This article was downloaded by: [The UC Irvine Libraries]On: 07 November 2014, At: 15:34Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Acta Agriculturae Scandinavica, Section B — Soil &Plant SciencePublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/sagb20

Impacts of land-use pattern on soil water-contentvariability on the Loess Plateau of ChinaD.L. She a b , M.A. Shao b , L.C. Timm c , I. Pla Sentís d , K. Reichardt e & S.E. Yu aa Key Laboratory of Efficient Irrigation-Drainage and Agricultural Soil-Water Environmentin Southern China, Ministry of Education, College of Agricultural Engineering , HohaiUniversity , Nanjing, 210098, Chinab State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau,Research Center of Soil and Water Conservation and Ecological Environment , ChineseAcademy of Sciences & Ministry of Education , Yangling, 712100, Chinac Faculty of Agronomy, Federal University of Pelotas, Department of Rural Engineering ,P.O. Box 354, 96001-970, Pelotas, RS, Brazild Departament de Medi Ambient i Ciències del Sòl , Universitat de Lleida , Av. AlcaldeRovira Roure 191, 25198, Lleida, Españae University of São Paulo/Center for Nuclear Energy in Agriculture – Soil PhysicsLaboratory , P.O. Box 96, 13418-900, Piracicaba, SP, BrazilPublished online: 22 Sep 2009.

To cite this article: D.L. She , M.A. Shao , L.C. Timm , I. Pla Sentís , K. Reichardt & S.E. Yu (2010) Impacts of land-usepattern on soil water-content variability on the Loess Plateau of China, Acta Agriculturae Scandinavica, Section B — Soil &Plant Science, 60:4, 369-380, DOI: 10.1080/09064710903049334

To link to this article: http://dx.doi.org/10.1080/09064710903049334

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose ofthe Content. Any opinions and views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be reliedupon and should be independently verified with primary sources of information. Taylor and Francis shallnot be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and otherliabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

ORIGINAL ARTICLE

Impacts of land-use pattern on soil water-content variability on theLoess Plateau of China

D.L. SHE1,2, M.A. SHAO2, L.C. TIMM3, I. PLA SENTIS4, K. REICHARDT5 & S.E. YU1

1Key Laboratory of Efficient Irrigation-Drainage and Agricultural Soil-Water Environment in Southern China, Ministry of

Education, College of Agricultural Engineering, Hohai University, Nanjing, 210098, China, 2State Key Laboratory of Soil

Erosion and Dryland Farming on the Loess Plateau, Research Center of Soil and Water Conservation and Ecological

Environment, Chinese Academy of Sciences & Ministry of Education, Yangling 712100, China, 3Faculty of Agronomy,

Federal University of Pelotas, Department of Rural Engineering, P.O. Box 354, 96001-970, Pelotas, RS, Brazil,4Departament de Medi Ambient i Ciencies del Sol, Universitat de Lleida. Av. Alcalde Rovira Roure 191, 25198 Lleida

(Espana), 5University of Sao Paulo/Center for Nuclear Energy in Agriculture � Soil Physics Laboratory, P.O. Box 96, 13418-

900, Piracicaba, SP-Brazil

AbstractLong-term vegetation restoration carried out on the slopes of the Loess Plateau of China employed different spatial andtemporal land-use patterns but very little is known about the effects of these patterns on soil water-content variability. Forthis study the small Donggou catchment was selected to investigate soil water-content distributions for three spatial scales,including the entire catchment area, sampling transects, and land-use systems. Gravimetric soil water contents weredetermined incrementally to a soil depth of 1.20 m, on 10 occasions from April to October, 2007, at approximately 20-dayintervals. Results indicated that soil water contents were affected by the six land-use types, resulting in four distinct patternsof vertical distribution of soil moisture (uniform, increasing, decreasing, and fluctuating with soil depth). The soil watercontent and its variation were also influenced in a complex manner by five land-use patterns distributed along transectsfollowing the gradients of five similar slopes. These patterns with contrasting hydrological responses in differentcomponents, such as forage land (alfalfa)�cropland�shrubland or shrubland�grassland (bunge needlegrass)�cropland�grassland, showed the highest soil water-content variability. Soil water at the catchment scale exhibited a moderatevariability for each measurement date, and the variability of soil water content decreased exponentially with increasing soilwater content. The minimum sample size for accurate data for use in a hydrological model for the catchment, for example,required many more samples for drier (69) than for wet (10) conditions. To enhance erosion and runoff control, this studysuggested two strategies for land management: (i) to create a mosaic pattern by land-use arrangement that located units withhigher infiltration capacities downslope from those with lower soil infiltrabilities; and (ii) raising the soil-infiltration capacityof units within the spatial mosaic pattern where possible.

Keywords: Hydrological modelling, land-use patterns, runoff and erosion control, soil moisture variation.

Introduction

Soil water content is one of the most important

hydrological variables at the land surface, and exhibits

a great degree of heterogeneity due to its interface,

either directly or indirectly, with the atmosphere

(Gomez-Plaza et al., 2000). Vegetative soil cover plays

an important role in the establishment of the spatio-

temporal distributions of soil water content, mainly by

influencing infiltration rates, runoff intensity, and

evapotranspiration (Cubera & Moreno, 2007).

Therefore, the evaluation of these distributions is

helpful when modelling runoff generation, soil eva-

poration, and plant transpiration, and also for further

controlling soil erosion and improving land-manage-

ment practices (Fitzjohn et al., 1998).

Variability of soil water content of agricultural

fields has been studied extensively (Qiu et al., 2003;

Timm et al., 2004, 2006; Brocca et al., 2007; Hu

Correspondence: Prof. Mingan Shao, State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Research Center of Soil and Water

Conservation and Ecological Environment, Chinese Academy of Sciences & Ministry of Education, Yangling 712100, China. E-mail: mashao@ms.iswc.ac.cn

Acta Agriculturae Scandinavica Section B � Soil and Plant Science, 2010; 60: 369�380

(Received 18 February 2009; accepted 18 May 2009)

ISSN 0906-4710 print/ISSN 1651-1913 online # 2010 Taylor & Francis

DOI: 10.1080/09064710903049334

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

et al., 2008b), but not in relation to comparisons

with long term vegetation cover. The establishment

of a soil water distribution at a given location is also

affected by the seasonal distribution of precipitation,

topographic variables, soil properties, and mean soil

water content (Fu et al., 2000; Cubera & Moreno,

2007). Evaluating the effects of land use and its

spatial distribution or pattern on soil water content is

a difficult task because the relationships between

them were found to be very variable (Famiglietti

et al., 1998).

The Loess Plateau is characterized by a unique

landscape overlying very deep loess deposits, which

has a variety of landforms created by severe soil

erosion including deep gullies, steep slopes, and

plateaus (Fu et al., 2000). To control soil losses

and restore the ecosystem, the Chinese government

has implemented large-scale projects in this area,

including revegetation programmes, since the

1950s. Although there has been some evaluation

of the land uses (Fu & Gulinck, 1994; Chen et al.,

2003), few, if any studies, have been directed

towards the effects of land-use pattern on soil

water distributions at various spatial or temporal

scales.

The objective of this study was to investigate the

spatiotemporal distribution of soil water content at

three spatial scales including the whole catchment,

sampling transects, and land-use systems during the

course of a rainy season. In particular, this work

intended: (1) to characterize the spatiotemporal

distribution of soil water content for six land uses

and for five land-use patterns; (2) to obtain the

sampling size required for an accurate estimation of

the mean soil water content for the catchment area;

and (3) to propose some possible management

systems by which runoff and erosion can be better

controlled based on our data.

Material and methods

Study site description

The experiments were carried out in the Donggou

catchment located in Shenmu County, Shannxi

Province, China (38846?�38851?N, 110821?�110823?E), a typical ‘wind-water erosion crisscross

zone’ of the Loess Plateau, where severe erosion

occurs by wind during the dry period and by water

during the wet summer (Figure 1). The climate in

the area is semi-arid temperate with an annual mean

precipitation of 430 mm, about 70% of which falls

from June to September. Rain storms can be intense

and a daily rainfall exceeding 25 mm should occur

on average 3.2 times per year, while one exceeding

100 mm should occur once every ten years based on

historical data (Mu, 1993). The mean annual pan

evaporation is 785 mm. Soils, with a sandy loam

texture, were developed over wind-accumulated

loess parent material. The dominant soil type in

the catchment is the loessal mein soil (Calcaric

Regosol, FAO/UNESCO, 1988), with some occur-

rence of the red loessal soil (Eutric Regosol), the

aeolian sand soil (Calcaric Arenosol), and the warp

soil (Calcaric Fluvisol). More details about this area

can be found in Hu et al. (2008b).

Revegetation systems in the Donggou catchment

were established in a patchwork or mosaic pattern of

land uses. Major plant species found in these land-

use units were: in cropland (C), millet [Setaria italica

(L.) Beauv.], beans (Phaseolus vulgaris), potatoes

(Solanum tuberosum L.), and spring wheat (Triticum

aestivum L.) grown at different times according to

the needs of individual farmers; in shrubland (S),

Korshinsk peashrub (Caragana Korshinskii Kom); in

orchards (O), apricot (Prunus armeniaca); in grass-

land (A), bunge needlegrass (Stipa bungeana Trin.);

and in forage land (B), legume alfalfa (Medicago

sativa) grown for 5 to 7 years without harvesting or

grazing. Fallow land (F) consisted of cultivated plots

that had been abandoned for one year before

measurements, weeded, and during the study period

vegetation such as annual grasses was allowed to

grow.

Experimental design

Five spatial transects containing a total of 49

sampling locations, each located on a downslope

strip, were selected to pass through typical land-use

patterns on adjacent hill slopes of the catchment

(Figure 1). The elevation ranges for the transects

were generally the same, descending from about

1117 m to 1099 m with the exception of M5, which

descended to 1078 m. Slope aspects ranged between

200o and 340o, but along a transect the aspect

remained relatively constant (B55o). Transects

M1, M2, and M5 were along convex slopes, M3

was concave, while the gradient of the slope for M4

fluctuated.

A further 21 sampling sites, which included some

in orchards, were located throughout the catchment,

making a total of 70 locations at which to measure

soil water content. All the sites were located on the

main soil type, the loessal mein soil. The main plant

species, percentage distributions, and the number of

sampling points for the different land uses are shown

in Table I. The area comprising roads and other

man-made structures as well as inaccessible areas

such as gullies accounted for 31% of the area. Ten

sets of soil samples were collected for gravimetric soil

water-content analysis to a depth of 1.20 m in 0.10

370 D.L. She et al.

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

m increments using an auger (sampling diameter,

0.04 m) during the growing season from April to

October, 2007, at approximately 20-day intervals.

Each complete set of samples was collected within 2

days to reduce the effect of sampling time on our

results. Soil samples were oven dried at 1058C. A

total of 403.52 mm of rainfall was recorded during

the experimental period by a rain gauge located in

the catchment area.

Methods of statistical analysis

One-way analysis of variance (ANOVA) analysis was

used to test the effects of land use on soil moisture

40N

104E 108E 112E

38N

36N

Loess Plateau boundary

Wind-water erosion crisscross region boundary

Sampling sites scattered in the catchment

Sampling sites on transects

Contour line

0 62.5 250Meters

M3M4

M5

M2M1

N

Donggou catchment

A F S

A

170 m

220 m

240 m

F

C

M2M1

Downslope

M3

CC

F

S

B

BS

F

A

A

A

M4

M5

130 m

140 m

125

Hohhot

Yinchuan

Lanzhou

Taiyuan

Xian Yellow River

Figure 1. Location of the study area, distribution of the sampling sites, and land-use patterns (M1, M2, M3, M4, and M5 refer to the land-

use patterns, and A, F, C, S, and B refer to land uses: Grassland, Fallow land, Cropland, Shrubland, and Forage land, respectively).

Impacts of land-use pattern on soil water 371

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

content, and multiple comparisons among the dif-

ferent land-use patterns were made using the least

significant difference (LSD) method. Taking the soil

water content of plot (i ), layer ( j ), and sampling

occasion (/k) as/Mi;j;k; the coefficient of variation (%)

of the averaged soil layer-moisture content on a

measurement occasion, k; CV can be calculated

from Equation (1):

CV �100 � Np � Nl

XNp

i�1

XNl

j�1

Mi;j;k

�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiNp

XNp

i�1

�1

Nl

XNl

j�1

Mi;j;k

�2

��XNp

i�1

�1

Nl

XNl

j�1

Mi;j;k

��2

Np � (Np � 1)

vuuuuut

(1)

where Np is the number of sampled sites (�70), and

Nl represents the number of sampled soil layers (�12). Heterogeneity is considered weak when CV%5

10%, moderate when 10%BCV%B100%, and

strong when CV%]100% (Hu et al., 2008b).

Based on the assumption that soil water-content

data were normally distributed (Buttafuoco et al.,

2005; Brocca et al., 2007), the minimum sample size

(/Nr: number of samplings) required for mean soil

water-content prediction at a chosen level of accu-

racy (/k%) is given by Equation (2):

Nr�m2a

�CV%

k

�2

(2)

where ma is a critical m-value for a t-distribution

corresponding to a certain confidence limit pl (e.g.,/

pl �95%); and k can be set from 5 to 20% depending

on the accuracy required (10% in this study).

Results

Temporal distribution of soil water content

Temporal variations of the mean soil water content,

calculated from the soil moisture content in the entire

0�1.20-m soil layer for each site within each of the six

land uses, as well as the corresponding daily rainfall,

are shown in Figure 2. Under different vegetation

types, the seasonal variation of the soil water profiles

was similar, approximately following the same pat-

tern as that of the precipitation (Figure 2), albeit

with quantitative differences among vegetation types

(Table II). The highest mean soil water contents were

found under crops and fallow sites, which were

significantly higher than those of the other land

uses. The reason for these differences in soil moisture

content under various land uses receiving the same

amount of rainfall may be due to differences in

evapotranspiration from different vegetation types

and soil surface physical properties such as infiltra-

tion capacity (Giertz et al., 2005; Zhang & Schilling,

2006).

Soil water-content variability among the various

land uses changed seasonally, so that greater differ-

ences were observed in the period April to August

than in September and October (Figure 2). This

may be related to the distribution pattern of the

rainfall events. Compared with the period April to

August, during September and October the rainfall

events were more frequent and yielded greater

quantities of rainwater, leading to a greater degree

of soil water recharge. Such frequent rainfall events

decreased the variability of soil moisture resulting

from differences in evapotranspiration and soil

hydraulic properties among land uses (Qiu et al.,

2001; Hu et al., 2008a).

Vertical distribution of soil water content

We identified four different types of vertical distribu-

tion of the soil water content in soil profiles under

the six land uses (Figure 3), including those that

were uniform (Figure 3a, b, c), those that increased

with soil depth (Figure 3e, g), those that decreased

with depth (Figure 3j), and those that fluctuated

with depth (Figure 3d, f, h, i).

Soil water content changed only slightly during the

early growing period (April to early June) due to the

balance between evapotranspiration and rainfall

supply (Figures 2 and 3). In late June, following a

succession of rain storms, the soil water at the depth

Table I. Main plant species, percentage distributions, and sampling-point numbers for the various land uses.

Land-use types Main plant species

Percentage distribution

(%)

Number of sampling

points

Shrubland Korshinsk peashrub (Caragana Korshinskii Kom) 7 8

Orchard Apricot (Prunus armeniaca) 5 3

Grassland Bunge needlegrass (Stipa bungeana Trin.) 25 20

Forage land Alfalfa (Medicago sativa) 13 18

Fallow land Annual grass 9 11

Cropland Millet in husk [Setaria italica (L.) Beauv.], Bean (Phaseolus vulgaris) 10 10

372 D.L. She et al.

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

of 0�0.3 m was replenished by rainfall, while the

deeper soil layers below 0.3 m were relatively

unaffected for all land uses except in the cases of

the soils under crops and those lying fallow where

soil water recharge occurred at greater depths

(Figure 3d). The relatively undeveloped root systems

of the annual crops and weeds on fallow land are less

capable of intercepting percolating rainwater, per-

mitting recharge to depth. Furthermore, the soil

under both of these land uses had received cultiva-

tion that would improve infiltration capacity com-

pared with no-tillage land-use systems. Some drying

out in the deeper orchard soil layers was also

apparent. In early July, soil water losses occurred

from both surface and deeper soil layers due to

continuous evapotranspiration until mid-August,

despite a large water recharge of the upper soil layers

in late July (Figure 3f). Decreasing evapotranspira-

tion due to lower temperatures and the occurrence

of more frequent rain events during September and

October resulted in increased soil water content,

mostly in the upper soil layers but also in the deeper

layers due to percolation of rainwater (Figure 3h and

3j). This was especially noticeable by October 11,

when the soil water recharge reached soil layers

deeper than 1.20 m for all land uses except for

orchards and grassland.

Variations in soil water content for the five land-use

patterns

The mean gravimetric soil water content of the

0�1.20-m soil layers within a land-use plot (Figure 1)

significantly increased with a decrease in elevation

along the slope under the M1 uniform land-use

pattern of grassland (Figure 4), in agreement with

the findings of Fu et al. (2003). The mean soil

moisture content along this transect was 7.6%, which

was the lowest among the five land-use patterns. In

contrast, along transect M2, the soil water contents

were almost uniform (Figure 4) and the mean soil

moisture content for M2 was the highest among the

five land-use patterns (PB0.05). Therefore, given

other similar conditions including those of the soil

type, geographic characteristics, and rainfall events,

soil erosion will possibly be more likely to occur in

this land-use pattern since a direct relationship exists

between the magnitude of the erosion and the pre-

existing degree of soil saturation (Luk, 1985). The

relatively lower, but statistically insignificant, water

content of the soil at the middle slope position

(Figure 4, M2) may indicate a slight effect on water

infiltration due to the relative steeper gradients

present there (Qiu et al., 2001). For the transects

M3 and M5, soil water contents of the upper and

lower parts of the slope were significantly less than in

the middle part (Figure 4). Therefore, the soil water

distribution for these two transects presented an

inverted ‘‘v’’ shape from the top to the foot of the

slope, which was opposite to the pattern observed in

M4 that had a ‘‘v’’-shaped distribution. Therefore,

it appears that these three land-use patterns

0

2

4

6

8

10

12

14

16

18

20 0

50

100

150

200

250

Shrubland Orchard Grassland Forage land Fallow land Cropland

3-Sep12-May 11-Oct31-Jul23-Jun

Time (day-month) 10-Jul

Soil

wat

er c

onte

nt (

%)

Prec

ipita

tion

(mm

)

22-Sep20-Apr 17-Aug1-Jun

Figure 2. Temporal changes in mean gravimetric soil water content in the 0�1.2-m soil layer for six land uses, and corresponding daily

rainfall (mm, ). (Error bars represent91 standard deviation.)

Table II. Comparisons of the mean soil moisture content mea-

sured to 1.2 m depth on 10 occasions between 20 April and 11

October, 2007 for six land-use systems.

Land-use type Mean soil water content (% m/m)

Shrubland 6.8a

Orchard 7.0a

Grassland 7.0a

Forage land 6.3a

Fallow land 10.1b

Cropland 11.6b

Values in each column with the same letter are not significantly

(PB0.05, LSD) different among land uses.

Impacts of land-use pattern on soil water 373

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

significantly altered the monotonic distribution of

soil water along the slopes that was observed in M1

and M2. Soil moisture content along these transects

did not appear to be significantly affected by the slope

shape. Differences between soil moisture contents

within land-use patterns were usually greater when

00 2 4 6 8 10

Soil water content (%)

Soi

l dep

th (

cm)

Soi

l dep

th (

cm)

Soi

l dep

th (

cm)

Soi

l dep

th (

cm)

Soi

l dep

th (

cm)

Soil water content (%)

20 - Apr

1 - Jun

10 - Jul

17 - Aug

22 - Sep

12 - May

23 - Jun

31 - Jul

3 - Sep

11 - Oct

a

c

e

g

i

b

d

f

h

j

12 14 16 18 0 2 4 6 8 10 12 14 16 18

20

40

60

80

100

120

140

0

20

40

60

80

100

120

140

0

20

40

60

80

100

120

140

20

0

40

60

80

100

120

0

20

40

60

80

100

120

140

Shrubland

Fallow land

Orchard

Cropland

Grassland

Standard deviation

Forage land

140

Figure 3. Profile distribution and temporal changes in mean gravimetric soil water content for six land uses, with their related standard

deviations (%).

374 D.L. She et al.

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

4

6

8

10

12

14

4

6

8

10

12

14

4

6

8

10

12

14

20 65 110 22 67 115

18 48 98 180 10 35 75 140

30 160 200

Slope position (m)

Slope position (m) Slope position (m)

b b

a

a

aa

bb b

a

ab

c

ab

b

b

a

Grassland-Grassland-Grassland (M1) Fallow land-Fallow land-Cropland (M2)

Shrubland-Grassland-Cropland-Grassland (M3) Fallow land-Shrubland-Forage land-Fallow land (M4)

Forage land-Cropland-Shrubland (M5)

Soil

wat

er c

onte

nt (

%)

Soil

wat

er c

onte

nt (

%)

Soil

wat

er c

onte

nt (

%)

Slope position (m)

Grassland

Fallow land

Cropland

Forage land

Shrubland

Slope position (m)

Figure 4. Slope distribution of mean gravimetric soil water contents for five land-use patterns. [Error bars represent91 standard deviation.

Bars with the same lowercase letter within a land-use pattern are not significantly different (P�0.05)].

Table III. Temporal changes in the mean and coefficient of variation (CV) of the mean soil water contents measured to 1.2 m depth for five

land-use patterns.

Land-use pattern 20-Apr 12-May 1-Jun 23-Jun 10-Jul 31-Jul 17-Aug 3-Sep 22-Sep 11-Oct

Soil water content (%, m/m)

M1 6.2a 6.1a 6.7a 7.5a 6.5a 7.5a 6.3a 9.9ab 7.8a 12.0a

M2 8.8b 8.6b 10.1b 10.8b 10.1b 0.4b 9.1b 11.5b 10.3b 14.6b

M3 7.7ab 6.6ab 7.3a 8.4ab 6.7a 8.0a 6.6a 9.6a 7.9a 12.8ab

M4 8.2ab 7.3ab 8.3ab 8.4ab 7.0a 7.7a 6.6a 10.0ab 8.0a 12.7ab

M5 7.7ab 7.0ab 7.8ab 8.1a 6.6a 7.8a 7.2ab 9.6a 7.9a 12.2a

CV (%)

M1 19.8 15.8 17.3 17.4 16.5 15.4 19.7 9.8 14.0 9.5

M2 8.6 8.6 9.9 9.7 13.0 16.5 18.2 16.9 11.9 10.3

M3 39.7 51.5 52.2 42.3 48.2 38.3 33.7 22.0 29.3 20.5

M4 27.9 28.2 32.0 28.5 36.2 29.4 25.3 14.5 25.3 14.5

M5 50.6 52.4 47.2 51.2 56.2 49.2 46.3 29.6 33.1 24.1

Values in each column with the same letter are not significantly (PB0.05, LSD) different among land-use patterns. M1, M2, M3, M4 and

M5 refer to different land-use patterns.

Impacts of land-use pattern on soil water 375

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

considering the soil profile to a depth of 1.2 m than

when considering the upper 0�0.2-m layer alone

(data not shown), but followed the same trends.

Soil water-content variability, as indicated by the

CV%, also differed with land-use patterns (Table III).

Patterns M5 and M3 had the highest soil water

variabilities, with CVs varying from 24.1 to 56.2%

and from 20.5 to 52.2%, respectively. In contrast,

pattern M2 had the lowest CV values, ranging from

8.6 to 18.2%. The differences in terms of variability

among the different land-use patterns generally

decreased at the end of the rainy season in September

and October, although fluctuations in variability were

common during the experimental period. From the

ecological point of view, soil water-content variability

may be beneficial for supporting a diversity of

ecosystems (Ibanez et al., 1995; Fitzjohn et al.,

1998), and may also be helpful in runoff and erosion

0

20

40

60

80

100 0

20

40

60

80

100

120guA-71rpA-02 tcO-11luJ-13 22-Sep23-Jun peS-3yaM-21 10-Jul 1-Jun

2

4

6

8

10

12

14

16 0

20

40

60

80

100

120

Time (day-month)

Soi

l wat

er c

onte

nt (

%)

/Var

ianc

e C

oeff

icie

nt o

f va

riat

ion

(%)

/ Min

imum

sam

ple

size

Prec

ipita

tion

(mm

) Pr

ecip

itatio

n (m

m)

Figure 5. Temporal changes in the mean (%, ) /variance ( ) and coefficient of variation (CV,%, ) of the layer-averaged soil water

content at the catchment scale. Daily precipitation depths (mm; ) and the minimum sample size ( ) are also shown.

Min

imum

sam

ple

size

A

10

20

30

40

50B

0

20

40

60

80

4 6 8 10 12 14 16 4 6 8 10 12 14 16

Coe

ffic

ient

of

vari

atio

n (%

)

)%(tnetnocretawlioS)%(tnetnocretawlioS

0.1446

2

94.489

0.7861

x

R

−==

0.2892

2

342.85

0.7861

xy e y e

R

−==

Figure 6. Correlation between statistical parameters for the catchment: (A) coefficient of variation versus mean soil water content, (B)

minimum sample size to estimate the mean versus mean soil water content.

376 D.L. She et al.

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

control (Bergkamp et al., 1996; Fitzjohn et al.,

1998).

Relationships between spatial variability of soil water and

mean values at the catchment scale

The mean soil water contents for the soil to a depth of

1.2 m and the spatial variability at the catchment scale

(Figure 5) show that the mean catchment soil water

content closely followed the distribution of rainfall

events. For example, the highest value of the mean soil

water content of 12.2% on October 11 was observed

following the period of highest rainfall (41.80 mm on

October 4 and 5), while the lowest mean value of 6.5%

on July 10 was measured during a period of low

rainfall and also when the highest evapotranspiration

rates occurred in the area (Kimura et al., 2006).

Similar results have been reported for other areas of

the Loess Plateau and for other environments (Barling

et al., 1994; Famiglietii et al., 1998; Fu et al., 2003;

Chen et al., 2007).

Spatial variability of soil water as indicated by

the variance and CV% followed a trend that was the

reverse of that of the mean (Figure 5). During the

observed period, CV% ranged from 18.0% (October

11) to 42.4% (July 10), indicating a moderate spatial

variability of soil water at the catchment scale.

Plotting the relationship between CV% and the

mean soil water content [Figure 6(A)] shows that

the variability decreased exponentially with increas-

ing soil water content. This can partly be explained

by the uniformly wet conditions of the soils attained

across the entire catchment following the exposure

to several heavy rainfall events and the accumulation

of rainwater from the entire rainfall season that was

not utilized by plants. Following such rainfall events,

the land uses (Figure 2) and spatial heterogeneity of

the soil physical properties would have minimal

influence on the soil water content (Grayson et al.,

1997). This phenomenon makes the measurement

error relatively more important for wetter conditions

because, when the variability is lower, the ratio of the

measurement error to the total variability becomes

higher, thus increasing the importance of the mea-

surement error.

Sampling size for soil moisture estimation

Assuming normally distributed soil water content, the

sampling size required for mean soil water-content

estimation measured on 10 occasions (Figure 5) was

calculated using Equation (2). The required sample

size for an accurate estimation of the mean soil water

content decreased exponentially with increasing soil

moisture content [Figure 6(B)] since the sample

number is directly related to the CV% of the soil

water content by Equation (2) and the CV% was

inversely related to the soil water content. Therefore,

for an accurate estimation of soil water content, many

more measurements are required under dry condi-

tions due to the greater heterogeneity of the soil water

conditions. Conversely, when the soil is wetter, fewer

samples would be required. Thus, to accurately

estimate the mean value for the 0�1.2-m soil layer

across the catchment with a precision level of 10%, 69

sampling points would be required under the driest

conditions (July 10), while only 10 sampling points

are needed under the wettest conditions (October

11). Based on these calculations, our sample size (n�70) can be considered adequate to predict the

catchment-mean for all 10 observations during the

experimental period. Considering the large differ-

ences of sampling numbers required for mean soil

water estimation, care should be taken to ensure that

an adequate number of samples are taken to deter-

mine soil moisture values for use in hydrological

modelling. In order to get a representative mean soil

moisture content of the catchment, the proportion of

samples taken for each land-use unit should reflect the

proportion of the catchment covered by that unit.

This would be more important in drier conditions

than under wetter conditions. Furthermore, in this

study we did not consider the area covered by man-

made structures including roads, or inaccessible

areas, since our focus was on land-use type. An

estimation of the soil water content in these areas,

where applicable, and the runoff generated from them

would normally need to be considered in a hydro-

logical model.

Discussion

Implications for hydrological modelling

Soil water content is a very important variable in

hydrological modelling, especially for distributed

hydrological modelling (Gomez-Plaza et al., 2000).

It is necessary to consider all aspects of the soil water-

content regime including the soil water-content level,

the magnitude of the variability, the spatial distribu-

tion, and the various influencing factors and pro-

cesses in order to construct a realistic model. Usually,

the modelled area, such as a catchment or a slope, is

subdivided into homogeneous areas in respect of

their hydrological responses known as ‘hydrological

response units’ (Flugel, 1995), which are often

related to topographic properties such as the relative

elevation. The difficulty for such investigations

comes mainly from the large heterogeneity of soil

water content in space and time, even in a small

catchment like the area considered in our study

(Gomez-Plaza et al., 2000). Since the 1950s, when

Impacts of land-use pattern on soil water 377

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

extensive vegetation-restoration programmes were

started, land use and vegetative cover have changed

over large areas of the Loess Plateau, which has made

the determination of the hydrological response units

more complex due to the presence of multiple land

uses that have increased soil water-content variability.

Our data for the Donggou catchment in the wind-

water erosion crisscross region on the Loess Plateau

indicate that soil water content varied with land-use

type (Figure 2 and Table II), and that there were four

types of vertical distribution of water content in the

soil under six land uses (Figure 3), which were

attributable to differences in the infiltration of rain-

water. The soil water-content distribution was also

very different under five land-use patterns (Figure 4).

The differences in CV% among the five land-use

patterns were evident, and CV% gradually became

smaller towards the end of the rainy season in

September and October (Table III). In addition,

since precipitation is the sole source of soil water in

our study area, the balance of precipitation and

evapotranspiration controlled the soil water-content

level and its variation at the catchment scale. During

September and October, soil water content was

relatively uniform across the catchment because of

the greater rainfall occurring compared with that in

other months in addition to some accumulation of

water from those previous storms (Figure 5), and

considerably fewer measurements were required for

an accurate estimation of the mean soil water content

(Figures 5 and 6). Land use and the spatial hetero-

geneity of soil physical properties had the lowest

influence on the distribution of soil water content

under the uniformly wet conditions at this time

(Grayson et al., 1997). Based on our analysis of soil

water-content variations for the three scales, i.e., the

entire catchment area, the sampling transects, and

the land-use systems, we concluded that the hydro-

logical response unit is not only decided by topo-

graphical characteristics, as suggested by previous

work, but also by land use and land-use patterns.

Failing to include the factors of distribution within

the soil profile and temporal variability, when con-

sidering antecedent soil water content down a slope

or across a catchment when modelling hydrological

processes, may lead to inaccuracies in the model’s

output.

Implications for controlling erosion and improving land-

use management

On the Loess Plateau, overland flow results from

rainfall intensities that exceed the infiltration capa-

cities of the soils. Therefore, every hydrological

response unit may have a different threshold value

for an infiltration rate in order for runoff to occur

(Liu & Kang, 1999). Only when the infiltration rate

threshold is exceeded for a specific storm will over-

land flow from a unit occur (Fu et al., 2003). Land-

use change can produce changes in the hydrological

processes and soil physical properties that affect

infiltration capacity (Giertz et al., 2005). Moreover,

the results presented here also indicated that soil

water content under the six land-use systems ex-

hibited differences in both temporal dynamics and

profile distribution, which are related to the infiltra-

tion capacity (Figures 2 and 3, and Table II). Mosaic

patterns on a slope can be achieved by different land-

use arrangements (Fu et al., 2003). The presence of

the same, or similar, land uses down the length of a

slope resulted in increasing or uniform types of soil

water-content profile distributions when descending

the slope (Figure 4, M1 and M2). This may be a

consequence of the entire slope having a similar

infiltration rate (Fu et al., 2003), which results in

runoff-producing areas being connected, thus in-

creasing the possibility of widespread runoff and

severe erosion down the entire length of the slope.

To change this situation, land-use patterns were

developed with spatial arrangements of different

land-use types (vegetation strips) (Figure 4, M3

and M5) to absorb the runoff and to trap sediments

from the upper slopes (Morgan, 1992; Fu et al.,

2003). In these land-use patterns, soil water-content

variability increased greatly (Table III, M3 and M5).

Soil variability, including the initial soil water con-

tent (Figure 4 and Table III) and infiltration rates (Li

et al., 1995), determines the differences in the time

to commencement of surface runoff from the differ-

ent land uses. Therefore, soil variability created by a

spatial mosaic pattern of contrasting hydrological

response units may create a self-regulating system in

which runoff-producing areas are bordered on their

lower sides by zones of higher infiltration capacity

that are capable of absorbing such runoff (Fitzjohn

et al., 1998). Thus, separating runoff-producing

areas on a slope can result in a reduction in overall

runoff and erosion. At the catchment scale, wide-

spread runoff and erosion may require storms that

are prolonged or of greater intensity in order to

overcome the spatial arrangement and threshold

values of each hydrological response unit in all the

smaller-scale landforms. However, in the relatively

wetter conditions, such as those that occurred during

September and October, with smaller soil water-

content variability (Figure 5), most of the thresholds

of the individual hydrological units may be exceeded

easily (Fitzjohn et al., 1998), so that larger areas will

contribute to surface runoff regardless of the spatial

distribution of land use.

Therefore, in terms of runoff and erosion control,

two land-use management strategies can be derived

378 D.L. She et al.

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

from the preceding analysis. First, creating a mosaic

pattern in order to increase the spatial variation by

arranging land uses, whereby land-use units that are

susceptible to runoff production would be located in

upslope positions relative to units with higher

infiltration capacities, would be an effective manage-

ment strategy for runoff and erosion control. Sec-

ond, raising the soil-infiltration capacity of the

hydrological response units within the spatial mosaic

pattern would increase the threshold for runoff

production from individual hydrological response

units. This may not always be practical but it may be

possible for certain land uses to be rotated, such as

forage, crops, and fallow, so that the soil can be

periodically cultivated, which can increase the short-

term infiltration capacity. Such rotations would have

to be coordinated with the objective of our first

proposed land-use management system. The surface

of temporarily bare soils, such as those being used

for crop production and those laying fallow, might

also be protected from surface seal formation caused

by the impact of raindrops, which results in reduced

infiltration capacity, by the application of mulch or

soil stabilizers.

Acknowledgements

We thank the National Natural Science Foundation

of China (90502006, 50839002) and 2007-2010

Principal Investigator for ‘Soil and water transferring

and accommodating mechanism in the watershed

ecosystem on Loess Plateau’, of the Ministry of

Education (3 000 000 RMB) for the financial

support of this research.

References

Barling, R.D., Moore, I.D., & Grayson, R.B. (1994). A quasi-

dynamic wetness index for characterizing the spatial dis-

tribution of zones of surface saturation and soil water

content. Water Resources Research, 30, 1029�1044.

Bergkamp, G., Cammeraat, L.H., & Martinez-Fernandez, J.

(1996). Water movement and vegetation patterns on

shrubland and an abandoned field in two desertification-

threatened areas in Spain. Earth Surface Processes and Land-

forms, 21, 1073�1090.

Brocca, L., Morbidelli, R., Melone, F., & Moramarco, T. (2007).

Soil moisture spatial variability in experimental areas of

central Italy. Journal of Hydrology, 333, 356�373.

Buttafuoco, G., Castrignano, A., Busoni, E., & Dimase, A.C.

(2005). Studying the spatial structure evolution of soil water

content using multivariate geostatistics. Journal of Hydrology,

311, 202�218.

Chen, L.D., Messing, I., Zhang, S.R., Fu, B.J., & Ledin, S.

(2003). Land use evaluation and scenario analysis towards

sustainable planning on the Loess Plateau in China-case

study in a small catchment. Catena, 54, 303�316.

Chen, L.D., Huang, Z.L., Gong, J., Fu, B.J., & Huang, Y.L.

(2007). The effect of land cover/vegetation on soil water

dynamic in the hilly area of the loess plateau, China. Catena,

70, 200�208.

Cubera, E., & Moreno, G. (2007). Effect of land-use on soil water

dynamic in dehesas of Central-Western Spain. Catena, 71,

298�308.

FAO/UNESCO (1988). Soil Map of the World. Revised Legend.

FAO/UNESCO, Rome.

Famiglietti, J.S., Rudnicki, J.W., & Rodell, M. (1998). Variability

in surface moisture content along a hillslope transect:

Rattlesnake Hill, Texas. Journal of Hydrology, 210, 259�281.

Fitzjohn, C., Ternan, J.L., & Williams, A.G. (1998). Soil moisture

variability in a semi-arid gully catchment: implications for

runoff and erosion control. Catena, 32, 55�70.

Flugel, W.A. (1995). Delineating hydrological response units by

geographical information system analyses for regional hydro-

logical modeling using PRMS/MMS in the drainage basin of

the river Brol, Germany. Hydrological Processes, 9, 423�436.

Fu, B.J., & Gulinck, H. (1994). Land evaluation in area of severe

erosion, the Loess Plateau of China. Land Degradation and

Rehabilitation, 5, 33�40.

Fu, B.J., Chen, L.D., Ma, K.M., Zhou, H.F., & Wang, J. (2000).

The relationships between land use and soil conditions in the

hilly area of the loess plateau in northern Shannxi, China.

Catena, 39, 69�78.

Fu, B.J., Wang, J., Chen, L.D., & Qiu, Y. (2003). The effects of

land use on soil moisture variation in the Danangou

catchment of the Loess Plateau, China. Catena, 54, 197�213.

Giertz, S., Junge, B., & Diekkruger, B. (2005). Assessing the

effects of land use change on soil physical properties and

hydrological processes in the sub-humid tropical environ-

ment of West Africa. Physics and Chemistry of the Earth, 30,

485�496.

Gomez-Plaza, A., Alvarez-Rogel, J., Albaladejo, J., & Castillo,

V.M. (2000). Spatial patterns and temporal stability of soil

moisture across a range of scales in a semi-arid environment.

Hydrological Processes, 14, 1261�1277.

Grayson, R.B., Western, A.W., Chiew, F.H.S., & Bloschl, G.

(1997). Preferred states in spatial soil moisture patterns:

local and nonlocal controls. Water Resources Research, 33,

2897�2908.

Hu, W., Shao, M.A., Wang, Q.J., Fan, J., & Reichardt, K. (2008a).

Spatial variability of soil hydraulic properties on a steep slope

in the Loess Plateau of China. Scientia Agricola, 65, 268�276.

Hu, W., Shao, M.A., Wang, Q.J., & Reichardt, K. (2008b). Soil

water content temporal-spatial variability of the surface layer

of a Loess Plateau hillside in China. Scientia Agricola, 65,

277�289.

Ibanez, J.J., De-Alba, S., Bermudez, F.F., & Garcia-Alvarez, A.

(1995). Pedodiversity: concepts and measures. Catena, 24,

215�232.

Kimura, R., Fan, J., Zhang, X.C., Takayama, N., Kamichika, M.,

& Matsuoka, N. (2006). Evapotranspiration over the Grass-

land Field in the Liudaogou Basin of the Loess Plateau,

China. Acta Oecologica, 29, 45�53.

Li, G., Luk, S.H., & Cai, Q.G. (1995). Topographic zonation of

infiltration in the hilly loess region, North China. Hydro-

logical Processes, 9, 227�235.

Liu, X.Z., & Kang, S.Z. (1999). Some developments and review

of rainfall-infiltration-runoff yield research. Bulletin of Soil

and Water Conservation, 19, 57�62 (in Chinese).

Luk, S.H. (1985). Effects of antecedent soil moisture on rainwash

erosion. Catena, 12, 129�139.

Morgan, R.P.C. (1992). Soil conservation options in the U.K. Soil

Use and Management, 8, 176�180.

Mu, X. (1993). The analysis on agro-climate resources and

disaster weather in Shenmu county. Memoir of Northwestern

Impacts of land-use pattern on soil water 379

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14

Institute of Soil and Water Conservation, Academia Sinica and

Ministry of Water Resources, 18, 24�35 (in Chinese).

Qiu, Y., Fu, B.J., Wang, J., & Chen, L.D. (2001). Soil moisture

variation in relation to topography and land use in a hillslope

catchment of the Loess Plateau, China. Journal of Hydrology,

240, 243�263.

Qiu, Y., Fu, B.J., Wang, J., & Chen, L.D. (2003). Spatiotemporal

prediction of soil moisture content using multiple-linear

regression in a small catchment of the Loess Plateau, China.

Catena, 54, 173�195.

Timm, L.C., Reichardt, K., Oliveira, J.C.M., Cassaro, F.A.M.,

Tominaga, T.T., Bacchi, O.O.S., Dourado-Neto, D., &

Nielsen, D.R. (2004). State-space approach to evaluate the

relation between soil physical and chemical properties.

Brazilian Journal of Soil Science, 28, 49�58.

Timm, L.C., Pires, L.F., Roveratti, R., Arthur, R.C.J., Reichardt,

K., Oliveira, J.C.M., & Bacchi, O.O.S. (2006). Field spatial

and temporal patterns of soil water content and bulk density

changes. Scientia Agricola, 63, 55�64.

Zhang, Y.K., & Schilling, K.E. (2006). Effects of land cover on

water table, soil moisture, evapotranspiration, and ground-

water recharge: A Field observation and analysis. Journal of

Hydrology, 319, 328�338.

380 D.L. She et al.

Dow

nloa

ded

by [

The

UC

Irv

ine

Lib

rari

es]

at 1

5:34

07

Nov

embe

r 20

14