Journal of Environmental Science and Engineering B 2 (2013) 238-247 Formerly part of Journal of Environmental Science and Engineering, ISSN 1934-8932
D
DAVID
PUBLISHING
Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for Saltation Activity Reiji Kimura1, Masao Moriyama2, Weizhen Wang3 and Abulitipu Abulaiti1 1. Arid Land Research Center, Tottori University, Tottori 6800001, Japan 2. Graduate School of Engineering, Nagasaki University, Nagasaki 8528521, Japan 3. Cold and Arid Regions Environmental and Engineering Research Institute, CAS (Chinese Academy of Sciences), Lanzhou 730000, China Received: March 01, 2013 / Accepted: April 1, 2013 / Published: April 20, 2013. Abstract: Land-surface conditions, such as surface roughness and SWC (soil-water content), control the saltation activity and dust emission in northeast Asia. Information on spatial and temporal changes in surface SWC is needed for dust-modeling systems used to predict dust events with the aim of preventing the damage they cause. A MTVDI (modified temperature-vegetation dryness index) was tested to see if it could reproduce the surface SWC observed in Zhangye, China, and the Tottori Sand Dunes of Japan, and the threshold wind speed at the Tottori Sand Dunes. MTVDI was calculated from land-surface temperature using the MODIS (moderate resolution imaging spectroradiometer) product, and the aerodynamic minimum and maximum surface temperatures were estimated based on meteorological data. A greater correlation is seen between MTVDI and SWC than between SWC from AMSR-E (advanced microwave scanning radiometer-earth observing system) and SWC in Zhangye. The threshold wind speed for saltation activity decreased with increasing MTVDI, that is, with drying of the soil surface of the Tottori Sand Dunes. The correlation between MTVDI and threshold wind speed is statistically significant (R2 = 0.2987). Key words: Asian dust, arid region, particulate matter, northeast Asia, remote sensing.
1. Introduction In January and February 2013, cross-border pollution by PM2.5 became quite serious in Japan. Dust soil, industry emissions, coal burning, vehicle exhaust emissions and waste incineration were thought to be the major sources of the emission of particulate pollution in large cities of China [1, 2]. It has been reported that PM2.5 has detrimental effects on human health. For example, it has been proven that PM2.5 leads to the spread of health problems and is closely correlated with increased respiratory morbidity and mortality [1, 3]. Although the Ministry of the Environment of Japan has determined that the air Corresponding author: Reiji Kimura, Ph.D., main research field: meteorology. E-mail: rkimura@alrc.tottori-u.ac.jp.
quality standard for PM2.5 is less than a daily average of 35 μg·m-3, PM2.5 concentrations over this standard were frequently observed, especially in western Japan. Another problem in Japan is the Asian dust event that occurs in springtime, from March through May. Most of the particles of Asian dust flowing into Japan are smaller than 4 μm, and of course PM2.5 is also included. Asian dust consists of soil or mineral particles originating mainly from severe dust storms in arid and semi-arid regions of northeast Asia, particularly the Taklimakan Desert, Gobi Desert, Hexi corridor, and Loess Plateau regions of China and Mongolia. Asian dust particles mostly consist of rock-forming minerals like quartz and feldspar, although ammonium ions, sulfate ions, and nitrate ions, which do not originate in the soil, are also
Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for 239 Saltation Activity
detected. This means that suspended dust soil may absorb air pollutants originating from the human activity. The possibilities of adhesion in mold, bacteria, and virus have also been pointed out [4]. The Asian dust event that occurs in springtime has been a well known seasonal meteorological event in Japan since time immemorial [5]. The main problems caused by Asian dust in Japan are visibility degradation and pollution of laundry, cars and glass houses for agriculture, in which solar radiation is prevented. In recent years, dust events have had detrimental effects on human health. For example, it has been statistically proven that Asian dust leads to the spread of health problems (e.g., worsening of symptoms of asthmatic patients in springtime in western Japan) [6, 7]. In Japan, an early warning and/or monitoring system based on dust modeling, remote sensing, and weather forecasts is in strong demand to prevent damage due to Asian dust [8]. It has been reported that increased desertification, along with deforestation and land degradation in Asian dust source areas, was a factor contributing to the increased number of dust events in northeast Asia [8, 9]. Therefore, to clarify the effect of land surface conditions (e.g., vegetation cover ratio, soil water content and distribution of soil particle size) on these dust outbreaks is an important part of increasing the accuracy of the dust emission model. An analysis based on SYNOP weather reports and actual observation suggested that the threshold vegetation cover ratio for preventing dust outbreaks exceeded 20% (above 0.2 in NDVI) in the Loess Plateau of China and the grassland of Mongolia [10, 11]. The target area for the dust source area in northeast Asia (35° N to 45° N and 100° E to 115° E) with reference to past results was found, and it was reported that a decreasing trend in Asian dust events in Japan could be recognized since 2000, corresponding to an increasing trend of vegetation in the target area [12, 13]. In 2002, a national project of the Chinese government (the Grain for Green Program) was initiated [14], and
natural vegetation has gradually increased as a result of this project’s efforts to reduce farmland and promote greening in certain river basins on the Loess Plateau [15]. The afforested area increased drastically, especially from 2002 to 2004, according to the China science portal. This is interesting, although it can not be definitely concluded at this stage that the decreasing trend in Asian dust events in Japan since 2000 is an outcome of the grain for green program in China because the study period (2000 to 2011) was short. SWC is also an important factor in preventing dust emission [16]. The effect of surface soil moisture on wind erosion in the Taklimakan Desert in China was demonstrated [17]. It was shown that dust events were rare on the Loess Plateau in China when the surface volumetric soil water content exceeded a threshold value [10]. These studies suggest that it is very important to monitor the spatial and temporal distributions of surface soil water content and to use these data in dust-modeling systems. This study used a MTVDI based on land-surface temperature and compared it with the surface SWC of field observations in Zhangye in China and the Tottori Sand Dunes in Japan. This MTVDI was also compared with the threshold wind speed for saltation activity at the Tottori Sand Dunes.
2. A Modified Dryness Index
Temperature-Vegetation
The MTVDI was defined as follows [18]: T − Ts min MTVDI = s Ts max − Ts min
(1)
where, Ts is the observed daytime surface temperature (°C or K), and Tsmin and Tsmax are the minimum and maximum surface temperatures for the reference crop [19], simulated using energy balance equations and meteorological data (°C or K). The effects of physiological activities on Ts can be separated from meteorological effects by normalization. For a dry surface, MTVDI is close to unity. It was found that
240 Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for Saltation Activity
MTVDI corresponds well to seasonal variations in moisture availability (ma, calculated as the ratio of actual to reference evapotranspiration [19]) in an area of varied land use [18]. ma is constant during a given day [20], so MTVDI corresponding to ma is also presumed to be constant during a given day [18]. This study examined the application of MTVDI by assuming that actual evapotranspiration from the land surface during the dust season was dominated by the surface soil water content [21] because the vegetation consisted only of dead (yellow) vegetation with low vegetation cover during the corresponding period in Zhangye. The observed daytime surface temperature (Ts in Eq. (1)) can be derived from the Terra/MODIS daily global land surface temperature or long-wave radiation data as follows:
{( L
↑
Ts =
− (1 − ε ) L↓ )/ε σ
}
0.25
(2)
↑
where, L is the upward long-wave radiation (W﹒m-2), ε is the surface emissivity, L↓ is the downward long-wave
radiation
-2
(W·m ),
and
σ -8
is
the
-2
λ E = λ ρ CH U β
soil was determined to be 0.98 and 0.95, respectively
(7)
and
Rn = (1 − ref ) S ↓ − ε (σ Ts − L↓ ) 4
(3)
R ↓ = (1 − ref ) S ↓ + ε L↓
(4)
4
where
and R↓ is the total incident radiation (W·m-2), G is the soil heat flux (W·m-2), H is the sensible heat flux (W·m-2), λE is the latent heat flux (W·m-2), ref is the surface albedo, and S↓ is global solar radiation (W·m-2). The fluxes H and λE can be expressed by the bulk transfer equations:
H = cp ρ CH U (Ts − T )
(5)
(8)
Here, Rn is the net radiation (W·m-2). The aerodynamic resistance (ra) of a hypothetical reference crop with an assumed height of 0.12 m and surface albedo of 0.23 can be defined and expressed in terms of CH and U as follows [19]. CHU =
1 = ra ⎡ ⎛ z − d ⎢ ln ⎜⎜ ⎣⎢ ⎝ z 0
⎞ ⎟⎟ ⎠
k 2U ⎤⎡ ⎛ z − d − ψ M ⎥ ⎢ ln ⎜⎜ ⎦⎥ ⎣⎢ ⎝ z T
⎞ ⎟⎟ − ψ ⎠
H
⎤ ⎥ ⎦⎥
(9) Here,
2 Hc 3 zo = 0.123 H c
The basic equation for the heat balance of a land
R ↓ − G = ε σ Ts + H + λ E
(6)
-1
G = 0.1× Rn
d=
surface is used to estimate Tsmin and Tsmax in Eq. (1):
}
where, cp is the specific heat of air (J·kg ·K ), ρ is the density of air (kg·m-3), CH is the bulk transfer coefficient, U is the wind speed at the observation height (m·s-1), T is the air temperature (°C or K), λ is the latent heat of vaporization (J·kg-1), β is the evapotranspiration efficiency (equals 1 under completely wet conditions and 0 under extremely dry conditions), qsat (Ts) is the specific humidity at saturation Ts (kg·kg-1), and q is the specific humidity (kg·kg-1). The soil heat flux G [19] in Eq. (3) is:
[22].
and
(Ts ) − q
sat
-1
-4
Stefan-Boltzmann constant (5.67 × 10 W·m ·K ). The surface emissivity of the vegetation area and bare
{q
(10) (11)
and
zT = 0.1 zo
(12)
where k is Von Karman’s constant (0.4), z is the observation height above the surface (m), d is the zero-plane displacement height (m), zo and zT are the roughness lengths for wind speed and temperature (m), ΨM and ΨH are the stability correction functions for momentum and sensible heat, and Hc is the plant height (0.12 m). The stability correction functions, which were not considered in the MTVDI [18], can be introduced by using the Businger-Dyer formulation [23, 24].
[
]
ψ M = 2 ln[(1 + x) / 2] + ln (1 + x 2 ) / 2 − 2 tan −1 ( x) + π / 2 : unstable, (13)
Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for 241 Saltation Activity
[
]
ψ H = 2 ln (1 + x 2 ) / 2 : unstable, ψ M = ψ H = −5ζ : stable,
(14)
x = ( 1 − 16 ζ )
0.25
(16)
ζ = (z − d ) / L
(17)
(15)
Here, L is the Obukhov stability length. The buoyancy parameter ζ can be replaced by the Richardson number (Ri) based on the following relationship [23]. Ri = ζ : unstable (18) Ri = ζ /(1 + 5ζ ) : stable (19) The Richardson number is given by:
Ri =
ranges from 5 °C to 10 °C. The annual mean precipitation ranges from 50 mm to 150 mm. The mean pan evaporation rate is 2,000 to 2,200 mm·y-1 [25, 26]. The site is dominated by the desert shrub plant Salsola passerina (≈ > 95%) with z = 10 ± 4 cm based on 258 samples. Other plants include Salsola arbuscula and Reaumuria soongorica, and the annual herb Bassia dasyphylla. The vegetation cover was low (almost constant at 17%) during the corresponding period. The following meteorological data were measured at the observation site: solar radiation (reflected solar
g (T − Ts ) ( z − d )
(20)
TU2 -2
radiation, upward long-wave radiation, and downward long-wave
radiation;
Kipp
&
Zonen
CNR-1;
where, g is the acceleration due to gravity (m·s ). Assuming that the vegetation canopy and ground can be regarded as a single plane surface, substitution of Eqs. (5) and (6) into Eq. (3) yields the following equation for Ts.
instrument height 2.5 m), air temperature and
R − G − ε σ Ts − cp ρ CH U (Ts − T )
(Young; CYG-3102 anemometer; instrument height
4
↓
{
}
− λ ρ CH β qsat (Ts ) − q = 0
(21)
In this study, Tsmax (Tsmin) in Eq. (1) is defined as Ts for a reference crop with β = 0 (1). The solution for Ts can be found through successive approximations of Eq. (21) with the fluxes H and λE evaluated from Eqs. (5) and (6) (Fig. 1). In some cases, Ts by Eq. (1) might be higher than Tsmax for the hypothetical reference crop [19] because the parameters used for the estimation of Tsmax are not site-specific. When it appears that Ts >Tsmax during the calculation, MTVDI is set to one.
3. Description of Sites and Experiment Method
humidity (Vaisala; HMP 45A; instrument height 2.94 m), air pressure (Vaisala; PTB 210 barometer; instrument
height
1
m),
precipitation
(OTA;
CTK-15PC; instrument height 0.68 m), wind speed 2.99 m), and wind direction (Young; CYG-3302 sensor; instrument height 4.01 m). SWC was measured with amplitude domain reflectometry probes (Delta-T; ML 2x) set horizontally at a depth of 0.02 m at three points, and the average value of these three points was obtained. All data were sampled every 5 s with a data logger (Campbell Scientific; CR 1000), and averages were computed every 30 min. Meteorological data at 0300 UTC (Coordinated Universal Time) was used to estimate MTVDI because overpasses of the MODIS satellite used for Ts in Eq. (1) during the study period occurred around 0230 to 0300 UTC.
3.1 Zhangye Site in China
3.2 Tottori Sand Dune Site in Japan
The observation station is located in Huazhaizi, Zhangye City, Gansu Province, China, at the center of the Hexi Corridor (38°45' N, 100°19' E, 1731 m) (Fig. 2). The data were collected during the springtime (April through May) in 2010 and 2011. The observation site is in a semiarid region. The annual mean temperature
The Tottori Sand Dunes are located near Tottori City in western Japan (35°32'33" N, 134°13'02" E) (Fig. 2). The data were collected from April 2011 to April 2012. The Tottori Sand Dunes are the largest sand hills in Japan. They are 16 km long from east to west and 2 km wide from north to south. The dunes
242 Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for Saltation Activity
Input the meteorological data S↓, L↓, U, T, q Calculate CHU
･･･Assuming that atmospheric stability is neutral
Determine Ts1 Calculate H, λE No
Converge ?
･･･ ｜R↓-G-σTs4-H-λE｜≒ 0 (a)
Yes Calculate Ri
Ri<0 (unstable) or Ri>0 (stable)
Calculate CHU
｜Ri｜<0.05 (neutral), Ts1=Ts
Calculate Ts2
Output Ts Ts２=Ts
Yes
Converge ?
･･･ same as (a)
･･･ ｜Ts1-Ts2｜≒ 0
No Fig. 1 Flowchart for determining aerodynamic minimum and maximum surface temperatures from meteorological data. Minimum surface temperature is the surface temperature for a reference crop when β (evapotranspiration efficiency) is 1, and maximum surface temperature is that when β = 0.
were created by sediment deposits carried by the Sendai River from the Chugoku Mountains into the Sea of Japan. Ocean winds blowing from the Sea of Japan formed the dunes over a period of almost 100,000 years. The following meteorological data were measured at the observation site: solar radiation and reflected solar radiation (Eko instruments; ML-020VM; instrument height 1.48 m), upward long-wave radiation (Kipp & Zonen; CNR-3; instrument height 1.47 m), air temperature and humidity (Vaisala; HMP45A; instrument height 1.8 m), precipitation (Ota keiki seisakusho; CTK-15PC; instrument height 0.2
m), wind speed and wind direction (Young; CYG-5106 anemometer; instrument height 2.47 m). SWC was measured with amplitude-domain reflectometry probes (Delta-T; ML 2x) set horizontally at a depth of 0.02 m at two points, and the average value of these two points was obtained. All data were sampled every second with a data logger (Campbell Scientific; CR 1000), and averages were computed every 10 min except for the SWC, which was sampled only at noon to save electricity. A Sensit piezoelectric sensor (Sensit Company, Portland, ND, USA) for measuring the amount of saltation, i.e., the saltation number, was mounted 0.04
Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for 243 Saltation Activity
m above ground. This sensor can falsely record rain drops and splashes as saltating sediment, so saltation data for days during which precipitation was measured were not used. Meteorological data and upward long-wave radiation for Ts in Eq. (1) at noon were used to estimate MTVDI, since very little MODIS data for Ts could be obtained because Tottori was covered with clouds most of the time. We determined the threshold wind speed (Ut) for saltation activity using the following equation [27], which is similar to that proposed for threshold friction velocity [28]: 2
SN = D ⋅ U (U 2 − U t )
U ≥ Ut
(22)
where, SN is the saltation number, D is an empirical coefficient depending on the sand particle size (s3·m-3), U is the wind speed (m·s-1), and Ut is the threshold wind speed (m·s-1). We solved for D and Ut through successive approximations of Eq. (22) given the observed saltation number. 3.3 Satellite Data Among the many products Terra/MODIS provides, the daily global land surface temperature product
MOD11C1 was used to estimate the MTVDI in Zhangye. The spatial resolution of this product is 0.05 deg, and the processing software is collection 5. A total of 59 images including Zhangye were obtained from April through May in 2011 and 2012. The soil-water product provided by AMSR-E installed on the Aqua satellite was also used to examine the SWC in Zhangye (https://gcom-w1.jaxa.jp/auth.html). The spatial resolution of this product is 0.25 deg, and the unit is g cm-3 × 103. A total of 84 images including Zhangye were obtained from April through May in 2011 and 2012. The Terra/MODIS MOD09A1 (ftp://ladsweb.nascom.nasa/gov/) product was used to determine the spatial distribution of vegetation (the Normalized Difference Vegetation Index, i.e., NDVI) over northeast Asia (Fig. 2).
4. Results and Discussion 4.1 Application of MTVDI to Estimate SWC The relationships among MTVDI, SWC by AMSR-E, and SWC in Zhangye are depicted in Fig. 3. The MTVDI was significantly correlated with surface
Fig. 2 Spatial distribution of NDVI (normalized differential vegetation index) in northeast Asia obtained from an image acquired in April 2010. Dots indicate the meteorological stations (Zhangye and Tottori) used in this study.
244 Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for Saltation Activity
Fig. 3 Relationship among the MTVDI, SWC by AMSR-E, and SWC in Zhangye, China.
4.2 Application of MTVDI for Estimating Threshold Wind Speed The erosion threshold velocity ratio increased with increasing gravimetric soil moisture for different soil types [31]. Fig. 4 illustrates the relationship between SWC and threshold wind speed at the Tottori Sand Dunes. The threshold wind speed increased with increasing SWC as discussed previously [31]. Fig. 5 illustrates the relationships among the
10 8
Ut (m s‐1)
SWC (R2 = 0.332). Because the surface temperature observed by MODIS is closely associated with the temperature in the very thin surface soil layer, soil water content measured at depths of 0.02 m may be too deep. If the SWC in the very thin surface soil layer could be measured continuously with improvements in the SWC sensor, the correlation between MTVDI and SWC might improve. Dust emission is also affected by the SWC of the very thin surface soil layer [17], so the MTVDI should be compared with SWC in that layer. The correlation between SWC by AMSR-E and SWC is lower than that for MTVDI, primarily because the spatial resolution of this product was 0.25 deg, which is rougher than that for MODIS surface temperature. Another reason is that there are some issues in using the AMSR-E soil water product when the vegetation amount is high because of difficulties in monitoring the SWC itself independent of the vegetation structure [29, 30].
6 4 y = 47.427x + 3.3121 R² = 0.5914 (n=44)
2 0 0
0.02
0.04
θ (m3
0.06
0.08
0.1
m‐3)
Fig. 4 Relationship between SWC and threshold wind speed at the Tottori Sand Dunes in Japan.
MTVDI, SWC, and threshold wind speed Ut at the Tottori Sand Dunes. MTVDI was significantly correlated with surface SWC (R2 = 0.5042). The threshold wind speed for saltation activity Ut decreased with increasing MTVDI, that is, with the drying of the soil surface. The correlation between MTVDI and Ut is significant (R2 = 0.2987). It was demonstrated experimentally that dust fluxes are proportional to saltation flux and confirmed that saltation bombardment is the major mechanism responsible for dust emission, and that therefore the Ut for saltation activity will also be the Ut for dust emission in the actual dust outbreak source area [16]. From this result, MTVDI will be an appropriate index for estimating Ut in dust source regions in the near future, except for the following limitations: y A large amount of dead vegetation may affect the land surface temperature, so the effect of dead vegetation on land surface temperature should be
Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for 245 Saltation Activity
y = ‐0.0601x + 0.0778 R² = 0.5042 (n=44)
0.1
0.06 0.04
6 4
0.02
2
0
0 0
0.2
0.4
0.6
0.8
1
1.2
y = ‐2.8549x + 7.0028 R² = 0.2987 (n=44)
8 Ut (m s‐1)
θ (m3 m‐3)
0.08
10
0
0.2
0.4
MTVDI Fig. 5
0.6
0.8
1
1.2
MTVDI
Relationship among the MTVDI, SWC, and threshold wind speed at the Tottori Sand Dunes in Japan.
separated to estimate the SWC and/or Ut from MTVDI. However, Ts in Eq. (1) can be corrected by using the following weighted-average equation to remove the effect of dead vegetation. (23) Tscor = (Ts - VCr Tsmax )/(1 - VCr ) Here, Tscor is the corrected surface temperature of the soil surface (°C or K), VCr is the dead vegetation cover ratio, and Tsmax is the assumed surface temperature of the dead vegetation derived as described in Fig. 1 for hypothetical dead vegetation of some height with β = 0 and a surface albedo of 0.23 [21]. In the dust source areas of northeast Asia, however, vegetation cover rarely exceeds 20% and the vegetation height is low [11, 32]. y MTVDI was significantly affected by meteorological conditions when calculating the aerodynamic surface temperature (Tsmin and Tsmax), so it is limited to small-scale use. For large-scale use, a detailed grid of meteorological data is needed. If a detailed grid of vegetation cover, vegetation height, and meteorological data can be applied, MTVDI will be a useful tool for estimating the threshold wind speed for dust emission in northeast Asia.
5. Conclusions Wind erosion and/or dust outbreaks in northeast Asia are controlled by land surface conditions such as vegetation cover, SWC, snow cover and type of surface crust. To predict dust events and thus prevent
damage in dust source areas and surrounding countries like Korea and Japan, the spatial and temporal distribution of surface SWC must be monitored for input to dust-modeling systems. The effectiveness of the MTVDI for estimating the surface SWC and threshold wind speed was examined to assist future dust-modeling research using satellite data. MTVDI and SWC were better correlated than SWC by AMSR-E and SWC in Zhangye in China. The threshold wind speed for saltation activity decreased with increasing MTVDI, and the correlation is statistically significant (R2 = 0.2987) at the Tottori Sand Dunes in Japan. MTVDI will be affected by dead vegetation cover in addition to meteorological conditions. For large-scale use, a detailed grid of vegetation cover, vegetation height, and meteorological data are needed. As a future challenge, we are planning to use satellite data and reanalysis data to create a detailed grid of vegetation and meteorological conditions. This will facilitate more precisely estimating the spatial and temporal SWC and threshold wind speed for dust emission in northeast Asia.
Acknowledgments This research was supported by KAKENHI Grant No. 25304037 of the Japan Ministry of Education, Culture, Sports, Science and Technology and the MEXT project of integrated research on Asian dust.
246 Application of Index Based on the Land Surface Temperature to Estimate the Threshold Wind Speed for Saltation Activity
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