Observation and modeling of black soil wind-blown erosion from cropland in Northeastern China

Aeolian Research 19 (2015) 153–162

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Observation and modeling of black soil wind-blown erosion from cropland in Northeastern China Xuelei Zhang a,⇑, Qinqian Zhou a, Weiwei Chen a,b, Yiyong Wang a, Daniel Q. Tong b a b

Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China U.S. NOAA Air Resources Laboratory, College Park, MD 20740, USA

a r t i c l e

i n f o

Article history: Received 27 November 2014 Revised 29 July 2015 Accepted 29 July 2015 Available online 14 August 2015 Keywords: Wind erosion Cropland Field observation Numerical modeling Crust WRF-CMAQ-FENGSHA

a b s t r a c t As the nation’s bread basket, Northeastern China has experienced dramatic land use changes in the past decades, with much natural land being converted into cropland to feed the growing population. The long dormant season, coupled with frequent cold fronts and strong spring winds, makes the exposed cropland vulnerable to wind erosion. However, the rates and spatial–temporal characteristics of wind erosion in this particular soil type have been poorly studied. The present study aimed to measure and simulated the wind erosion characteristics from black soil cropland in the Dehui region of Northeastern China. Our results showed that wind-blown erosion was positively correlated with wind speed and negatively linked to soil moisture, vegetation and soil roughness in this region. The measured threshold friction velocity was 4.47 m/s at 2 m height, corresponding to 0.37 m/s at the surface ground. Based on WRF-CMAQ-FENGSHA model, we localized the parameters and simulated a significant wind erosion event in the Dehui region on May 31, 2013. The relationships between modeled dust flux and ground measurement were correlated (R2 = 0.78). In addition, the modeled aerosol optical depths were also captured by satellite observations (MODIS and CALIPSO). Our results indicate that the bare farmland areas over Northeastern China are important dust sources over this region, and should not be neglected in regional air quality models. The use of protective farming techniques, protection of grassland and plowing in autumn for cropland areas should be considered to combat dust emission. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction A crucial criterion for the existence of wind erosion is the availability of fine particles which can be lifted from the ground when surface wind velocity exceeds a certain threshold wind speed. The wind erosion decreases soil texture, nutrient content, vegetation growth and productivity (Chameides et al., 1999); it effects atmospheric visibility and climate change (Chen et al., 2014), transports micro-nutrients to terrestrial and marine ecosystems (Quinton et al., 2010), and even endangers human health (Goudie, 2014). The wind-blown dust which generally arises from natural processes is commonly at severe levels in arid or semi-arid areas (e.g. desert, Gobi and alluvial deposits), but agricultural activities that disturb the soil can greatly increase the frequency and amount of wind-blown dust (Zender et al., 2004; Engelstaedter et al., 2006; Ginoux et al., 2012). Wind-blown erosion from cultivated and grazed soils is a global problem that has inspired studies in North America (Saxton et al., 2000; Nordstrom and Hotta, 2004; ⇑ Corresponding author. E-mail address: [email protected] (X. Zhang). http://dx.doi.org/10.1016/j.aeolia.2015.07.009 1875-9637/Ó 2015 Elsevier B.V. All rights reserved.

Singh et al., 2012), South America (Mendez and Buschiazzo, 2010), Africa (Toure et al., 2011; Wiggs and Holmes, 2011), Europe (Gomes et al., 2003a,b; Borrelli et al., 2014), Asia (Li et al., 2004; Guo et al., 2013; Wang et al., 2013) and Australia (Webb et al., 2006; Harper et al., 2010; Chappell et al., 2014). Only a few studies have attempted to estimate the contribution of anthropogenic sources to global dust emission by comparing scenarios in numerical dust models with ground-based observations as well as satellite products (Tegen and Fung, 1995; Sokolik and Toon, 1996; Tegen et al., 2004; Park et al., 2010; Ginoux et al., 2012). Ginoux et al. (2012) have estimated that wind erosion and transport from cropland contribute approximately 25% of the total amount of atmospheric dust (Ginoux et al., 2012); however, these processes are important to humans as they occur in inhabited areas, where human health and soil fertility are adversely affected by enhanced dust emissions. Anthropogenic dust sources are associated with land use, and thus, field measurements are essential to further understand wind-erosion processes from cropland and verify wind-erosion models, but there are very few studies relevant to wind-blown erosion of cropland in China (Li et al., 2004; Zhao et al., 2006; Chen et al., 2007).

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Land use changes must be considered in dust emission models (Tegen et al., 2004). Land cover in the Northeastern China dust source region includes grassland, dry river beds and lakes, salt lakes, croplands, and mines. Agricultural land and playas, and their margins, have been identified as focus areas for dust emission in the Chihuahuan Desert (Rivera Rivera et al., 2010). As the nation’s bread basket, Northeastern China has experienced dramatic land use change in the past century, with much of the wild land being converted into cropland to feed a growing population (Wang et al., 2011; Zhao et al., 2014). The long cold season, coupled with frequent cold fronts and strong spring winds (Dickerson et al., 2007), makes the exposed cropland vulnerable to wind erosion (Fig. 10 in (Ginoux et al., 2012)). The loss of top soil caused by wind-blown erosion not only reduces soil productivity, but also degrades air quality and contributes to regional haze (Zhang et al., 2012). To understand, predict, and mitigate the impact of dust aerosol on air quality and climate, it is necessary to accurately parameterize the emission rate of dust particles in the wind erosion processes on croplands. However, windblown dust emission from croplands is poorly represented in existing air quality models. The black soil in the Northeastern China is rich in organic carbons and crusts, which can dramatically alter the threshold wind velocity required to initiate wind erosion (Sharratt and Vaddella, 2014). The effects of such rich organic carbon content on the susceptibility of cropland to wind erosion are expressed as: (1) promotion of the formation of a surface crust, which acts to hold in soil moisture and resist erosion, and (2) significant aggregation, which impacts on aggregate disintegration and vertical dust flux. However, there is a lack of observation data on the parameterization of dust emission such as threshold wind speed, saltation flux, dust concentration, vegetation cover and soil properties in this area. In this paper, we firstly report the field observations relevant to windblown erosion of cropland, and present the numerical modeling of a typical dust event sourced from black soil areas with consideration of the crust effects, in Northeastern China. 2. Observation and modeling methods 2.1. Experimental site A wind-erosion monitoring study was initiated in the spring of 2013 at the Dehui Experimental Station (44° 120 N, 125°330 E) of the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Jilin Province, China (Fig. 1). The observation site is located in the black soil area with a continental monsoon climate in Northeastern China. The mean annual temperature is 4.4 °C, and the mean annual precipitation is 520.3 mm, with more than 70% occurring in June, July and August. The studied soil is a black soil (Udolls, USDA Soil Taxonomy, or Black Chernozem, Canadian soil classification) with the texture of clay loam (Typic Hapludoll) containing 36% clay, 24% silt and 40% sand. The mean soil organic carbon content of the top soil is 1.65% (Liu et al., 2006a,b). More detailed physical and chemical properties were presented by (Liang et al., 2007). Prior to our observations, the land had been used for continuous maize production under conventional tillage management for more than 20 years (Fig. 1). 2.2. Instrumentation This study was conducted from April to June, 2013. Only one dust storm event (May 31) was observed and analysed in detail. A 5 m instrument tower has been installed at the downwind edge of the selected cropland site. The tower provided a platform to

measure the wind velocity profile (5 heights, 0.2 m, 0.5 m, 1.0 m, 2.0 m and 5.0 m, 010C, Met One Instruments Inc.), wind direction (instrument height 2.0 m, 020C, Met One Instruments Inc.), air temperature and relative humidity (instrument height 2.0 m, HMP45C, Campbell Sci. Inc.). Volumetric soil moisture content was measured with three amplitude domain reflectometry probes (CS616-L, Campbell Sci. Inc.) that were placed 2 m away and average around the tower. Saltating particles were detected with a SENSITÒ (Model H11-LIN, Sensit, Co.) particle impact detector in the center of the field. The SENSIT uses a piezoelectric crystal to detect saltating sand grains at a frequency of 1 Hz and amplifies the signals 10 to increase the sensitivity. In this study, the piezoelectric crystal was mounted at 0.02 m above the soil surface. Instantaneous measurements were taken at intervals of 1 s for saltation, wind speed and direction and at 6 s for soil moisture, air temperature and humidity, and recorded by a data logger (CR1000, Campbell Scientific). Finally, all data were averaged over 1 min intervals. The whole monitoring system for wind-blown erosion was powered by solar panels and a colloidal battery. PM10 dust concentrations (mg m3) were measured using DustTrakÒ (Model 8520, TSI, Inc.) aerosol monitors mounted on the tower at 0.5 and 1.5 m height. The vertical flux was calculated according to the method described by Zobeck and Van Pelt (2006). The surface roughness of the soil was measured by the chain method (Saleh, 1993; Zobeck et al., 2003). 2.3. Numerical modeling 2.3.1. Model description and configuration The U.S. EPA’s Community Multi-scale Air Quality (CMAQ) modeling system (Byun and Schere, 2006) was adopted in this study. CMAQ is a three-dimensional Eulerian modeling system that can calculate the mass balance within each grid cell by solving the transport across each cell boundary and chemical transformations within each cell during a given time period. One-way double nesting simulation domains were used in this study, as shown in Fig. 1. Domain 1 covered the whole of East Asia with a horizontal grid resolution of 54 km  54 km, and Domain 2 covered the Northeastern China with a grid resolution of 18 km  18 km. All two-level domains used the same vertical domain set up with 14 vertical layers that reach approximately 20 km above the ground. The first layer thickness was approximately 38 m. The Weather Research and Forecasting (WRF) version 3.2.1 was employed to generate the meteorological fields for CMAQ (Skamarock et al., 2005). The initial and boundary fields were obtained from final operational global analysis data of the National Center for Environmental Prediction (NCEP) with 1°  1° resolution (available at http://rda.ucar.edu/datasets/ds083.2). The physical options used in the WRF model were WRF Double-Moment 6-class microphysics scheme (Lim and Hong, 2010), the Rapid Radiative Transfer Model for GCMs (RRTMG) longwave and shortwave radiation scheme (Mlawer et al., 1997, 1998), Pleim–Xiu land surface scheme (Xiu and Pleim, 2001) ACM2 PBL scheme (Pleim, 2007), and Grell–Devenyi ensemble cumulus scheme (Grell and Dévényi, 2002). In this study, the INDEX-B inventory was employed as the anthropogenic emission inventory (Zhang et al., 2009). 2.3.2. Windblown dust module FENGSHA in CMAQ A new windblown dust emission module (FENGSHA) was developed for CMAQv4.7. The FENGSHA model considers four land types as potential erodible dust sources (barren land, shrub-grass land, shrub land and agricultural land: see Fig. 2b). The vertical flux F (g m2 s1) is calculated based on a modified Owen saltation formula (Owen, 1964; Gillette and Passi, 1988),

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Fig. 1. Field observation site and nested domains used for WRF-CMAQ-FENGSHA.

Fig. 2. Geographic information data sets used in WRF-CMAQ-FENGSHA. (a) Threshold friction velocity, (b) Land use types, (c) Soil textures.

F¼

M X N X q f tr  f cs  K  A   Si  SEP  u  ðu2  u2ti;j Þ g i¼1 j¼1

for u > ut ;

to horizontal emission fluxes, and is associated with the clay content (in percentage); it is calculated as follows (Marticorena and Bergametti, 1995):

ð1Þ

( K¼

where M is the number of erodible land types; N is the number of soil types; A is a constant representing particle supply limitation, which is set to zero for a complete supply limited surface and 1.1 for a source area that is not supply limited (Gillette and Chen, 2001); q is air density; g is the gravitational constant (9.8 ms2); Si is the dust source area for land type i; and SEP is the soil erodible potential for each soil type, determined by the following equation:

SEP ¼ 0:08  %clay þ 1:00  %silt þ 0:12  %sand:

ð2Þ

The terms %clay, %silt and %sand represent the mass fractions of clay, silt and sand in the surface soil, respectively. The soil texture parameters were obtained from the Land Surface Model (Pleim– Xiu scheme in this study) embedded in the meteorological WRF model. The land use and soil texture maps of the Northeastern China are shown in Fig. 2b and c. K represents the ratio of vertical

100:134clay%6

for clay% < 20%

0:0002

for clay% P 20%

;

ð3Þ

u⁄ is the surface friction velocity (from the meteorological model), and uti;j is the empirically-based threshold friction velocity for soil type j and land use type i (Fig. 2a), which controls both the onset and intensity of dust emissions. However, the threshold friction velocity can be modified by surface roughness (f d ), soil moisture and snow cover (f m ). The drag partitioning caused by surface roughness was parameterized in a simplified format following (Marticorena et al., 1997):

f d ¼ ln

    d d = ln ; Z0 Z 0s

ð4Þ

where d is the internal boundary layer height; Z 0 and Z 0s are the aerodynamic roughness length and local roughness length of the intervening surface, where the former is calculated in the WRF

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model and the latter is derived by extrapolation form the wind profile. The parameterization of the soil moisture effect follows (Fécan et al., 1999) as:

( Fm ¼

½1 þ 0:21ðw  w0 Þ0:68  1

0:5

for w P w0 for w 6 w0 ;

ð5Þ

where w0 ¼ 0:08  clay% þ 1:00  silt% þ 0:12  sand%, w is the gravimetric soil water content obtained by an extra conversion from the volumetric soil water content in the WRF model based on the method of (Zender et al., 2003). Finally, the vertical dust flux from the cropland should be further modified by the tillage-ridge factor ftr (Hagen and Armbrust, 1992; Kardous et al., 2005; Liu et al., 2006a,b) and the crusting factor fcs. Fryrear and Saleh (1996) and Fryrear et al. (1998) established the crust correction function based on the relationship between soil crust and soil clay content and organic matter, which expressed as: 2

1

f cs ðFÞ ¼ ð0:0066  ð%clayÞ þ 0:021  ð%OMÞ2 þ 1Þ ;

ð6Þ

where %clay is limited to the range 5.0–39.3% and %OM is limited to the range 0.32–4.74%.

In this study, the values of threshold friction velocities were obtained from the previously reported with direct field or wind tunnel measurements over desert and agricultural land. To account for the spatial variation of threshold friction velocity, different values were assigned to each erodible land use type and each surface soil texture (Fig. 2c).

2.3.3. Satellite data acquisition In order to investigate dust intensity and transport with a regional overview of this dust event, data from multiple satellite platforms were used in this study. The MODIS detector is a moderate resolution spectrometer with a high number of narrow spectral bands and daily global coverage. A number of aerosol parameters are retrieved by MODIS (Remer et al., 2005). Relevant to this study is the aerosol optical depth (AOD), which can be related to aerosol concentration. MODIS provides good spatial resolution data with a number of useful spectral band widths and is widely used in dust studies. The Level 2 data are averaged onto a 1°  1° global grid to produce the Level 3 data used in this study. Level 3 AOD data were generated for multiplatform comparisons using the NASA Giovanni website at http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.

Fig. 3. Hourly variations in air temperature, relative humidity, wind speeds, wind direction, soil moistures, visibility and saltation particle counts from May 30 to June 1, 2013.

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cgi?instance_id=aerosol_daily and https://earthdata.nasa.gov/ labs/worldview/. Moreover, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was used to retrieve the vertical distribution of different aerosol types, especially for dust in this study (Omar et al., 2009). For irregular particles like dust, the lidar return signal is depolarized and hence the depolarization ratio is frequently used to discriminate nonspherical particles from spherical particles (Liu et al., 2013). The smoke layers are easily distinguished from the dust by their very low depolarization ratios and by the strong wavelength dependence of the signal attenuation within the layer. Note that the time difference between Aqua and CALIPSO footprints in the dust region is about 8 h for the CALIPSO daytime orbit.

3. Results and analysis 3.1. Observations A summary of the wind profile characteristics determined for periods when saltation was active is demonstrated in Fig. 3b. The

157

selected observation period is May 30 to June 1, 2013. Note that no precipitation was observed during this period. Frequency distributions of wind direction (Fig. 3b) show that winds from the S to SW are predominant, and that the most frequent and strongest winds (>10 m s1) were from the SSW. Winds during saltation were predominantly from SSW to S (Fig. 3b and Fig. 3d). These observations indicate that wind directions were almost parallel to the direction of soil ridges and thus the flux effects of ridge direction can be neglected in this study. The surface soil moisture and threshold wind speed can be significantly affected by changes of atmospheric temperature and humidity in arid regions (Neuman, 2003; Ravi and D’Odorico, 2005), thus a summary of the air temperature and humidity characteristics determined for the observed period was also drawn in Fig. 3a. An obvious negative relation between air temperature and relative humidity was evident, in accordance with other reported field results (Shao and Leslie, 1997; De Oro and Buschiazzo, 2009). The atmospheric humidity had a weak influence on the variability of surface soil moisture in this cropland: all observed surface soil moisture series from the three sensors showed relatively consistent smoothness (the average volumetric

Fig. 4. Simulated results for dust emission fluxes from cropland over Northeastern China on (a) 09:00, (b) 13:00, (c) 15:00 and (d) 21:00 of May 31, 2013.

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water content was 36.9%), indicating that the soil moisture was not a leading factor controlling soil erosion over this period. Visibility reduction was related to the accumulated progress of the mass concentration of dust in the atmosphere. Thus, an obviously hysteretic visibility reduction was observed during our study (Fig. 3c). A crucial criterion for the existence of wind erosion is the availability of fine particles which can be lifted from the ground when the surface wind velocity exceeds a certain threshold wind speed. The observed threshold friction velocity was 0.37 m/s at the soil surface, corresponding to 4.47 m/s at a height of 2 m. This value is the same order of magnitude as those of agricultural soil investigated by previous scientists (Gomes et al., 2003a,b; Zobeck and Van Pelt, 2006; Park and Park, 2013). Two very weak dust saltation events occurred when the wind speed exceeded 4.47 m/s at local times (LT) 12:30 and 14:30 on May 30 (Fig. 3d). On May 31, 2013, the weather conditions were ideal for wind erosion with a strong prefrontal southwesterly wind accompanied by high air temperature, low relative humidity, and moderate soil moisture. The saltation was initiated when the 2-m wind speed exceeded 4.47 m/s, at 7:30, and continued as the wind speed gradually increased to 11.93 m/s at 3:30; and the wind speed then decreased sharply to 6.73 m/s at 5:30 and finally dropped back below 4.5 m/s at 8:45. The relationship between SENSIT counts and wind speed show that the saltation only started when the wind speed exceed the threshold friction velocity of 4.5 m/s during the daytime, which implies that different soil moisture conditions between daytime and night time affected the generation of dust in Northeastern China.

WRF-CMAQ-FENGSHA with locally-determined parameters. The observed threshold friction velocity (0.37 m/s for the land surface) was input into the FENGSHA module to replace the threshold friction velocity of a land surface with the soil texture of clay loam. The measured surface roughness of 0.018 m was also used in FENGSHA module. Fig. 4 shows the patterns of dust emission at 4 different times from 7:30 AM to 9:30 PM, May 31. The numerical simulations suggest that for this dust event, the dust source region was not the desert area, but the croplands over the Northeastern Plain of China (Fig. 2c). Weak wind erosion started at 9:00 from croplands over the central part of Jilin Province, the western part of Liaoning Province and Eastern Inner Mongolia (Fig. 4a), and reached a maximum emission along a long strip from the plain area of western Liaoning Province to the central part of Jilin Province at 3:30 PM (Fig. 4b). The erosion then gradually decreased in strength until 9:30, May 31 (Fig. 4d). Furthermore, the modeled peak distribution range of the wind erosion was extended to two extra dust emission sources: one located in the southwestern area of Heilongjiang Province, with saline-alkali soils, and another located in the southeastern area of Inner Mongolia Autonomous Region, on farmland adjacent to the Horqin Sandy Land (Fig. 4c). The maximum dust emission rate exceeded 0.69 mg m2 s1. Note that the simulated distribution of wind erosion was consistent with the ground-based and satellite observations in precious studies, e.g. Fig. 6 in (Xuan et al., 2004) and Fig. 10 in (Ginoux et al., 2012).

3.2. Modeling results

In order to test the model performance in terms of the ability to better reproduce dust emissions, we compared the observations with predictions in the single simulation grid for our observed site. Since the surface friction velocity u⁄ is a crucial parameter in the calculation of dust emission flux in Eq. (2), we need to check the accuracy of u⁄ simulated by WRF. Simulated friction velocities

Since agricultural soil surfaces are frequently more complex than desert soils, it is important to test the ability of numerical models to evaluate dust emissions from a variety of agricultural soils. The wind erosion event at Dehui was simulated by

3.3. Validation

Fig. 5. Temporal comparison and correlation analysis for wind speed at 10 m height (a) and vertical dust flux (b) between simulated and observed results.

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are compared with measured wind speeds in Fig. 5a. Note that the selected assembly of physical parameterizations in WRF, as described in Section 2.3.1, is the optimal scheme with highest correlation coefficient (R2 = 0.9807); the detail information further details will not be discussed in this paper. Obviously, a high correlation was present between the observed and simulated velocities, but the simulated wind speed was approximately 16% lower than that of observations. The hourly observed PM10 concentrations from DustTracks were converted to the vertical dust flux (with units of mg m2 s1) and compared to with the simulated values

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in Fig. 5b. Simulation results were in good agreement with the measured values, with a correlation coefficient of 0.78 during the dust event of May 31, 2013. This model can therefore capture the general characteristics of the outbreak event and its temporal evolution during 31 May. The spatial distributions of AOD retrieved from MODIS Aura and Terra satellite images of dust on May 31 are shown in Figs. 6a and c. The vertical distribution of total attenuated backscatter at 532 nm and identified aerosol subtypes from CALIPSO lidar are also presented in Fig. 6g. A comparison between

Fig. 6. Spatial comparison between satellite observations and simulation results. Panels (a) and (c), MODIS Aqua and Terra aerosol optical depth (AODs) at 10:30 LT and 13:30 LT; (b) and (d), simulated total dust fluxes of WRF-CMAQ-FENGSHA at 11:00 LT and 14:00 LT; (e) and (f), MODIS true-color RGB image with daily AODs and simulated daily mean total dust fluxes; (g) and (h), vertical CALIPSO profiles of total attenuated backscatter signals at 532 nm and classified aerosol subtypes between 10:08 LT and 10:21 LT. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Figs. 6a and 4 confirms that the predicted dust source region is in good agreement with the satellite observations. Fig. 6a–d compare the spatial distribution of MODIS AODs from the Terra and Aura satellites and the simulated total dust emission flux. During our observation period, AODs were identified using MODIS data from the Terra (morning) and Aura (afternoon) satellites, passing over the Northeastern China at local time 10:50 and 12:35 on 31 May, 2013 (http://lance-modis.eosdis.nasa.gov/cgi-bin/imagery/ realtime.cgi). The simulation results for the corresponding hours are presented in Fig. 6b, d and f. The similar distribution patterns imply that dust was the dominant aerosol type over Northeastern China on 31 May. Fig. 6e shows a daily true-color image overlaid with the retrieved daily AODs from Terra and Aura on 31 May, 2013. Cloudless conditions were prevalent across the Northeastern China, and most of the AODs retrieved were well in excess of 0.35, consistent with higher aerosol loadings. A comparison between Fig. 6f (simulated daily dust flux) and Fig. 6e confirms that the predicted dust source region is in good agreement with the satellite observations. Nevertheless, high AODs > 0.8 were generally observed over the Bohai Sea and Liaoning Peninsula (Figs. 6a, c and e), which may have resulted from the transportation of anthropogenic aerosols from the Beijing–Tianjin–Hebei metropolitan region. This deduction was further confirmed by the results of vertical aerosol classification from the CALIPSO lidar (Fig. 6g and h). An obvious air mass with high dust aerosol content and top height of 5 km was observed from 44°N to 38°N, but polluted dust with smoke aerosols was present at a lower height, of 1 km, from 41°N to 38°N; mainly covering the Bohai Sea. In summary, the dust emissions simulated by WRFCMAQ-FENGSHA for the Northeastern China can be well verified with single-point ground-based and multi-satellite observations.

4. Discussion Research on dust emissions from cropland regions, and dust distribution in the atmosphere, has received much greater focus over the last decade (Claiborn et al., 1998; Nordstrom and Hotta, 2004; Van Oost et al., 2007; Harper et al., 2010; Tatarko et al., 2013). An important driver of this increased research effort stems from recognition of the impacts of dust from wind erosion on climate change, agricultural productivity and air quality. Wind erosion is a highly nonlinear, threshold-controlled process. Unfortunately, most of the numerical models of this process need a preferential dust source map in their dust emission evolution component (Ginoux et al., 2001; Zender et al., 2003; Koven and Fung, 2008; Draxler et al., 2010; Menut et al., 2013), thus limiting their applicability to wind erosion from agricultural regions. Downscaling to regional scale, the Northeastern China is an important dust source for Korea, Japan and even Western Russia, and thus has been considered in different regional dust models— CUACE/Dust (Gong et al., 2003), ADAM (Park and In, 2003), ADAM2 (Park et al., 2010), CFORS/Dust (Uno et al., 2003) and MASINGAR (Tanaka and Chiba, 2005). All the above numerical models also need a regional dust source map, such as the expanded dust source map implemented in ADAM2 adopted to improve model performance when compared with ADAM (Park et al., 2012). There is no doubt that the WRF-CMAQ-FENGSHA modeling system without considering the dust source map, has produced a reasonable record of the temporal and spatial evolution of dust emissions from croplands over Northeastern China. Unfortunately, the simulated results cannot be verified by optical thickness data from the ground-based observations of the Aerosol Robotic Network (AERONET), as such data are extremely scarce in this region, and the single site (Changchun) was deployed by the authors after this dust event. Since CMAQ-FENGSHA has

removed the restriction of requiring an emission source map, the accuracies of the databases of soil textures and land use types have become the key factors affecting the modeling results over the Northeastern China. Mineralogy of the soil will be incorporated into FENGSHA model to further constrain the threshold friction velocity by following the method in Gillette et al. (1982). A comparison between different soil databases (e.g. Harmonized World Soil Database on a 30 by 30 arc-seconds global grid in version 1.21 (Nachtergaele and Batjes, 2012), ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global grid in version 1.2 (Avellan et al., 2012), gridded Global Soil Dataset for use in Earth System Models (GSDE) on a 30 by 30 arc-seconds global grid in version 1.0 (Shangguan et al., 2014) and the default database in WRF will be conducted in our imminent future studies. Furthermore, additional wind-blown erosion monitoring systems should be deployed at different locations over Northeastern China, and thus the original empirically-based threshold friction velocity for different soil textures over four potential erodible land types (barren land, shrub-grass land, shrub land and agricultural land) could be measured in future field observations. Further chemical analysis of dust samples, combined with the PM2.5/PM10 ratio, could be employed to characterize local dust events (Tong et al., 2012; Appel et al., 2013) and verify the accuracy of our model.

5. Conclusions This study is the first joint application of filed observations and numerical modeling to investigate a typical wind erosion event, in a case study of black soil cropland in Northeastern China on 31 May, 2013. The observed threshold friction velocity was 0.37 m/s over a clay loam soil surface, corresponding to 4.47 m/s at 2 m height. The main factors affecting soil erosion processes in farmland include wind speed, soil moisture, soil texture, land use type, surface roughness and furrow direction. The simulation results show that the source region for wind erosion from farmland is a long strip covering the plain area of western Liaoning Province to the central part of Jilin Province and even the southeastern area of Inner Mongolia. The simulated maximum dust emission rate exceeded 0.69 mg m2 s1. The single-point ground-based and multi-satellite observations also verify that the WRF-CMAQ-FENGSHA modeling system has accurately reproduced the temporal and spatial evolution of dust emissions from croplands over the Northeastern China. However, it is a pity that no more typical dust events with corresponded satellite observations were found during the field experiment to validate the findings of this study. More long-term field monitoring over the Northeastern China and other cropland regions (e.g. Huabei plain and Guanzhong plain) needs to be conducted to further validate the WRF-CMAQ-FENGSHA model. Continued and improved evaluation of the WRFCMAQ-FENGSHA model for significant dust events over the Northeastern China (and elsewhere) will help improve their forecasting and mitigation of their impacts. In Northeastern China, attention to dust-producing agricultural activities is more important, the use of protective farming techniques, protection of grassland, plowing in autumn and the application of furrow direction that perpendicular to prevalent wind direction for cropland areas should be considered to combat dust emission.

Acknowledgements This work was financially supported by the National Natural Science Foundation of China (NSFC) (No. 41205108 and No.

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41275158), and the CAS/SAFEA International Partnership Program for Creative Research Teams (No. KZZD-EW-TZ-07).

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