Comparison of Wind Erosion Based on Measurements and SWEEP

Home , Kangbao County

Soil & Tillage Research 165 (2017) 169–180

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Soil & Tillage Research

journa l homepage: www.elsevier.com/locate/still

Comparison of wind erosion based on measurements and SWEEP

simulation: A case study in Kangbao County, Hebei Province, China

a b, c d a

Zhang Jia-Qiong , Zhang Chun-Lai *, Chang Chun-Ping , Wang Ren-De , Liu Gang

State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling,

Shaanxi 712100, China

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

College of Resource and Environment Sciences/Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Normal University,

Shijiazhuang 050024, China

Institute of Geographical Sciences, Hebei Science College, Shijiazhuang, Hebei 050000, China

A R T I C L E I N F O A B S T R A C T

Article history:

Received 3 January 2016 Farmland especially dry farmland managed in traditional ways has high wind erosion risk and

Received in revised form 8 August 2016 contributes mainly to dust emission in arid area. Modeling predicting provides a general view to soil

Accepted 9 August 2016

erosion susceptibility, and is very helpful for the understanding of potential spatial source of wind

Available online xxx

erosion. This study applied the Single-event Wind Erosion Evaluation Program (SWEEP) to predict soil

wind erosion of farmland in the study area. SWEEP is a standalone version of the erosion sub-model from

Keywords:

the Wind Erosion Prediction System (WEPS). It needs fewer calculation parameters than WEPS and is

Soil erosion

often used for single erosion events of limited size. The objective of this study was to test the feasibility of

Wind erosion

using SWEEP to estimate annual wind erosion of farmland over large areas (downwind distance >1600 m)

SWEEP

with limited wind data (2005–2011) from weather stations. We validated the simulation results by

Friction velocity

comparing them with ﬁeld measurements and wind tunnel data for the same soils. The soil material

Threshold velocity

eroded by wind included PM10, suspension, saltation, and creep particles. Suspension particles were the

main component involved in the soil loss (averaged 61.8%). However, saltation and creep particles

dominate the particle clouds for ﬁelds with a downwind length of less than 550 m (averaged 56.6%), and

the mass ﬂux dominated by suspension particles stabilizes when this length is longer than 1000 m

(averaged 83.4%). PM 10 always has very low proportion (<2.3%). Our validation results suggest that it is

feasible to use SWEEP for large areas with limited wind data. However, SWEEP could not simulate the

small soil losses that occur especially at low wind velocities well. Many factors contribute to this problem,

but the main one is overestimation of the threshold wind velocity. Previous research suggest that it will

be difﬁcult to replace SWEEP’s calculation algorithms for the threshold wind velocity, but both these

algorithms and some SWEEP parameters must be improved to provide accurate predictions of soil

erosion.

1. Introduction (Brevik et al., 2015; Smith et al., 2015). Unfortunately, soil erosion

especially on agricultural land, are several magnitude higher than

Soil is the key factor in major cycles of global biogeochemical, it its formation (Verheijen et al., 2009). The area affected by wind

provides fundamental ecosystem services, and related closely to erosion took more than one third of the land surface of the earth

biodiversity, energy security, climate change, ecosystem services, (Chen and Fryrear, 1996). Severe soil erosion by wind usually

food security, human health, land degradation, and water security related close to land use. Farmland strongly affected by frequent

human activities commonly increased in soil erosion and has high

erosion risk (Ginoux et al., 2012; Chen et al., 2014).

In China, soil erosion by the wind affects about half of the

* Corresponding author at: State Key Laboratory of Earth Surface Processes and

Resource Ecology, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing national territorial area (Chen et al., 1994). Previous studies has

100875, China. clariﬁed the inﬂuencing factors of wind erosion and its affects to

E-mail addresses: [email protected] (J.-Q. Zhang), [email protected]

environment, established control measurements, found effective

(C.-L. Zhang), [email protected] (C.-P. Chang), [email protected]

evaluation indexes for wind erosion, and improved study

(R.-D. Wang), [email protected] (G. Liu).

http://dx.doi.org/10.1016/j.still.2016.08.006

170 J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180

technique and methods (Gao et al., 2016; Jiang et al., 2016; Wang elevation (m AMSL), daily temperatures (maximum, minimum and

et al., 2015; Yang et al., 2013). The main issues have to be solved in mean values) ( C), precipitation (mm), and solar radiation (ly d )

the future include promoting study on multiple-forces erosion, (Van Donk et al., 2005; USDA, 2010). Although WEPS permits wind

finding sustainable land use patterns that benefit both environ- erosion simulations based on field-measured data (Funk et al.,

ment and economy. North China is the area with high wind erosion 2004; Hagen, 2004) or limited data (Van Donk et al., 2008; Chen

susceptibility. Although many farmlands have turned into grass- et al., 2012), but basic information about the study area’s climate,

lands and woodlands with the implementation of “Grain for soils, surface conditions, crop managements, and residues are still

Green” project, there are still wide farmlands in cultivation. required (USDA, 2010). It is not easy to collect sufﬁcient data, even

Moreover, Farmers lost farmlands living on the government for a small area.

subsidy will not last forever. Farmers have to ﬁnd livelihoods. One alternative would be to use the Single-event Wind Erosion

Therefore, it is important and urgent to ﬁnd suitable cultivate Evaluation Program (SWEEP). SWEEP is a standalone version of the

practices of farmland beneﬁt both environment and economy to WEPS’s wind erosion sub-model. Soil loss and deposition can be

instead of “Grain for Green” project. Prior to this, it is necessary to calculated for a single erosion event in which the friction velocity

clear the wind erosion condition in China. Kangbao County in exceeds the static threshold friction velocity required for entrain-

Hebei province is located in a farming and pastoral zone in ment of soil particles. The calculation of both the friction velocity

northern China. Dry farmland in this region that has been managed and the threshold friction velocity by SWEEP is based on surface

in traditional ways has undergone serious wind erosion and conditions in a simulated rectangular region. Users must provide

become a major dust emission source that creates dusty weather data on the ﬁeld size, plant biomass, soils, surface conditions, and

farther east, in the Beijing and Tianjin areas (Liu et al., 2003). weather to support the simulation (Hagen, 1995; USDA, 2008).

Although large national ecological restoration projects such as Previous research using SWEEP to simulate wind erosion were

“Grain for Green” have reduced the area of easily eroded land and carried out for limited study areas and short durations using data

weakened wind erosion (Gao et al., 2008), farmland still remains from wind tunnel and ﬁeld tests (Feng and Sharratt, 2009; Gao

the main land use type in the dust source region for eastern China, et al., 2014; Liu et al., 2014; Pi et al., 2014b). The feasibility of

where much of the land is vulnerable to strong soil erosion by the SWEEP for soil wind erosion prediction in large area (such as a

wind. county) and relative long duration (such as years) is unknown. This

Previous research studied the factors and mechanisms that study assumes that SWEEP should achieve to predict soil wind

inﬂuence wind erosion, control methods, and the temporal and erosion during long time in a relative large area outside of the USA.

spatial distributions of soil erosion in the ﬁeld by measurements The present study applied SWEEP (http://www.weru.ksu.edu/

and investigations (Hagen, 2004; Li et al., 1994; Zhang et al., 2010), weps/) to simulate wind erosion over the course of years (2005–

wind tunnel experiments (Liu et al., 2007; He et al., 2013) and 2011) using hourly-resolution wind data recorded at a weather

remote sensing method (Wang et al., 1991; Yue et al., 2015). These station, and then to validate the SWEEP’s estimation of soil loss in

study methods have limitations. Wind tunnel experiments are comparison with ﬁeld measurements and the results of wind

easier to control but difﬁcult to apply to the real world, whereas tunnel experiments using the same soils. The goal was to evaluate

ﬁeld experiments allow researchers to more closely emulate the feasibility of using SWEEP to simulate wind erosion using a

natural conditions, but are more difﬁcult to control and have been small wind dataset for a large simulation area. Based on the results

carried out at a limited range of sites, so the results may not be of this analysis, we discuss the feasibility of simulating annual

applicable to other sites (Cole, 1984; Zhao et al., 2006). Though average wind erosion based on individual erosion events and needs

remote sensing method could estimate soil loss on a large scale, for future research.

results were barely matched with ﬁeld ones or difﬁcult to verify for

lacking of information in large scale. Moreover, it often predicts the 2. Materials and methods

erosion risk rather than estimates actual erosion (Chappell, 1998;

Jiang et al., 2003). However, remote sensing is useful to develop 2.1. Study area

models and improve their prediction accuracy (Gong et al., 2014;

Yue et al., 2015). Modeling predicts soil erosion potential usually Kangbao County is located in northern China’s Inner Mongolia

0 00 0 00

and output verify-needed results, which is similar to remote plateau. It is located between 41 25 24 N and 42 08 57 N, and

0 00 0 00

sensing, it may overcome the problems of ﬁeld measurements, between 114 11 21 E and 114 55 57 E. The average elevation is

wind tunnels experiments and remote sensing by estimating and 1450 m AMSL. Low mountains and hills with a gentle slope cover

predicting wind erosion. Of the currently available models, the the eastern and northern parts of the county; plains with

Wind Erosion Prediction System (WEPS) is the most advanced and undulating terrain cover the southern and western parts. The

widely used (Woodruff and Siddoway, 1965; Cole et al., 1983; climate is a mid-latitude temperate monsoon climate with obvious

Hagen, 1991a, 2004; Fryrear et al., 1996; Funk et al., 2004). continental characteristics. The mean annual temperature is 1.2 C.

WEPS is a process-based and modular program that can The annual precipitation averages 351.7 mm, 66.7% of which occurs

simulate weather, ﬁeld conditions, and wind erosion in farmland at from June to August. Both temperature and precipitation show

a daily time-step (Hagen et al., 1995). The sub-models of WEPS are high inter-annual variation. The average wind velocity is 3.7 m s ,

supported by large databases on erosion-control barriers, soils, and the dominant winds blow from the north and northwest in the

agricultural operations, crop growth, decomposition of residues, winter and spring averaged 4.1 m s , but from the west and

and climate. Implementation of WEPS required databases that southwest during the growing season averaged 3.1 m s . Blown

covered the whole United States (Wagner, 2013). Unfortunately, it sand and dust events occur most frequently in the spring, with a

is difﬁcult to apply WEPS outside the United States due to a lack of cumulative duration more than 1440 h per year. The main soil type

data with acceptable resolution and accuracy to parameterize the is a Chestnut soil (a Mollisolin the USDA classiﬁcation) with a high

model and allow an accurate simulation. For example, when the erosion risk.

model is used for areas outside the United States, wind data are In 2000, farmland and grassland accounted for 69.0 and 21.0% of

usually generated stochastically because historical data are this Kangbao County (total county area is 3365.7 km ), respective-

unavailable. For quality control, the stochastic wind data must ly, and forest covered only 2.6% of the area before implementation

be based on historical wind data recorded for more than 5 years at of the national “Grain for Green” project in areas that served as

hourly intervals. Other required weather data includes the station sandstorm sources for the Beijing-Tianjin area. By 2009, this

J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180 171

Fig. 1. The distribution of land uses in Kangbao County in 2009.

project had decreased the farmland area 30.0% of the total and rectilinear, deviations from the rectangular shape will inﬂuence

increased the areas of grassland and forest 10.4 and 19.7% of the the side-lengths calculation of each ﬁeld. Thus, the dimensions of

total, respectively. All the farmland is dry land, 94.0% of which lies cultivated area were adjusted according to size investigation in

in the plains part of the study area (Fig. 1). The main crops in this ﬁeld and length measurements of the polygons in the ArcMap 10.0

area are naked oats (Avena chinensis), winter wheat (Triticum (https://desktop.arcgis.com/en/desktop/) when the calculated size

aestivum), and potato (Solanum tuberosum). or the ratio of the length in X direction to that in Y direction is too

big. Wind speed used in the simulation was recorded at hourly

2.2. Methods intervals day by day at the Kangbao meteorological station locating

in Kangbao city during 2005 and 2011 (Table 1). The wind direction

2.2.1. Data for the SWEEP simulation was considered to be constant (from the north) throughout the

In SWEEP, the simulation areas are deﬁned as rectangles. The simulation period rather than accounting for angle changes

sizes of farmland areas in the present study were calculated based between the wind and the sides of the farmland. The surface

on the areas and perimeters recorded in the attribute table of a condition included residue retained, plowed and smoothed,

digital land-use map created by Hebei provincial department of plowed and created low ridges. Standing residues of winter wheat

land and resources, in 2009. Since the borders of most ﬁelds are not and naked oats (5–20 cm tall) are generally left in the ﬁelds after

172 J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180

Table 1

distributed as uniformly as possible in farmlands except those in

The cumulative hours of hourly average wind speed during 2005 and 2011 except

mountains. There were 30 soil samples collected in total, including

June to September each year.

12 wheat field samples, 10 oats field samples, and 8 potato field

Cumulative hours of wind (h)

samples. We collected soil in each ﬁeld at three sites and combined

2005 2006 2007 2008 2009 2010 them as one sample. For each sample, we measured the grain size

distribution with Mastersizer 2000 (Malvern Instruments, Mal-

Wind 5–6 468 491 427 524 486 536 502

speed 6–7 330 360 286 350 364 421 310 vern, UK) dry bulk density with cutting ring method, the organic

–

(m s ) 7 8 305 213 223 231 209 252 175 matter contents with potassium dichromate external heating

8–9 194 157 140 193 96 193 98

method, and calcium carbonate contents with calcimetric method.

9–10 125 125 114 106 52 111 37

Several parameters of the soil aggregates were studied: density,

10–11 81 70 79 60 32 36 16

dry stability, geometric mean diameter (GMD), geometric standard

11–12 50 58 36 37 24 15 10

12–13 15 26 22 20 18 12 7 deviation (GSD), and maximum and minimum sizes. These

–

13 14 14 9 12 6 5 4 2 aggregate-related parameters were estimated from soil properties

14–15 2 9 4 2 1 1 1

using the formulas provided in the WEPS user manual (USDA,

15–16 0 1 1 0 0 0 1

2010):

16–17 0 0 2 0 0 0 0

aggregate density = 2.01 (0.72 + 0.00092layer depth) (1)

Cumulative hour 17 19 17 19 10 5 4

of erosion (h)

Days erosion event 5 6 7 5 3 2 2 2

occurred (d)

dry aggregate stability = 0.83 + 15.7clay 23.8clay (2)

harvesting in the study region, but little residue remained for a

GMD = e (1.0 + 0.006layer depth) (3)

potato. Flat residues were not considered, since the loose and ﬂat

0:0238 sand

; ¼ ð : : : þ

residues produced by harvesting using combines mostly grazed by in which a 1 343 2 235sand 1 226silt clay

sheep, and only standing stems remained in the ﬁeld. Besides, local

33:6om þ 6:85CaCO3Þ

farmers may plow and smooth the ﬁeld or plow and leave low ridges

before the next planting (Fig. 2). The tillage depth is about 25 cm by

1:0

tractor-pulled blade plow, and a rake was added behind plow if GSD ¼ ð4Þ

p0:ffiffiffiffiffiffiffiffi074

0:0203 þ 0:00193GMD þ

surface smoothing implemented. Surface soil crusts were omitted GMD

from the simulation because they would have been disrupted

annually by cultivation. Surface was almost non-erodible by wind in

rainy season (June to September), and when the recorded daily maximum aggregate size = (GSD) GMD + 0.84 (5)

precipitation (both rain and snow) was greater than 20 mm. 0.449

2 2 in which, p = 1.52GSD where “clay”, “sand”, and “silt”

When residue retained, the stem area index (SAI, m m ) was 1

represents the clay, sand, and silt contents (mg mg ), respectively,

used to describe the quantity of standing stems during the post-

and “om” and “CaCO3” represent the organic matter and calcium

harvest season (Hagen, 1996). SAI was the product of stem height 1

2 carbonate contents (mg kg ), respectively.

(m), stem population density (stems m ), and stem diameter (m).

Since the shapes of the residues of winter wheat and naked oats are

2.2.2. Field measurements for comparison with the simulation results

similar, we used data on the population density and diameter of

The ﬁeld measurements of erosion were obtained in April and

naked oats in the simulation of both crop types. When ridges were

May 2011 (Chen, 2012). Two types of ﬁeld were tested: a naked oats

created, ridges were described by their height (mm), spacing (mm),

ﬁeld (1000 m 400 m) with standing residues 20 cm tall and a row

and width (mm) based on ﬁeld plow conditions. Farmland areas in

spacing of 30 cm, and a plowed ﬁeld (1600 m 600 m) after

the northern mountains were not simulated in this study, because

harvest of potato with no residues and no ridges. Their

farmland in the mountains took low percentage in total farmland

aerodynamic roughness lengths were 0.0487 and 0.0138 cm,

area (6%), moreover, SWEEP could not account for the effects of

respectively. The near-surface wind data was observed using a

mountain topography based on the current version. Table 2

set of anemometers at the center of each ﬁeld. The set consisted of

summarizes the main parameters and their values used in the

a mast and rotating-cup anemometers mounted at nine heights

SWEEP. The distribution of erosion rates for farmland was mapped

(5–400 cm). The wind velocities were recorded at 1-min intervals

using ArcMap 10.0.

using a data logger. Eroded particles were collected with 30 layers

Soil samples were collected to a depth of 10 cm with soil auger

60-cm-tall vertical segmented sand traps (LDDSEG samplers)

core samplers in June 2005 and July 2007. The sampling sites

installed in a middle-ﬁeld line along the prevailing wind direction.

Fig. 2. Photographs of typical ﬁelds with (a) stubble retained, (b) the surface plowed and smoothed, and (c) the surface plowed to create ridges.

J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180 173

Table 2

Parameters and their values used in the SWEEP simulation.

Item Parameters Values

Field X length (perpendicular to the wind) range (m) 10.7–1668.9

Y length (downwind distance) range (m) 26.3–3825.8

Residues Height (mm) 50, 100, 150, 200

Population density (stems m ) 120

Stem diameter (mm) 3.220

2 2 *

Coverage by ﬂat residues (m m ) 0

Soil Composition (sand, silt, and clay contents) (mg mg ) 0.57, 0.31, 0.12

Soil depth (mm) 100

Dry bulk density for plowed ﬁelds (mg m ) 1.361

Dry bulk density for stubble ﬁelds (mg m ) 1.388

Aggregates Density (mg m ) 1.632

Dry stability (ln(J kg )) 2.469

GMD (mm) 5.820

GSD (mm mm ) 16.075

Max. and min. size (mm) 20.418, 0.01

Ridges Height (mm) 0, 30, 50, 100

Spacing (mm) 300

Width (mm) 50

Random roughness Stubble ﬁelds (mm) 6

Plowed ﬁelds (mm) 10

1 1

Wind Wind velocity (m s ) >8 m s

Wind direction (degrees) 0

Flat residues were not considered in the simulations, as they were mostly grazed by sheep after harvest, and only standing stems were left in the ﬁeld.

0 represents a wind from the north.

The nearest sampler was 5–10 m from the anemometers. There Armbrust, 1994; Hagen et al., 1999; Panofsky and Dutton, 1984).

were 9 traps in naked oats ﬁeld and 7 traps in plowed ﬁeld with The parameters used in these calculations were the same as those

intervals ranged 10–70 m. The sand collection efﬁciency of the trap in Table 2. The main calculation equations were obtained from

1 1

averaged 80% (Zhang et al., 2014). Soil erosion rate (t ha yr ) was USDA (1996):

calculated by performing an self-developed exponential-function The friction velocity for bare ground (FVb):

correlated with wind velocity (Wang, 2010), combined with wind

zb 0:067

¼ ð Þ ð Þ

data recorded at the Kangbao weather station from 1990 to 2010. FVb FVs 6

Wind tunnel results obtained by Niu (2010) were also used to

validate the SWEEP results. Undisturbed samples of the Chestnut where FVs represents the friction velocity of the weather station,

soil from Hebei Province (80 cm in length, 30 cm in width, and and zb and zs represent surface aerodynamic roughness lengths (m)

20 cm in height) were collected from a ﬁeld of naked oats with for bare ground and weather station, respectively.

standing residues 5 and 10 cm tall and from a plowed ﬁeld that had The above-canopy friction velocity (FVar) for the standing

been raked smooth. The wind velocities in the tests were residues was calculated as follows:

6–18 m s . zr 0:067

FVar ¼ FVsð Þ ð7Þ

In the SWEEP simulation, wind data measured at the height of

4 m in Chen’s study were averaged to produce 15-min values for

where zr represents the surface aerodynamic roughness length (m)

simulation, and wind data recorded at the height of 60 cm in Niu’s

for standing residues.

study were applied in simulation and each wind velocity was

The near-surface (under canopy) friction velocity (FVur) for the

assumed to last for an hour. Soil properties in both Chen’s ﬁeld

standing residues was calculated as follows:

measurements and Niu’s wind tunnel tests were identical to those

SAI SAI in Table 2.

0:0298 0:356

FVur ¼ 0:86FVare þ 0:025e ð8Þ

2.2.3. Calculation of the friction velocity and threshold friction velocity in which, SAI = nDh,where SAI represents the stem area index, n

in SWEEP represents the number of stems per unit area (stems m ), D

The SWEEP erosion submodel considered aggregate size and represents the stem diameter (m), and h represents the stem

density, the coverage of the surface by unerodible materials, height (m).

surface roughness, the cover of ﬂat biomass, and surface soil The threshold friction velocity for bare ground (TFVb) was

wetness in its calculation of friction velocities and threshold calculated as follows:

friction velocities for each simulated region. Bare ground, ﬁelds

bSFcv

¼ : : ð Þ

TFVb 1 7 1 35e 9

with ridges, and ﬁelds with standing biomass were considered

separately. The friction velocities were calculated using wind 1

in which, b = : ,where SF represents the fraction of the 1p111ffiffiffi0:076 cv

velocity proﬁles at the weather station ﬁtted to a logarithmic b

soil surface that is unerodible because it is covered by aggregates,

curve, and the threshold friction velocities were estimated from

crusts, or rocks:

empirical data on the coverage by a non-erodible surface and the

aerodynamic roughness length (Hagen, 1991b, 1996; Hagen and

¼½ð Þð Þ þ ð Þ þ ð Þ SFcv 1 SFcr 1 SF84 SFcr SFlos 1 SVroc SVroc 10

174 J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180

where SFcr represents the fraction of the soil surface that is bare ground regardless existing of ridges. Wind erosion occurred

unerodible because it is covered by crusts, SFlos represents the only when recorded wind velocities were greater than the

fraction of the soil surface that is erodible because it is covered by threshold wind velocity. The threshold wind velocity at a height

loose soil, SVroc represents the proportion of the soil volume with a of 10 m was about 12.0 m s according to the SWEEP simulation.

3 3 2 1

particle diameter greater than 2.0 mm (m m ), and SF84 repre- The total erosion averaged 4.79 kg m yr for bare smooth

2 1

sents the fraction of the uncrusted surface covered by aggregates ﬁelds and 4.01 kg m yr for ﬁelds with 10-cm-tall ridges.

smaller than 0.84 mm in diameter, and is calculated as follows: Particles traveling in suspension were the main component of

soil loss, accounting more than 60.9% of the eroded soil, and this

lnSLT

pffiffiffi

SF84 ¼ 0:5½1 þ erfð Þ ð11Þ proportion varied much more than for the particles traveling by

2lnGSD

creep and saltation (i.e., its SD was higher; Table 3). The content of

where erf represents error function, and SLT is a parameter that PM10 particles remained low and stable (at ca. 2% of the total),

represents the aggregate size distribution, and is calculated as regardless of the ridge height, and amounted to approximately

2 1

follows: 0.2 kg m yr . Soil erosion decreased with increasing ridge

height. When the ridge height increased from 0 to 10 cm, the

ð0:84 GSminÞðGSmax GSminÞ 2 1

SLT ¼ ð12Þ total erosion decreased by an average of 0. 78 kg m yr , which

ðGSmin 0:84ÞGMD 2 1

was composed of 0.47 kg m yr traveling in suspension and

2 1

where GSmax and GSmin represent the maximum and minimum 0.30 kg m yr traveling by creep and saltation. This shows that

grain sizes (mm), respectively. although ridges could reduce wind erosion and higher ridges

The threshold friction velocity for biomass-covered ground increased the protection, soil ridges alone cannot prevent soil loss

(TFVr) is calculated as follows: by wind erosion.

Compared with other studies in Kangbao County, the wind

¼ þ : þ ð Þ

TFVr TFVb 0 02 SFCcv 13

erosion values simulated by SWEEP were lower than those of

where SFCcv is the fraction of the soil surface area protected from Zhang et al. (2011) but higher than those of Wang et al. (2013).

erosion by residues, in which SFCcv = (1 SFcv)RC, and RC is the Zhang et al. (2011) reported a farmland erosion rate of 8.95 kg

2 1 137

friction velocity for the biomass cover. m yr by using Cs as a tracer, and validated this result using a

The friction velocity for bare ground in the ﬁeld measurements real-time kinematic GPS method. Wang et al. (2013) estimated

ﬁ ﬁ

is calculated as follows: wind erosion for a plowed eld and a stubble eld (with a stubble

2 1 2 1

height of 10–12 cm) as 3.82 kg m yr and 1.79 kg m yr ,

u z

uz ¼ lnð Þ ð14Þ respectively, by comparing the grain size distribution of surface

k zb

soils (to a depth of 1.0 cm) before and after erosion. This means that

where uz is the wind velocity (m s ) at height z (m) and k is the von SWEEP provided an acceptable estimate of the erosion rate for

ﬁ ﬁ

Karman constant (0.4). plowed bare elds but signi cantly underestimated erosion for the

ﬁelds with stubble.

3. Results Fig. 3 shows the simulated spatial distribution of soil loss as

total erosion, particles transported by creep and saltation, and

3.1. Soil loss by wind erosion in the SWEEP simulation based on local particles transported in suspension. PM10 accounted for a very low

wind data percentage of the total erosion mass (Table 3), and is not presented

here. In the southern plains, wind erosion was serious based on the

ﬁ

In the ﬁelds with standing residues simulated by SWEEP during classi cation standard of Zachar (1982) and the soil bulk density

2005 and 2011, there was no soil loss, but soil erosion occurred on (1.374 g m ). Most farmland had a higher soil erosion rate than the

Table 3

Average wind erosion for ﬁelds with different ridge heights by SWEEP simulation during 2005 and 2011.

2 1

Ridge height (cm) Soil loss Average wind erosion rate (kg m yr ) Ex > Em (%)

Min. Max. Mean SD Proportion of total (%)

0 Total 0.25 6.27 4.79 1.43 100.0 95.6

Creep and saltation 0.20 4.02 1.82 0.65 37.8 13.0

Suspension 0.05 5.87 2.97 1.63 61.9 91.5

PM10 0.0007 0.21 0.11 0.10 2.3 92.1

3 Total 0.24 6.29 4.76 1.45 100.0 95.7

Creep and saltation 0.19 3.00 1.79 0.63 37.7 13.5

Suspension 0.05 5.88 2.97 1.65 62.3 93.6

PM10 0.0007 0.21 0.10 0.06 2.1 93.5

5 Total 0.22 6.21 4.55 1.46 100.0 95.5

Creep and saltation 0.17 2.82 1.73 0.63 37.3 14.2

Suspension 0.04 5.81 2.82 1.60 60.9 91.6

PM10 0.0006 0.21 0.10 0.06 2.2 93.2

10 Total 0.18 5.35 4.01 1.35 100.0 96.3

Creep and saltation 0.14 4.34 1.51 0.59 37.5 15.4

Suspension 0.04 5.02 2.50 1.40 62.2 93.5

PM10 0.0005 0.17 0.09 0.09 2.3 92.4

1. The ridge height of 0 cm means that the ﬁeld was plowed and then raked smooth to eliminate ridges.

2. Ex is the simulated wind erosion rate for the agricultural ﬁeld, and Em is the average erosion rate for all agricultural ﬁelds in simulation. The term Ex > Em represents the

proportion of the ﬁelds with greater erosion than the average value for the whole county.

3. There was no soil loss simulated for ﬁelds with standing stubble.

J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180 175

Fig. 3. The distribution of representative wind erosion rates in Kangbao County in 2011 for total erosion, particles transported by creep and saltation, and particles

transported by suspension as a function of the ridge height in plowed ﬁelds. Complete data for all ridge heights is shown in Supplemental Fig. A1. Erosion rates were simulated

by the SWEEP software. Farmland on sloping land in the northern mountain area was not simulated in this study.

176 J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180

average for the whole county, but some areas had lower erosion composition of the eroded soil also changed with increasing

rates. In the simulation results for total erosion, more than 95% of downwind length. Particles transported by creep and saltation

the area of farmland had a greater erosion rate than the county were the main component for a downwind length of less than

average (Table 3). about 550 m (averaged 56.6%), on average, it kept stable during 250

Farmlandswith differenterosionrateswerescatteredthroughout and 550 m (averaged 52.2%), but this component decreased

the study area, and there was no continuously distributed area with steadily with increasing downwind length. Whereas the propor-

similar erosion rates. This result can be mostly attributed to the tion of the particles transported by suspension kept increasing and

pattern of land use (Fig. 1), but the undulating terrain also played a gradually became the dominant component of the eroded soil for a

role. In the study area, cultivation and harvesting are performed downwind length greater than 1000 m (averaged 83.4%). The rate

mainly by large machines, and big ﬁelds are created to facilitate of increase in the proportion of particles transported by suspension

management. However, small farmland also exists, particularly gradually slowed with increasing downwind distance. The amount

around Kangbao City, as a result of limitations on the land available of PM10 was quite small (<2.3%), but increased slowly with

for farming. The big areas of farmland are also partitioned into increasing downwind length. These results suggest a signiﬁcant

smaller areas by windbreaks, bodies of water, villages, abandoned inﬂuence of the fetch effect on wind erosion in the ﬁeld, both in

farmland, and farmland returning to grassland or forest. These land- terms of the total erosion and in the size distribution of the eroded

use conditions affect the ﬁeld size, wind velocity, and quantity of soil particles. Since particles in suspension have the potential to be

erodible material, and cause variation of the erosion rate. The transported over long distances, farmland with a long downwind

undulating terrain of this rolling plains area also contributes to the length will require more measures to reduce the wind’s force and

spatial differences in the erosion rate to some extent. control soil erosion. Such measures include the creation of

In the SWEEP model, the downwind length (the “Y length” in windbreaks and retention of stubble residues to weaken the fetch

the simulation of SWEEP) is a very important parameter. The effect.

SWEEP simulation showed that total wind erosion increased

sharply and linearly as this length increased from 0 to about 600 m 3.2. Comparison of simulated and measured soil loss by wind erosion

(Fig. 4). Beyond this distance, the erosion rate stabilized; it seemed

to reach the maximum erosion capacity of the wind, and no longer Soil wind erosion was simulated by SWEEP using the data on

increased with increasing fetch (downwind length) of the ﬁeld. The wind velocities obtained by Chen (2012). Unfortunately, this

Fig. 4. The variation of eroded soil transported in different ways as a function of the downwind length of the simulated ﬁelds.

J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180 177

simulation revealed no soil loss, possibly because the greatest Skidmore, 2003). Feng and Sharratt (2009) found that SWEEP

wind velocity in that study was 9.4 m s , recorded at a height of estimated zero wind erosion under conditions that led to

4 m minutely. However, in the present study, the sand traps observation of soil loss in the ﬁeld during some strong wind

captured soil particles from across the whole ﬁeld, even in ﬁelds events. Our results also revealed problems with erosion estimates

with standing residues about 20 cm tall, and much more in the bare at lower wind velocities; SWEEP predicted no erosion at low

plowed field. The wind erosion rate in the naked oats stubble field velocities, despite observation of often significant erosion at these

and the plowed ﬁeld averaged 0.68 and 1.77 kg m , respectively, velocities both in the ﬁeld and in wind tunnel tests. Liu et al. (2014)

based on wind data from weather stations between 1990 and 2010. compared wind tunnel measurements with SWEEP simulations

The great difference between the ﬁeld measurements and SWEEP and found that SWEEP gave acceptable results at typical wind

simulations revealed that SWEEP estimates are inaccurate when velocities, but underestimated soil loss at high wind velocities

wind velocities are low. (>18 m s for free-stream in the wind tunnel). Nevertheless, the

Niu (2010) conducted a wind tunnel erosion experiment using inability of SWEEP to predict zero erosion at low wind velocity

soil samples from Kangbao County. We used SWEEP to simulate seems serious. For example, SWEEP predicted no erosion even

wind erosion based on their data, and found that the soil erosion though soil loss in the stubble ﬁeld reached 0.68 and 1.77 kg m in

calculated with SWEEP differed greatly from that in the wind studies by Chen (2012) and Wang et al. (2013), respectively.

tunnel experiments at low wind velocities. Soil loss began at The reasons for the serious underestimation of soil erosion by

6 m s in the wind tunnel experiments, but in the present study, it SWEEP are complex. One possibility is that surface and residue

1 1

did not begun until about 10 m s for bare ground, 12 m s when conditions in the simulation differed from those in the ﬁeld. For

standing residues existed, and 18 m s when the height of the instance, standing residues in the ﬁeld often existed in rows, which

standing stems was 10 cm. This showed the poor estimation at create a consistent physical structure, but SWEEP expresses the

lower wind velocities. Although the simulated results were effect of SAI, which does not account for that structure (Hagen,

reasonably close to the measured values at high wind velocities, 1996). The use of SAI is based on the assumption that the stems are

it also showed instability based on the difference between distributed uniformly, but does not account for bare ground

measured and simulated results (Fig. 5). This comparison between the rows. In addition, it fails to account for the

conﬁrmed the poor accuracy of SAI index for soil erosion relationship between the row direction and the wind direction.

simulation at low wind velocities and the improved estimation Another possibility is the choice of parameters for simulation. For

at high wind velocities obtained from Chen’s data. instance, we averaged 15 one-min recording wind velocity into one

value for simulation. However, the most likely and most important

4. Discussion reason appears to be overestimation of the threshold wind

velocity. This overestimation is clear in comparisons between

4.1. The feasibility and limitations of simulation by SWEEP based on ﬁeld and wind tunnel data and the SWEEP simulations; the

limited wind data overestimation means that SWEEP will predict no entrainment of

particles at wind velocities that lead to sediment transport in the

In previous research with WEPS, the main reason for inaccurate ﬁeld. The overestimation of threshold wind velocity increased in

estimation of erosion was that the erosion submodel was severity for tall residues. Therefore, the current version of SWEEP

established basing on erosion events with a large soil loss; as a cannot be applied at low wind velocities until the model is

result, for erosion events with a soil loss of less than 0.1 kg m , the modiﬁed to predict erosion at these lower velocities. At

model performed poorly (Hagen, 1991a, 2004; Van Donk and

Fig. 5. Soil erosion comparison between data from wind tunnel experiments (Niu, 2010) and SWEEP simulations for the same wind velocities used by Niu. (a) Plowed ﬁelds

with no ridges. (b, c) Fields with standing stems of different heights.

178 J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180

Table 4

Comparison of the average friction velocities in the SWEEP simulation and in the ﬁeld measurements.

Data source Surface Friction velocity simulated by SWEEP Friction velocity obtained from SD of simulated and measured friction

1 1

condition (m s ) measurements (m s ) velocities

Above Under Threshold Above Under Threshold Above Under Threshold canopy canopy canopy canopy canopy canopy (max.) (max.) (max.) (max.) (max.) (max.)

y y *

Weather station Bare ground – 0.946 0.732 – – – – – –

5-cm stems 0.785 0.372 0.770

10-cm stems 0.886 0.228 0.787

15-cm stems 0.922 0.133 0.805

20-cm stems 0.986 0.083 0.822

* * y

Chen’s (2012) ﬁeld tests Bare ground – 0.456 0.732 – 0.374 <0.403 – 0.058 >0.233

20-cm stems 0.764 0.040 0.822 0.652 0.168 <0.168 0.086 0.091 >0.462

* *

Niu’s (2010) wind Bare ground – 1.130 0.732 – 0.779 0.260 – 0.248 0.334

tunnel experiments

5-cm stems 0.938 0.444 0.770 0.889 0.421 0.296 0.035 0.016 0.335

10-cm stems 0.826 0.213 0.787 1.144 0.295 0.381 0.225 0.058 0.287

1. In calculating the friction velocity, the recording height of the wind velocity was 10 m for the weather station data, 10 m for the ﬁeld measurements data of Chen (2012)

calculated basing on empirically derived log-law wind proﬁles, and 0.6 m for Niu’s (2010) wind tunnel experiments.

2. The friction velocity was calculated by SWEEP based on the aerodynamic roughness length obtained from the ﬁeld measurements. It was 0.138 mm for bare ground, and

0.487 mm for naked oat ﬁelds with 20-cm stubble.

3. “Bare ground” were plowed and smoothed ﬁeld.

Represents the maximum friction velocity for bare ground in the ﬁeld.

In the ﬁeld measurements, the author presented no information about the threshold wind velocity or threshold friction wind velocity, but this value can be inferred from

the material collected in the sand traps during wind velocity recording periods.

y y

Wind speed data from weather station were from 2005 to 2011, months during June and September was not included for each year. The maximum friction velocity

simulated by SWEEP with weather station data was the average value of the maximum value of each year.

intermediate wind velocities, it may produce acceptable erosion than the algorithm used by SWEEP. This suggests that the

prediction accuracy for agricultural ﬁelds. algorithms used to estimate the threshold friction velocities and

the parameters of SWEEP must be improved simultaneously to

4.2. Potential improvements for SWEEP account for erosion by winds with a low velocity (but one that is

higher than the threshold) without overestimating soil loss.

SWEEP is the erosion submodel used by WEPS, and therefore

uses the same simulation methods and processes. In both 5. Conclusions

programs, the friction velocity is the driving force responsible

for erosion. Erosion only begins when the friction velocity exceeds In conclusion, SWEEP provided reasonable estimates of wind

the threshold value. The model calculates the maximum friction erosion over a large area using limited wind data based on a

velocities of each subregion in the ﬁeld and compares them with its comparison with the results of ﬁeld and wind tunnel experiments.

threshold friction velocities (USDA, 1996). Therefore, the accuracy It provided good estimates of the PM10 component of the eroded

of both the friction velocities and the threshold velocities will soil, as well as of the particles transported by suspension, saltation

determine the reliability of the simulated results. and creep, and of the changes in each component as a function of

SWEEP estimated friction velocities well both in the ﬁeld and in the ﬁeld length in the downwind direction. However, SWEEP

wind tunnel experiments, but greatly overestimated threshold greatly underestimated wind erosion at low wind velocity and

friction velocities (Table 4). The estimation of friction velocities could not predict the small soil loss observed in the ﬁeld and in

was based on the aerodynamic roughness length in Eqs. (6)–(8), wind tunnel experiments at low wind velocity. The main cause of

whereas the threshold friction velocities were mainly calculated this problem was overestimation of the threshold friction velocity.

based on surface conditions and soil aggregate characteristics in To improve SWEEP’s ability to well predict soil loss, an alternative

Eqs. (9) and (10). This suggests that the empirical Eqs. ((6)–(8)) algorithm with high sensitivity to ﬁeld characteristics will be

provide acceptably accurate estimates of friction velocities, but required, though previous research suggests that it will be difﬁcult

that the methods used to estimate the threshold friction velocities to replace the current algorithm used by SWEEP.

must be improved. The overestimation of threshold friction

velocity observed in the present study, also observed by Pi et al. Acknowledgments

(2014c) through comparing measured and SWEEP-simulated

threshold friction velocities. They also suggested it would be This study was ﬁnancially supported by the National Natural

necessary to improve estimation of the threshold friction velocity. Science Foundation of China (Grant Nos. 41401314 and 41330746),

A simple strategy to improve calculation of the threshold and by the West Light Foundation of the Chinese Academy of

friction velocity would be to replace the current algorithm with a Sciences. Sincerely gratitude goes to Dr. Larry E Wagner for guiding

better one. However, Pi et al. (2014a) tried this unsuccessfully the application of SWEEP, and Dr. Geoff Hart for the language

using four different algorithms in a study conducted for the editing.

Columbia Plateau region of the United States. These included

algorithms developed by Shao (2000), Lu and Shao (2001), Böhner Appendix A

et al. (2003), and Gregory et al. (2004). The algorithms of Shao

(2000) and Lu and Shao (2001) improved estimates of the Fig. A1.

threshold friction velocities, but resulted in substantial overesti-

mation of soil loss. The other algorithms produced worse results

J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180 179

Fig. A1. The distribution of wind erosion rates in Kangbao County in 2011 for total erosion, particles transported by creep and saltation, and particles transported by

suspension as a function of the ridge height in plowed ﬁelds.

180 J.-Q. Zhang et al. / Soil & Tillage Research 165 (2017) 169–180

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