 1.20 in August to 1.50 in April. Seasonal variation of *α* in the Priestley-Taylor equation for calculat‐ ing *LE*w can be considered an indicator of CR variability.

This chapter tried to examine quantitatively the seasonal and regional variability of the CR on the basis of observation data from 6 flux experiment sites and 108 catchments in the Asian mon‐ soon region, and then to find an explanation for CR seasonal and regional variability.

### **2. Study area and data available**

### **2.1. Flux experiment sites**

Theoretically, the CR has been heuristically proven based on a series of restrictive assump‐ tions [2, 3]. Also, it has been proved in many applications, such as interpreting the evapora‐ tion paradox and estimating actual evaporation. The evaporation paradox was referred as that an increase in actual evaporation estimated by water balance methods over large areas, and a decrease in pan evaporation from measurements in many regions have been recently reported [4, 5], which can be interpreted based on the CR [6]. Using the CR, Brutsaert [7] estimated actual evaporation increase at about 0.44 mm/a2, according to typical values of global trends of net radiation, temperature and pan evaporation. Direct measurement of ac‐ tual evaporation over large areas is still difficult [8]. Consequently, the CR in which the feedback of potential evaporation with actual evaporation is considered suggests an attrac‐ tive method for estimating *LE* over a large region, without knowing underlying surface con‐ ditions such as soil moisture. This has been widely applied for actual evaporation estimation

over different time scales, such as monthly [9-13], daily [14, 15], and hourly [16].

( ) w n *LE R G* D a

= - +

D g

entropy at the system reference height, causing change in the proportion*α*

observed variability.

62 Evapotranspiration - An Overview

ley-Taylor equation [21]:

ble energy transforming into latent heat*α*

Nevertheless, it was found that the Bouchet hypothesis (Equation 3) was only partially ful‐ filled [17,18]. In fact, Bouchet [1] documented that Equation (3) was generally modified with consideration of changes to water vapor and energy exchanges of the system with its sur‐ roundings, so that*LE* + *L E*<sup>p</sup> ≤2*L E*w. Whereupon the expression was modified [19, 20] as *LE* + *L E*<sup>p</sup> =*mL E*w, where *m* is a constant of proportionality. Based on 192 data pairs from 25 basins over the United States, Ramirez et al. [19] determined a mean m of 1.97, but with high

In the CR, wet environment evaporation (*LE*w) was suggested [14] to be given by the Priest‐

where *α* is a parameter,.*Δ*. (kPa/oC) is the slope of saturated vapor pressure at the air tempera‐ ture, *γ*(kPa/oC) is a psychometric constant, *R*n(mm/day) is net radiation, and *G* (mm/day) is soil heat flux. Central to wet environment evaporation is the concept of equilibrium evaporation. According to a theory for surface energy exchange in partly open systems, embracing a fully open system and fully closed system as limits, Raupach [22] asserted that a steady state with a steady-state *LE*w could be attained; the time to reach steady state (a steady proportion of availa‐

*Δ*

dary layer. Because of water vapor and energy exchanges between the system and surroundings, the proportion of available energy transforming into latent heat is usually modi‐ fied. Raupach [23] parameterized the effect of air exchange between system and surroundings on equilibrium evaporation, and suggested conservation equations for entropy and water va‐ por in an open system. This revealed that advection was likely to modify air temperature and

*<sup>Δ</sup>* <sup>+</sup> *<sup>γ</sup>* ) was 1–10 hours for a shallow convective boun‐

*Δ <sup>Δ</sup>* <sup>+</sup> *<sup>γ</sup>* . (4)

A flux observation data set was collected from six flux experiment sites (the information was shown in Table 1 and Figure 1). These sites covered a wide range of climate and vegetation conditions from low latitude to high latitude in Asia. Therein five sites belong to the GE‐ WEX (Global Energy and Water cycle Experiment) Asian Monsoon Experiment (GAME), in‐ cluding the Kogma site in the Thailand, Tibet\_MS3637 site (renamed as Tibet site in this paper) and Hefei site in China, Yakutsk site and Tiksi site in Russia. Another site (Weishan experiment site) was located at the downstream of the Yellow River, China, which was set up by Tsinghua University. This data set includes meteorological elements (air temperature, relative humidity, wind direction and speed, air pressure), radiation (longwave and short‐ wave radiation, net radiation), soil temperature, precipitation, soil moisture, skin tempera‐ ture, sensible heat flux, latent heat flux, and soil heat flux. Data were recorded as hourly averages. The energy balance closure problem was solved before data release at five of the six sites except Weishan site. At Weishan site, closure of the energy balance of approximate‐ ly 0.8 was evaluated, according to data from 2005 to 2006.

The Yakutsk site is located in the middle reaches of the Lena and is in a region of continuous permafrost, the Sakha Republic of Russian, where the climate exhibits a strong continentali‐ ty [36]. Air temperature and humidity were measured using sensors (HMP-35D, Vaisala) at 17.2 and 13.4 m. Wind speed was measured at 9.8 m using an anemometer (AC-750, Maki‐ no). Downward and upward solar radiations were measured with pyranometers (CM-6F, Kipp-Zonen) at 18.2 and 15.9, respectively. Downward and upward long-wave radiations were measured with pyrgeometers (MS-201F, EKO). Sensible and latent heat flexes were measured using the eddy correlation system with a sonic anemometer- thermometer

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in

The Tiksi site is performed in the Siberian tundraregion near Tiksi, Sakha Republic, Russian Federation. Air temperature and humidity were measured using sensors (HMP-45D, Vaisa‐ la) at 10 m. Wind speed was measured at 10 m using an anemometer (AC860, Makino). Downward solar radiation was measured at 1.5 m with a pyranometer (MS-802F, EKO). Downward and upward long-wave radiations were measured at 1.5 m with pyrgeometers (MS-802F, EKO). Sensible and latent heat flexes were measured using the eddy correlation system with a sonic anemometer- thermometer. Soil heat flux was measured with a probe (MF-81, EKO) at 0.01 and 0.08 m. More details about the five sites are provided at the GAME

> Vegetation type

Hefei 32°34.8'N, 116°46.2'E 23 Rice paddy Aug., 1998 , Apr., Nov., and Dec., 1999

The Weishan site is located in a downstream reach of the Yellow River. Most of this region is farmland, with flat topography. Winter wheat and maize are the two major crops, rotation‐ ally cultivated [37]. Winter wheat planting season is in early October, and the growing peri‐ od is from March to mid-June. The experimental field is near the center of the irrigation district, and is a 400 m by 500 m rectangular field. Typical meteorological instruments are installed atop a 10 m tall tower, along with a radiometer and an eddy correlation system for sensible and latent heat fluxes. Air temperature and humidity were measured using sensors (HMP-45C, Vaisala) at 10 m. Wind speed was measured at 10 m using an anemometer (05103, Young Co.). Downward and upward solar and long-wave radiations were measured

Weishan 36°38.9'N, 116°03.3'E 30 Wheat, corn May 18, 2005 – Dec. 31, 2006

**Data period**

the Asian Monsoon Region http://dx.doi.org/10.5772/53067 65

Apr. – Jul., 1999

forest Feb. – Dec., 1998

(DA-600, KAIJO) and an open pathe H2O gas analyser (AH-300, KAIJO).

Altitude (m)

Tibet 31°01.0'N, 91°39.4'E 4820 Grass May – Sep., 1998

Yakutsk 62°15.3'N, 129°37.1'E 210 Larch forest Apr. – Aug., 1998

Tiksi 71°35.2'N, 128°46.5'E 40 Tundra Jun. – Sep., 2000

website (http://aan.suiri.tsukuba.ac.jp/).

**Table 1.** Descriptions of the flux experiment sites

Kogma 18°48.8'N, 98°54.0'E 1268 Evergreen

**Site Location**

The Kogma watershed is covered by a hilly evergreen forest in which only a few species lose their leaves, and canopy top is about 30 m [34, 35]. The Kogma site, part of the GEWEX (Global Energy and Water Cycle Experiment) Asian Monsoon Experiment (GAME), is locat‐ ed in the Kogma watershed of northern Thailand, with a 50 m observation tower. Air tem‐ perature and humidity were measured at 43.4 m using a psychrometer (HMP45D, Vaisala). Wind speed was measured at 43.4 m using an anemometer (AC750, Makino Ohyosokki). Downward and upward solar radiations were measured with pyranometers (MS-801 and MS-42, Eiko Seiki Co.) at 50.5 and 43.4 m, respectively. Downward and upward long-wave radiations were measured with an infrared radiometer (MS-200, Eiko Seiki Co.) at 50.5 and 43.4 m. Sensible and latent heat flexes were measured using the eddy correlation system, and the sonic anemometer-thermometer (DA-600, Kaijo) was installed at 41.5 m. Soil heat flux was measured using a probe (MF81, Eiko Seiki Co.)

The Tibet site was setup on May 1998 in the wet grassland between Amdo and Naqu, in the GAME-Tibet region, which was closed in September 1998. Air temperature and humidity were measured using sensors (50Y, Vaisala) at 7.8 and 2.3 m. Wind speed was measured at 9.8 m using an anemometer (R.M. Young Prop-Vane). Downward and upward solar radia‐ tions were measured with two pyranometers (CM21, Kipp-Zonen) respectively. Downward and upward long-wave radiations were measured with two pyrgeometers (PSP, Eppley). Sensible and latent heat flexes were measured using the eddy correlation system with a son‐ ic anemometer- thermometer (R3A, Gill). Soil heat flux was measured with a probe (HFT-3.1, REBS).

The Hefei site is set up in the Shouxian Meteorological Observatory, Anhui province for sur‐ face flux observation in the Huaihe River Basin. The vegetation of surrounding area consists of mostly rice paddy and partly farmland. Shouxian is located in the middle of intensified observation area of GAME-HUBEX. Air temperature and humidity were measured using sensors (50Y, Vaisala). Wind speed was measured at 9.8 m using an anemometer (09101, R.M. Young Prop-Vane). Downward and upward solar radiations were measured with two pyranometers (Kipp-Zonenn), respectively. Downward and upward long-wave radiations were measured with two pyrgeometers (PIR, Eppley). Sensible and latent heat flexes were measured using the eddy correlation system with a sonic anemometer- thermometer (Gill). Soil heat flux was measured with a probe (HFT-3.1, REBS).

The Yakutsk site is located in the middle reaches of the Lena and is in a region of continuous permafrost, the Sakha Republic of Russian, where the climate exhibits a strong continentali‐ ty [36]. Air temperature and humidity were measured using sensors (HMP-35D, Vaisala) at 17.2 and 13.4 m. Wind speed was measured at 9.8 m using an anemometer (AC-750, Maki‐ no). Downward and upward solar radiations were measured with pyranometers (CM-6F, Kipp-Zonen) at 18.2 and 15.9, respectively. Downward and upward long-wave radiations were measured with pyrgeometers (MS-201F, EKO). Sensible and latent heat flexes were measured using the eddy correlation system with a sonic anemometer- thermometer (DA-600, KAIJO) and an open pathe H2O gas analyser (AH-300, KAIJO).

The Tiksi site is performed in the Siberian tundraregion near Tiksi, Sakha Republic, Russian Federation. Air temperature and humidity were measured using sensors (HMP-45D, Vaisa‐ la) at 10 m. Wind speed was measured at 10 m using an anemometer (AC860, Makino). Downward solar radiation was measured at 1.5 m with a pyranometer (MS-802F, EKO). Downward and upward long-wave radiations were measured at 1.5 m with pyrgeometers (MS-802F, EKO). Sensible and latent heat flexes were measured using the eddy correlation system with a sonic anemometer- thermometer. Soil heat flux was measured with a probe (MF-81, EKO) at 0.01 and 0.08 m. More details about the five sites are provided at the GAME website (http://aan.suiri.tsukuba.ac.jp/).


**Table 1.** Descriptions of the flux experiment sites

cluding the Kogma site in the Thailand, Tibet\_MS3637 site (renamed as Tibet site in this paper) and Hefei site in China, Yakutsk site and Tiksi site in Russia. Another site (Weishan experiment site) was located at the downstream of the Yellow River, China, which was set up by Tsinghua University. This data set includes meteorological elements (air temperature, relative humidity, wind direction and speed, air pressure), radiation (longwave and short‐ wave radiation, net radiation), soil temperature, precipitation, soil moisture, skin tempera‐ ture, sensible heat flux, latent heat flux, and soil heat flux. Data were recorded as hourly averages. The energy balance closure problem was solved before data release at five of the six sites except Weishan site. At Weishan site, closure of the energy balance of approximate‐

The Kogma watershed is covered by a hilly evergreen forest in which only a few species lose their leaves, and canopy top is about 30 m [34, 35]. The Kogma site, part of the GEWEX (Global Energy and Water Cycle Experiment) Asian Monsoon Experiment (GAME), is locat‐ ed in the Kogma watershed of northern Thailand, with a 50 m observation tower. Air tem‐ perature and humidity were measured at 43.4 m using a psychrometer (HMP45D, Vaisala). Wind speed was measured at 43.4 m using an anemometer (AC750, Makino Ohyosokki). Downward and upward solar radiations were measured with pyranometers (MS-801 and MS-42, Eiko Seiki Co.) at 50.5 and 43.4 m, respectively. Downward and upward long-wave radiations were measured with an infrared radiometer (MS-200, Eiko Seiki Co.) at 50.5 and 43.4 m. Sensible and latent heat flexes were measured using the eddy correlation system, and the sonic anemometer-thermometer (DA-600, Kaijo) was installed at 41.5 m. Soil heat

The Tibet site was setup on May 1998 in the wet grassland between Amdo and Naqu, in the GAME-Tibet region, which was closed in September 1998. Air temperature and humidity were measured using sensors (50Y, Vaisala) at 7.8 and 2.3 m. Wind speed was measured at 9.8 m using an anemometer (R.M. Young Prop-Vane). Downward and upward solar radia‐ tions were measured with two pyranometers (CM21, Kipp-Zonen) respectively. Downward and upward long-wave radiations were measured with two pyrgeometers (PSP, Eppley). Sensible and latent heat flexes were measured using the eddy correlation system with a son‐ ic anemometer- thermometer (R3A, Gill). Soil heat flux was measured with a probe

The Hefei site is set up in the Shouxian Meteorological Observatory, Anhui province for sur‐ face flux observation in the Huaihe River Basin. The vegetation of surrounding area consists of mostly rice paddy and partly farmland. Shouxian is located in the middle of intensified observation area of GAME-HUBEX. Air temperature and humidity were measured using sensors (50Y, Vaisala). Wind speed was measured at 9.8 m using an anemometer (09101, R.M. Young Prop-Vane). Downward and upward solar radiations were measured with two pyranometers (Kipp-Zonenn), respectively. Downward and upward long-wave radiations were measured with two pyrgeometers (PIR, Eppley). Sensible and latent heat flexes were measured using the eddy correlation system with a sonic anemometer- thermometer (Gill).

ly 0.8 was evaluated, according to data from 2005 to 2006.

flux was measured using a probe (MF81, Eiko Seiki Co.)

Soil heat flux was measured with a probe (HFT-3.1, REBS).

(HFT-3.1, REBS).

64 Evapotranspiration - An Overview

The Weishan site is located in a downstream reach of the Yellow River. Most of this region is farmland, with flat topography. Winter wheat and maize are the two major crops, rotation‐ ally cultivated [37]. Winter wheat planting season is in early October, and the growing peri‐ od is from March to mid-June. The experimental field is near the center of the irrigation district, and is a 400 m by 500 m rectangular field. Typical meteorological instruments are installed atop a 10 m tall tower, along with a radiometer and an eddy correlation system for sensible and latent heat fluxes. Air temperature and humidity were measured using sensors (HMP-45C, Vaisala) at 10 m. Wind speed was measured at 10 m using an anemometer (05103, Young Co.). Downward and upward solar and long-wave radiations were measured at 3.5 m with pyranometers (CNR-1, Kipp-Zonen). Sensible and latent heat flexes were measured using the eddy correlation system at 3.7 m with a sonic anemometer-thermometer (CSAT3, Campbell). Soil heat flux was measured with a probe (HFP01SC, Hukseflux). Ob‐ servations were recorded as 30-minute averages.

**Figure 2.** Distribution of the hydrologic and meteorological stations in the 108 catchments in the non-humid regions of China (the solid triangle represents a hydrologic station at the outlet of catchments and the grey solid circle repre‐

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in

In a closed system without advection, the CR can be expressed as equation (3) in which *LE*<sup>w</sup> is estimated using equation (4) with *α* = 1.26. However, in a real open environment the hori‐ zontal advection can't be neglected. Therefore, taking the effect of the horizontal advection

D

(6)

the Asian Monsoon Region http://dx.doi.org/10.5772/53067 67

+

i.e., the CR is modified as*LE* + *L E*<sup>p</sup> =2*L E*<sup>w</sup> + *A*. The value of *A* indicated the effect of the horizontal advection of both energy and water vapor. Morton [10] suggested a similar equa‐

advection. If there is a seasonal and regional variability in the CR, *A*should have a seasonal and regional variation. The other one is focusing on the Priestley-Taylor parameter *α* to re‐ veal seasonal and regional variability of the CR. According to Equations (3) and (4), *α*can be

*<sup>Δ</sup>* <sup>+</sup> *<sup>γ</sup>* (*R*<sup>n</sup> <sup>−</sup>*<sup>G</sup>* <sup>+</sup> *<sup>A</sup>*m), where *A*m was an empirical correction factor for

D g

( ) p n *A LE LE* 2 1.26 *R G* ,

= + -´ -

sents a meteorological station)

tion*LE* + *L E*<sup>p</sup> =2×1.26

calculated as

into account, we have two methods, and the one is

*Δ*

**3. Method**

**Figure 1.** Location of the two flux experiment sites

#### **2.2. Study catchments**

To examine the regional variability, 108 catchments, locating in the Yellow River basin, the Hai River basin, and the Inland Rivers basin in the non-humid region of China were chose. Their drainage areas cover a range 272–94,800 km2 . Climate arid index covers from 1–7, and the runoff coefficient ranges 0.02–0.32. The hydrologic and meteorological data were collect‐ ed from each catchment from 1953–1998. Figure 2 presents the distribution of hydrologic and meteorological stations in the study region. Furthermore, more information on the 108 catchments was given by [38].

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in the Asian Monsoon Region http://dx.doi.org/10.5772/53067 67

**Figure 2.** Distribution of the hydrologic and meteorological stations in the 108 catchments in the non-humid regions of China (the solid triangle represents a hydrologic station at the outlet of catchments and the grey solid circle repre‐ sents a meteorological station)

### **3. Method**

at 3.5 m with pyranometers (CNR-1, Kipp-Zonen). Sensible and latent heat flexes were measured using the eddy correlation system at 3.7 m with a sonic anemometer-thermometer (CSAT3, Campbell). Soil heat flux was measured with a probe (HFP01SC, Hukseflux). Ob‐

To examine the regional variability, 108 catchments, locating in the Yellow River basin, the Hai River basin, and the Inland Rivers basin in the non-humid region of China were chose.

the runoff coefficient ranges 0.02–0.32. The hydrologic and meteorological data were collect‐ ed from each catchment from 1953–1998. Figure 2 presents the distribution of hydrologic and meteorological stations in the study region. Furthermore, more information on the 108

. Climate arid index covers from 1–7, and

servations were recorded as 30-minute averages.

66 Evapotranspiration - An Overview

**Figure 1.** Location of the two flux experiment sites

Their drainage areas cover a range 272–94,800 km2

**2.2. Study catchments**

catchments was given by [38].

In a closed system without advection, the CR can be expressed as equation (3) in which *LE*<sup>w</sup> is estimated using equation (4) with *α* = 1.26. However, in a real open environment the hori‐ zontal advection can't be neglected. Therefore, taking the effect of the horizontal advection into account, we have two methods, and the one is

$$A = LE + LE\_p - 2 \times 1.26 \frac{A}{A+\chi} \left(R\_n - G\right),\tag{6}$$

i.e., the CR is modified as*LE* + *L E*<sup>p</sup> =2*L E*<sup>w</sup> + *A*. The value of *A* indicated the effect of the horizontal advection of both energy and water vapor. Morton [10] suggested a similar equa‐ tion*LE* + *L E*<sup>p</sup> =2×1.26 *Δ <sup>Δ</sup>* <sup>+</sup> *<sup>γ</sup>* (*R*<sup>n</sup> <sup>−</sup>*<sup>G</sup>* <sup>+</sup> *<sup>A</sup>*m), where *A*m was an empirical correction factor for advection. If there is a seasonal and regional variability in the CR, *A*should have a seasonal and regional variation. The other one is focusing on the Priestley-Taylor parameter *α* to re‐ veal seasonal and regional variability of the CR. According to Equations (3) and (4), *α*can be calculated as

$$\alpha = \frac{\gamma + A}{2A} \cdot \frac{LE + LE\_p}{R\_n - G} \tag{7}$$

and decreases at last until July or August. Similarly, Figure 4 shows the Priestley-Taylor's parameter *α* has a seasonal variation with a maximum in winter and a minimum in summ‐ er. In general, we discern seasonal variation of*α*, although the points are scattered in winter.

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in

the Asian Monsoon Region http://dx.doi.org/10.5772/53067 69

In particular, at Kogma, *α*has a maximum of 1.7 approximately in February, then falls to a minimum of 1.1 approximately in summer; it increases thereafter, until winter, which ranges from 1.08–1.40 between April and October. At Weishan, *α*has a mean of 1.18 during the summer monsoon period, and about 1.92 during the winter monsoon. Monthly *α* varies

Figure 3 also shows that the Priestley-Taylor's parameter *α* increases with latitude increas‐ ing, which is the largest at the Yakutsk site in Siberia of Russia and the smallest at the Kog‐

12312312312312312312312312312312312 3123123123123123123123123123123123123 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

( B)

12312312312312312312312312312312312 3123123123123123123123123123123123123 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1998 1999

( A)

Yakutsk Kogma Tibet Hefei

from 1.02–1.40 between May and October.

ma site in Thailand in the same season.

Weishan(2005-2006) Tiski(2000)

**Figure 3.** Seasonal and regional variability of the horizontal advection.



0

1

*A*(mm/d)

2

3

*A*(mm/d)

Similar to*A*, *α*should have a seasonal and regional variation if there is a seasonal and region‐ al variability in the CR.

The wet environment evaporation *LE*w was estimated by the Priestley-Taylor equation. To calculate potential evaporation *LE*p, the Penman equation [39] has been suggested by [10, 14, 40]

$$LE\_p = \frac{A}{A+\chi} \left(R\_n - G\right) + \frac{\chi}{A+\chi} E\_{\chi\sigma} \tag{8}$$

where *E*A is the drying power of the air. This can be estimated by

$$E\_{\rm A} = f(\mu) \left( e^\* - e \right) \tag{9}$$

where *e*\* (kPa) and *e* (kPa) are the saturated and actual vapor pressures at the same air tem‐ perature, respectively. The wind function *f* (*u*) can be estimated as

$$f(\mu) = 0.26 \left( 1 + 0.54\mu \right) \tag{10}$$

where *u* (m/s) is mean wind speed at 2 m height.

Actual evaporation *LE* was observed using the eddy correlation technique at the flux experi‐ ment sites, and was calculated from the annual water balance by ignoring the inter-annual change of water storage in these catchments.

### **4. Results**

#### **4.1. Seasonal and regional variability observed in the flux experiment sites**

The horizontal advection term *A* in the modified CR and the Priestley-Taylor parameter *α* at the six sites were calculated using equations (6) and (7) respectively based on the daily data. Then the daily values were averaged on each ten-day from the starting date, and the results were shown in Figures 3 and 4. With regard to the value of *A*, Figure 3 shows different var‐ iance ranges at different sites. However, a seasonal variation is observable, i.e. the value of *A* reaches the minimum in July or August, then rises up to the maximum in March or April, and decreases at last until July or August. Similarly, Figure 4 shows the Priestley-Taylor's parameter *α* has a seasonal variation with a maximum in winter and a minimum in summ‐ er. In general, we discern seasonal variation of*α*, although the points are scattered in winter.

p

 g

 Dg

( ) \*

where *e*\* (kPa) and *e* (kPa) are the saturated and actual vapor pressures at the same air tem‐

Actual evaporation *LE* was observed using the eddy correlation technique at the flux experi‐ ment sites, and was calculated from the annual water balance by ignoring the inter-annual

The horizontal advection term *A* in the modified CR and the Priestley-Taylor parameter *α* at the six sites were calculated using equations (6) and (7) respectively based on the daily data. Then the daily values were averaged on each ten-day from the starting date, and the results were shown in Figures 3 and 4. With regard to the value of *A*, Figure 3 shows different var‐ iance ranges at different sites. However, a seasonal variation is observable, i.e. the value of *A* reaches the minimum in July or August, then rises up to the maximum in March or April,

**4.1. Seasonal and regional variability observed in the flux experiment sites**

<sup>A</sup>*E fu e e* = - () , (9)

*f u*( ) 0.26 1 0.54 = + ( ) *u* , (10)


(8)

*LE LE R G*

Similar to*A*, *α*should have a seasonal and regional variation if there is a seasonal and region‐

The wet environment evaporation *LE*w was estimated by the Priestley-Taylor equation. To calculate potential evaporation *LE*p, the Penman equation [39] has been suggested by [10, 14,

<sup>n</sup> 2

( ) pn A *LE R G E* ,

= -+ + +

D

Dg

where *E*A is the drying power of the air. This can be estimated by

perature, respectively. The wind function *f* (*u*) can be estimated as

where *u* (m/s) is mean wind speed at 2 m height.

change of water storage in these catchments.

**4. Results**

+ +

D

= ×

g D

a

al variability in the CR.

68 Evapotranspiration - An Overview

40]

In particular, at Kogma, *α*has a maximum of 1.7 approximately in February, then falls to a minimum of 1.1 approximately in summer; it increases thereafter, until winter, which ranges from 1.08–1.40 between April and October. At Weishan, *α*has a mean of 1.18 during the summer monsoon period, and about 1.92 during the winter monsoon. Monthly *α* varies from 1.02–1.40 between May and October.

Figure 3 also shows that the Priestley-Taylor's parameter *α* increases with latitude increas‐ ing, which is the largest at the Yakutsk site in Siberia of Russia and the smallest at the Kog‐ ma site in Thailand in the same season.

**Figure 3.** Seasonal and regional variability of the horizontal advection.

#### **4.2. Regional variability observed in the 108 catchments**

The parameter *α* for the 108 catchments has a large variance, which ranges from 0.87 to 1.48 (with an average of 1.17). Nevertheless, the relation of *α* with latitude can be revealed, as shown in Figure 3. The parameter *α* increases with the latitude increasing over the region ranging 33–40 °N, while it decreases with the latitude increasing over the region ranging 40– 42 °N. Also, the relation of the parameter *α* with the longitude was plotted in Figure 4. It can be found that the catchment with larger longitude is approximately closer to the ocean. Therefore, Figure 4 shows that *α* increases with the distance from ocean decreasing.

### **5. Discussion**

#### **5.1. Seasonal variability**

The value of *A* reaches the minimum in July or August, then rises up to the maximum in March or April, and decreases at last until July or August. As shown in Figure 4, the Priest‐ ley-Taylor's parameter *α* has a seasonal variation with a maximum in winter and a mini‐ mum in summer [41], which is similar to that *A* has. Also, most studies did not introduce an advection item, but instead adjusted the Priestley-Taylor parameter for advection. There‐ fore, we focus on the variation of *α* in this chapter.

**Figure 5.** Relation of parameter α with latitude

**Figure 6.** Relation of parameter α with longitude (larger longitude indicating the catchment being nearer the ocean)

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in

the Asian Monsoon Region http://dx.doi.org/10.5772/53067 71

DeBruin and Keijman [33] reported that *α* differed slightly from 1.26 in May–September, but was about 1.50 in April and October. Similarly, the *α* value at Kogma was between 1.07–1.26 in May–September and was 1.40 in April. At the Weishan site, *α*had a similar seasonal varia‐ tion but larger values, up to 1.60 in April and 1.38 in October. At the same time, another phenomenon, that the Asian monsoon is from ocean to continent in June–October and from continent to ocean in October–June [42], should be noticing. As a result, the monsoon leads to air temperature decreasing and humid increasing in June–October, while both air temper‐ ature and humidity increasing in October–June above the continent. It was therefore specu‐ lated that there is a certain relation between the seasonal variation in *α* and the monsoon.

**Figure 4.** Seasonal and regional variability of the Priestley-Taylor's parameter α

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in the Asian Monsoon Region http://dx.doi.org/10.5772/53067 71

**Figure 5.** Relation of parameter α with latitude

**4.2. Regional variability observed in the 108 catchments**

fore, we focus on the variation of *α* in this chapter.

**Figure 4.** Seasonal and regional variability of the Priestley-Taylor's parameter α

**5. Discussion**

**5.1. Seasonal variability**

70 Evapotranspiration - An Overview

The parameter *α* for the 108 catchments has a large variance, which ranges from 0.87 to 1.48 (with an average of 1.17). Nevertheless, the relation of *α* with latitude can be revealed, as shown in Figure 3. The parameter *α* increases with the latitude increasing over the region ranging 33–40 °N, while it decreases with the latitude increasing over the region ranging 40– 42 °N. Also, the relation of the parameter *α* with the longitude was plotted in Figure 4. It can be found that the catchment with larger longitude is approximately closer to the ocean.

The value of *A* reaches the minimum in July or August, then rises up to the maximum in March or April, and decreases at last until July or August. As shown in Figure 4, the Priest‐ ley-Taylor's parameter *α* has a seasonal variation with a maximum in winter and a mini‐ mum in summer [41], which is similar to that *A* has. Also, most studies did not introduce an advection item, but instead adjusted the Priestley-Taylor parameter for advection. There‐

Therefore, Figure 4 shows that *α* increases with the distance from ocean decreasing.

**Figure 6.** Relation of parameter α with longitude (larger longitude indicating the catchment being nearer the ocean)

DeBruin and Keijman [33] reported that *α* differed slightly from 1.26 in May–September, but was about 1.50 in April and October. Similarly, the *α* value at Kogma was between 1.07–1.26 in May–September and was 1.40 in April. At the Weishan site, *α*had a similar seasonal varia‐ tion but larger values, up to 1.60 in April and 1.38 in October. At the same time, another phenomenon, that the Asian monsoon is from ocean to continent in June–October and from continent to ocean in October–June [42], should be noticing. As a result, the monsoon leads to air temperature decreasing and humid increasing in June–October, while both air temper‐ ature and humidity increasing in October–June above the continent. It was therefore specu‐ lated that there is a certain relation between the seasonal variation in *α* and the monsoon.

The energy balance near the ground surface can be expressed as

$$R\_{\rm in} - G = LE + H \tag{11}$$

vapor transported from the lower atmospheric layer over the ocean to that over land. This

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in

the Asian Monsoon Region http://dx.doi.org/10.5772/53067 73

From the Figure 3, it can be seen that the magnitude of the horizontal advection effect is the largest at the Yakutsk site and the smallest at the Kogma site in the same season. This indi‐ cates that the magnitude of the horizontal advection effect increases with latitude increas‐ ing. Figure 4 shows that the Priestley-Taylor's parameter *α* increases with latitude increasing, which is the largest at the Yakutsk site and the smallest at the Kogma site in the same season. This is also consistent with the results given by Xu and Singh [23], in which *α* =1.0, 1.04 and 1.18 at the catchments in Eastern China (29°15′N, 121°10′E), Northwestern Cy‐ prus (about 35°N), and Central Sweden (59°53′N, 17°35′E) respectively for a long-term

Across the 108 catchments, the parameter *α* increases with the latitude increasing over the region ranging 33–40 °N but decreases with the latitude increasing over the region ranging 40–42 °N. As shown in Figure 2, the catchments ranging 40–42 °N belong to the Hai River basin, which are adjacent to the Bohai Sea. Those catchments have an increasing distance from the sea with their latitude increasing. The possible cause is that the change in distance from the sea has a larger effect on the horizontal advection than increasing latitude has. This was revealed by Figure 6, i.e. the catchments farther from the sea have larger parameter*α*. In addition, Figure 6 shows larger dispersion in the relation between *α* and the longitude, the possible cause for which was that the flexuous coastline results in the catchments with same

The complementary relationship (CR) between actual evaporation and potential evaporation has been widely used to explain the evaporation paradox, as well as to estimate regional evaporation. The theoretical foundation of the CR is the Bouchet hypothesis, including the constraint that exchanges of water vapor and energy between the considered system and its exterior are constant. In reality, the atmosphere does not always satisfy the constraint. In the Asian monsoon region, atmospheric motions have a significant seasonal variation, accompa‐ nied by transport of water vapor and energy. Through analyzing seasonal variation in pa‐ rameter *α* of the Priestley-Taylor equation for calculating wet environment evaporation, this chapter analyzed effects of horizontal advection on the CR. *α*has a significant seasonal varia‐ tion, which is larger in winter than in summer. The possible cause is that the summer mon‐ soon increases water vapor content and decreases air temperature, whereas the winter monsoon increases both water vapor and air temperature. The parameter *α* increases with latitude, as a result of the annual transport of the energy and vapor from low latitudes to high latitudes through the atmospheric and oceanic flows. Atmospheric circulation between continent and ocean transports vapor from the oceans to the land, so *α* decreases with dis‐

therefore leads to a regional variability in the CR.

longitude having different distance from the ocean.

mean.

**6. Conclusion**

tance from ocean.

Advection impacts the CR by modifying air temperature, water vapor pressure, and others. As a result, the partition of available energy into latent and sensible heats will change, and the presence of advection causes *LE* >*R*<sup>n</sup> −*G* [43-45] when the direction of sensible heat *H* is downward. Differences in thermodynamic properties between land and ocean produce gen‐ erally higher temperatures and less water vapor over continents than over oceans in summ‐ er, and lower temperatures and less water vapor over continents than oceans in winter. Consequently over continents, atmospheric circulation between land and ocean decreases temperature and increases vapor during summer, and increases both temperature and va‐ por in winter. It seems paradoxical that the winter monsoon increases temperature over con‐ tinents. In fact, we find that the distribution of isotherms is not completely latitudinal; temperature has an inverse relationship with distance from the ocean in identical latitude continental regions. This indicates heat transport from ocean to continent by advection. We speculate that the circulation increases temperature over land, and the increase weakens with distance from the ocean, as a result of sensible heat transport.

Advection possibly affects the major assumption of the CR, that energy release from a de‐ crease in actual evaporation compensates the increase in potential evaporation [46]. The monsoon transports water vapor and sensible heat between ocean and continent, which causes additional seasonal changes to air humidity and temperature. The effects of these changes on the two sides of Equation (3) are asymmetric. On the left side, the terms *LE*p and *LE* can be determined by climate variables (such as air temperature and vapor pressure), which include the effect of horizontal advection. On the right side, the effect of horizontal advection on *LE*w is parameterized as only the change of air temperature (if the effect of ra‐ diation is neglected), not including changes of wind speed and humidity.

We assume a system without horizontal advection, where Equation (3) is satisfied. Since the summer monsoon imports a large amount of water vapor and reduces latent heat, the dry‐ ing power of the air *E*<sup>A</sup> decreases, and increases the ratio *H*/(*R*n – *G*) (i.e., *LE* decreases). This reduces (*LE* + *LE*p) but causes less change in *LEw*. This translates into a smaller *α* in Equation (3). The winter monsoon increases *E*A and *LE*/(*R*n – *G*), which produces an increase in (*LE* + *LE*p) but less change in *LE*w, resulting in a larger in Equation (3). Following the same reason‐ ing, we can explain the seasonal variation in *α* revealed by [33]. According to the CR, with an unlimited water supply above a lake, the evaporation *LE* equals the potential evaporation *LE*p. In summer, horizontal advection reduces (*LE* + *LE*p), resulting in a small *α* value, but a large *α* in winter.

#### **5.2. Regional variability**

In addition, the effect of horizontal advection also has a regional variation. Since energy is transported by atmospheric and oceanic circulations from low to high latitudes, and water vapor transported from the lower atmospheric layer over the ocean to that over land. This therefore leads to a regional variability in the CR.

From the Figure 3, it can be seen that the magnitude of the horizontal advection effect is the largest at the Yakutsk site and the smallest at the Kogma site in the same season. This indi‐ cates that the magnitude of the horizontal advection effect increases with latitude increas‐ ing. Figure 4 shows that the Priestley-Taylor's parameter *α* increases with latitude increasing, which is the largest at the Yakutsk site and the smallest at the Kogma site in the same season. This is also consistent with the results given by Xu and Singh [23], in which *α* =1.0, 1.04 and 1.18 at the catchments in Eastern China (29°15′N, 121°10′E), Northwestern Cy‐ prus (about 35°N), and Central Sweden (59°53′N, 17°35′E) respectively for a long-term mean.

Across the 108 catchments, the parameter *α* increases with the latitude increasing over the region ranging 33–40 °N but decreases with the latitude increasing over the region ranging 40–42 °N. As shown in Figure 2, the catchments ranging 40–42 °N belong to the Hai River basin, which are adjacent to the Bohai Sea. Those catchments have an increasing distance from the sea with their latitude increasing. The possible cause is that the change in distance from the sea has a larger effect on the horizontal advection than increasing latitude has. This was revealed by Figure 6, i.e. the catchments farther from the sea have larger parameter*α*. In addition, Figure 6 shows larger dispersion in the relation between *α* and the longitude, the possible cause for which was that the flexuous coastline results in the catchments with same longitude having different distance from the ocean.

### **6. Conclusion**

The energy balance near the ground surface can be expressed as

72 Evapotranspiration - An Overview

with distance from the ocean, as a result of sensible heat transport.

diation is neglected), not including changes of wind speed and humidity.

large *α* in winter.

**5.2. Regional variability**

Advection impacts the CR by modifying air temperature, water vapor pressure, and others. As a result, the partition of available energy into latent and sensible heats will change, and the presence of advection causes *LE* >*R*<sup>n</sup> −*G* [43-45] when the direction of sensible heat *H* is downward. Differences in thermodynamic properties between land and ocean produce gen‐ erally higher temperatures and less water vapor over continents than over oceans in summ‐ er, and lower temperatures and less water vapor over continents than oceans in winter. Consequently over continents, atmospheric circulation between land and ocean decreases temperature and increases vapor during summer, and increases both temperature and va‐ por in winter. It seems paradoxical that the winter monsoon increases temperature over con‐ tinents. In fact, we find that the distribution of isotherms is not completely latitudinal; temperature has an inverse relationship with distance from the ocean in identical latitude continental regions. This indicates heat transport from ocean to continent by advection. We speculate that the circulation increases temperature over land, and the increase weakens

Advection possibly affects the major assumption of the CR, that energy release from a de‐ crease in actual evaporation compensates the increase in potential evaporation [46]. The monsoon transports water vapor and sensible heat between ocean and continent, which causes additional seasonal changes to air humidity and temperature. The effects of these changes on the two sides of Equation (3) are asymmetric. On the left side, the terms *LE*p and *LE* can be determined by climate variables (such as air temperature and vapor pressure), which include the effect of horizontal advection. On the right side, the effect of horizontal advection on *LE*w is parameterized as only the change of air temperature (if the effect of ra‐

We assume a system without horizontal advection, where Equation (3) is satisfied. Since the summer monsoon imports a large amount of water vapor and reduces latent heat, the dry‐ ing power of the air *E*<sup>A</sup> decreases, and increases the ratio *H*/(*R*n – *G*) (i.e., *LE* decreases). This reduces (*LE* + *LE*p) but causes less change in *LEw*. This translates into a smaller *α* in Equation (3). The winter monsoon increases *E*A and *LE*/(*R*n – *G*), which produces an increase in (*LE* + *LE*p) but less change in *LE*w, resulting in a larger in Equation (3). Following the same reason‐ ing, we can explain the seasonal variation in *α* revealed by [33]. According to the CR, with an unlimited water supply above a lake, the evaporation *LE* equals the potential evaporation *LE*p. In summer, horizontal advection reduces (*LE* + *LE*p), resulting in a small *α* value, but a

In addition, the effect of horizontal advection also has a regional variation. Since energy is transported by atmospheric and oceanic circulations from low to high latitudes, and water

<sup>n</sup> *R G LE H* -= + (11)

The complementary relationship (CR) between actual evaporation and potential evaporation has been widely used to explain the evaporation paradox, as well as to estimate regional evaporation. The theoretical foundation of the CR is the Bouchet hypothesis, including the constraint that exchanges of water vapor and energy between the considered system and its exterior are constant. In reality, the atmosphere does not always satisfy the constraint. In the Asian monsoon region, atmospheric motions have a significant seasonal variation, accompa‐ nied by transport of water vapor and energy. Through analyzing seasonal variation in pa‐ rameter *α* of the Priestley-Taylor equation for calculating wet environment evaporation, this chapter analyzed effects of horizontal advection on the CR. *α*has a significant seasonal varia‐ tion, which is larger in winter than in summer. The possible cause is that the summer mon‐ soon increases water vapor content and decreases air temperature, whereas the winter monsoon increases both water vapor and air temperature. The parameter *α* increases with latitude, as a result of the annual transport of the energy and vapor from low latitudes to high latitudes through the atmospheric and oceanic flows. Atmospheric circulation between continent and ocean transports vapor from the oceans to the land, so *α* decreases with dis‐ tance from ocean.

### **Acknowledgements**

Data were from the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment in a Tropical region (GAME-T), and Weishan flux observation was supported by the National Natural Science Foundation of China (grant nos. 50909051, 50939004, and 51025931). This research was also supported by the Ministry of Science and Technology of China (2011IM011000).

[9] Hobbins, M. T., J. A. Ramirez, and T. C. Brown (2001), The complementary relation‐ ship in estimation of regional evapotranspiration: An enhanced Advection-Aridity

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in

the Asian Monsoon Region http://dx.doi.org/10.5772/53067 75

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### **Author details**

Hanbo Yang\* and Dawen Yang

\*Address all correspondence to: yanghanbo@tsinghua.edu.cn

State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engi‐ neering, Tsinghua University, Beijing, China

### **References**


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**Acknowledgements**

74 Evapotranspiration - An Overview

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[25] Yang, H.B., Yang, D.W., Lei, Z.D., Sun, F.B. and Cong, Z.T., 2008. Reginal varibility of the complementary relationship between actual and potential evapotranspirations (in Chinese). Journal of Tsinghua University (Science and Technique), 48(9): 1413-1416.

[38] Yang, D. W., F. B. Sun, Z. Y. Liu, Z. T. Cong, G. H. Ni, and Z. D. Lei (2007), Analyz‐ ing spatial and temporal variability of annual water-energy balance in nonhumid re‐ gions of China using the Budyko hypothesis, Water Resour Res, 43, W04426, doi:

Seasonal and Regional Variability of the Complementary Relationship Between Actual and Potential Evaporations in

the Asian Monsoon Region http://dx.doi.org/10.5772/53067 77

[39] Penman, H. L. (1948), Natural evaporation from open water, bare soil and grass, Pro‐ ceeding of the Royal Society of London. Series A, Mathematical and Physical Scien‐

[40] Morton, F. I. (1976), Climatological estimates of evapotranspiration, J. Hydraul. Div.

[41] Yang, H. B., D. W. Yang, and Z. D. Lei (2012), Seasonal variability of the complemen‐ tary relationship in the Asian monsoon region, Hydrol Process, DOI: 10.1002/hyp.

[42] Ye, D. Z., Tao, S. Y., and Li, M. C., 1958. The abrupt change of circulafion over the Northern Hemisphere during June and October (in Chinese). Acta Meteorologica

[43] Rijks, D. A. (1971), Water Use by Irrigated Cotton in Sudan. III. Bowen Ratios and

[44] Rosenberg, N. J., and S. B. Verma (1976), Extreme Evapotranspiration by Irrigated Al‐ falfa: A Consequence of the 1976 Midwestern Drought, J Appl Meteorol, 17, 934-941.

[45] Wright, J. L., and M. E. Jensen (1972), Peak water requirements of crops in southern

[46] Lhomme, J. P., and L. Guilioni (2006), Comments on some articles about the comple‐

Advective Energy, The Journal of Applied Ecology, 8, 643-663.

Idaho, Journal of Irrigation and Drainage Division, 96, 193-201.

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[25] Yang, H.B., Yang, D.W., Lei, Z.D., Sun, F.B. and Cong, Z.T., 2008. Reginal varibility of the complementary relationship between actual and potential evapotranspirations (in Chinese). Journal of Tsinghua University (Science and Technique), 48(9):

[26] Gao, G., C. Xu, D. Chen, and V. P. Singh (2012), Spatial and temporal characteristics of actual evapotranspiration over Haihe River basin in China, Stoch Env Res Risk A,

[27] Pettijohn, J. C., and G. D. Salvucci (2006), Impact of an unstressed canopy conduc‐ tance on the Bouchet-Morton complementary relationship, Water Resour Res, 42(9),

[28] Yang, H. B., D. W. Yang, Z. D. Lei, F. B. Sun, and Z. T. Cong (2009), Variability of complementary relationship and its mechanism on different time scales F-5023-2011

[29] Davies, J. A., and C. D. Allen (1973), Equilibrium, potential, and actual evaporation

[30] Eichinger, W. E., M. B. Parlange, and H. Stricker (1996), On the concept of equilibri‐ um evaporation and the value of the Priestley-Taylor coefficient, Water Resour Res,

[31] Stewart, R. B., and W. R. Rouse (1976), A simple method for determining the evapo‐

[32] Stewart, R. B., and W. R. Rouse (1977), Substantiation of the Priestley-Taylor parame‐ ter α= 1.26 for potential evaporation in high latitudes, J Appl Meteorol, 16, 649-650.

[33] DeBruin, H. A. R., and J. Q. Keijman (1979), The Priestley-Taylor evaporation model applied to a large shallow lake in the Netherland, J Appl Meteorol, 18, 898-903.

[34] Komatsu, H., N. Yoshida, H. Takizawa, I. Kosaka, C. Tantasirin, and M. Suzuki (2003), Seasonal trend in the occurrence of nocturnal drainage flow on a forested

slope under a tropical monsoon climate, Bound-Lay Meteorol, 106(3), 573-592.

[35] Kume, T., H. Takizawa, N. Yoshifuji, K. Tanaka, C. Tantasirin, N. Tanaka, and M. Su‐ zuki (2007), Impact of soil drought on sap flow and water status of evergreen trees in a tropical monsoon forest in northern Thailand, Forest Ecol Manag, 238(1-3), 220-230.

[36] Dolman, A. J., T. C. Maximov, E. J. Moors, A. P. Maximov, J. A. Elbers, A. V. Kono‐ nov, M. J. Waterloo, and M. K. van der Molen (2004), Net ecosystem exchange of car‐ bon dioxide and water of far eastern Siberian Larch (Larix cajanderii) on permafrost,

[37] Lei, H. M., and D. W. Yang (2010), Interannual and seasonal variability in evapo‐ transpiration and energy partitioning over an irrigated cropland in the North China

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G-4441-2010, Sci China Ser E, 52(4), 1059-1067.


**Chapter 5**

**Satellite-Based Energy Balance Approach to Assess**

Previous studies across the High Plains and the Arid West of the United States have pro‐ duced widely varying impacts of riparian evapotranspiration (ET) on surface and ground water. Many producers as well as various state agencies have advocated removing all trees along the river basins as a method of riparian control for water reclamation. Although eradi‐ cation of trees might be an effective method for water reclamation in the short-term, it has not been yet proven whether such water savings are possible on a stream level. Mean water use of riparian trees has been reported in relatively few studies, and most of the previous studies have been of short duration. The water use for saltcedar (Tamarix spp.) was estimat‐

for Fremont cottonwood (Populus fremontii S. Wats.) varied from 57.6 L d-1 for 33 cm2 swa

plant communities are complex ecosystems that, through an intimate relationship with the fluvial dynamics of river systems, are as much described by their continual cycle of disturb‐ ance and succession as by the vegetation that makes up their multi-storied habitats. Current‐ ly, there is uncertainty in the water use of riparian systems due to the narrow and sparse vegetation commonly associated with them. Local, state and federal water management reg‐ ulatory agencies need good quality water use estimates on unmanaged riparian systems. High frequency micrometeorological flux measurements such as Eddy Correlation System (ECS) have been used to estimate water use by balancing fluxes of sensible and latent heat with total energy incident on a riparian area. However, the technique is most effective when

sap wood area (swa) (Smith et al. 1998), 56.8 L d-1 for 33 cm2 swa

© 2013 Kamble et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Kamble et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

swa (Owens and Moore, 2007). The water use

swa (Schaeffer et al. 2000). Riparian

**Riparian Water Use**

http://dx.doi.org/10.5772/52929

**1. Introduction**

ed at 15.9 L d-1 for 10 cm2

(Nagler et al. 2003), and 29.9 L d-1 for 100 cm2

(Nagler et al. 2003) to as high as 499.7 L d-1 for 833 cm2

Baburao Kamble, Ayse Irmak, Derrel L. Martin, Kenneth G. Hubbard, Ian Ratcliffe, Gary Hergert,

Sunil Narumalani and Robert J. Oglesby

Additional information is available at the end of the chapter

## **Satellite-Based Energy Balance Approach to Assess Riparian Water Use**

Baburao Kamble, Ayse Irmak, Derrel L. Martin, Kenneth G. Hubbard, Ian Ratcliffe, Gary Hergert, Sunil Narumalani and Robert J. Oglesby

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/52929

### **1. Introduction**

Previous studies across the High Plains and the Arid West of the United States have pro‐ duced widely varying impacts of riparian evapotranspiration (ET) on surface and ground water. Many producers as well as various state agencies have advocated removing all trees along the river basins as a method of riparian control for water reclamation. Although eradi‐ cation of trees might be an effective method for water reclamation in the short-term, it has not been yet proven whether such water savings are possible on a stream level. Mean water use of riparian trees has been reported in relatively few studies, and most of the previous studies have been of short duration. The water use for saltcedar (Tamarix spp.) was estimat‐ ed at 15.9 L d-1 for 10 cm2 sap wood area (swa) (Smith et al. 1998), 56.8 L d-1 for 33 cm2 swa (Nagler et al. 2003), and 29.9 L d-1 for 100 cm2 swa (Owens and Moore, 2007). The water use for Fremont cottonwood (Populus fremontii S. Wats.) varied from 57.6 L d-1 for 33 cm2 swa (Nagler et al. 2003) to as high as 499.7 L d-1 for 833 cm2 swa (Schaeffer et al. 2000). Riparian plant communities are complex ecosystems that, through an intimate relationship with the fluvial dynamics of river systems, are as much described by their continual cycle of disturb‐ ance and succession as by the vegetation that makes up their multi-storied habitats. Current‐ ly, there is uncertainty in the water use of riparian systems due to the narrow and sparse vegetation commonly associated with them. Local, state and federal water management reg‐ ulatory agencies need good quality water use estimates on unmanaged riparian systems. High frequency micrometeorological flux measurements such as Eddy Correlation System (ECS) have been used to estimate water use by balancing fluxes of sensible and latent heat with total energy incident on a riparian area. However, the technique is most effective when

© 2013 Kamble et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Kamble et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

applied to an agricultural land where the plant canopy is relatively homogeneous both in composition and in height, and the fetch is relatively large. Estimating riparian ET in semiarid river basins is difficult because of the complicated geometry of a typical riparian zone (Goodrich et al., 2000). Riparian forests are typically characterized by long narrow strips of vegetation directly adjacent to stream channels. These strips of forest are often relatively high (~5-20m), not more than 20 m wide on either side of a watercourse and may consist of several different species and size classes of trees (Goodrich et al., 2000). This geometry pre‐ cludes the use of classical meteorological flux measurements as the required fetch require‐ ments usually are not satisfied. Without the required fetch, the total flux measured using this system will not representf the riparian zone. Water use varies spatially even in the same riparian species because of variation in tree age, height, density, and surroundings. There‐ fore, the estimation based on in-situ measurements is at stand scale and adds uncertainty when applied to riparian corridors of larger scale. In addition, application of high frequency meteorological flux measurements to quantify ET along stream channels in a basin is limited due to the number of measurement sites needed and the operational expense of such a dense network. The tree sap flow measurements capture variations in transpiration demand as a function of atmospheric demand and water availability. However, there is some uncer‐ tainty associated with estimation of stand-level transpiration from individual plant sap flow measurements.

able method (Irmak and Kamble, 2009;, Kamble and Irmak, 2011;2008; Irmak et al., 2011). The literature also shows that the thermal signatures of the riparian systems indicate a pretty complete picture of the amount of evaporative cooling within boundaries of the ecosystem, including evaporation from wet soil or under structure. Therefore, the MET‐ RICtm or related remote sensing based energy balance approach is one of the better means to make the ET estimates and to monitor before and after vegetation and land-

Satellite-Based Energy Balance Approach to Assess Riparian Water Use

http://dx.doi.org/10.5772/52929

81

The information presented here is taken from the results of projects conducted at the Uni‐ versity of Nebraska. Our primary goal in this chapter is to illustrate spatiotemporal estima‐ tion of ET using satellite-and aerial derived spectral radiances in conjunction with a land surface energy balance model and flux measurements to evaluate water use and water dis‐ tribution within and between riparian systems, including invasive species, along the North Platte River Basin (NPRB) in Nebraska (NE). Specific objectives were to quantify daily and seasonal distributions of ET in riparian systems within the NPRB, quantify surface energy balance flux components for riparian systems, and compare water use among riparian spe‐ cies, both native and invasive, by utilizing ET maps together with riparian species distribu‐

The Nebraska Panhandle is an area in western Nebraska bounded by the mountainous (Rocky Mountains) states of Wyoming and Colorado on the west and the city of North Platte,Nebraska on the east, wherein the main stem of the North Platte River flows from west to east. The Nebraska panhandle roughly encompasses the area in Nebraska between 102° and 104°W longitude and 41° and 43°N latitude. The elevation ranges from 3,000-5,000 ft and the growing season is characterized by hot days and cool nights. The Panhandle addi‐ tionally has a high desert-type semi-arid climate receiving 14-16 inches rainfall per year. Flows in the North Platte River are derived mainly from snowmelt runoff from the Rocky Mountains and surface runoff and ground-water discharge (Bentall and Shaffer, 1979; Gu‐ tentag et. al., 1984). The eastern and central parts of Nebraska predominantly are cultivatedagricultural land with increasing amounts of rangeland to the west (Center for Advanced Land Management Information Technologies, 2000; U.S. Geological Survey, 1999-2000). Ri‐ parian vegetation is restricted to lowlands located along the North Platte River and its tribu‐ taries. Forests and grasslands (including wet meadows) in the riparian zone are the predominant communities, but other categories are present including herbaceous, shrub,

A total of 8 Landsat5 and Landsat7 satellite images (Path 33, Row 31) from 2005 were used for this study (table 2). Each Landsat scene size is approximately 170 km X 185 km with a

use modification.

tion map.

**2.1. Study area**

**2. Material and methods**

and emergent wetlands (Currier et. al., 1985).

**2.2. Satellite image processing**

Remote sensing measurements can provide information with a broad spatial coverage and a repeat temporal coverage and avoid the need to rely on field databases. Calcula‐ tion of water consumption by remote sensing has benefited from significant research ef‐ forts over the last 30 years, especially the dedicated energy balance models like the TSEB (Norman et al., 1995), SEBAL (Bastiaanssen, 1998a; 1998b), SEBI (Menenti, 2000), and METRICtm(Allen et al. 2007b). In this chapter, the method used to estimate ET in riparian areas employs the model known as Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRICtm) by Allen (2007b). The METRICtm model (Allen et al. 2007b) requires parameterization of the energy balance and estimates surface energy flux‐ es based on spectral satellite measurements. The model has auto-calibration capabilities for each satellite image using ground-based calculation of alfalfa reference ET based on hourly weather data. Modifications to the energy balance algorithms for narrow riparian regions that may experience advection and different turbulence characteristics than short‐ er and more homogeneous surfaces is accomplished by running the METRICtm model with airborne data collected from June through the end of October 2009 using AISA hy‐ perspectral system hyperspectral system at Center for Advanced Land Management In‐ formation's (CALMIT's), onboard Piper Saratoga aircraft. Furthermore, frequent and intensive aerial images help to investigate patterns of surface temperature at various spacings inside riparian structures and the variation with wind speed and wind direc‐ tion. This data is also used to understand the partitioning of the available energy be‐ tween understory and overstory in the riparian system. There is some uncertainty in estimating ET from riparian systems with SRS based energy balance due to the narrow and sparse vegetation commonly associated with them. However, the high resolution of Landsat images is extremely useful for assessing ET patterns and might be the most suit‐ able method (Irmak and Kamble, 2009;, Kamble and Irmak, 2011;2008; Irmak et al., 2011). The literature also shows that the thermal signatures of the riparian systems indicate a pretty complete picture of the amount of evaporative cooling within boundaries of the ecosystem, including evaporation from wet soil or under structure. Therefore, the MET‐ RICtm or related remote sensing based energy balance approach is one of the better means to make the ET estimates and to monitor before and after vegetation and landuse modification.

The information presented here is taken from the results of projects conducted at the Uni‐ versity of Nebraska. Our primary goal in this chapter is to illustrate spatiotemporal estima‐ tion of ET using satellite-and aerial derived spectral radiances in conjunction with a land surface energy balance model and flux measurements to evaluate water use and water dis‐ tribution within and between riparian systems, including invasive species, along the North Platte River Basin (NPRB) in Nebraska (NE). Specific objectives were to quantify daily and seasonal distributions of ET in riparian systems within the NPRB, quantify surface energy balance flux components for riparian systems, and compare water use among riparian spe‐ cies, both native and invasive, by utilizing ET maps together with riparian species distribu‐ tion map.

### **2. Material and methods**

#### **2.1. Study area**

applied to an agricultural land where the plant canopy is relatively homogeneous both in composition and in height, and the fetch is relatively large. Estimating riparian ET in semiarid river basins is difficult because of the complicated geometry of a typical riparian zone (Goodrich et al., 2000). Riparian forests are typically characterized by long narrow strips of vegetation directly adjacent to stream channels. These strips of forest are often relatively high (~5-20m), not more than 20 m wide on either side of a watercourse and may consist of several different species and size classes of trees (Goodrich et al., 2000). This geometry pre‐ cludes the use of classical meteorological flux measurements as the required fetch require‐ ments usually are not satisfied. Without the required fetch, the total flux measured using this system will not representf the riparian zone. Water use varies spatially even in the same riparian species because of variation in tree age, height, density, and surroundings. There‐ fore, the estimation based on in-situ measurements is at stand scale and adds uncertainty when applied to riparian corridors of larger scale. In addition, application of high frequency meteorological flux measurements to quantify ET along stream channels in a basin is limited due to the number of measurement sites needed and the operational expense of such a dense network. The tree sap flow measurements capture variations in transpiration demand as a function of atmospheric demand and water availability. However, there is some uncer‐ tainty associated with estimation of stand-level transpiration from individual plant sap flow

Remote sensing measurements can provide information with a broad spatial coverage and a repeat temporal coverage and avoid the need to rely on field databases. Calcula‐ tion of water consumption by remote sensing has benefited from significant research ef‐ forts over the last 30 years, especially the dedicated energy balance models like the TSEB (Norman et al., 1995), SEBAL (Bastiaanssen, 1998a; 1998b), SEBI (Menenti, 2000), and METRICtm(Allen et al. 2007b). In this chapter, the method used to estimate ET in riparian areas employs the model known as Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRICtm) by Allen (2007b). The METRICtm model (Allen et al. 2007b) requires parameterization of the energy balance and estimates surface energy flux‐ es based on spectral satellite measurements. The model has auto-calibration capabilities for each satellite image using ground-based calculation of alfalfa reference ET based on hourly weather data. Modifications to the energy balance algorithms for narrow riparian regions that may experience advection and different turbulence characteristics than short‐ er and more homogeneous surfaces is accomplished by running the METRICtm model with airborne data collected from June through the end of October 2009 using AISA hy‐ perspectral system hyperspectral system at Center for Advanced Land Management In‐ formation's (CALMIT's), onboard Piper Saratoga aircraft. Furthermore, frequent and intensive aerial images help to investigate patterns of surface temperature at various spacings inside riparian structures and the variation with wind speed and wind direc‐ tion. This data is also used to understand the partitioning of the available energy be‐ tween understory and overstory in the riparian system. There is some uncertainty in estimating ET from riparian systems with SRS based energy balance due to the narrow and sparse vegetation commonly associated with them. However, the high resolution of Landsat images is extremely useful for assessing ET patterns and might be the most suit‐

measurements.

80 Evapotranspiration - An Overview

The Nebraska Panhandle is an area in western Nebraska bounded by the mountainous (Rocky Mountains) states of Wyoming and Colorado on the west and the city of North Platte,Nebraska on the east, wherein the main stem of the North Platte River flows from west to east. The Nebraska panhandle roughly encompasses the area in Nebraska between 102° and 104°W longitude and 41° and 43°N latitude. The elevation ranges from 3,000-5,000 ft and the growing season is characterized by hot days and cool nights. The Panhandle addi‐ tionally has a high desert-type semi-arid climate receiving 14-16 inches rainfall per year. Flows in the North Platte River are derived mainly from snowmelt runoff from the Rocky Mountains and surface runoff and ground-water discharge (Bentall and Shaffer, 1979; Gu‐ tentag et. al., 1984). The eastern and central parts of Nebraska predominantly are cultivatedagricultural land with increasing amounts of rangeland to the west (Center for Advanced Land Management Information Technologies, 2000; U.S. Geological Survey, 1999-2000). Ri‐ parian vegetation is restricted to lowlands located along the North Platte River and its tribu‐ taries. Forests and grasslands (including wet meadows) in the riparian zone are the predominant communities, but other categories are present including herbaceous, shrub, and emergent wetlands (Currier et. al., 1985).

#### **2.2. Satellite image processing**

A total of 8 Landsat5 and Landsat7 satellite images (Path 33, Row 31) from 2005 were used for this study (table 2). Each Landsat scene size is approximately 170 km X 185 km with a repeat cycle of 16 days. The Thematic Mapper (TM) sensor on-board Landsat 5 has seven spectral bands with 30 m spatial resolution in reflective bands and 120 m resolution in ther‐ mal bands. The Enhanced Thematic Mapper (ETM) on-board Landsat 7 has eight spectral bands including panchromatic band (not used in this study). UTM zone 13 and NAD 1983 was the projection and datum used. For Landsat 7 imagery, the high gain on the thermal band was used. The Landsat 7 thermal band is acquired in both low and high gain. The low gain provides an expanded dynamic range generating less saturation at high values, but lower radiometric resolution (sensitivity).

tool of Erdas Imagine® image processing software (Leica Geosystems Geospatial Imaging, LLC) was used to code the METRICtm algorithms. An iterative procedure was followed for sensible heat flux estimation using hot and cold pixels. From each processed image, average of 9 (3 X 3) pixels cantered over the field measurement location was used for the comparison

Satellite-Based Energy Balance Approach to Assess Riparian Water Use

http://dx.doi.org/10.5772/52929

83

High quality hourly weather data consisting of air temperature, relative humidity, wind speed, incoming solar radiation, and precipitation are required for the operation of the METRICtm model. Hourly weather data were acquired from the High Plains Regional Cli‐ mate Center's (HPRCC) Automated Weather Data Network (AWDN). Weather data were acquired for 2005 from the Scottsbluff (latitude: 41.22 N; longitude: 103.02 W; elevation=1208 m) AWDN station to calibrate METRICtm model. The weather data was quality controlled following the recommendations of Allen et al., 1996; 1998; 2005 and by ASCE-EWRI (2005) for all the weather stations in and out of Landsat path. Table 2 shows the list of AWDN sta‐ tions used in this analysis. Hourly and daily observed solar radiation (Rs) values were com‐

**Figure 2.** Scottsbluff 2005 corrected observed solar radiation (W/m2) and calculated clear sky solar radiation (W/m2).

Rso is the theoretical estimate of incoming solar radiation to the ground surface on a clear sky day with low atmospheric aerosol content (e.g. no haze, dust, smoke from fires, etc.) and is calculated based on atmospheric pressure, sun angle, and precipitable water in the atmos‐ phere (i.e. figure 2). Corrections are only applied when the data exhibits systematic errors eg. R eg. Rs>Rso. Individual values are corrected. Reasons for errors in the solar radiation values can be due to misalignment or a nonrepresentative calibration of the sensor. reference evapotranspiration (ETr) values were calculated using the ASCE-EWRI (2005) standardized Penman-Monteith equation for alfalfa reference. These calculations were carried out using

of model estimated fluxes with the field measurements.

pared with that of calculated clear sky solar radiation (Rso).

For DOY 1-123 (03 May) a 6% decrease in Rs values were applied.

Ref-ET software (University of Idaho and Allen, 2003

**2.3. Meteorological data**

**Figure 1.** Geographic footprint of Landsat path 33, row 31. Images cover parts of the Nebraska Panhandle, Wyoming, and Colorado.

The thermal high gain band has higher radiometric resolution but provides a less dynamic range.The spatial resolution of ETM in reflective bands is 30 m and 60 m in the thermal bands. Landsat7 ETM images have missing data in the form of a wedge due to failure of Scan Line Corrector (SLC) on May 31, 2003 referred to as SLC off images. Processing of SLCoff images requires replacing the missing data. Gap filling was used utilizing same time im‐ ages with spectral information taken from the neighbouring pixels. The convolution filtering algorithm with majority function was used to replace the missing data. The METRICtm mod‐ el (Allen et al. 2007b) estimated energy fluxes using the remotely sensed data as input (A general overview of the METRICtm model is presented in the next section). The model maker tool of Erdas Imagine® image processing software (Leica Geosystems Geospatial Imaging, LLC) was used to code the METRICtm algorithms. An iterative procedure was followed for sensible heat flux estimation using hot and cold pixels. From each processed image, average of 9 (3 X 3) pixels cantered over the field measurement location was used for the comparison of model estimated fluxes with the field measurements.

#### **2.3. Meteorological data**

repeat cycle of 16 days. The Thematic Mapper (TM) sensor on-board Landsat 5 has seven spectral bands with 30 m spatial resolution in reflective bands and 120 m resolution in ther‐ mal bands. The Enhanced Thematic Mapper (ETM) on-board Landsat 7 has eight spectral bands including panchromatic band (not used in this study). UTM zone 13 and NAD 1983 was the projection and datum used. For Landsat 7 imagery, the high gain on the thermal band was used. The Landsat 7 thermal band is acquired in both low and high gain. The low gain provides an expanded dynamic range generating less saturation at high values, but

**Figure 1.** Geographic footprint of Landsat path 33, row 31. Images cover parts of the Nebraska Panhandle, Wyoming,

The thermal high gain band has higher radiometric resolution but provides a less dynamic range.The spatial resolution of ETM in reflective bands is 30 m and 60 m in the thermal bands. Landsat7 ETM images have missing data in the form of a wedge due to failure of Scan Line Corrector (SLC) on May 31, 2003 referred to as SLC off images. Processing of SLCoff images requires replacing the missing data. Gap filling was used utilizing same time im‐ ages with spectral information taken from the neighbouring pixels. The convolution filtering algorithm with majority function was used to replace the missing data. The METRICtm mod‐ el (Allen et al. 2007b) estimated energy fluxes using the remotely sensed data as input (A general overview of the METRICtm model is presented in the next section). The model maker

lower radiometric resolution (sensitivity).

82 Evapotranspiration - An Overview

and Colorado.

High quality hourly weather data consisting of air temperature, relative humidity, wind speed, incoming solar radiation, and precipitation are required for the operation of the METRICtm model. Hourly weather data were acquired from the High Plains Regional Cli‐ mate Center's (HPRCC) Automated Weather Data Network (AWDN). Weather data were acquired for 2005 from the Scottsbluff (latitude: 41.22 N; longitude: 103.02 W; elevation=1208 m) AWDN station to calibrate METRICtm model. The weather data was quality controlled following the recommendations of Allen et al., 1996; 1998; 2005 and by ASCE-EWRI (2005) for all the weather stations in and out of Landsat path. Table 2 shows the list of AWDN sta‐ tions used in this analysis. Hourly and daily observed solar radiation (Rs) values were com‐ pared with that of calculated clear sky solar radiation (Rso).

**Figure 2.** Scottsbluff 2005 corrected observed solar radiation (W/m2) and calculated clear sky solar radiation (W/m2). For DOY 1-123 (03 May) a 6% decrease in Rs values were applied.

Rso is the theoretical estimate of incoming solar radiation to the ground surface on a clear sky day with low atmospheric aerosol content (e.g. no haze, dust, smoke from fires, etc.) and is calculated based on atmospheric pressure, sun angle, and precipitable water in the atmos‐ phere (i.e. figure 2). Corrections are only applied when the data exhibits systematic errors eg. R eg. Rs>Rso. Individual values are corrected. Reasons for errors in the solar radiation values can be due to misalignment or a nonrepresentative calibration of the sensor. reference evapotranspiration (ETr) values were calculated using the ASCE-EWRI (2005) standardized Penman-Monteith equation for alfalfa reference. These calculations were carried out using Ref-ET software (University of Idaho and Allen, 2003


**2.4. Land surface energy balance model**

hour ETr as:

weights (λ<sup>i</sup>

The landsat images for 2005 for Path32 Row 31 were processed using the algorithms in the METRICtm (2007a; 200b) model which requires parameterization of the energy balance and estimation of surface energy fluxes based on spectral satellite measurements (Allen et al., 2007). The model is originated from the SEBAL model and a "hybrid" energy balance model that uses thermal bands from Landsat imagery to compute ET. In particularly, it combines remotely-sensed energy balance (satellite) data and ground-based ETr (reference ET) data to

where Rn is the net radiation, G is the soil heat flux, H is the sensible heat flux, and LE is the latent heat flux. The units for all the fluxes are in W m-2. METRICtm calculates net radiation (Rn) as the difference between incoming radiation at all wavelengths and reflected shortwavelength (~ 0.3 – 3 μm) and both reflected and emitted long-wavelength (3~60 - μm) radi‐ ation (Allen et al., 2007a). The LE time integration was split into two steps. The first step was to convert the instantaneous value of LE into daily values of actual ET (*ET*24) values by hold‐ ing the reference ET fraction constant (Allen et al., 2007b). An instantaneous value of ET (ETinst) in equivalent evaporation depth is the ratio of LE to the latent heat of vaporization. usually range from 0 to 1.05 and is defined as the ratio of instantaneous ET (ET*inst*) for each pixel to the alfalfa-reference ET calculated using the standardized ASCE Penman-Monteith

> inst r

The procedures outlined in ASCE-EWRI (2005) were used to calculate parameters in the hourly ETr equation. The daily ET at each pixel was estimated by consideringETrF and 24

where ET24 is the daily value of actual ET (mm day-1), ETr-24 is 24 hour ETr for the day of im‐ age and calculated by summing hourly ETr values over the day of image. In order to pro‐ duce monthly and seasonal ET maps, individual ETrF maps from each image in the analysis were generated from METRIC and interpolated using a cubic spline model. The spline mod‐ el is deterministic interpolation method which fits a mathematical function through data points to create a surface (Hartkamp, 1999). The spline surface was achieved through

) and number of points (N). A regularized spline was used because this method

*<sup>n</sup> LE R G H* = -- (1)

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ET <sup>=</sup> (2)

24 r r 24 ET ET F xET - = (3)

determine ET. METRICtm computes LE as a residual of the energy balance as:

equation for alfalfa (ETr) following the procedures given in ASCE-EWRI (2005):

ET ETrF

**Table 1.** Lists of observation stations for calibration and validation

Figure 3 shows the riparian species as documented by the AISA system onboard CALMIT's Piper Saratoga aircraft on NE Panhandle. This map was integrated with seasonal ET maps obtained with METRICtm model. Hyperspectral remote sensing holds great promise for re‐ search on invasive species. Spectral information provided by hyperspectral sensors on AISA system can detect invaders at the species level across a range of community and ecosystem types. This gigh resolution classification provides a valuable description of spatial variation in riparian, serves as a baseline to measure ET in riparian, and is essential for prioritizing riparian restoration treatments in the basin.

**Figure 3.** Invasive species distribution in 2005 for flight line #3 on Path 33, Row 31.

#### **2.4. Land surface energy balance model**

**Station Latitude Longitude Elevation (m)**

Alliance-North, NE 42.18 102.92 3980.10 Alliance-West, NE 42.02 103.13 3980.00 Arapahoe Prairie, NE 41.48 101.85 3600.00 Arthur, NE 41.65 101.52 3598.08 Gordon, NE 42.73 102.17 3638.45 Gudmundsen, NE 42.07 101.43 3441.60 Mitchell Farms, NE 41.93 103.70 3602.36 Scottsbluff, NE 41.88 103.67 3963.25 Sidney, NE 41.22 103.02 4320.87 Sterling, CO 40.47 103.02 3937.01 Torrington, WY 42.03 104.18 3989.50

Figure 3 shows the riparian species as documented by the AISA system onboard CALMIT's Piper Saratoga aircraft on NE Panhandle. This map was integrated with seasonal ET maps obtained with METRICtm model. Hyperspectral remote sensing holds great promise for re‐ search on invasive species. Spectral information provided by hyperspectral sensors on AISA system can detect invaders at the species level across a range of community and ecosystem types. This gigh resolution classification provides a valuable description of spatial variation in riparian, serves as a baseline to measure ET in riparian, and is essential for prioritizing

**Table 1.** Lists of observation stations for calibration and validation

84 Evapotranspiration - An Overview

riparian restoration treatments in the basin.

**Figure 3.** Invasive species distribution in 2005 for flight line #3 on Path 33, Row 31.

The landsat images for 2005 for Path32 Row 31 were processed using the algorithms in the METRICtm (2007a; 200b) model which requires parameterization of the energy balance and estimation of surface energy fluxes based on spectral satellite measurements (Allen et al., 2007). The model is originated from the SEBAL model and a "hybrid" energy balance model that uses thermal bands from Landsat imagery to compute ET. In particularly, it combines remotely-sensed energy balance (satellite) data and ground-based ETr (reference ET) data to determine ET. METRICtm computes LE as a residual of the energy balance as:

$$LE = \mathcal{R}\_n - G - H \tag{1}$$

where Rn is the net radiation, G is the soil heat flux, H is the sensible heat flux, and LE is the latent heat flux. The units for all the fluxes are in W m-2. METRICtm calculates net radiation (Rn) as the difference between incoming radiation at all wavelengths and reflected shortwavelength (~ 0.3 – 3 μm) and both reflected and emitted long-wavelength (3~60 - μm) radi‐ ation (Allen et al., 2007a). The LE time integration was split into two steps. The first step was to convert the instantaneous value of LE into daily values of actual ET (*ET*24) values by hold‐ ing the reference ET fraction constant (Allen et al., 2007b). An instantaneous value of ET (ETinst) in equivalent evaporation depth is the ratio of LE to the latent heat of vaporization. usually range from 0 to 1.05 and is defined as the ratio of instantaneous ET (ET*inst*) for each pixel to the alfalfa-reference ET calculated using the standardized ASCE Penman-Monteith equation for alfalfa (ETr) following the procedures given in ASCE-EWRI (2005):

$$\text{ETrF} = \frac{\text{ET}\_{\text{inst}}}{\text{ET}\_{\text{r}}} \tag{2}$$

The procedures outlined in ASCE-EWRI (2005) were used to calculate parameters in the hourly ETr equation. The daily ET at each pixel was estimated by consideringETrF and 24 hour ETr as:

$$\text{ET}\_{24} = \text{ET}\_{\text{r}} \text{F} \times \text{ET}\_{\text{r}-24} \tag{3}$$

where ET24 is the daily value of actual ET (mm day-1), ETr-24 is 24 hour ETr for the day of im‐ age and calculated by summing hourly ETr values over the day of image. In order to pro‐ duce monthly and seasonal ET maps, individual ETrF maps from each image in the analysis were generated from METRIC and interpolated using a cubic spline model. The spline mod‐ el is deterministic interpolation method which fits a mathematical function through data points to create a surface (Hartkamp, 1999). The spline surface was achieved through weights (λ<sup>i</sup> ) and number of points (N). A regularized spline was used because this method results in a smoother surface. Daily images were generated by interpolation used for month‐ ly and seasonal ET calculation.

integrated ET maps with invasive species map to estimate the mean and the range of water use for each riparian species. The invasive species map developed using hyperspectral aerial imagery (AISA) in 2005 at 1.5 meter resolution for the North Platte River Basin was used. The measured components of the water balance (precipitation and ETrF based on ETr ) from Scottsbluff, NE were evaluated to determine ETrF for the hot pixel selection and to deter‐ mine the net ground-water recharge that occurred during the study. Precipitation and ETrF were the dominant components of the water balance with ground-water storage being a comparatively minor term. The ETrF is highly variable over the landscape because of the variability in landuse, climate, soil properties, and management practices. Soil properties af‐ fect surface soil evaporation and energy balances, including soil heat flux and sensible flux; causing within-field and across field variability in ETrF. Much of this variability occurs at the field scale, making it nearly impossible to quantify ET spatially using more traditional and conventional methods. Figure 6 shows monthly ETrF for path 33, row 31 in 2005. The spline model requires two images each in the preceding and subsequent months for the month to be interpolated. Because only one image was available for the month of April, a new ETrF image was created for April 23rd from MODIS 250m NDVI data. The methods

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used were the same as the cloud filling method using MODIS 250m NDVI data.

**Figure 5.** Calculated ETrF (reference ET faction) for individual Landsat dates in 2005 path 33, row 31.

### **3. Result and discussion**

A daily soil water balance model was applied for 2005 using precipitation and ETr from the for all weather stations (Table1). The water balance model estimates residual evaporation from bare soil for each of the Landsat image dates. The model is based on the two-stage dai‐ ly soil evaporation model of the United Nations Food and Agriculture Organization's Irriga‐ tion and Drainage Paper 56 (Allen et al., 1998). The soil water balance is set up assuming a loam soil having a water content at field capacity and the wilting point of 0.3 cm3 / cm3 and 0.15 cm3 / cm3 , respectively and having 10 mm of readily evaporable water in the upper 12.5 cm of soil. Figure 4 shows a simulation of evaporation from bare soil. The results from the soil water balance were used to determine reference evapotranspiration fraction (EtrF) for hot pixel selection, an internal calibration step for running METRICtm.

**Figure 4.** Soil water balance for bare soil calculated from meteorological data from Scottsbluff, NE 2005.

An example of soil water balance simulations for the top 0.125 meter of soil based on soil properties and meteorological data from the Scottsbluff, NE AWDN station is shown in Fig‐ ure 4. It should be noted, that the soil water balance indicates that the residual evaporation from bare soil on 10/14/2005 corresponds to an ETrF of 1.0 as a result of relatively large amounts of precipitation a few days prior to the image date. An ETrF value for the hot pixel of this magnitude leaves a very small margin up to the ETrF of 1.05 generally assigned to the cold pixel. For reasons discussed under the individual images below, the ETrF for the hot pixel has been assigned the value 0.8 for this image.

We utilized both satellite and air-bone remote sensing data with an energy balance model to provide a better understanding and quantification of evapotrabspiration for selected inva‐ sive species. We utilized METRICtm to quantify spatial distribution and seasonal variation of actual ET over riparian zone in North Platte River during growing season for 2005. Next, we integrated ET maps with invasive species map to estimate the mean and the range of water use for each riparian species. The invasive species map developed using hyperspectral aerial imagery (AISA) in 2005 at 1.5 meter resolution for the North Platte River Basin was used. The measured components of the water balance (precipitation and ETrF based on ETr ) from Scottsbluff, NE were evaluated to determine ETrF for the hot pixel selection and to deter‐ mine the net ground-water recharge that occurred during the study. Precipitation and ETrF were the dominant components of the water balance with ground-water storage being a comparatively minor term. The ETrF is highly variable over the landscape because of the variability in landuse, climate, soil properties, and management practices. Soil properties af‐ fect surface soil evaporation and energy balances, including soil heat flux and sensible flux; causing within-field and across field variability in ETrF. Much of this variability occurs at the field scale, making it nearly impossible to quantify ET spatially using more traditional and conventional methods. Figure 6 shows monthly ETrF for path 33, row 31 in 2005. The spline model requires two images each in the preceding and subsequent months for the month to be interpolated. Because only one image was available for the month of April, a new ETrF image was created for April 23rd from MODIS 250m NDVI data. The methods used were the same as the cloud filling method using MODIS 250m NDVI data.

results in a smoother surface. Daily images were generated by interpolation used for month‐

A daily soil water balance model was applied for 2005 using precipitation and ETr from the for all weather stations (Table1). The water balance model estimates residual evaporation from bare soil for each of the Landsat image dates. The model is based on the two-stage dai‐ ly soil evaporation model of the United Nations Food and Agriculture Organization's Irriga‐ tion and Drainage Paper 56 (Allen et al., 1998). The soil water balance is set up assuming a

cm of soil. Figure 4 shows a simulation of evaporation from bare soil. The results from the soil water balance were used to determine reference evapotranspiration fraction (EtrF) for

, respectively and having 10 mm of readily evaporable water in the upper 12.5

/ cm3

and

loam soil having a water content at field capacity and the wilting point of 0.3 cm3

**Figure 4.** Soil water balance for bare soil calculated from meteorological data from Scottsbluff, NE 2005.

pixel has been assigned the value 0.8 for this image.

An example of soil water balance simulations for the top 0.125 meter of soil based on soil properties and meteorological data from the Scottsbluff, NE AWDN station is shown in Fig‐ ure 4. It should be noted, that the soil water balance indicates that the residual evaporation from bare soil on 10/14/2005 corresponds to an ETrF of 1.0 as a result of relatively large amounts of precipitation a few days prior to the image date. An ETrF value for the hot pixel of this magnitude leaves a very small margin up to the ETrF of 1.05 generally assigned to the cold pixel. For reasons discussed under the individual images below, the ETrF for the hot

We utilized both satellite and air-bone remote sensing data with an energy balance model to provide a better understanding and quantification of evapotrabspiration for selected inva‐ sive species. We utilized METRICtm to quantify spatial distribution and seasonal variation of actual ET over riparian zone in North Platte River during growing season for 2005. Next, we

hot pixel selection, an internal calibration step for running METRICtm.

ly and seasonal ET calculation.

86 Evapotranspiration - An Overview

**3. Result and discussion**

0.15 cm3

/ cm3

**Figure 5.** Calculated ETrF (reference ET faction) for individual Landsat dates in 2005 path 33, row 31.

Figure 6 shows the expected progression of ETrF during a growing season as surface condi‐ tions changed. The spatial distribution of daily ETrF estimations using the Landsat overpass on May 31, 2005 and August 19 2005 were highly variable ranging from 0.6 mm day-1 to as high as 0.9 mm day-1 across the images. Most of the variability was due to differences in land use and riparian species. The land use in the top part of the study area is mainly agri‐ cultural land that is devoid of standing crops in early May. The bottom part of the study area is mostly grazed rangeland or natural vegetation dominated by green vegetation in ear‐ ly spring, resulting in higher ET.

time. Information gained enables the prediction of the timing and the spatial extent of po‐ tential depletions or gains in both the short-term and in the long-term management of sur‐

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**Figure 6.** Monthly ET (mm) maps produced with METRICtm for Path 33 Row 31 in 2005.

than the water consuption for other landuse in the Panhandle.

from 27 inches to 30 inches for all the invasive species.

The seasonal ET (mm) maps generated by the METRICtm model showed spatial and tempo‐ ral distribution of relative ET during the 2005 season as land surface conditions continuous‐ ly changed (Figure. 8). The information also allowed us to follow the seasonal trend in ET for major land use classes on the image. Water consumption by the riperian species is higher

The frequency distribution and the basic statistics for seasonal ET including all species are presented in Figure 8 and and summarized in Table 2, respectively. Statistics provided in Table 2 are for the water use during the growing season for individual invasive species. Comparison is difficult due to the non-uniform distribution of species and difference in age. Water use varied considerable even in the same species due to plant density, plant distribu‐ tion and plant height of individual species. ET roughly ranged from 12in to as high as 43 in for all the invasive species for May 1st to September 31st. Average actual seasonal ET ranged

face and ground water.

Monthly ET maps were summed to obtain total seasonal ET for the study area. Figure 6 shows the progression of ET from May through September in 2007 across the Panhandle de‐ rived using spline interpolation algorithm. The monthly ET maps generated by the MET‐ RICtm model showed a good progression of ET during the growing season as surface conditions continuously changed. Results showed that salt cedar water use was lowest com‐ pared to other invasive species. Russian olive also has substantial water use during growing season. For most species seasonal actual ET ranged from 20 to 35 inches. From figure 2 of hyperspectral image of invasive species distribution and figure 6 of monthly ET maps pro‐ duced with METRICtm for Landsat path 33 row 31 in 2005 we have an indication of the wa‐ ter use during the growing season for individual invasive species. Comparison is difficult due to non-uniform distribution of species and differences in age. Water use varied consid‐ erable even in the same species due to plant density, plant distribution and plant height of individual species. ET roughly ranged from 12 in to as high as 43 in for all the invasive spe‐ cies for May 1st to September 31st. Average actual seasonal ET ranged from 27 inches to 30 inches for all the invasive species. Overall, the remote sensing based energy balance ap‐ proach based on landsat image in conjunction with high resolution hyperspectral image was useful to obtain distribution of ET estimates from riparian systems.

The ET was lower early in the growing season and gradually increased as the riparian spe‐ cies increasingly transpire water towards the mid season. The METRICtm model was also able to estimate the decreasing evaporative losses towards the end of the season and after the harvest. However, in figures 6 subfigures July, August and September show visible dis‐ tinctions in ET among the riparian species. To calculate species wise distribution one needs full knowledge of the study area land use with hyperspectral imagery classification. Since requirement of accurate riparian species type identification can increase costs of ET map‐ ping at larger scales, this is an advantage of METRICtm because the model does not require information on soil and management practices.

July is usually the peak ET month with high incoming solar radiation, high temperatures, and large vapor pressure deficit all contributing to increased ET. The ET shows variation throughout the district as a function of different ET rates of various land covers, including riperian species, agriculture crops and natural vegetation, etc. With physiological maturity, leaf aging and senescence, ET starts to decrease gradually in September. At the start of fall, leaves of plants start falling in October, most of the ET in this month represents the soil evaporation component of ET. As shown in monthly ET maps, mapping ET on large scales can provide vital information on the progression of ET for various vegetation surfaces over time. Information gained enables the prediction of the timing and the spatial extent of po‐ tential depletions or gains in both the short-term and in the long-term management of sur‐ face and ground water.

Figure 6 shows the expected progression of ETrF during a growing season as surface condi‐ tions changed. The spatial distribution of daily ETrF estimations using the Landsat overpass on May 31, 2005 and August 19 2005 were highly variable ranging from 0.6 mm day-1 to as high as 0.9 mm day-1 across the images. Most of the variability was due to differences in land use and riparian species. The land use in the top part of the study area is mainly agri‐ cultural land that is devoid of standing crops in early May. The bottom part of the study area is mostly grazed rangeland or natural vegetation dominated by green vegetation in ear‐

Monthly ET maps were summed to obtain total seasonal ET for the study area. Figure 6 shows the progression of ET from May through September in 2007 across the Panhandle de‐ rived using spline interpolation algorithm. The monthly ET maps generated by the MET‐ RICtm model showed a good progression of ET during the growing season as surface conditions continuously changed. Results showed that salt cedar water use was lowest com‐ pared to other invasive species. Russian olive also has substantial water use during growing season. For most species seasonal actual ET ranged from 20 to 35 inches. From figure 2 of hyperspectral image of invasive species distribution and figure 6 of monthly ET maps pro‐ duced with METRICtm for Landsat path 33 row 31 in 2005 we have an indication of the wa‐ ter use during the growing season for individual invasive species. Comparison is difficult due to non-uniform distribution of species and differences in age. Water use varied consid‐ erable even in the same species due to plant density, plant distribution and plant height of individual species. ET roughly ranged from 12 in to as high as 43 in for all the invasive spe‐ cies for May 1st to September 31st. Average actual seasonal ET ranged from 27 inches to 30 inches for all the invasive species. Overall, the remote sensing based energy balance ap‐ proach based on landsat image in conjunction with high resolution hyperspectral image was

The ET was lower early in the growing season and gradually increased as the riparian spe‐ cies increasingly transpire water towards the mid season. The METRICtm model was also able to estimate the decreasing evaporative losses towards the end of the season and after the harvest. However, in figures 6 subfigures July, August and September show visible dis‐ tinctions in ET among the riparian species. To calculate species wise distribution one needs full knowledge of the study area land use with hyperspectral imagery classification. Since requirement of accurate riparian species type identification can increase costs of ET map‐ ping at larger scales, this is an advantage of METRICtm because the model does not require

July is usually the peak ET month with high incoming solar radiation, high temperatures, and large vapor pressure deficit all contributing to increased ET. The ET shows variation throughout the district as a function of different ET rates of various land covers, including riperian species, agriculture crops and natural vegetation, etc. With physiological maturity, leaf aging and senescence, ET starts to decrease gradually in September. At the start of fall, leaves of plants start falling in October, most of the ET in this month represents the soil evaporation component of ET. As shown in monthly ET maps, mapping ET on large scales can provide vital information on the progression of ET for various vegetation surfaces over

useful to obtain distribution of ET estimates from riparian systems.

information on soil and management practices.

ly spring, resulting in higher ET.

88 Evapotranspiration - An Overview

**Figure 6.** Monthly ET (mm) maps produced with METRICtm for Path 33 Row 31 in 2005.

The seasonal ET (mm) maps generated by the METRICtm model showed spatial and tempo‐ ral distribution of relative ET during the 2005 season as land surface conditions continuous‐ ly changed (Figure. 8). The information also allowed us to follow the seasonal trend in ET for major land use classes on the image. Water consumption by the riperian species is higher than the water consuption for other landuse in the Panhandle.

The frequency distribution and the basic statistics for seasonal ET including all species are presented in Figure 8 and and summarized in Table 2, respectively. Statistics provided in Table 2 are for the water use during the growing season for individual invasive species. Comparison is difficult due to the non-uniform distribution of species and difference in age. Water use varied considerable even in the same species due to plant density, plant distribu‐ tion and plant height of individual species. ET roughly ranged from 12in to as high as 43 in for all the invasive species for May 1st to September 31st. Average actual seasonal ET ranged from 27 inches to 30 inches for all the invasive species.

**Figure 7.** Seasonal ET (mm) map from May 01 through September 30 in 2005 for Path 33 Row 31.


**Figure 8.** The comparison of water use (seasonal actual ET, inch) for individual and combination of various riparian

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This study focused on the use of multi-temporal landsat data and hyperspectral remote sensing data to calculate daily and seasonal actual ET based upon satellite energy balance model such as METRICtm. The remotely sensed measurements using METRICtm.provide the estimation of spatial distribution of instantaneous ET, which can be integrated into daily and seasonal ET values. The seasonal spatial distribution maps help to explain the water consumption for the different riperean species season. Based on maps we have developed guidelines on riparian water use which will be of benefit to the state, particularly with re‐ gard to riparian control for water acclamation. Satellite-based measurements can provide such information and avoid the need to rely on field databases. The ET values for a given species varies due to the density and age of the plant species. By integrating the hyperspec‐ tral images with the METRIC™ model, we obtain the following seasonal ET values (May 1 through September 1, 2005) for the following invasive species: Salt Cedar (Tamarisk) 26.8 in‐ ches (680 mm) or 2.23 acre-feet water, Russian Olive 26.7 inches (677 mm) or 2.23 acre-feet water, average Canada and Musk Thistle 27.9 inches (708 mm). Using developed figures, this vegetation transpires approximately 30,453 acre-feet of water per season. Since willows, cottonwood trees and some grasses transpire approximately the same amount of water, revegetation with these species would result in a net "no gain" of water conservation. Based

species.

**4. Conclusion**

**Table 2.** Comparison of seasonal water use in inches from May 1st to September 30th in 2005.

Figure 8 shows the histogram of seasonal actual ET for individual and combination of vari‐ ous riparian species. The y axis on the figure (histogram) shows the number of AISA pixels (1.5 m) for each riparian species while x axis shows corresponding actual ET values in in‐ ches. We observed that there is no single ET value for invasive species. Results showed that salt cedar water use was lowest compared to other invasive species. Russian olive also has substantial water use during growing season. For most species seasonal actual ET ranged from 20 to 35 inches. Overall, the remote sensing based energy balance approach based on the landsat image in conjunction with high resolution hyperspectral image was useful to ob‐ tain distribution of ET estimates from riparian systems.

**Figure 8.** The comparison of water use (seasonal actual ET, inch) for individual and combination of various riparian species.

### **4. Conclusion**

**Figure 7.** Seasonal ET (mm) map from May 01 through September 30 in 2005 for Path 33 Row 31.

Thistle+Salt Cedar+Reed Canary Grass

90 Evapotranspiration - An Overview

**Species Minimum Maximum Average** Russian Olive 12 42 29 Salt Cedar 14 40 27 Thistle 12 42 30

Thistle+Reed Canary Grass 16 43 30

Figure 8 shows the histogram of seasonal actual ET for individual and combination of vari‐ ous riparian species. The y axis on the figure (histogram) shows the number of AISA pixels (1.5 m) for each riparian species while x axis shows corresponding actual ET values in in‐ ches. We observed that there is no single ET value for invasive species. Results showed that salt cedar water use was lowest compared to other invasive species. Russian olive also has substantial water use during growing season. For most species seasonal actual ET ranged from 20 to 35 inches. Overall, the remote sensing based energy balance approach based on the landsat image in conjunction with high resolution hyperspectral image was useful to ob‐

**Table 2.** Comparison of seasonal water use in inches from May 1st to September 30th in 2005.

tain distribution of ET estimates from riparian systems.

15 41 28

This study focused on the use of multi-temporal landsat data and hyperspectral remote sensing data to calculate daily and seasonal actual ET based upon satellite energy balance model such as METRICtm. The remotely sensed measurements using METRICtm.provide the estimation of spatial distribution of instantaneous ET, which can be integrated into daily and seasonal ET values. The seasonal spatial distribution maps help to explain the water consumption for the different riperean species season. Based on maps we have developed guidelines on riparian water use which will be of benefit to the state, particularly with re‐ gard to riparian control for water acclamation. Satellite-based measurements can provide such information and avoid the need to rely on field databases. The ET values for a given species varies due to the density and age of the plant species. By integrating the hyperspec‐ tral images with the METRIC™ model, we obtain the following seasonal ET values (May 1 through September 1, 2005) for the following invasive species: Salt Cedar (Tamarisk) 26.8 in‐ ches (680 mm) or 2.23 acre-feet water, Russian Olive 26.7 inches (677 mm) or 2.23 acre-feet water, average Canada and Musk Thistle 27.9 inches (708 mm). Using developed figures, this vegetation transpires approximately 30,453 acre-feet of water per season. Since willows, cottonwood trees and some grasses transpire approximately the same amount of water, revegetation with these species would result in a net "no gain" of water conservation. Based on the analysis of images from AISA and Landsat, we show that ET varies even in the same species (i.e. salt cedar) because of variation in tree age, height, density, and surrounding area. Nevertheless, further studies are necessary to expand this method in conjuction with hyperspectral data to obtain species wise water use distribution. The METRICtm model should be tested for its suitability for other climate conditions found in Nebraska and an as‐ sessment made of the spatial variability of the calibration parameters is needed.

[6] Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998a). A remote sensing surface energy balance algortithm for land (SEBAL). Part 1: Formula‐

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[8] Bentall, Ray., & Shaffer, F. B. (1979). Availability and use of water in Nebraska, 1975: Conservation and Survey Division, University of Nebraska-Lincoln, Nebraska Water

[9] Center for Advanced Land Management Information Technologies (CALMIT), 2000, Delineation of 1997 land use patterns for the Cooperative Hydrology Study in the central Platte River Basin: Lincoln, Nebraska, CALMIT, 73 p., accessed August 29,

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28:101-112 (DOIs00271-009-01939).

### **Acknowledgements**

The. authors thank to the University of Nebraska Foundation, Anna H. Elliot Fund grants which supported this work and University of Idaho for METRICtm modeling support.

### **Author details**

Baburao Kamble, Ayse Irmak, Derrel L. Martin, Kenneth G. Hubbard, Ian Ratcliffe, Gary Hergert, Sunil Narumalani and Robert J. Oglesby

University of Nebraska-Lincoln (UNL), Lincoln, USA

### **References**


[6] Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998a). A remote sensing surface energy balance algortithm for land (SEBAL). Part 1: Formula‐ tion. J. of Hydrology , 198-212.

on the analysis of images from AISA and Landsat, we show that ET varies even in the same species (i.e. salt cedar) because of variation in tree age, height, density, and surrounding area. Nevertheless, further studies are necessary to expand this method in conjuction with hyperspectral data to obtain species wise water use distribution. The METRICtm model should be tested for its suitability for other climate conditions found in Nebraska and an as‐

The. authors thank to the University of Nebraska Foundation, Anna H. Elliot Fund grants which supported this work and University of Idaho for METRICtm modeling support.

[1] Allen, R. G. (1996). Assessing Integrity of Weather Data for Use in Reference Evapo‐

[2] Allen, R. G., Pereira, L., Raes, D., & Smith, M. (1998). Crop Evapotranspiration, Food and Agriculture Organization of the United Nations, Rome, It. 925-1-04219-530-0p.

[3] Allen, R. G., Tasumi, M., & Trezza, R. (2007a). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)- Model. ASCE

[4] Allen, R. G., Tasumi, M., Morse, A. T., Trezza, R., Kramber, W., Lorite, I., & Robison, C. W. (2007b). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)- Applications. ASCE J. Irrigation and Drainage

[5] -E, A. S. C. E., & , W. R. I. (2005). The ASCE Standardized reference evapotranspira‐ tion equation. ASCE-EWRI Standardization of Reference Evapotranspiration Task

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transpiration Estimation. J. Irrig. Drain. Eng ASCE, , 122, 97-106.

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sessment made of the spatial variability of the calibration parameters is needed.

**Acknowledgements**

92 Evapotranspiration - An Overview

**Author details**

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[30] Schaeffer, S. M., Williams, D. G., & Goodrich, D. C. (2000). Transpiration of cotton‐ wood/willow forest estimated from sap flux. Agricultural and Forest Meteorology

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[32] Smith, S. D., Devitt, D. A., Sala, A., Cleverly, J. R., & Busch, D. E. (1998). Water rela‐ tions of riparian plants from warm desert regions. Wetlands , 18, 687-696.

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[29] Ranade, P. (2010). Spatial Water Balance for Bare Soil. University of Nebraska. Re‐ port, 10 pp.

[17] Irmak, A., Ratcliffe, I., Ranade, P., Hubbard, K. G., Singh, R. K., Kamble, B., Allen, R. G., & Kjaersgaard, J. (2010). Estimation of land surface evapotranspiration: A Satellite

[18] Irmak, A., Rundquist, D., , S., Narumalani, G., Hergert, , & Stone, G. (2009). Satellite-Based Energy Balance to Assess Riparian Water Use. UNL. Lincoln, NE. USGS 104b

[19] Irmak, A., Ratcliffe, I., Ranade, P., Irmak, S., Allen, R. G., Kjaersgaard, J., Kamble, B., Choragudi, R., Hubbard, K. G., Singh, R., Mutiibwa, D., & Healey, N. (2010). Season‐ al Evapotranspiration Mapping Using Landsat Visible and Thermal Data with an En‐ ergy Balance Approach in Central Nebraska. Remote Sensing and Hydrology.

[20] Irmak, S., Howell, T. A., Allen, R. G., Payero, J. O., & Martin, D. L. (2005). Standar‐ dized ASCE Penman-Monteith: impact of sum-of-hourly vs. 24-hour time step com‐

[21] Kamble, B., & Irmak, A. (2011). Remotely Sensed Evapotranspiration Data Assimila‐ tion for Crop Growth Modeling, Evapotranspiration, Leszek Labedzki (Ed.),

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**Chapter 6**

**Influence of Vegetation Cover on Regional**

**Northwest China**

http://dx.doi.org/10.5772/52812

**1. Introduction**

ries, namely:

Mir A. Matin and Charles P.-A. Bourque

Additional information is available at the end of the chapter

wind velocity, and air humidity;

aperture [1, 5, 6].

**Evapotranspiration in Semi-Arid Watersheds in**

Evapotranspiration (ET) is an important biospheric process whereby liquid water is vapor‐ ized from moist surfaces and from plant tissues [1]. It is one of the key processes in the hy‐ drological cycle and the energy balance of watersheds. Level of ET from a surface is primarily dependent on the availability of moisture on the surface and the amount of energy available to evaporate that moisture. To understand and quantify ET, three conceptual defi‐ nitions of ET exist in the scientific literature. Actual ET (AET) refers to the actual evapora‐ tion of liquid water from a surface under given atmospheric conditions. For the same atmospheric conditions, potential ET (PET) refers to the elevated evaporation of water when the amount of surface moisture is unlimited and vegetation conditions are ideal. Ideal con‐ ditions regarding vegetation is characterized by actively growing short vegetation covering a large surface area with unlimited supply of soil water [2, 3]. If the vegetation cover is standardized to grass or alfalfa, PET is considered as reference ET (i.e., ETo) [1]. Understand‐ ing spatiotemporal trends in ET is critical to interpreting eco-hydrometeorological processes of complex landscapes [4]. Specific factors affecting ET are divided into three main catego‐

**i.** meteorological factors, including solar radiation, near-surface air temperature,

**iii.** plant factors, including rooting depth, leaf structure, and stomatal density and

© 2013 Matin and Bourque; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Matin and Bourque; licensee InTech. This is a paper distributed under the terms of the Creative Commons

**ii.** surface factors, including surface water and soil water content; and
