**Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China**

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

Additional information is available at the end of the chapter

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

### **1. Introduction**

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‐ ries, namely:


© 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. © 2013 Matin and Bourque; 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.

Spatiotemporal variability in these factors determines the variability of ET across large areas. This variability increases as the complexity of the underlying terrain and vegetation cover intensifies.

*2.1.1. Hydrological methods*

ter-budget equation, such that

change in soil-water storage.

*2.1.2. Micrometeorological methods*

expressed as

runoff are critical for the accurate assessment of AET [19].

Hydrological methods include the soil-water balance and weighing-lysimeter methods [18]. AET-determination from the soil-water balance is possible with a rearrangement of the wa‐

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

where P is the precipitation, R is the surface runoff, D is the soil drainage, and ∆S is the

Each term in Eqn. (1) is given in unit catchment volume, in which proper delineation of catchment boundaries, distribution of precipitation and streamflow gauges, and accuracy of

Lysimeters are measuring devices with containers for soil and plants that estimate ET by measuring the difference in weight of water in individual containers [20]. Accuracy of AET with lysimeters rests on the precision of the weighing instrument and sampling frequency [18].

Micrometeorological methods estimate latent heat fluxes at potentially high temporal fre‐ quencies over homogeneous vegetation [18]. The Bowen ratio method estimates latent heat fluxes [i.e., λET (W m-2), where λ is the latent heat of vaporization expressed in J kg-1] from

> , <sup>1</sup> *R G <sup>n</sup>*

where Rn is the net surface radiation (W m-2), G is the soil-heat flux (W m-2), and β (Bowen ratio) is the ratio to sensible heat and latent heat fluxes estimated from air temperature and

> , *a a T e*

where γ is the psychometric constant (0.67 hPa K-1, at sea level) and ∆Ta (K) and ∆ea (hPa) are differences in air temperature and water vapor pressure taken at two separate levels [16].

The aerodynamic resistance method uses the atmospheric boundary-layer resistance to cal‐ culate the transfer of sensible heat (H; W m-2) from the surface to the air [22] and is formally

b

the ratio of surface energy to the Bowen ratio (non-dimensional), such that

l*ET*

water vapor pressure gradients taken within the same sample of air [21], or

b g

*AET P R D S* = - - -D , (1)

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

99


<sup>D</sup> <sup>=</sup> <sup>D</sup> (3)

Back and forth exchange of water vapor and liquid water from oases at the base of the Qili‐ an Mountains (Northwest China) and from the Qilian Mountains to oases as surface and shallow subsurface flow has been previously shown to be a potentially significant mecha‐ nism in the long term maintenance of oases in westcentral Gansu, NW China [7]. In general, direct precipitation (rain + snow) to the foothills and oases at the base of the Qilian Moun‐ tains is inadequate to maintain vegetation in these areas. Maintenance of oases vegetation is shown to be dependent on the surface water flowing from the higher regions of the moun‐ tain range, where direct precipitation and snowmelt are greatest. Supply of atmospheric moisture that leads to the formation of precipitation in the mountains is sustained by sea‐ sonal ET at the base of the mountain range.

Influence of vegetation and landcover on local and regional climate is well documented in the scientific literature [7-9]. Changes in landcover result in changes in surface albedo, sur‐ face roughness, leaf area index (LAI), stomatal conductance, rooting depth, and soil texture and structure [10-12]. Changes in surface vegetation have been known to impact


Agricultural landuse affects climate by modifying the physiological attributes (e.g., canopy conductance) of the land [14].

### **2. AET-determination methods**

### **2.1. Point measurements**

Suitability of AET-measurement methods depend on the reasons for assessment and on the spatiotemporal dimensions of the problem. Measurement of AET is mainly done indirectly by measuring the effect of AET on the water and energy balance. *Rose and Sharma* [15] cate‐ gorized AET-measurement methods into three broad categories, i.e.,


### *2.1.1. Hydrological methods*

Spatiotemporal variability in these factors determines the variability of ET across large areas. This variability increases as the complexity of the underlying terrain and vegetation

Back and forth exchange of water vapor and liquid water from oases at the base of the Qili‐ an Mountains (Northwest China) and from the Qilian Mountains to oases as surface and shallow subsurface flow has been previously shown to be a potentially significant mecha‐ nism in the long term maintenance of oases in westcentral Gansu, NW China [7]. In general, direct precipitation (rain + snow) to the foothills and oases at the base of the Qilian Moun‐ tains is inadequate to maintain vegetation in these areas. Maintenance of oases vegetation is shown to be dependent on the surface water flowing from the higher regions of the moun‐ tain range, where direct precipitation and snowmelt are greatest. Supply of atmospheric moisture that leads to the formation of precipitation in the mountains is sustained by sea‐

Influence of vegetation and landcover on local and regional climate is well documented in the scientific literature [7-9]. Changes in landcover result in changes in surface albedo, sur‐ face roughness, leaf area index (LAI), stomatal conductance, rooting depth, and soil texture

and structure [10-12]. Changes in surface vegetation have been known to impact

**i.** the partitioning of net radiative energy into sensible and latent heat fluxes, and

**ii.** the formation of convective rainfall by affecting the state of the convective boun‐

Agricultural landuse affects climate by modifying the physiological attributes (e.g., canopy

Suitability of AET-measurement methods depend on the reasons for assessment and on the spatiotemporal dimensions of the problem. Measurement of AET is mainly done indirectly by measuring the effect of AET on the water and energy balance. *Rose and Sharma* [15] cate‐

**ii.** micrometeorological approaches, including the Bowen ratio [16], aerodynamic, and

**iii.** plant-physiological approaches at the scale of individual plants or groups of plants

**i.** hydrological approaches, based on the residual of the water-budget equation;

gorized AET-measurement methods into three broad categories, i.e.,

by collecting sap-flow or chamber measurements.

eddy covariance-based methods [17], and

cover intensifies.

98 Evapotranspiration - An Overview

sonal ET at the base of the mountain range.

dary layer [13].

**2. AET-determination methods**

conductance) of the land [14].

**2.1. Point measurements**

Hydrological methods include the soil-water balance and weighing-lysimeter methods [18]. AET-determination from the soil-water balance is possible with a rearrangement of the wa‐ ter-budget equation, such that

$$AET = P - R - D - \Delta S,\tag{1}$$

where P is the precipitation, R is the surface runoff, D is the soil drainage, and ∆S is the change in soil-water storage.

Each term in Eqn. (1) is given in unit catchment volume, in which proper delineation of catchment boundaries, distribution of precipitation and streamflow gauges, and accuracy of runoff are critical for the accurate assessment of AET [19].

Lysimeters are measuring devices with containers for soil and plants that estimate ET by measuring the difference in weight of water in individual containers [20]. Accuracy of AET with lysimeters rests on the precision of the weighing instrument and sampling frequency [18].

### *2.1.2. Micrometeorological methods*

Micrometeorological methods estimate latent heat fluxes at potentially high temporal fre‐ quencies over homogeneous vegetation [18]. The Bowen ratio method estimates latent heat fluxes [i.e., λET (W m-2), where λ is the latent heat of vaporization expressed in J kg-1] from the ratio of surface energy to the Bowen ratio (non-dimensional), such that

$$
\lambda ET = \frac{R\_n - G}{1 + \beta},
\tag{2}
$$

where Rn is the net surface radiation (W m-2), G is the soil-heat flux (W m-2), and β (Bowen ratio) is the ratio to sensible heat and latent heat fluxes estimated from air temperature and water vapor pressure gradients taken within the same sample of air [21], or

$$
\beta = \gamma \frac{\Delta T\_a}{\Delta e\_a},
\tag{3}
$$

where γ is the psychometric constant (0.67 hPa K-1, at sea level) and ∆Ta (K) and ∆ea (hPa) are differences in air temperature and water vapor pressure taken at two separate levels [16].

The aerodynamic resistance method uses the atmospheric boundary-layer resistance to cal‐ culate the transfer of sensible heat (H; W m-2) from the surface to the air [22] and is formally expressed as

$$H = \mathfrak{g} C\_p \frac{T\_s - T\_a}{r\_a},\tag{4}$$

**2.2. Regional estimates of AET from remote sensing-based data**

categorized into three main groups, i.e., methods based on:

**ii.** Penman-Monteith and Priestley-Taylor equations [30-32]; and

l

primary input, they differ in the way they partition the energy into H and λET.

**i.** the surface-energy balance equation [27-29];

**iii.** the complementary relationship [33, 34].

*2.2.1. Surface energy balance*

those identified in Table 1.

where

pression of latent heat flux becomes:

For expansive landscapes, remote sensing (RS)-based methods have been gaining popularity during the past few decades with regard to estimating regional AET at daily, monthly, and annual time scales. Characterizing land-surface conditions with RS-based methods provides an important way of overcoming the difficulty of interpolating AET for complex landscapes [26]. Most methods using earth-observation data in approximating regional AET can be

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

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

101

While all methods require an assessment of available net energy at the surface (i.e., Rn-G) as

The surface-energy balance is based on the assumption that Rn is equal to the sum of H, λET, and G; the energy required for photosynthesis is insignificant here as it accounts for < 1% of incoming solar radiation [35]. Based on a rewriting of the surface-energy balance, the ex‐

In Eqn. (8), *Rs* ↓ and *Rs* ↑ represent incoming and outgoing shortwave radiation and *RL* ↓ and *RL* ↑ , incoming and outgoing longwave radiation emitted by the atmosphere (includ‐ ing clouds, if present) and earth surface. Typically, Rn and G (generally expressed as a frac‐ tion of *Rs* ↓ ) are determined from sun-earth geometric relations and illumination angles imposed by variable terrain. Most of the functions and information for the determination of Rn and G are available in present day geographic information systems (GIS) and digital ter‐ rain models [36-39]. Reflected and emitted radiative components are estimated from surface albedo, land surface and air temperatures, and emissivities derived from RS-data, e.g., as

Most popular among surface-energy balance models are the surface-energy balance system (SEBS; [29]) and surface-energy balance algorithm for land (SEBAL; [27]). A comprehensive review of various surface-energy balance procedures appearing in the scientitific literature can be found in [40]. The SEBS model uses Monin-Obukhov similarity theory for the atmos‐

,

*ET R G H* = -- *<sup>n</sup>* (7)

. *RR R R R ns s L L* = ¯- + ¯- (8)

where ra is the aerodynamic resistance (s m-1), ς and Cp are the density and specific heat of the airmass involved (kg m-3 and J kg-1 K-1, respectively), and Ts and Ta are the surface and air temperature (K). The aerodynamic resistance in Eqn. (4) is the most important and most difficult to define. Various methods have been proposed to estimate this resistance, many of which are summarized in [23]. Once H is determined, AET (i.e., AET = λET. λ-1) can be determined from

$$
\lambda ET = R\_n - G - H,\tag{5}
$$

given corresponding measurements of Rn and G.

Eddy covariance facilitates determination of vertical fluxes of atmospheric gases (e.g., wa‐ ter vapor, carbon dioxide, and trace gases) and heat within the atmospheric boundary lay‐ er. Historical advancement of eddy-covariance concepts is summarized in [17]. Assuming that the vertical wind velocity is responsible for vertical fluxes of atmospheric gases and heat, there should be a high positive correlation between vertical wind velocities and indi‐ vidual fluxes [24]. The method monitors high-frequency fluctuations in vertical wind veloc‐ ity (m s-1) and water vapor mixing ratios (kg kg-1), in estimating AET from the covariance of the two, i.e.,

$$
\mathcal{A}ET = \mathcal{A}\overline{\mathcal{w}^\*q^\*},
\tag{6}
$$

where w' and q' are the instantaneous fluctuation (deviation from the interval mean) in ver‐ tical wind velocity and water vapor mixing ratio in a parcel of air.

The physiological approach determines AET for individual plants or groups of plants. Two of the more broadly used physiological methods, includes


In the sap-flow method, sap flow in plants is assumed to be closely linked to plant transpira‐ tion and is quantified by applying heat pulses to the stem of plants and analyzing correspond‐ ing heat balances [18]. In a chamber setting, transpiring plants are enclosed in a transparent chamber and changes in within-chamber water vapor concentrations are quantified [25].

While AET-determination methods based on principles of micrometeorology and plant physiology provide suitable overall accuracy at point scales (within a few tens of metres), spatial interpolation of their results across entire landscapes is wholly inappropriate because of the inherent complexity of natural landscapes, particularly with respect to topography and landcover.

### **2.2. Regional estimates of AET from remote sensing-based data**

For expansive landscapes, remote sensing (RS)-based methods have been gaining popularity during the past few decades with regard to estimating regional AET at daily, monthly, and annual time scales. Characterizing land-surface conditions with RS-based methods provides an important way of overcoming the difficulty of interpolating AET for complex landscapes [26]. Most methods using earth-observation data in approximating regional AET can be categorized into three main groups, i.e., methods based on:


While all methods require an assessment of available net energy at the surface (i.e., Rn-G) as primary input, they differ in the way they partition the energy into H and λET.

#### *2.2.1. Surface energy balance*

The surface-energy balance is based on the assumption that Rn is equal to the sum of H, λET, and G; the energy required for photosynthesis is insignificant here as it accounts for < 1% of incoming solar radiation [35]. Based on a rewriting of the surface-energy balance, the ex‐ pression of latent heat flux becomes:

$$
\lambda ET = R\_n - G - H,\tag{7}
$$

where

, *s a*


*ET R G H* = -- *<sup>n</sup>* (5)

*ET w q* = ' ', (6)

λ-1) can be determined from

*a*

*r*

where ra is the aerodynamic resistance (s m-1), ς and Cp are the density and specific heat of the airmass involved (kg m-3 and J kg-1 K-1, respectively), and Ts and Ta are the surface and air temperature (K). The aerodynamic resistance in Eqn. (4) is the most important and most difficult to define. Various methods have been proposed to estimate this resistance, many of which are

Eddy covariance facilitates determination of vertical fluxes of atmospheric gases (e.g., wa‐ ter vapor, carbon dioxide, and trace gases) and heat within the atmospheric boundary lay‐ er. Historical advancement of eddy-covariance concepts is summarized in [17]. Assuming that the vertical wind velocity is responsible for vertical fluxes of atmospheric gases and heat, there should be a high positive correlation between vertical wind velocities and indi‐ vidual fluxes [24]. The method monitors high-frequency fluctuations in vertical wind veloc‐ ity (m s-1) and water vapor mixing ratios (kg kg-1), in estimating AET from the covariance

,

*p*

*T T H C*

V

summarized in [23]. Once H is determined, AET (i.e., AET = λET.

given corresponding measurements of Rn and G.

of the two, i.e.,

100 Evapotranspiration - An Overview

**1.** the sap-flow method, and

**2.** the chamber method.

and landcover.

l

l

tical wind velocity and water vapor mixing ratio in a parcel of air.

of the more broadly used physiological methods, includes

 l

where w' and q' are the instantaneous fluctuation (deviation from the interval mean) in ver‐

The physiological approach determines AET for individual plants or groups of plants. Two

In the sap-flow method, sap flow in plants is assumed to be closely linked to plant transpira‐ tion and is quantified by applying heat pulses to the stem of plants and analyzing correspond‐ ing heat balances [18]. In a chamber setting, transpiring plants are enclosed in a transparent chamber and changes in within-chamber water vapor concentrations are quantified [25].

While AET-determination methods based on principles of micrometeorology and plant physiology provide suitable overall accuracy at point scales (within a few tens of metres), spatial interpolation of their results across entire landscapes is wholly inappropriate because of the inherent complexity of natural landscapes, particularly with respect to topography

$$R\_n = R\_s \downarrow - R\_s \uparrow + R\_L \downarrow - R\_L \uparrow \,. \tag{8}$$

In Eqn. (8), *Rs* ↓ and *Rs* ↑ represent incoming and outgoing shortwave radiation and *RL* ↓ and *RL* ↑ , incoming and outgoing longwave radiation emitted by the atmosphere (includ‐ ing clouds, if present) and earth surface. Typically, Rn and G (generally expressed as a frac‐ tion of *Rs* ↓ ) are determined from sun-earth geometric relations and illumination angles imposed by variable terrain. Most of the functions and information for the determination of Rn and G are available in present day geographic information systems (GIS) and digital ter‐ rain models [36-39]. Reflected and emitted radiative components are estimated from surface albedo, land surface and air temperatures, and emissivities derived from RS-data, e.g., as those identified in Table 1.

Most popular among surface-energy balance models are the surface-energy balance system (SEBS; [29]) and surface-energy balance algorithm for land (SEBAL; [27]). A comprehensive review of various surface-energy balance procedures appearing in the scientitific literature can be found in [40]. The SEBS model uses Monin-Obukhov similarity theory for the atmos‐ pheric surface layer to derive land surface physical parameters and roughness lengths in de‐ termining the evaporative fraction at the limiting ends of accessible water, i.e., dry and wet limits [29]. At the dry end, evaporation (ETdry) is assumed to be zero due to the limitation in soil moisture and, as a result, sensible heat (Hdry) is maximum, equalling net available ener‐ gy (i.e., Hdry = Rn - G). At the wet end, evaporation (ETwet) is assumed to be at its potential rate (i.e. λETwet= λPET) and sensible heat (Hwet) is minimum. Relative evaporation fraction is defined as:

$$EF\_r = 1 - \frac{H - H\_{\text{wor}}}{H\_{\text{dry}} - H\_{\text{wor}}},\tag{9}$$

sure (hPa). In their model, input to Eqn. (12) are based on MODIS-derived vegetation data and daily surface meteorological data. Surface conductance is estimated in the model as a

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

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103

The model was later modified by *Mu et al.* [5] by adding vapor pressure deficit (VPD) and minimum air temperature as two important factors influencing stomatal conduc‐ tance. In this modification, EVI (enhanced vegetation index; [45] ) was used in replac‐ ing NDVI in the calculation of surface-vegetation fraction. Also, treatment of soil-water evaporation was incorporated. The model was further modified by *Mu et al.* [46] to in‐ clude calculation of daytime and nighttime ET, soil-heat flux, and canopy conductance for both dry and wet foliar conditions. This variant of the model along with climate-sta‐ tion data worldwide were used in the production of a global product of AET (i.e., MOD16; [46]) that was subsequently validated against flux data acquired from 46 AmeriFlux sites

Complementary-based methods were first introduced by *Bouchet* [33], considering that whenever a well-watered surface dries, the decrease in AET is coupled with a correspond‐

where *E*w is defined as the value of potential evaporation, when AET and PET are equal for

The complementary relationship is based on the assumption that the energy required for evaporation is permanently available [48]. In such instance, drying of a wet surface would result in a reduction in AET and a corresponding reduction in energy consumption. The en‐ ergy saved would lead to an increase in PET [49]. The complementary relationship was ex‐ tended by *Granger* [34] for non-saturated surfaces and non-equal changes in AET and PET.

*Granger* [34] defined PET, AET, and Ew using a Dalton-type mass-transfer equation and in‐

 gD +

. *AET PET Ew* g

+ =

∂ *AET* <sup>∂</sup>*PET* <sup>=</sup> -γ

troduced an alternative equation for the complementary relationship, i.e.,

2 , *AET PET E* + = *<sup>w</sup>* (13)

*<sup>Δ</sup>* . (14)

D D (15)

function of MODIS LAI and NDVI.

distributed throughout the Americas.

*2.2.3. Complementary-based methods*

ing increase in PET and, as a result

an unlimited moist surface [47].

He expressed these changes as

where H is the actual sensible heat flux and determined by solving a system of non-linear equations proposed by *Brutsaert* [29, 41]. AET is subsequently calculated with:

$$AET = \frac{1}{\lambda} EF\_r \times \lambda PET\_r \tag{10}$$

where

$$
\lambda PET = \mathbb{R}\_n - G - H\_{wet}.\tag{11}
$$

Hwet is calculated using an equation proposed by *Meneti* [42] based on the Penman-Monteith combination equation [43].

The SEBAL model [27] was designed to estimate regional AET from RS-data consisting of surface temperature, surface reflectance, Normalized Difference Vegetation Index (NDVI), and their corresponding relations. The SEBAL model calculates H from a linear relationship between Ts and Ta derived from plotted distributions of wet and dry pixels.

#### *2.2.2. Penman-Monteith equation*

To estimate regional ET using MODIS (or Moderate Resolution Imaging Spectroradiometer) data, *Cleugh et al.* [44] proposed the use of the Penman-Monteith equation, i.e.,

$$\lambda ET = \frac{\Delta (R\_n - G) + \xi C\_p \frac{\mathbf{e}\_s - \mathbf{e}\_a}{r\_a}}{\Delta + \mathcal{Y} \left(1 + \frac{r\_s}{r\_a}\right)},\tag{12}$$

where ∆ is the slope of the saturation-vapor-pressure-to-temperature-curve, ra and rs are the aerodynamic (atmospheric) and surface resistances (s m-1) to the transfer of surface water vapor to the atmosphere, es is saturation vapor pressure (hpa), and ea is actual vapor pres‐ sure (hPa). In their model, input to Eqn. (12) are based on MODIS-derived vegetation data and daily surface meteorological data. Surface conductance is estimated in the model as a function of MODIS LAI and NDVI.

The model was later modified by *Mu et al.* [5] by adding vapor pressure deficit (VPD) and minimum air temperature as two important factors influencing stomatal conduc‐ tance. In this modification, EVI (enhanced vegetation index; [45] ) was used in replac‐ ing NDVI in the calculation of surface-vegetation fraction. Also, treatment of soil-water evaporation was incorporated. The model was further modified by *Mu et al.* [46] to in‐ clude calculation of daytime and nighttime ET, soil-heat flux, and canopy conductance for both dry and wet foliar conditions. This variant of the model along with climate-sta‐ tion data worldwide were used in the production of a global product of AET (i.e., MOD16; [46]) that was subsequently validated against flux data acquired from 46 AmeriFlux sites distributed throughout the Americas.

### *2.2.3. Complementary-based methods*

pheric surface layer to derive land surface physical parameters and roughness lengths in de‐ termining the evaporative fraction at the limiting ends of accessible water, i.e., dry and wet limits [29]. At the dry end, evaporation (ETdry) is assumed to be zero due to the limitation in soil moisture and, as a result, sensible heat (Hdry) is maximum, equalling net available ener‐ gy (i.e., Hdry = Rn - G). At the wet end, evaporation (ETwet) is assumed to be at its potential rate (i.e. λETwet= λPET) and sensible heat (Hwet) is minimum. Relative evaporation fraction is

1 , *wet*

*H H*

where H is the actual sensible heat flux and determined by solving a system of non-linear

Hwet is calculated using an equation proposed by *Meneti* [42] based on the Penman-Monteith

The SEBAL model [27] was designed to estimate regional AET from RS-data consisting of surface temperature, surface reflectance, Normalized Difference Vegetation Index (NDVI), and their corresponding relations. The SEBAL model calculates H from a linear relationship

To estimate regional ET using MODIS (or Moderate Resolution Imaging Spectroradiometer)

1

where ∆ is the slope of the saturation-vapor-pressure-to-temperature-curve, ra and rs are the aerodynamic (atmospheric) and surface resistances (s m-1) to the transfer of surface water vapor to the atmosphere, es is saturation vapor pressure (hpa), and ea is actual vapor pres‐

*e e RG C*

V

*s a*

*r r*

è ø

*n p*


g

,

(12)

*s a*

*a*

*H H EF*

equations proposed by *Brutsaert* [29, 41]. AET is subsequently calculated with:

*AET* <sup>=</sup> <sup>1</sup>

between Ts and Ta derived from plotted distributions of wet and dry pixels.

data, *Cleugh et al.* [44] proposed the use of the Penman-Monteith equation, i.e.,

( )

l

*<sup>r</sup> ET*

<sup>=</sup> æ ö D+ + ç ÷

*dry wet*


*<sup>λ</sup> EFr* <sup>×</sup>*λPET* , (10)

*λPET* =*Rn* −*G* −*Hwet*. (11)

*r*

defined as:

102 Evapotranspiration - An Overview

where

combination equation [43].

*2.2.2. Penman-Monteith equation*

Complementary-based methods were first introduced by *Bouchet* [33], considering that whenever a well-watered surface dries, the decrease in AET is coupled with a correspond‐ ing increase in PET and, as a result

$$AET + PET = \mathcal{Z}E\_w,\tag{13}$$

where *E*w is defined as the value of potential evaporation, when AET and PET are equal for an unlimited moist surface [47].

The complementary relationship is based on the assumption that the energy required for evaporation is permanently available [48]. In such instance, drying of a wet surface would result in a reduction in AET and a corresponding reduction in energy consumption. The en‐ ergy saved would lead to an increase in PET [49]. The complementary relationship was ex‐ tended by *Granger* [34] for non-saturated surfaces and non-equal changes in AET and PET. He expressed these changes as

$$\frac{\partial AET}{\partial PET} = \frac{\mathbf{\cdot}\mathbf{\cdot}}{\Delta}.\tag{14}$$

*Granger* [34] defined PET, AET, and Ew using a Dalton-type mass-transfer equation and in‐ troduced an alternative equation for the complementary relationship, i.e.,

$$AET + PET \frac{\mathcal{Y}}{\Delta} = E\_{\text{w}} \frac{\Delta + \mathcal{Y}}{\Delta}. \tag{15}$$

For non-saturated surfaces, *Granger and Gray* [50] proposed a relative evaporation fraction with the notion that wind velocity (u) has an equal impact on AET and PET, i.e.,

$$EF\_r = \frac{AET}{PET} = \frac{f(\mu)(e\_a^s - e\_a)}{f(\mu)(e\_s^s - e\_a)} = \frac{e\_a^s - e\_a}{e\_s^s - e\_a},\tag{16}$$

*λPET* = *f <sup>T</sup>* (*es*

*<sup>p</sup>* is saturation vapor pressure at Tp and Tp is the equilibrium temperature. The equi‐

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

librium temperature is defined as the temperature at which the energy balance [Eqn. (19)] and mass transfer [Eqn. (20)] give the same result for PET, when solved numerically. Param‐ eter fT is a vapor-transfer coefficient considered constant for a specific pressure level and in‐ dependent of wind velocity. Parameter fP is a function of atmospheric pressure (Ψ; hPa) and

<sup>3</sup> 4 ( 273) / . *p sp T f Tf* = Y+ +

*ΔTP* ) −1

where b1 and b2 are coefficients determined by regression, ∆TP is the slope of the saturationvapor-pressure-curve at Tp, and RnTP is the net surface energy at Tp, εs is the surface emissivi‐ ty (non-dimensional), and σ is the Stefan-Boltzmann constant (i.e., 5.67 × 10-8 W m-2 K-4).

The two models were validated by *Hobbins et al* [53] using long term water-balance estimates from 120 watersheds distributed throughout the United States. Outcomes of this validation have revealed that the AA-model underestimated the annual AET by 10.6% of annual pre‐ cipitation and the CRAE-model overestimated the same variable by 2.5% of annual precipi‐ tation. For highly arid watersheds, both models tended to overestimate AET. Validation of complementary-based models in other regions of the world, as well as comparing their re‐ sults with direct observations of AET and PET strongly support the prevalence of the com‐ plementary relationship. *Ramirez et al* [54] found that complementary-based models tended to work well for temperate, humid regions of the world and less well for arid regions [55].

One important constraint of conventional complementary-based methods is the requirement of wind velocity in the calculation of relative evaporation, which is not always available or reliable. To overcome this limitation, *Venturini et al* [56] modified the relative evaporation fraction (EFr) of Eqn. (16) by replacing the expression of water vapor pressure with the cor‐ responding temperature used in calculating it. In the case of actual surface water vapor pressure, a hypothetical temperature is used. This hypothetical temperature (Tu) is defined as the temperature at which the surface becomes saturated without changing water vapor pressure; this is equivalent to the definition of dew point temperature for air (Td; K). Using

these temperatures, *Venturini et al.* [56] modified Eqn. (16) to the following:

 es

*<sup>λ</sup><sup>E</sup>* <sup>=</sup>*b*<sup>1</sup> <sup>+</sup> *<sup>b</sup>*2(1 <sup>+</sup> *γΨ*

g

Ew is calculated with a regression equation,

where *es*

Tp, such that

*<sup>p</sup>* <sup>−</sup>*ea*), (20)

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

105

(21)

*RnTP*, (22)

where *es s* and *ea s* are the saturated and actual water vapor pressure at the surface (hPa), ea is the actual water vapor pressure of the air (hPa), and f(u) is a function of wind velocity.

Two of the most widely used models based on the complementary relationship are


$$
\lambda \text{A} \, E \, T = (2\alpha - 1) \frac{\Delta}{\Delta + \gamma} (R\_u - G) - \frac{\gamma}{\Delta + \gamma} 0.35 \, (0.5 + 0.54u\_2)(e\_s - e\_o) \,. \tag{17}
$$

where α is the Priestley-Taylor constant (i.e., 1.26) and *u*2 is the wind velocity at 2-m above the ground or canopy surface (m s-1).

The CRAE model estimates AET from PET and Ew, i.e.,

$$AET = \heartsuit E\_w - PET.\tag{18}$$

PET is addressed by decomposing the Penman equation into its two main components: one component to address the energy balance at the surface and another, the transfer of water vapor from a moist surface, or

$$
\lambda PET = \mathbf{R\_n} - f\_P f\_T (T\_P - T\_a)\_\prime \tag{19}
$$

and

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China http://dx.doi.org/10.5772/52812 105

$$
\lambda PET = f\_{\,\,T}(e\_s^p - e\_a)\_{\,\,\,\,} \tag{20}
$$

where *es <sup>p</sup>* is saturation vapor pressure at Tp and Tp is the equilibrium temperature. The equi‐ librium temperature is defined as the temperature at which the energy balance [Eqn. (19)] and mass transfer [Eqn. (20)] give the same result for PET, when solved numerically. Param‐ eter fT is a vapor-transfer coefficient considered constant for a specific pressure level and in‐ dependent of wind velocity. Parameter fP is a function of atmospheric pressure (Ψ; hPa) and Tp, such that

$$\mathcal{L}f\_p = \mathfrak{P}\Psi + 4\varepsilon\_s \sigma (T\_p + 273)^3 / f\_T. \tag{21}$$

Ew is calculated with a regression equation,

For non-saturated surfaces, *Granger and Gray* [50] proposed a relative evaporation fraction

( )( ) , ( )( ) *s s aa aa*

*sa sa*

are the saturated and actual water vapor pressure at the surface (hPa), ea is


0.35 (0.5 + 0.54*u*2)(*es* −*ea*), (17)

2 . *AET E PET* = - *<sup>w</sup>* (18)

*λPET* =*Rn* − *f <sup>P</sup> f <sup>T</sup>* (*TP* −*Ta*), (19)

with the notion that wind velocity (u) has an equal impact on AET and PET, i.e.,

*r s s*

the actual water vapor pressure of the air (hPa), and f(u) is a function of wind velocity.

**ii.** the complementary relationship aerial evaporation (CRAE) model of *Morton* [52].

*Δ* + *γ*

where α is the Priestley-Taylor constant (i.e., 1.26) and *u*2 is the wind velocity at 2-m above

PET is addressed by decomposing the Penman equation into its two main components: one component to address the energy balance at the surface and another, the transfer of water

Two of the most widely used models based on the complementary relationship are

**i.** the advection-aridity (AA) model of *Brutsaert and Stricker* [51] and

**3.** an empirical wind-velocity function for relative evaporation [31], and

*<sup>Δ</sup>* <sup>+</sup> *<sup>γ</sup>* (*Rn* <sup>−</sup>*G*)<sup>−</sup> *<sup>γ</sup>*

where *es s* and *ea s*

104 Evapotranspiration - An Overview

The AA-model combines

**1.** the Priestley-Taylor equation [32],

λA*ET* =(2*α* −1)

the ground or canopy surface (m s-1).

vapor from a moist surface, or

and

**4.** the complementary relationship, giving rise to

*Δ*

The CRAE model estimates AET from PET and Ew, i.e.,

**2.** the Penman equation [30],

*AET fu e e e e EF PET f u e e e e*

$$\lambda\_E = b\_1 + b\_2 \left( 1 + \frac{\gamma^\prime \mathcal{V}}{\Delta\_{TP}} \right)^{-1} R\_{nTP\prime} \tag{22}$$

where b1 and b2 are coefficients determined by regression, ∆TP is the slope of the saturationvapor-pressure-curve at Tp, and RnTP is the net surface energy at Tp, εs is the surface emissivi‐ ty (non-dimensional), and σ is the Stefan-Boltzmann constant (i.e., 5.67 × 10-8 W m-2 K-4).

The two models were validated by *Hobbins et al* [53] using long term water-balance estimates from 120 watersheds distributed throughout the United States. Outcomes of this validation have revealed that the AA-model underestimated the annual AET by 10.6% of annual pre‐ cipitation and the CRAE-model overestimated the same variable by 2.5% of annual precipi‐ tation. For highly arid watersheds, both models tended to overestimate AET. Validation of complementary-based models in other regions of the world, as well as comparing their re‐ sults with direct observations of AET and PET strongly support the prevalence of the com‐ plementary relationship. *Ramirez et al* [54] found that complementary-based models tended to work well for temperate, humid regions of the world and less well for arid regions [55].

One important constraint of conventional complementary-based methods is the requirement of wind velocity in the calculation of relative evaporation, which is not always available or reliable. To overcome this limitation, *Venturini et al* [56] modified the relative evaporation fraction (EFr) of Eqn. (16) by replacing the expression of water vapor pressure with the cor‐ responding temperature used in calculating it. In the case of actual surface water vapor pressure, a hypothetical temperature is used. This hypothetical temperature (Tu) is defined as the temperature at which the surface becomes saturated without changing water vapor pressure; this is equivalent to the definition of dew point temperature for air (Td; K). Using these temperatures, *Venturini et al.* [56] modified Eqn. (16) to the following:

$$EF\_r = \frac{AET}{PET} = \frac{T\_u - T\_d}{T\_s - T\_d}.\tag{23}$$

These data can be

regional AET.

(NDVI)

Land surface temperature (Ts) Land surface emissivity (εs)

Enhanced vegetation index (EVI)

Air temperature (Ta) Dew point temperature (Td)

spatiotemporal resolutions.

Normalized difference vegetation index

**i.** acquired by direct measurement in the field,

that are largely inadequate for regional-estimate of AET.

**ii.** derived from other related variables by means of regression, or

**iii.** derived from RS optical reflectance or thermal emission data.

While optical and thermal RS has been providing spatially-distributed values for the assess‐ ment of landscape AET, it poses several important challenges. RS-images from LANDSAT, ASTER, and SPOT-systems have high spatial resolution, but are acquired infrequently (i.e., every 16 days for LANDSAT and ASTER-systems and every 26 days for SPOT). In contrast, high temporal-acquisitions with geostationary satellites provide coarse spatial resolutions

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

The MODIS-instrument is crucial to the production of important global image-products for land- and ocean-surface monitoring [58]. MODIS-instruments were launched to space by NASA (National Aeronautics and Space Administration) as part of the earth-observing sys‐ tem (EOS). MODIS Terra and Aqua image-products are available since February 24, 2000 and June 24, 2002, respectively [59]. Raw image-products have a temporal-acquisition inter‐ val of one or two days, covering 36 spectral bands between 0.405 and 14.385 μm [59, 60]. Be‐ sides daily image scenes, several composite-products are also available at multiple-day intervals at different processing levels. Level-1 products provide the instrument data at full resolution for individual MODIS scenes, which can be exploited after converting to radiance values, surface reflectance, or brightness temperature. Level-2, 2G, and 3 images contain de‐ rived geo-biophysical parameters, while Level-4 images contain model-generated products. Level-2G and -3 image-products consist of aggregations of daily images to give 8 and 16-day cloud-free images. Table 1 lists a number of MODIS image-products valuable in estimating

**Variables MODIS image-products Spatiotemporal**

MODIS land surface temperature and

MODIS vegetation indices (**MOD13Q1**) 250 16

emissivity data (**MOD11A2**)

MODIS atmospheric profile data

albedo products

**Table 1.** Data needed in the calculation of regional AET and corresponding MODIS image-product sources and

(**MOD07**)

Land surface albedo (As) MODIS products combined with **BRDF**-

**Resolution m days**

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

107

1000 8

1000 16

5000 1

The hypothetical temperature (Tu) is derived using the slope of the water vapor pressure curve at Td (i.e., Δd) and Ts (Δs) and the relationship between water vapor pressures and cor‐ responding temperatures, such that

$$T\_{\mu} = \frac{e\_s^s - e\_a - \Delta\_s T\_s + \Delta\_d T\_d}{\Delta\_d - \Delta\_s}. \tag{24}$$

Combining Eqn.'s (15) and (23) and the Priestley-Taylor equation [32] with the definition of Ew, the equation of AET then becomes

$$
\lambda A \, E \, T = \alpha \frac{E F\_r \, \Delta}{E F\_r \, \Delta \, \star \mathcal{V}} (\mathcal{R}\_n \text{--G}).\tag{25}
$$

These changes render the method less error prone and simpler regarding its data require‐ ments. The method is numerically robust, producing errors < 15% of daily AET-measure‐ ments for heterogeneous landscapes of the Southern Great Plains of the United States [56]. *Kalma et al.* [57] summarized results of validation studies of 30 RS-based AET methods, where it was found that *Venturini et al.*'s [56] method produced the highest accuracy among the methods using MODIS-derived data. In the current study (Section 5.2), we use the *Ven‐ turini et al.* [56] method to calculate regional AET for the extremely complex landscape of westcentral Gansu, NW China.

#### **3. Data requirements and accessibility**

Data requirements in the calculation of AET depend on the method or model used in the calculation procedure. In general, input data for all methods can be grouped into four main categories:


These data can be

. *u d*


*EFr<sup>Δ</sup>* <sup>+</sup> *<sup>γ</sup>* (*Rn* <sup>−</sup>*G*). (25)

. (24)

*s d*

The hypothetical temperature (Tu) is derived using the slope of the water vapor pressure curve at Td (i.e., Δd) and Ts (Δs) and the relationship between water vapor pressures and cor‐

> *<sup>s</sup>* <sup>−</sup>*ea* <sup>−</sup>*ΔsTs* <sup>+</sup> *<sup>Δ</sup>dTd Δ<sup>d</sup>* −*Δ<sup>s</sup>*

Combining Eqn.'s (15) and (23) and the Priestley-Taylor equation [32] with the definition of

These changes render the method less error prone and simpler regarding its data require‐ ments. The method is numerically robust, producing errors < 15% of daily AET-measure‐ ments for heterogeneous landscapes of the Southern Great Plains of the United States [56]. *Kalma et al.* [57] summarized results of validation studies of 30 RS-based AET methods, where it was found that *Venturini et al.*'s [56] method produced the highest accuracy among the methods using MODIS-derived data. In the current study (Section 5.2), we use the *Ven‐ turini et al.* [56] method to calculate regional AET for the extremely complex landscape of

Data requirements in the calculation of AET depend on the method or model used in the calculation procedure. In general, input data for all methods can be grouped into four

**i.** surface meteorological data, including near surface air temperature, wind velocity,

**ii.** radiative energy fluxes, including incoming shortwave ( *Rs* ↓ ) and longwave radia‐

**iii.** surface attributes, including temperature, emissivity, albedo, and soil water con‐

**iv.** vegetation attributes, including LAI, vegetation-cover density and extent, and sto‐

tion ( *RL* ↓ ) and their outgoing counterparts (i.e., *Rs* ↑ and *RL* ↑ );

*EFrΔ*

*r*

*Tu* = *es*

*λAET* =*α*

responding temperatures, such that

106 Evapotranspiration - An Overview

Ew, the equation of AET then becomes

westcentral Gansu, NW China.

main categories:

tent; and

**3. Data requirements and accessibility**

matal density and aperture.

and in-air water vapor pressure and humidity;

*AET T T EF PET T T*


While optical and thermal RS has been providing spatially-distributed values for the assess‐ ment of landscape AET, it poses several important challenges. RS-images from LANDSAT, ASTER, and SPOT-systems have high spatial resolution, but are acquired infrequently (i.e., every 16 days for LANDSAT and ASTER-systems and every 26 days for SPOT). In contrast, high temporal-acquisitions with geostationary satellites provide coarse spatial resolutions that are largely inadequate for regional-estimate of AET.

The MODIS-instrument is crucial to the production of important global image-products for land- and ocean-surface monitoring [58]. MODIS-instruments were launched to space by NASA (National Aeronautics and Space Administration) as part of the earth-observing sys‐ tem (EOS). MODIS Terra and Aqua image-products are available since February 24, 2000 and June 24, 2002, respectively [59]. Raw image-products have a temporal-acquisition inter‐ val of one or two days, covering 36 spectral bands between 0.405 and 14.385 μm [59, 60]. Be‐ sides daily image scenes, several composite-products are also available at multiple-day intervals at different processing levels. Level-1 products provide the instrument data at full resolution for individual MODIS scenes, which can be exploited after converting to radiance values, surface reflectance, or brightness temperature. Level-2, 2G, and 3 images contain de‐ rived geo-biophysical parameters, while Level-4 images contain model-generated products. Level-2G and -3 image-products consist of aggregations of daily images to give 8 and 16-day cloud-free images. Table 1 lists a number of MODIS image-products valuable in estimating regional AET.


**Table 1.** Data needed in the calculation of regional AET and corresponding MODIS image-product sources and spatiotemporal resolutions.

#### **3.1. Land surface temperature**

MODIS land surface temperatures (Ts; MOD11A2) are estimated from MODIS thermal infra‐ red (TIR) data collected in near-cloud-free conditions with the application of a split-window algorithm to avoid misidentification with cloud-top conditions [61]. The algorithm uses MODIS-emissivity bands 31 and 32 in the calculation of Ts [62]. Details of the calculationprocedure can be found in [63].

#### **3.2. Vegetation indices**

MODIS vegetation-products (MOD13Q1) provide two vegetation-indices (VI) both at 250-m and 16-day resolution [64]. NDVI is calculated as the normalized ratio of near infrared (NIR) and red bands. EVI (introduced earlier) was developed to improve the sensitivity of NDVI for high biomass regions [45]. NDVI and EVI are calculated from

$$\text{NDVI} = \frac{\rho\_{\text{NIR}} - \rho\_{red}}{\rho\_{\text{NIR}} + \rho\_{red}},\tag{26}$$

albedo acquired with the Terra and Aqua satellites [68], covering all seven spectral bands

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

Air and dew point temperatures provided by the MODIS-MOD07 product (at 5-km resolu‐ tion) is currently the best possible estimate of the two variables derived from RS methods [69]. MOD07 atmospheric profile information includes temperature and atmospheric hu‐

The study area consists of the Shiyang and Hei River watersheds in westcentral Gansu, NW China (Fig. 1). The Shiyang River originates from the Qilian Mountains and flows north‐ westward before terminating in the Minqin-lake district [71]. The total basin area is approxi‐

mean sea level (AMSL), with an average elevation of 1,871 m AMSL. The Hei River water‐

er basin in NW China [72]. The Hei River watershed includes the Zhangye watershed, with

The natural landscape of the study area comprises of mountains, oases, and deserts, all in‐ teracting with each other [73]. The oases play an important role in the sustainability of the region's overall ecological and socio-economic integrity [74]. The area overlaps four distinct ecoregions [75]. The northern part, noted for its arid to semi-arid conditions, includes por‐ tion of the Badain Jaran and Tengger deserts and oases in the southwest portions of the Ala‐ shan Plateau. Liangzhou, at the south, and Minqin, at the north, are two important oases in the Shiyang River watershed [71]. Zhangye is the main oasis in the Zhangye watershed. Spring wheat is the main food crop grown in the oases, which is usually supported by irri‐ gation [76]. In the deserts, salt-tolerant, xerophytic shrub species, i.e., saxaul (*Haloxylon am‐*

Locally-generated rainfall in the oases is normally insufficient (< 170 mm yr-1) to support ag‐ riculture in the oases [7]. The main source of water to the oases is the runoff from the Qilian Mountains generated from snowmelt and in-mountain precipitation transported by the Shiyang and Hei River systems [78, 79]. Glacial meltwater contributes to about 3.8% and 8.3% of total runoff in the Shiyang and Hei Rivers, respectively [78]. The meltwater usually flows during the spring-summer period due to the warming of the mountain glaciers and

A primary source of water in the rivers during the summer is orographic precipitation [80] formed in the Qilian Mountains [81]. Annual precipitation in the watersheds is < 80 mm in the desert-portion of the watershed and > 800 mm in the Qilian Mountains, proper. Annual

. Elevation in the Shiyang River basin varies from 1,284 to 5,161 m above

. Elevation in the Zhangye watershed varies from 1,287

, is the second largest inland riv‐

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

109

midity at 20 different vertical pressure levels between 5 to 1000 hPa [70].

shed, with a land surface area of approximately 128,000 km2

*modendron*) and *Reaumuria soongolica* [77] are common.

previous snow-season's snow cover [74].

to 5,045 m AMSL, with an average elevation of 2,679 m AMSL (Fig. 1).

and three broad bands [60].

**3.4. Air and dew point temperature**

**4. Study area description**

a total land area of about 31,100 km2

mately 49,500 km2

and

$$EVI = B \frac{\rho\_{NIR} - \rho\_{red}}{\rho\_{NIR} + \mathbf{C}\_1 \rho\_{red} - \mathbf{C}\_2 \rho\_{blue} + \mathbf{L}} \,\tag{27}$$

where ρ's are wavelength-specific atmospherically-corrected or partially atmosphere-cor‐ rected (for Rayleigh and ozone absorption) surface reflectances; B is the gain factor; L is the canopy background adjustment which addresses nonlinear, differential NIR and red-radiant transfer through vegetative canopies; and C1, C2 are the coefficients of aerosol resistance, which uses the blue band to correct for aerosol influences on the red band.

Coefficients adopted in the EVI-algorithm are L=1, C1=6, C2 = 7.5, and G= 2.5 [45]. While NDVI shows some problems with dense forests [45], both vegetation indices provide similar accuracy when used for cropland delineation [65].

#### **3.3. Land surface albedo**

Land surface albedo is the fraction of shortwave radiation reflected in all directions and is a critical parameter in estimating surface net shortwave radiation [66]. MODIS-BRDF (bidirec‐ tional reflectance distribution function)-based albedo image-products combine atmospheri‐ cally-corrected surface reflectance from multiple dates and sensors in creating 16-day, 1-km resolution images [67]. The MODIS product, MCD43B3, includes both black and white sky albedo acquired with the Terra and Aqua satellites [68], covering all seven spectral bands and three broad bands [60].

### **3.4. Air and dew point temperature**

**3.1. Land surface temperature**

108 Evapotranspiration - An Overview

procedure can be found in [63].

**3.2. Vegetation indices**

and

MODIS land surface temperatures (Ts; MOD11A2) are estimated from MODIS thermal infra‐ red (TIR) data collected in near-cloud-free conditions with the application of a split-window algorithm to avoid misidentification with cloud-top conditions [61]. The algorithm uses MODIS-emissivity bands 31 and 32 in the calculation of Ts [62]. Details of the calculation-

MODIS vegetation-products (MOD13Q1) provide two vegetation-indices (VI) both at 250-m and 16-day resolution [64]. NDVI is calculated as the normalized ratio of near infrared (NIR) and red bands. EVI (introduced earlier) was developed to improve the sensitivity of NDVI

> *ρNIR* −*ρred ρNIR* + *ρred*

where ρ's are wavelength-specific atmospherically-corrected or partially atmosphere-cor‐ rected (for Rayleigh and ozone absorption) surface reflectances; B is the gain factor; L is the canopy background adjustment which addresses nonlinear, differential NIR and red-radiant transfer through vegetative canopies; and C1, C2 are the coefficients of aerosol resistance,

Coefficients adopted in the EVI-algorithm are L=1, C1=6, C2 = 7.5, and G= 2.5 [45]. While NDVI shows some problems with dense forests [45], both vegetation indices provide similar

Land surface albedo is the fraction of shortwave radiation reflected in all directions and is a critical parameter in estimating surface net shortwave radiation [66]. MODIS-BRDF (bidirec‐ tional reflectance distribution function)-based albedo image-products combine atmospheri‐ cally-corrected surface reflectance from multiple dates and sensors in creating 16-day, 1-km resolution images [67]. The MODIS product, MCD43B3, includes both black and white sky

, (26)

*<sup>ρ</sup>NIR* <sup>+</sup> *<sup>C</sup>*1*ρred* <sup>−</sup>*C*2*ρblue* <sup>+</sup> *<sup>L</sup>* , (27)

for high biomass regions [45]. NDVI and EVI are calculated from

*NDVI* =

*EVI* <sup>=</sup> *<sup>B</sup> <sup>ρ</sup>NIR* <sup>−</sup>*ρred*

which uses the blue band to correct for aerosol influences on the red band.

accuracy when used for cropland delineation [65].

**3.3. Land surface albedo**

Air and dew point temperatures provided by the MODIS-MOD07 product (at 5-km resolu‐ tion) is currently the best possible estimate of the two variables derived from RS methods [69]. MOD07 atmospheric profile information includes temperature and atmospheric hu‐ midity at 20 different vertical pressure levels between 5 to 1000 hPa [70].

### **4. Study area description**

The study area consists of the Shiyang and Hei River watersheds in westcentral Gansu, NW China (Fig. 1). The Shiyang River originates from the Qilian Mountains and flows north‐ westward before terminating in the Minqin-lake district [71]. The total basin area is approxi‐ mately 49,500 km2 . Elevation in the Shiyang River basin varies from 1,284 to 5,161 m above mean sea level (AMSL), with an average elevation of 1,871 m AMSL. The Hei River water‐ shed, with a land surface area of approximately 128,000 km2 , is the second largest inland riv‐ er basin in NW China [72]. The Hei River watershed includes the Zhangye watershed, with a total land area of about 31,100 km2 . Elevation in the Zhangye watershed varies from 1,287 to 5,045 m AMSL, with an average elevation of 2,679 m AMSL (Fig. 1).

The natural landscape of the study area comprises of mountains, oases, and deserts, all in‐ teracting with each other [73]. The oases play an important role in the sustainability of the region's overall ecological and socio-economic integrity [74]. The area overlaps four distinct ecoregions [75]. The northern part, noted for its arid to semi-arid conditions, includes por‐ tion of the Badain Jaran and Tengger deserts and oases in the southwest portions of the Ala‐ shan Plateau. Liangzhou, at the south, and Minqin, at the north, are two important oases in the Shiyang River watershed [71]. Zhangye is the main oasis in the Zhangye watershed. Spring wheat is the main food crop grown in the oases, which is usually supported by irri‐ gation [76]. In the deserts, salt-tolerant, xerophytic shrub species, i.e., saxaul (*Haloxylon am‐ modendron*) and *Reaumuria soongolica* [77] are common.

Locally-generated rainfall in the oases is normally insufficient (< 170 mm yr-1) to support ag‐ riculture in the oases [7]. The main source of water to the oases is the runoff from the Qilian Mountains generated from snowmelt and in-mountain precipitation transported by the Shiyang and Hei River systems [78, 79]. Glacial meltwater contributes to about 3.8% and 8.3% of total runoff in the Shiyang and Hei Rivers, respectively [78]. The meltwater usually flows during the spring-summer period due to the warming of the mountain glaciers and previous snow-season's snow cover [74].

A primary source of water in the rivers during the summer is orographic precipitation [80] formed in the Qilian Mountains [81]. Annual precipitation in the watersheds is < 80 mm in the desert-portion of the watershed and > 800 mm in the Qilian Mountains, proper. Annual PET in the deserts ranges between 2,000 to 2,600 mm and 700 to 1,200 mm in the mountains [82]. Most of the precipitation occurs during June-August. About 94% of the water delivered from the mountains is through surface runoff. Average annual runoff delivered by the Shiyang River is about 15.8 ×108 m3 and about 37.7 × 108 m3 by the Hei River [82].

and surface and shallow subsurface water in the region forms a nearly perfect close system [74]. ET in the series of oases at the base of the Qilian Mountains is shown to play an impor‐ tant role in the recycling of water in the region and seasonal evolution of snow cover in the

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

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

111

**5. Seasonal and annual variation of AET for different landcover types**

The MODIS annual global landcover map currently available (as of 2012) is produced from seven spectral maps, BRDF-adjusted reflectance, Ts, EVI, and an application of supervised classification using ground data from 1860 field sites [86]. Assessments of the product have shown that this map is not entirely realistic for zones of transition or for mountainous re‐ gions [87]. Improved landcover definition at regional or local scales with supervised classifi‐ cation usually involves much greater amounts of ground data that are normally available for most regions. Recently, decision tree-based classification has been applied to RS-data and has been shown to produce better results than other classification systems based on maximum likelihood or unsupervised clustering and labeling [88]. One advantage of deci‐ sion tree-based classification is that it is able to use local knowledge of vegetation character‐ istics together with other pertinent data, such as terrain characteristics. In the current study, we use chronological-sequences of MODIS-based EVI and digital terrain information (e.g.,

Vegetation distribution in the study area has a unique preferential association with eleva‐ tion, slope, and slope direction [89]. North-facing slopes of the Qilian Mountains support al‐ pine meadow at elevations between 2,500 to 3,300 m AMSL. At elevations above 3,300 m AMSL, deciduous shrubs represent the most dominant vegetation type. Isolated patches of conifer forests in the Qilian Mountains are classified as a separate ecoregion [90] found at elevations between 2,500 m to 3,300 m AMSL. Vegetation density and seasonal vegetation

Based on vegetation site preferences, the study area is subdivided into four main elevation

Different landcover types in these elevation zones were then identified based on EVI and terrain attributes, in particular slope orientation (aspect). Landcover types and their discrim‐

growth vary as a function of vegetation type and, consequently, landcover.

Qilian Mountains [7, 85].

**5.1. Landcover classification**

zones defined by elevations:

**ii.** between 2,500 to 3,300;

**iv.** > 3,900 m AMSL.

**iii.** between 3,300 to 3,900; and

ination are summarized in Table 2.

**i.** < 2,500;

aspect, elevation) to classify landcover with decision trees.

**Figure 1.** (a) Location of the study area along the northeast flank of the Qinghai-Tibetan Plateau. Yellow and or‐ ange areas correspond to mountain ranges and plateau, respectively. Map (b) gives the physical boundaries of the Shiyang and Hei River watersheds, respectively. Green areas in (b) illustrate the geographic extent of major oases at the base of the Qilian Mountains. Meteorological data relevant to the study were collected at individual climate sta‐ tions identified in (b).

Presence of the Qinghai-Tibetan Plateau at the south of the study area blocks the northward passage of southwest monsoonal precipitation and the westerly airflow on the northern side of the plateau interferes with southerly airflow from reaching the region [83]. Dry north‐ westerly winds during summer generated from the Azores high pressure system and cold dry northerly winds during the winter generated from the Siberian high pressure system limit the outside contribution of moisture to this area [84]. Cyclic exchange of atmospheric and surface and shallow subsurface water in the region forms a nearly perfect close system [74]. ET in the series of oases at the base of the Qilian Mountains is shown to play an impor‐ tant role in the recycling of water in the region and seasonal evolution of snow cover in the Qilian Mountains [7, 85].

### **5. Seasonal and annual variation of AET for different landcover types**

### **5.1. Landcover classification**

PET in the deserts ranges between 2,000 to 2,600 mm and 700 to 1,200 mm in the mountains [82]. Most of the precipitation occurs during June-August. About 94% of the water delivered from the mountains is through surface runoff. Average annual runoff delivered by the

and about 37.7 × 108

**Figure 1.** (a) Location of the study area along the northeast flank of the Qinghai-Tibetan Plateau. Yellow and or‐ ange areas correspond to mountain ranges and plateau, respectively. Map (b) gives the physical boundaries of the Shiyang and Hei River watersheds, respectively. Green areas in (b) illustrate the geographic extent of major oases at the base of the Qilian Mountains. Meteorological data relevant to the study were collected at individual climate sta‐

Presence of the Qinghai-Tibetan Plateau at the south of the study area blocks the northward passage of southwest monsoonal precipitation and the westerly airflow on the northern side of the plateau interferes with southerly airflow from reaching the region [83]. Dry north‐ westerly winds during summer generated from the Azores high pressure system and cold dry northerly winds during the winter generated from the Siberian high pressure system limit the outside contribution of moisture to this area [84]. Cyclic exchange of atmospheric

m3

by the Hei River [82].

m3

Shiyang River is about 15.8 ×108

110 Evapotranspiration - An Overview

tions identified in (b).

The MODIS annual global landcover map currently available (as of 2012) is produced from seven spectral maps, BRDF-adjusted reflectance, Ts, EVI, and an application of supervised classification using ground data from 1860 field sites [86]. Assessments of the product have shown that this map is not entirely realistic for zones of transition or for mountainous re‐ gions [87]. Improved landcover definition at regional or local scales with supervised classifi‐ cation usually involves much greater amounts of ground data that are normally available for most regions. Recently, decision tree-based classification has been applied to RS-data and has been shown to produce better results than other classification systems based on maximum likelihood or unsupervised clustering and labeling [88]. One advantage of deci‐ sion tree-based classification is that it is able to use local knowledge of vegetation character‐ istics together with other pertinent data, such as terrain characteristics. In the current study, we use chronological-sequences of MODIS-based EVI and digital terrain information (e.g., aspect, elevation) to classify landcover with decision trees.

Vegetation distribution in the study area has a unique preferential association with eleva‐ tion, slope, and slope direction [89]. North-facing slopes of the Qilian Mountains support al‐ pine meadow at elevations between 2,500 to 3,300 m AMSL. At elevations above 3,300 m AMSL, deciduous shrubs represent the most dominant vegetation type. Isolated patches of conifer forests in the Qilian Mountains are classified as a separate ecoregion [90] found at elevations between 2,500 m to 3,300 m AMSL. Vegetation density and seasonal vegetation growth vary as a function of vegetation type and, consequently, landcover.

Based on vegetation site preferences, the study area is subdivided into four main elevation zones defined by elevations:


Different landcover types in these elevation zones were then identified based on EVI and terrain attributes, in particular slope orientation (aspect). Landcover types and their discrim‐ ination are summarized in Table 2.


 2000 2009 () , *dom <sup>i</sup> for all pixels <sup>i</sup> for all pixels*

(2000-2009) and the dominant landcover over the same ten-year time period, respectively.

Monthly AET for the study area was calculated using the complementary method of *Ventur‐ ini et al.* [56] for the period of 2000-2009. Computed AET was compared with ETo calculated from pan evaporation data (Fig. 3) corrected with season-specific coefficients reported in [91]. Pan evaporation coefficients were calculated by relating 50 years of pan evaporation data collected at 580 climate stations distributed across China to ETo calculated for the same stations with the FAO Penman-Monteith equation. Pan evaporation coefficients by *Chen et al.* [91] varied from 0.45 to 0.54 from spring to winter. The scattergraphs in Fig. 3 show that

3a for the Shiyang and Fig. 3b for the Hei River watershed). The line and error graphs in Fig. 3 are generated from AET-values extracted at the location of four meteorological stations within the Liangzhou, Minqin, and Zhangye oases (Fig. 1). The lines represent mean total monthly AET based on 2000-2009 data; error bars represent standard deviation of corre‐ sponding values. Modeled AET display seasonal patterns similar to those expressed in ETo, but at substantially reduced levels (Fig. 3). This discrepancy can be rationalized by three im‐

**i.** modeled AET represents the average conditions of individual image pixels (cover‐

**ii.** surface moisture and vegetation conditions and water-vapor-transfer resistances

**iii.** pan evaporation coefficients are based on seasonal averages independent of location. Spatially-distributed average total monthly AET was calculated by averaging monthly AETimages generated for the 2000-2009 period (Fig. 4). Yearly total growing-season AET was calculated by summing AET from April-October of each year (Fig. 5). From Fig. 4, it is clear that AET in the watershed is very low (≤ 25 mm month-1) during winter (January-March and October-December periods) and progressively higher in summer (> 75 mm month-1). AET reaches its maximum during the June-August period and begins to decrease prior the start of winter. Both monthly and total growing-season AET reveals greatest AET in the oases and low-to-mid-slope positions of the Qilian Mountains and lowest in the deserts (not shown) and high-elevation portions of the Qilian Mountains. Within-year variation in AET also appears within the same elevation bands due to changes in vegetation type. Compari‐ son of chronological-series of monthly AET for three landcover types is shown in Fig. 6. For most years, forest landcover is shown to contribute the most to AET in the Shiyang River

are openly different from those driving ETo (i.e., open water vs. soil-plant environ‐

modeled AET has very high positive correlation with ETo for both watersheds (R2

and LCOVdom represent landcover at the pixel-level for individual years

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

= (28)

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

113

> 0.78; Fig.

= -

*LCOV Majority LCOV*

**5.2. Study-area AET variation for different landcover types**

) and not points;

where LCOVi

portant realities, i.e.,

ing 62,500 m2

ments); and

**Table 2.** Landcover definition as a function of elevation zone, EVI, and slope orientation (aspect).

**Figure 2.** Study-area distribution of dominant landcover types.

Ten landcover maps were generated for 2000-2009 using the classification standards sum‐ marized in Table 2. From these maps, a final landcover composite (LCOVdom for all image pixels; Fig. 2) was then created based on a pixel-level, landcover-dominance evaluation, i.e.,

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China http://dx.doi.org/10.5772/52812 113

$$\left.LCOV\_{\text{don}}\right|\_{\text{for all }\text{pixels}} = \left.M\_{\text{2000-2009}} \right|\_{\text{for all }\text{pixels}},\tag{28}$$

where LCOVi and LCOVdom represent landcover at the pixel-level for individual years (2000-2009) and the dominant landcover over the same ten-year time period, respectively.

#### **5.2. Study-area AET variation for different landcover types**

**Elevation zone Landcover Discrimination criteria**

Sparse grass and/or

Sparse grass and/or

shrub

shrub

**Figure 2.** Study-area distribution of dominant landcover types.

Desert Mean growing-season EVI < 0.1130

cropland

facing slopes

Bare land Mean growing-season EVI < 0.113

Snow and/or ice Mean growing-season EVI < 0.113

Ten landcover maps were generated for 2000-2009 using the classification standards sum‐ marized in Table 2. From these maps, a final landcover composite (LCOVdom for all image pixels; Fig. 2) was then created based on a pixel-level, landcover-dominance evaluation, i.e.,

**Table 2.** Landcover definition as a function of elevation zone, EVI, and slope orientation (aspect).

Deciduous shrub Maximum growing-season EVI > 0.27 Bare land Mean growing-season EVI < 0.113

Sparse shrub Mean growing-season EVI between 0.113-0.27

season EVI < 0.1130

Crop Maximum growing-season EVI > 0.27 and mean growing-

Dense grass Maximum growing-season EVI > 0.27, but different from

Alpine meadow Maximum growing-season EVI > 0.27 on north-facing slopes Coniferous forest Maximum growing-season EVI > 0.27 on other than north-

Mean growing-season EVI between 0.113-0.27

Mean growing-season EVI between 0.113 – 0.27

**Zone 1**

**Zone 2**

**Zone 3**

**Zone 4**

(< 2,500 m AMSL)

112 Evapotranspiration - An Overview

(2,500-3,300 m AMSL)

(3,300-3,900 m AMSL)

(> 3,900 m AMSL)

Monthly AET for the study area was calculated using the complementary method of *Ventur‐ ini et al.* [56] for the period of 2000-2009. Computed AET was compared with ETo calculated from pan evaporation data (Fig. 3) corrected with season-specific coefficients reported in [91]. Pan evaporation coefficients were calculated by relating 50 years of pan evaporation data collected at 580 climate stations distributed across China to ETo calculated for the same stations with the FAO Penman-Monteith equation. Pan evaporation coefficients by *Chen et al.* [91] varied from 0.45 to 0.54 from spring to winter. The scattergraphs in Fig. 3 show that modeled AET has very high positive correlation with ETo for both watersheds (R2 > 0.78; Fig. 3a for the Shiyang and Fig. 3b for the Hei River watershed). The line and error graphs in Fig. 3 are generated from AET-values extracted at the location of four meteorological stations within the Liangzhou, Minqin, and Zhangye oases (Fig. 1). The lines represent mean total monthly AET based on 2000-2009 data; error bars represent standard deviation of corre‐ sponding values. Modeled AET display seasonal patterns similar to those expressed in ETo, but at substantially reduced levels (Fig. 3). This discrepancy can be rationalized by three im‐ portant realities, i.e.,


Spatially-distributed average total monthly AET was calculated by averaging monthly AETimages generated for the 2000-2009 period (Fig. 4). Yearly total growing-season AET was calculated by summing AET from April-October of each year (Fig. 5). From Fig. 4, it is clear that AET in the watershed is very low (≤ 25 mm month-1) during winter (January-March and October-December periods) and progressively higher in summer (> 75 mm month-1). AET reaches its maximum during the June-August period and begins to decrease prior the start of winter. Both monthly and total growing-season AET reveals greatest AET in the oases and low-to-mid-slope positions of the Qilian Mountains and lowest in the deserts (not shown) and high-elevation portions of the Qilian Mountains. Within-year variation in AET also appears within the same elevation bands due to changes in vegetation type. Compari‐ son of chronological-series of monthly AET for three landcover types is shown in Fig. 6. For most years, forest landcover is shown to contribute the most to AET in the Shiyang River watershed; crop cover, however, contributes the most in the Hei River watershed (Fig. 6). For the two watersheds, areas sparse of vegetation (as defined in Table 2) consistently con‐ tribute the least to AET during the growing seasons of 2000-2009.

**Figure 4.** Study-area distribution of monthly AET averaged over the 2000-2009 period.

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

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115

**Figure 5.** Study-area distribution of total growing-season (April-October period) AET for the 2000-2009 period.

**Figure 3.** Comparison of monthly AET against ETo calculated from pan evaporation data over a ten-year (2000-2009) period. Dashed lines in (a) and (b) are lines of regression fitted to the ETo-to-AET data pairs; R2 is the coefficient of determination. Line and error graphs of monthly AET and ETo [i.e., (c)-(f)] are based on modeled AET-data extracted at the location of four climate stations, including Wuwei (c) and Minqin (d), representing the Shiyang River watershed, and Zhangye (e) and Shandan (f), representing the Hei River watershed and pan evaporation data.

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China http://dx.doi.org/10.5772/52812 115

**Figure 4.** Study-area distribution of monthly AET averaged over the 2000-2009 period.

watershed; crop cover, however, contributes the most in the Hei River watershed (Fig. 6). For the two watersheds, areas sparse of vegetation (as defined in Table 2) consistently con‐

**Figure 3.** Comparison of monthly AET against ETo calculated from pan evaporation data over a ten-year (2000-2009) period. Dashed lines in (a) and (b) are lines of regression fitted to the ETo-to-AET data pairs; R2 is the coefficient of determination. Line and error graphs of monthly AET and ETo [i.e., (c)-(f)] are based on modeled AET-data extracted at the location of four climate stations, including Wuwei (c) and Minqin (d), representing the Shiyang River watershed,

and Zhangye (e) and Shandan (f), representing the Hei River watershed and pan evaporation data.

tribute the least to AET during the growing seasons of 2000-2009.

114 Evapotranspiration - An Overview

**Figure 5.** Study-area distribution of total growing-season (April-October period) AET for the 2000-2009 period.

**Figure 6.** Monthly average AET (mm) for three landcover types; the upper graph gives monthly AET for the Shiyang River watershed, whereas the bottom graph gives it for the Hei River watershed.

**Figure 7.** Scattergraphs showing AET as a function of same-month EVI for different vegetation covertypes for the two watersheds (labels with 1 refer to the Shiyang River watershed and 2, to the Hei River watershed): a.1 and a.2 repre‐ sents all landcover types combined; b.1 and b.2 for crop cover; c.1 and c.2 for forest cover; d.1 and d.2 for dense grass

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

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**i.** a summary of methodologies available to estimate AET at point to regional scales,

**ii.** case-study calculations of AET for two watersheds in NW China based on the com‐

The complementary relationship of *Venturini et al.* is considered appropriate for complex landscapes of NW China, because of the method's independence from wind velocity, its overall accurracy, and its ability to regionalize AET-calculations with assistance of RS-data as input. Because of the frequent acquisition of MODIS data (primary input datatype to the calculation of AET), AET-calculations can be updated frequently. For NW China, AET-calcu‐ lations at 250-m resolution were carried out on a monthly interval over a ten-year time peri‐ od (2000-2009). Based on a landcover map generated from decision-tree classification and landcover-dominance analysis, regional AET was partitioned along three vegetation-domi‐ nated landcover types. Forest and crop landcover types were shown to contribute the most

plementary relationship of *Venturini et al.* [56].

cover; and e.1 and e.2 for sparse grass or shrub cover.

**6. Conclusions**

This chapter provides

and

#### **5.3. Vegetation influence on AET**

Using EVI as an indicator of vegetation density and vitality, monthly AET was compared with same-month EVI extracted at 220 randomly distributed points across the study area for different landcover types. Scattergraphs using all points and partitioned according to landcover type (Fig. 7) show that independent-evaluations of AET have an overall posi‐ tive correlation with same-month EVI; the strength of correlation (R2 ), however, varies with landcover type. Strongest correlation occurs for cropland areas and weakest, for areas sparse of vegetation.

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China http://dx.doi.org/10.5772/52812 117

**Figure 7.** Scattergraphs showing AET as a function of same-month EVI for different vegetation covertypes for the two watersheds (labels with 1 refer to the Shiyang River watershed and 2, to the Hei River watershed): a.1 and a.2 repre‐ sents all landcover types combined; b.1 and b.2 for crop cover; c.1 and c.2 for forest cover; d.1 and d.2 for dense grass cover; and e.1 and e.2 for sparse grass or shrub cover.

### **6. Conclusions**

**Figure 6.** Monthly average AET (mm) for three landcover types; the upper graph gives monthly AET for the Shiyang

Using EVI as an indicator of vegetation density and vitality, monthly AET was compared with same-month EVI extracted at 220 randomly distributed points across the study area for different landcover types. Scattergraphs using all points and partitioned according to landcover type (Fig. 7) show that independent-evaluations of AET have an overall posi‐

landcover type. Strongest correlation occurs for cropland areas and weakest, for areas sparse

), however, varies with

River watershed, whereas the bottom graph gives it for the Hei River watershed.

tive correlation with same-month EVI; the strength of correlation (R2

**5.3. Vegetation influence on AET**

116 Evapotranspiration - An Overview

of vegetation.

This chapter provides


The complementary relationship of *Venturini et al.* is considered appropriate for complex landscapes of NW China, because of the method's independence from wind velocity, its overall accurracy, and its ability to regionalize AET-calculations with assistance of RS-data as input. Because of the frequent acquisition of MODIS data (primary input datatype to the calculation of AET), AET-calculations can be updated frequently. For NW China, AET-calcu‐ lations at 250-m resolution were carried out on a monthly interval over a ten-year time peri‐ od (2000-2009). Based on a landcover map generated from decision-tree classification and landcover-dominance analysis, regional AET was partitioned along three vegetation-domi‐ nated landcover types. Forest and crop landcover types were shown to contribute the most to AET across the study area, particularly in lowland areas. Areas of sparse vegetation (among the three landcover types) contributed the least to regional AET. This supports the view that the state and abundance of vegetation (defined here by EVI), particularly in the lowlands and low-to-mid-slope positions of the Qilian Mountains, have an important influ‐ ence on regional AET and on the water budget of the study area.

[9] Pielke, R. A., & Avissar, R. (1990). Influence of landscape structure on local and re‐

Influence of Vegetation Cover on Regional Evapotranspiration in Semi-Arid Watersheds in Northwest China

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

119

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### **Author details**

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

\*Address all correspondence to: cbourque@unb.ca

Faculty of Forestry and Environmental Management, University of New Brunswick, Canada

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Faculty of Forestry and Environmental Management, University of New Brunswick, Canada

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**Chapter 7**

**Effect of Evapotranspiration on Hydrothermal**

Evapotranspiration plays an important role not only on hydrologic cycle but also on thermal changes in various ways. In China, hydro-climate is diverse between north and south (Fig. 1). Semi-arid north is heavily irrigated and combination of increased food demand and declining water availability is creating substantial pressures in Yellow River (Brown and Halweil, 1998; Yang et al., 2004; Nakayama et al., 2006, 2010; Nakayama, 2011a, 2011b), whereas flood storage ability around lakes has decreased and impact of Three Gorges Dam (TGD) on flood occurrence in Changjiang downstream against original purpose is increasing problem in humid south (Shankman and Liang, 2003; Zhao et al., 2005; Nakayama and Watanabe, 2008b). Irrigation has a different impact on evapotranspiration changes at rotation between winter wheat and summer maize in the semi-arid region in the north (downstream of Yellow River), and double-cropping of rice in the humid south (middle of Changjiang River) in China. This mechanism changes greatly hydrologic cycle such as river discharge and groundwater, and in particular, affects extremes of flood and drought under climatic change (Nakayama, 2011a, 2011b, 2012c; Nakayama and Watanabe, 2006, 2008b; Nakayama et al., 2006, 2010).

On the other hand, urban heat island (Oke, 1987), where the urban temperature is higher than its rural surroundings, has become a serious environmental problem with the expansion of cities and industrial areas in the world (Fig. 1). Surfaces covered by concrete or asphalt can absorb a large amount of heat during the day and release it to the atmosphere at night. The evaporation of water provides an important counter to this effect, and so open parks and water surfaces are vital in urban areas for creating urban cool-island (Spronken-Smith and Oke, 1999; Chang et al., 2007). Recent researches showed that cooling effect of water-holding pavements made of new symbiotic material (consisting of porous asphalt and water-holding filler made of steel by-products based on silica compound) in addition to that of natural green

> © 2013 Nakayama; 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,

© 2013 Nakayama; 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.

distribution, and reproduction in any medium, provided the original work is properly cited.

**Changes in Regional Scale**

Additional information is available at the end of the chapter

Tadanobu Nakayama

http://dx.doi.org/10.5772/52808

**1. Introduction**
