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Analysis and modeling of soil conservation measures in the Three Gorges Reservoir Area in China
Catena 81 (2010) 104–112
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Catena j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c a t e n a
Analysis and modeling of soil conservation measures in the Three Gorges Reservoir Area in China Zhenyao Shen a,⁎, Yongwei Gong a, Yanhong Li b, Ruimin Liu a a b
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China Beijing Guohuan-Tsinghua Environmental Engineering Design & Research Institute, Beijing, 100084, China
a r t i c l e
i n f o
Article history: Received 26 July 2009 Received in revised form 25 January 2010 Accepted 28 January 2010 Keywords: Soil erosion Experimental plot Hedgerows Terracing WEPP
a b s t r a c t Experimental plots were constructed in the Zhangjiachong Watershed of the Three Gorges Reservoir Area to evaluate soil erosion of traditional slope land farming and effects of soil conservation measures. Surface runoff and sediment from the watershed and each plot were collected and measured during 2004–2007. Field investigations indicated that hedgerows were the best for soil erosion control, followed by stone dike terraces and soil dike terraces. The Water Erosion Prediction Project (WEPP) model was used to simulate erosion of annual and rainfall events both at the watershed and plot levels. The low deviation, high coefficient of determination and model efficiency values for the simulations indicated that the WEPP model was a suitable model. The soil erosion rate distribution was modeled to determine where serious erosion would occur during rainfall events in the Zhangjiachong Watershed and so control measures can be taken. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Soil erosion is a major agricultural and environmental problem in the Three Gorges Reservoir Area, China and is one main cause of the loss of nutrient-rich topsoil and soil fertility decline. As an example, the soil erosion area of Chongqing City, which is the main part of the Three Gorges Reservoir Area, was 40,006 km2 in 2005, accounting for 48.6% of the total city area; with the total amount of soil loss 14.6 billon tonnes in 2005 (CQSBJC, 2006). Soil erosion and sedimentation in the Yangtze River is a potential threat to safety of the Three Gorges Dam. Additionally, most of the population living in the area depends on agriculture for their livelihood, hence it is imperative to preserve soil to sustain crop yields. Conventional slope land farming practices are far from sustainable and environmentally compatible from a soil and water conservation perspective. Therefore, alternative practices are needed for the Three Gorges Reservoir Area. The use of terraces and hedgerows are two main soil conservation measures in this area. Terracing of steep farmland is a way to conserve soil and water that divides a long slope into short slopes, thus reducing sheet and rill erosion (Abu Hammad et al., 2006; Aynsau and de Graaff, 2007; Zhang et al., 2008). Meanwhile, terracing that changes landform and reduces the slope gradient can reduce runoff amounts and rates. Terracing has
⁎ Corresponding author. E-mail address: [email protected] (Z. Shen). 0341-8162/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.catena.2010.01.009
been used in China for centuries, since the West-Han Dynasty (207 BC–25 AD) in foothills and valleys and since the South-Sung Dynasty (1127–1279 AD) in mountainous regions. Contour hedgerows were introduced to the Three Gorges Reservoir Area by the local government as a soil conservation measure in the late 1980s. The basic design is rows of trees or shrubs planted along contours at intervals of 3–6 m in a sloping field, with crop production between hedgerows. They can reduce runoff generation and intercept eroded sediment from the upper slope, thus conserving soil fertility (Poudel et al., 1999; Ng et al., 2008; Sun et al., 2008). The objective of this study was to evaluate the soil erosion of traditional slope land farming and the conservation measures. In the Three Gorges Reservoir Area, a number of experimental plot sets have been constructed to collect field data. However, these plots are expensive to construct and maintain. Computer simulation models are increasingly popular for simulating soil loss and to quantify the processes of detachment, transport and deposition of eroded soil. It would be useful to identify a soil erosion model that would be applicable for this area, which can be used in evaluation of the effects of different practices on soil erosion and to select the best practices. The Zhangjiachong Watershed was selected as the study area where experimental plots were constructed. The Water Erosion Prediction Project (WEPP) model is a physicallybased model capable of modeling runoff and soil loss for a wide range of environmental conditions (Foster et al., 1987; Nearing et al., 1989; Flanagan and Nearing, 1995). Its main advantage is that it can be extrapolated to conditions for which field testing is not feasible. It is intended for use on small agricultural watersheds in which the sediment yield at the outlet is significantly influenced by hill slope and channel processes, and has a recommended maximum field size of 2.6 km2
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(Foster et al., 1987). Despite this limitation, WEPP capabilities have been successfully applied to watersheds with bigger sizes (N2.6 km2) (Amore et al., 2004; Baigorria and Romero, 2007; Pandey et al., 2008). Shen et al. (2009) compared the modeling performances of WEPP and the Soil and Water Assessment Tool (SWAT) for the Zhangjiachong Watershed, and the results indicated that WEPP provided better predictions than SWAT for both runoff and sediment yields. Therefore, the WEPP model was selected to evaluate the effects of soil erosion and soil conservation measures in this study. 2. Method and materials
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Table 1 The experimental plots' details. Plot Number
Conservation type
1 2 3 4 5 6 7 8 9 10
Stone dike terraces — peanut Soil dike terraces — orange trees Soil dike terraces with hedgerows — orange trees Soil dike terraces with hedgerows — peanut Slope land with hedgerows — tea trees Slope land with hedgerows — orange trees Slope land — peanut Slope land — tea trees Slope land — orange trees Sloping wild grassland
2.1. Study area The Zhangjiachong Watershed covers an area of 1.62 km2 in the southwest of Zigui County in the Three Gorges Reservoir Area (Fig. 1). This region has an annual mean temperature of 18 °C, an annual mean precipitation of 1439 mm, and an elevation of 148–530 m. The mean slope of the watershed area is 22.0% and maximum slope of some hilly parts is 71.6%. The main land uses are soil dike terraces — 51.9%, stone dike terraces — 22.0%, paddy field — 14.1%, and mixed forest — 10.0%. Ten experimental plots were constructed in the Zhangjiachong Watershed, each of size 2 m × 10 m, with slope of 46.6%. The borders of the plots were built with concrete. Below each plot was a concrete tank to collect surface runoff and sediment from the plot during rainfall events. Of the ten plots, one with sloping wild grassland was set as the reference. Three types of plants were planted in the plots, including peanut, orange trees, and tea trees (Table 1). The orange and tea trees were one year old when planted. The two conservation measures were terraces and hedgerows. The hedgerow used in this study was a shrub, Amorpha fruticosa. 2.2. Model description The WEPP watershed model is a continuous simulation computer program that predicts sediment yield and deposition from overland flow on hill slopes, sediment yield and deposition from concentrated flow in small channels, and sediment deposition in impoundments. It computes spatial and temporal distributions of sediment yield and deposition, and provides explicit estimates of when and where in a watershed or on a hill slope that erosion occurs so that conservation measures can be selected to effectively control sediment yield (Flanagan and Nearing, 1995). The Green and Ampt equation modified by Mein and Larson (1973) and Chu (1978) was implemented in the model to estimate
infiltration. Before the model computes runoff it adjusts the amount of excess rainfall by depression storage. The surface runoff is routed using two procedures: a semi-analytical solution of the kinematic wave equation is used for a single event, and an approximate method is used for the continuous mode (Stone et al., 1995). The model uses a steady-state, sediment continuity equation to estimate net detachment and deposition (Flanagan and Nearing, 2000). dG = dx = Df + Di
ð1Þ
where G is sediment load (kg s− 1 m− 1), x represents distance downslope (m), Df is rill erosion rate (kg s− 1 m− 2) and Di is inter-rill sediment delivery to the rill (kg s− 1 m− 2). Di is considered independent of x, and always N0. Df is N0 for detachment and b0 for deposition. For model calculations, both Df and Di are computed on a per rill area basis, thus G is solved on a per unit rill width basis. After computations, sediment yield is expressed as sediment yield per unit land area. The simulated values were evaluated by visual inspection of the figures of observed and simulated values, and also by certain statistical criteria for goodness-of-fit, including the deviation (Re), the coefficient of determination (R2) and model efficiency values (ENS). Re of runoff and sediment values is given by the following equation (Yen, 1993): Re = ðPi −Oi Þ = Oi × 100%
ð2Þ
where Oi and Pi are the ith pair of the observed and simulated values. The smaller the |Re| the better the model result.
Fig. 1. The location and land use of the Zhangjiachong Watershed.
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Table 2 Details of the five selected rainfall events. No.
Date
Precipitation (mm)
Duration time (h)
Mean intensity (mm h− 1)
Max intensity (mm h− 1)
1 2 3 4 5
2005-07-09 2005-08-28 2006-06-22 2006-06-29 2006-07-17
158.9 23.9 49.9 36.2 26.1
33 30 36 6.5 22
4.82 0.80 1.39 5.57 1.19
70.5 3.8 120 120 7.5
R2 is calculated for each relationship, using the following formula (Legates and McCabe, 1999):
2
"
R =
n
∑
i=1
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi!#2 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n – – –2 –2 ∑ Pi − P Oi − O Pi − P = ð3Þ ∑ Oi − O i=1
i=1
where Ō is the mean of observed values, P̄ is the mean of simulated values, and n is the total number of paired values. ENS is expressed as (Nash and Sutcliffe, 1970): n
2
n
ENS = 1− ∑ ðOi −Pi Þ = ∑ i=1
i=1
– 2 Oi − O
ð4Þ
The range of ENS is from −∞ to 1, with 1 indicating a perfect fit. 2.3. Data The construction of the experimental plots was completed in April 2004, hence there were only observed data since then. Watershed surface runoff and sediment were measured for years 2004–2007. For every rainfall event the stream flow was measured. A water level gauge was installed at the watershed outlet to provide water levels and the flow velocity measured at the same site. The spillway was rectangular-shaped so that discharge could be well estimated. Since the sediment concentration was relatively low for non-rainfall days, the sediment flow samples were collected only for rainy days. The sediment concentration was obtained by filtration and evaporation methods. Water and eroded sediment from the plots were collected in the concrete tank after rainfall events. They were mixed uniformly to estimate the mean sediment concentration. The surface runoff was calculated according to the water level of the tank. The sediment yield was calculated by multiplying water volume in the tank and the mean sediment concentration. The model inputs include daily precipitation, temperature, solar radiation, dew points, wind speed, management, soil, slope, and land use. Rainfall data were collected at the Zigui Soil and Water Conservation Experiment Station. The other required climate data were collected at the Zigui County Weather Station. Management and soil input files were built from field investigations. The digital elevation model (DEM) and slope data were developed from the watershed contour map (at 3-m intervals). Land use information was obtained from interpretation of IRS P6 LISS-4 (5-m resolution) satellite imagery, based on field investigation.
Fig. 2. The annual soil erosion rates for the ten plots.
2.4. Modeling scheme The WEPP model was used to simulate the monthly stream flow and sediment yield of the watershed, the annual runoff and soil erosion of the plots, and the rainfall event runoff and soil erosion of the watershed and the plots for 2004–2007. Sensitivity analysis was performed for 2004 and 2005 by changing the value of a parameter within an acceptable range and observing the stream flow and sediment yield output. Effective hydraulic conductivity was found to be the most sensitive parameter for runoff, and baseline inter-rill erodibility, effective hydraulic conductivity, rill erodibility and critical hydraulic shear stress values were most sensitive for soil erosion. The parameter that produced the maximum sensitivity was adjusted first, followed by the other parameters. Once the model was calibrated, WEPP was run with the calibrated parameters, and the stream flow and sediment yield values simulated for the validation period. All the calibrated parameter values were within the physically meaningful value ranges. Five rainfall events (Table 2) were selected, with a range of mean rainfall intensity of 0.80–5.57 mm h− 1, and the runoff and soil erosion simulated. The five events were selected because there were relatively complete records of their runoff and soil erosion, and all could have resulted in soil erosion. The model parameters set for the previous simulation were used. To further investigate the effects of traditional slope land farming on soil erosion, Plots 7–10 (slope land with peanut, tea trees, orange trees and wild grassland, respectively) were selected and their runoff and soil erosion rates modeled by WEPP, using Plot 10 as the reference. 3. Results and discussion 3.1. Effects of conservation measures The soil erosion rates were analyzed at the annual and rainfall event scale. At the rainfall event level, 21 rainfall events were uniformly selected from four years when sediment yield was measured for the plots (Table 3).
Table 3 Dates and precipitations for the 21 rainfall events. Date Precipitation (mm) Date Precipitation (mm) Date Precipitation (mm)
2004-6-7 35.4 2005-5-24 65.3 2006-5-2 123.3
2004-6-21 38.6 2005-6-11 21.3 2006-6-22 49.9
2004-7-2 18 2005-7-10 158.7 2006-6-29 36.2
2004-7-11 125.3 2005-8-1 84.1 2006-7-7 45.4
2004-7-79 59.2 2005-8-4 68.4 2006-7-29 43.3
2004-8-7 150.7 2005-8-22 154.2 2006-9-4 115.3
2004-8-23 55.4 2005-8-29 32.5 2007-3-3 43.5
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3.1.1. Annual soil erosion rates The soil erosion rates for the ten plots were calculated based on the four years' observations of rainfall events (Fig. 2). The soil erosion rate was calculated from January to December except during 2004 May– December only, since the plots were not constructed until May 2004. The precipitation for the first four months of every year was relatively small, and so the soil erosion rate for 2004 was close to the real value of the whole year. The soil erosion rates decreased over time for most plots, except for Plots 4, 5, 8 and 10. When comparing 2007 with 2004, the values declined greatly, with the reduction from 74.7% to 100%. The reason that soil erosion rates decreased was that a gradual process stabilized soil and plants in the plots. During the construction of the plots there was great disturbance of soil in this process. The orange and tea trees needed a long period to stabilize soil. Therefore, soil erosion in 2004 was relatively high. As time passed, the plants grew, and soil stability was gradually enhanced. 3.1.2. Comparison of effects for different conservation measures 3.1.2.1. Hedgerows versus slope land. On an annual basis, Plot 5 that was planted with tea trees and hedgerows had less sediment load than Plot 8 (no hedgerows) (Fig. 2). The mean annual reduction rate was about 39.6%. However, Plot 6 that was planted with orange trees and hedgerows had no obvious control effects when compared with Plot 9 (no hedgerows) (Fig. 2); the soil erosion was greater in Plot 6 than Plot 9 in 2005 and 2006. For the event comparison, the result of soil erosion rates for planting tea trees on slope land with and without hedgerows (Plots 5 and 8,
Fig. 3. Soil erosion rate for planting with and without hedgerows.
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respectively) is shown in Fig. 3(a). Fifteen of the 21 rainfall events showed that planting tea trees on slope land with hedgerows was beneficial for controlling soil erosion. For other events (Nos. 1, 2, 4, 9, 11, and 19), there were opposite results. The reason may be that event Nos. 1, 2 and 4 were at the beginning of plot experiments, when hedgerows were small. Because there was bare land around hedgerows, they possibly had little effect at early stages. The result of soil erosion rates for planting orange trees on slope land with and without hedgerows (Plots 6 and 9, respectively) is shown in Fig. 3(b). Of the 21 rainfall events, 13 showed that planting orange trees was beneficial to controlling soil erosion, the other eight events had opposite results. The results indicate that hedgerows were beneficial in controlling soil erosion, similar to some previous studies (Jin et al., 2008; Knapen et al., 2008; Gomez et al., 2009).
3.1.2.2. Terraces versus slope land. On average there were good control effects on soil erosion of planting peanuts on stone dike terraces compared with slope land farming (Plots 1 and 7, respectively). The mean annual sediment reduction rate of terracing was 23.2%. On the contrary, there were little effects on soil erosion while planting orange trees on soil dike terraces (Plot 2) compared with slope land (Plot 9). The soil erosion rates of soil dike terraces were even greater than that of slope land. The soil erosion rates for planting peanuts on stone dike terraces and slope land (Plots 1 and 7, respectively) are compared in Fig. 4(a) on a rainfall event basis. Twelve of the 21 rainfall events showed that
Fig. 4. Soil erosion rate for stone dike terraces and slope land.
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Fig. 5. Observed and simulated stream flow for the calibration and validation periods.
planting peanuts on stone dike terraces was beneficial in controlling soil erosion. For the other nine events, there were opposite results. The result of soil erosion rates for planting orange trees on soil dike terraces and slope land (Plots 2 and 9, respectively) is shown in Fig. 4(b). Only five of 21 rainfall events showed that planting orange trees on soil dike terraces was beneficial in controlling soil erosion in the plots. For the other 16 events, there were opposite results. The stone dike terraces were better than soil dike terraces at controlling soil erosion in this study. There are two possible reasons. One is that the height of soil dikes was insufficient due to the relatively short length of the plots. Thus, compared with slope land, the soil dikes increased the area of bare land and could not effectively slow the surface flow. Therefore, more soil was eroded. The second reason is that peanuts were better at covering the soil surface than orange trees. Similar results were found in Sichuan Basin, China, where there was a better soil conservation effect from planting crops than trees on terraces (Zhang et al., 2008). 3.1.2.3. Hedgerows versus terraces. The above comparisons show that hedgerows had the best control effect on soil erosion, followed by stone dike terraces, and the soil dike terraces had the least effect. Table 4 Statistical analysis of observed and simulated stream flows. Periods
Calibration Validation
Mean stream flow (m3/s) Observed
Simulated
0.0435 0.0341
0.0475 0.0379
Fig. 6. Observed and simulated sediment yield for the calibration and validation periods.
In some studies, however, terraces are effective in soil conservation (Gachene et al., 1997; Wakindiki and Ben-Hur, 2002; Lue et al., 2009). Terraces can reduce the steepness and length of slopes, deactivating the natural slope–length function of the erosion process. Therefore, they increase the infiltration rate associated with a decrease in the velocity, quantity and energy of overland flow, thus reducing soil erosion. However, soil conservation is only one aspect of planting, and socioeconomic factors and the design of the conservation measures also need to be considered (Abu Hammad and Borresen, 2006; Cao et al., 2007) . The soil erosion rates estimated from plot experiments were used to assess the effects of conservation measures and to calibrate and validate the WEPP model for the study area. However, there are many methodological problems concerning to plot experiments, including the boundary effects, the appropriate number of replicated plots, and the uncertainty of data in the spatial or temporal context (Wendt et al., 1986; Nearing et al., 1999; Ollesch and Vacca, 2002; Parsons et al., 2006; Wainwright et al., 2008). Due to financial and resource constraints, the plots were not replicated in this study area. To
Table 5 Statistical analysis of observed and simulated sediment yield.
Re
R2
ENS
Periods
9.2% 11.1%
0.937 0.976
0.864 0.835
Calibration Validation
Mean sediment yield (t) Observed
Simulated
35.95 36.75
38.10 35.31
Re
R2
ENS
6.0% -3.9%
0.945 0.982
0.847 0.828
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3.2. WEPP modeling
overall simulations of monthly mean sediment were satisfactory and so used for further analysis. Pandey et al. (2008) also obtained satisfactory simulations of runoff and soil loss in a small Indian watershed using WEPP, but Raclot and Albergel (2006) got poor erosion simulation results for a Mediterranean watershed due to the seasonal effects. Therefore, the suitability of WEPP should be examined when using it in new regions.
3.2.1. The monthly erosion simulation of watershed The simulated monthly mean stream flow values of the WEPP model for the calibration and validation periods were compared with observed values (Fig. 5). The simulated peak values matched consistently well with the measured peak values for stream flow in all years (Fig. 5). Reasonably high ENS values for the calibration and validation periods (0.864 and 0.835, respectively) showed satisfactory performance of the model (Table 4). The Re values between observed and simulated monthly mean values were 9.2 and 11.1% for the whole calibration and validation periods, respectively. These results along with other criteria indicate satisfactory overall simulation of monthly mean stream flow by WEPP. The simulated monthly mean sediment yield values of the WEPP model for the calibration and validation periods were compared with observed values (Fig. 6). The simulated peak values matched consistently well with the measured peak values for sediment in all years (Fig. 6). Reasonably high ENS values for the calibration and validation periods (0.847 and 0.828, respectively) along with other criteria showed that the model performed satisfactorily (Table 5). The
3.2.2. The annual erosion simulation of plots The observed and simulated annual runoff and soil erosion rates were compared (Fig. 7). The annual runoff and soil erosion rates were simulated reasonably well. The mean |Re| = 11.1 and 18.5% for runoff and soil erosion, respectively; and respective means of ENS = 0.787 and 0.929. Vegetation is important in reducing runoff and soil loss. It can add litter fall to the ground surface, thereby increasing soil organic matter and improving soil structure, which can increase soil infiltration capacity and decrease erodibility. The effectiveness of plants in improving soil quality and controlling runoff and soil loss depends on variations in plant morphology and architecture (Bochet et al., 1998; Gyssels and Poesen, 2003; Casermeiro et al., 2004; Xu et al., 2009). However, in this study, no conventional plant effectively decreased soil loss compared with sloping wild grassland. Among the three plants, peanuts had the most severe soil erosion; for three of the four years, the soil erosion rates were greater than the sloping wild grassland. The WEPP model reflected the real situation very well
avoid or minimize problems associated with non-replication, the plots were constructed and managed identically including layout, dimensions, material, location and personnel. Therefore, the effects of different conservation techniques were comparable in this study.
Fig. 7. Annual runoff and soil erosion rate simulations of traditional slope land farming.
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ENS = 0.960 and 0.964. Similar to the above annual situation, no conventional plant effectively decreased soil loss compared with sloping wild grassland, and the WEPP model simulated these successfully. Bhuyan et al. (2002) compared the model performances of WEPP, the Erosion Productivity Impact Calculator (EPIC), the Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS), and their results indicated that WEPP predictions were better than those by the other two models in most of the cases. De la Rosa et al. (2005) used the SIDASS model (an adaptation of the WEPP model) to model the runoff coefficient and the inter-rill soil erosion, and their results showed that the model replicated the natural runoff and soil erosion responses very well.
3.2.5. Soil erosion distribution over the watershed The above simulation showed that the WEPP model adequately simulated soil erosion of the Zhangjiachong Watershed. Using the same parameter set, the annual mean soil erosion rate distribution over the watershed was modeled (Fig. 10), which ranged from 20 to 380 t km− 2. The rate was similar with that of a nearby watershed in the same county (220–940 t km− 2) (Ng et al., 2008), but much lower than those of the Loess Plateau in the northwest China (1700–6300 t km− 2) (Li et al., 2003) and of Sichuan Province in the upper Yangtze River Basin (2415 t km− 2) (He et al., 2007). The most seriously eroded area was the region in the northwest within this watershed. Field investigation showed that the topography of this region varied greatly. There were many agricultural activities in this region, and most crops were planted on soil dike terraces, which had the poorest soil conservation effect in the former analysis. All these reasons made the region the most seriously eroded. The least eroded area of the watershed was the central region, where there were many agricultural activities, such as paddy rice. However, this region is almost the lowest part of the watershed and has low-gradient slopes, and soil erosion was not intense. The soil erosion rate simulation by WEPP was in accordance with the real situation. Fig. 8. Runoff and soil erosion simulations of the five rainfall events on the watershed.
3.3. Addressing future research and implementation efforts (Fig. 7), with the trends of the four plots very similar for observed and simulated results. 3.2.3. The rainfall events erosion simulation of watershed The observed and simulated watershed runoff and sediment yield for the five rainfall events were compared (Fig. 8). Comparing the observed and simulated values showed that the model simulated well with high R2 and ENS. Ramsankaran et al. (2009) also obtained satisfactory rainfall events simulation results of soil erosion at the watershed level using the WEPP model. The rainfall characteristics determine erosivity, an important factor in the cause–effect relationship in soil erosion. This relationship has been studied many times (Hastings et al., 2005; Marques et al., 2007; Dunkerley, 2008; Machado et al., 2008). The contribution of less intense but longer events was also significant. Abu Hammad et al. (2006) showed that runoff and soil erosion were both likely in rain events in which Rt30 (30 min mean rainfall rate) N 4 mm h− 1, a quite low rain rate threshold. In the present study, there was obvious soil erosion only in four rainfall events. Though event No. 5 had similar characteristics to No. 2, there was no soil loss monitored at the watershed level. WEPP successfully simulated these processes at the rainfall event step (Fig. 8). 3.2.4. The rainfall events erosion simulation of plots WEPP simulations of runoff and soil erosion were performed for Plots 7–10 for the five rainfall events (Fig. 9). The mean |Re| = 18.7 and 23.7% for runoff and soil erosion, respectively; and respective mean
Uncertainty is inevitable in any system and is likely to cause confusion in decision-making. Like other models, the WEPP simulations also have uncertainties in terms of input data, parameters and model structure. The input data may cause uncertainty because the weather data, the observed runoff and sediment yield data, land use information map, soil type map, and DEM cannot be absolutely accurate. The uncertainty attributed to the parameters can be explained by the fact that the calibrated parameter values may not adequately capture the temporal and spatial variability of the watershed. As for the model structure, it may cause uncertainty because there are a set of assumptions and simplifications in developing the equations and algorithms for the model. All these uncertainties should be assessed when using the modeling results to make better decisions in future. Most runoff and soil erosion occur in April–September and this period is critical for flood control in the Three Gorges Reservoir Area. According to the operation plan of the Three Gorges Reservoir, the water surface elevation is kept the lowest during this period (around 150 m for April–May and 145 m for June–September). The vast soil loss may threaten the safety of the Three Gorges Dam. Meanwhile, the eroded soil carries large amounts of nutrients and pesticides, which may pose a danger to the aquatic ecology. However, the conservation effects of the measures mentioned above are limited. Therefore, additional measures, such as filter strips, wetlands, grassed waterway, etc., are also needed to prevent the soil and contaminants into the reservoir during this period. Consequently, the research on their combined effects is necessary.
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Fig. 9. Rainfall events runoff and soil erosion simulations of four plots.
4. Conclusions Ten experimental plots were constructed to evaluate the effect of soil conservation in the Zhangjiachong Watershed. The soil conservation measures were analyzed by field investigation data. Hedgerows had the best control effect on soil erosion, followed by the stone dike terraces, and the soil dike terraces had the least. Soil conservation measures must be selected according to several factors, such as soil types, topographical conditions, hydrological processes, land uses and land management practices.
The application of WEPP for modeling runoff and soil erosion of the watershed and plots showed that it could be used adequately in this area. The annual mean soil erosion rate distribution over the watershed was modeled and indicated that the most seriously eroded area was the northwest. Therefore, control measures should be taken for this region. Plot experiments can provide the actual soil erosion situation, but are expensive. WEPP modeling performed well in this area, and combined with plot experiments can help with decision-making in selecting soil conservation practices.
Fig. 10. The simulated soil erosion distribution over the Zhangjiachong Watershed.
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Acknowledgements The research was supported by the National Natural Science Foundation of China (No. 40771193) and the Nonprofit Environment Protection Specific Project (No. 200709024). We are grateful to the Zigui Soil and Water Conservation Experiment Station personnel for the help with field observations. References Abu Hammad, A., Borresen, T., 2006. Socioeconomic factors affecting farmers' perceptions of land degradation and stonewall terraces in Central Palestine. Environmental Management 37 (3), 380–394. Abu Hammad, A.H., Borresen, T., Haugen, L.E., 2006. Effects of rain characteristics and terracing on runoff and erosion under the Mediterranean. Soil & Tillage Research 87 (1), 39–47. Amore, E., Modica, C., Nearing, M.A., Santoro, V.C., 2004. Scale effect in USLE and WEPP application for soil erosion computation from three Sicilian basins. Journal of Hydrology 293 (1–4), 100–114. Aynsau, A., de Graaff, J., 2007. Determinants of adoption and continued use of stone terraces for soil and water conservation in an Ethiopian highland watershed. Ecological Economics 61 (2–3), 294–302. Baigorria, G.A., Romero, C.C., 2007. Assessment of erosion hotspots in a watershed: integrating the WEPP model and GIS in a case study in the Peruvian Andes. Environmental Modelling & Software 22 (8), 1175–1183. Bhuyan, S.J., Kalita, P.K., Janssen, K.A., Barnes, P.L., 2002. Soil loss predictions with three erosion simulation models. Environmental Modelling & Software 17 (2), 135–144. Bochet, E., Rubio, J.L., Poesen, J., 1998. Relative efficiency of three representative matorral species in reducing water erosion at the microscale in a semi-arid climate (Valencia, Spain). Geomorphology 23 (2–4), 139–150. Cao, S.X., Chen, L., Feng, Q., Liu, Z.D., 2007. Soft-riser bench terrace design for the hilly loess region of Shaanxi Province, China. Landscape and Urban Planning 80 (1–2), 184–191. Casermeiro, M.A., et al., 2004. Influence of scrubs on runoff and sediment loss in soils of Mediterranean climate. Catena 57 (1), 91–107. Chu, S.T., 1978. Infiltration during an unsteady rain. Water Resources Research 14 (3), 461–466. CQSBJC, 2006. Chongqing soil erosion statistics for 2005. Chongqing Soil and Water Conservation Ecological Environment Monitoring Station. http://www.cqsbjc.com. De la Rosa, D., et al., 2005. SIDASS project. Part 2. Soil erosion as a function of soil type and agricultural management in a Sevilla olive area, southern Spain. Soil & Tillage Research 82 (1), 19–28. Dunkerley, D., 2008. Rain event properties in nature and in rainfall simulation experiments: a comparative review with recommendations for increasingly systematic study and reporting. Hydrological Processes 22 (22), 4415–4435. Flanagan, D.C., Nearing, M.A., 1995. USDA-water erosion prediction project: hillslope profile and watershed model documentation. NSERL Report, No. 10. USDA—ARS National Soil Erosion Research Laboratory, West Lafayette. Flanagan, D.C., Nearing, M.A., 2000. Sediment particle sorting on hillslope profiles in the WEPP model. Transactions of the ASAE 43 (3), 573–583. Foster, G.R., Lane, L.J., Schertz, D.L., Nordin, J.O., Wingate, G.D., 1987. User requirements USDA-water erosion prediction project (WEPP). NSERL Report, 1. USDA-ARS National Soil Erosion Research Laboratory, West Lafayette. Gachene, C.K.K., Jarvis, N.J., Linner, H., Mbuvi, J.P., 1997. Soil erosion effects on soil properties in a highland area of Central Kenya. Soil Science Society of America Journal 61 (2), 559–564. Gomez, J.A., Sobrinho, T.A., Giraldez, J.V., Fereres, E., 2009. Soil management effects on runoff, erosion and soil properties in an olive grove of Southern Spain. Soil & Tillage Research 102 (1), 5–13. Gyssels, G., Poesen, J., 2003. The importance of plant root characteristics in controlling concentrated flow erosion rates. Earth Surface Processes and Landforms 28 (4), 371–384. Hastings, B.K., Breshears, D.D., Smith, F.M., 2005. Spatial variability in rainfall erosivity versus rainfall depth: implications for sediment yield. Vadose Zone Journal 4 (3), 500–504. He, X., Xu, Y., Zhang, X., 2007. Traditional farming system for soil conservation on slope farmland in southwestern China. Soil and Tillage Research 94 (1), 193–200. Jin, K., et al., 2008. Soil management effects on runoff and soil loss from field rainfall simulation. Catena 75 (2), 191–199. Knapen, A., Poesen, J., Govers, G., De Baets, S., 2008. The effect of conservation tillage on runoff erosivity and soil erodibility during concentrated flow. Hydrological Processes 22 (10), 1497–1508.
Legates, D.R., McCabe Jr, G.J., 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research 35 (1), 233–241. Li, Y., Poesen, J., Yang, J.C., Fu, B., Zhang, J.H., 2003. Evaluating gully erosion using 137Cs and 210Pb/137Cs ratio in a reservoir catchment. Soil and Tillage Research 69 (1–2), 107–115. Lue, H., Zhu, Y., Skaggs, T.H., Yu, Z., 2009. Comparison of measured and simulated water storage in dryland terraces of the Loess Plateau, China. Agricultural Water Management 96 (2)10.1016/j.agwat.2008.08.010. Machado, R.L., de Carvalho, D.F., Costa, J.R., de Oliveira, D.H., Pinto, M.F., 2008. Analysis of rainfall erosivity associated to pluvial precipitation patterns in the Ribeirao Das Lajes Region, Rio De Janeiro State, Brazil. Revista Brasileira De Ciencia Do Solo 32 (5), 2113–2123. Marques, M.J., Bienes, R., Jimenez, L., Perez-Rodriguez, R., 2007. Effect of vegetal cover on runoff and soil erosion under light intensity events. Rainfall simulation over USLE plots. Science of the Total Environment 378 (1–2), 161–165. Mein, R.G., Larson, C.L., 1973. Modeling infiltration during a steady rain. Water Resources Research 9 (2), 384–394. Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — a discussion of principles. Journal of Hydrology 10 (3), 282–290. Nearing, M.A., Foster, G.R., Lane, L.J., Finkner, S.C., 1989. A process-based soil erosion model for USDA-water erosion prediction project technology. Transactions of the ASAE 32 (5), 1587–1593. Nearing, M.A., Govers, G., Norton, L.D., 1999. Variability in soil erosion data from replicated plots. Soil Science Society of America Journal 63 (6), 1829. Ng, S.L., Cai, Q.G., Ding, S.W., Chau, K.C., Qin, J., 2008. Effects of contour hedgerows on water and soil conservation, crop productivity and nutrient budget for slope farmland in the Three Gorges Region (TGR) of China. Agroforestry Systems 74 (3), 279–291. Ollesch, G., Vacca, A., 2002. Influence of time on measurement results of erosion plot studies. Soil & Tillage Research 67 (1), 23–39. Pandey, A., Chowdary, V.M., Mal, B.C., Billib, M., 2008. Runoff and sediment yield modeling from a small agricultural watershed in India using the WEPP model. Journal of Hydrology 348 (3–4), 305–319. Parsons, A.J., Brazier, R.E., Wainwright, J., Powell, D.M., 2006. Scale relationships in hillslope runoff and erosion. Earth Surface Processes and Landforms 31 (11), 1384–1393. Poudel, D.D., Midmore, D.J., West, L.T., 1999. Erosion and productivity of vegetable systems on sloping volcanic ash-derived Philippine soils. Soil Science Society of America Journal 63 (5), 1366–1376. Raclot, D., Albergel, J., 2006. Runoff and water erosion modelling using WEPP on a Mediterranean cultivated catchment. Physics and Chemistry of the Earth 31 (17), 1038–1047. Ramsankaran, R., Kothyari, U.C., Rawat, J.S., 2009. Simulation of surface runoff and sediment yield using the water erosion prediction project (WEPP) model: a study in Kaneli watershed, Himalaya, India. Hydrological Sciences Journal–Journal Des Sciences Hydrologiques 54 (3), 513–525. Shen, Z.Y., Gong, Y.W., Li, Y.H., Hong, Q., Xu, L., Liu, R.M., 2009. A comparison of WEPP and SWAT for modeling soil erosion of the Zhangjiachong Watershed in the Three Gorges Reservoir Area. Agricultural Water Management 96 (10), 1435–1442. Stone, J.J., Lane, L.J., Shirley, E.D., Hernandez, M., 1995. Chapter 4. Hillslope surface hydrology, USDA-water erosion prediction project: hillslope profile and watershed model documentation. NSERL Report, 10. USDA-ARS National Soil Erosion Research Laboratory, West Lafayette, IN, pp. 4.1–4.20. Sun, H., Tang, Y., Xie, J.S., 2008. Contour hedgerow intercropping in the mountains of China: a review. Agroforestry Systems 73 (1), 65–76. Wainwright, J., et al., 2008. A transport-distance approach to scaling erosion rates: 3. Evaluating scaling characteristics of Mahleran. Earth Surface Processes and Landforms 33 (7), 1113–1128. Wakindiki, I.I.C., Ben-Hur, M., 2002. Indigenous soil and water conservation techniques: effects on runoff, erosion, and crop yields under semi-arid conditions. Australian Journal of Soil Research 40 (3)10.1071/SR01037|UNSP 0004-9573/02/030367. Wendt, R.C., Alberts, E.E., Hjelmfelt Jr, A.T., 1986. Variability of runoff and soil loss from fallow experimental plots. Soil Science Society of America Journal 50 (3), 730. Xu, X.L., Ma, K.M., Fu, B.J., Liu, W., Song, C.J., 2009. Soil and water erosion under different plant species in a semiarid river valley, SW China: the effects of plant morphology. Ecological Research 24 (1), 37–46. Yen, B.C., 1993. Criteria for evaluation of watershed models. Journal of Irrigation and Drainage Engineering-ASCE 119 (3), 429–442. Zhang, J.H., Su, Z.A., Liu, G.C., 2008. Effects of terracing and agroforestry on soil and water loss in hilly areas of the Sichuan Basin, China. Journal of Mountain Science 5 (3), 241–248.
Contents lists available at ScienceDirect
Catena j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c a t e n a
Analysis and modeling of soil conservation measures in the Three Gorges Reservoir Area in China Zhenyao Shen a,⁎, Yongwei Gong a, Yanhong Li b, Ruimin Liu a a b
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China Beijing Guohuan-Tsinghua Environmental Engineering Design & Research Institute, Beijing, 100084, China
a r t i c l e
i n f o
Article history: Received 26 July 2009 Received in revised form 25 January 2010 Accepted 28 January 2010 Keywords: Soil erosion Experimental plot Hedgerows Terracing WEPP
a b s t r a c t Experimental plots were constructed in the Zhangjiachong Watershed of the Three Gorges Reservoir Area to evaluate soil erosion of traditional slope land farming and effects of soil conservation measures. Surface runoff and sediment from the watershed and each plot were collected and measured during 2004–2007. Field investigations indicated that hedgerows were the best for soil erosion control, followed by stone dike terraces and soil dike terraces. The Water Erosion Prediction Project (WEPP) model was used to simulate erosion of annual and rainfall events both at the watershed and plot levels. The low deviation, high coefficient of determination and model efficiency values for the simulations indicated that the WEPP model was a suitable model. The soil erosion rate distribution was modeled to determine where serious erosion would occur during rainfall events in the Zhangjiachong Watershed and so control measures can be taken. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Soil erosion is a major agricultural and environmental problem in the Three Gorges Reservoir Area, China and is one main cause of the loss of nutrient-rich topsoil and soil fertility decline. As an example, the soil erosion area of Chongqing City, which is the main part of the Three Gorges Reservoir Area, was 40,006 km2 in 2005, accounting for 48.6% of the total city area; with the total amount of soil loss 14.6 billon tonnes in 2005 (CQSBJC, 2006). Soil erosion and sedimentation in the Yangtze River is a potential threat to safety of the Three Gorges Dam. Additionally, most of the population living in the area depends on agriculture for their livelihood, hence it is imperative to preserve soil to sustain crop yields. Conventional slope land farming practices are far from sustainable and environmentally compatible from a soil and water conservation perspective. Therefore, alternative practices are needed for the Three Gorges Reservoir Area. The use of terraces and hedgerows are two main soil conservation measures in this area. Terracing of steep farmland is a way to conserve soil and water that divides a long slope into short slopes, thus reducing sheet and rill erosion (Abu Hammad et al., 2006; Aynsau and de Graaff, 2007; Zhang et al., 2008). Meanwhile, terracing that changes landform and reduces the slope gradient can reduce runoff amounts and rates. Terracing has
⁎ Corresponding author. E-mail address: [email protected] (Z. Shen). 0341-8162/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.catena.2010.01.009
been used in China for centuries, since the West-Han Dynasty (207 BC–25 AD) in foothills and valleys and since the South-Sung Dynasty (1127–1279 AD) in mountainous regions. Contour hedgerows were introduced to the Three Gorges Reservoir Area by the local government as a soil conservation measure in the late 1980s. The basic design is rows of trees or shrubs planted along contours at intervals of 3–6 m in a sloping field, with crop production between hedgerows. They can reduce runoff generation and intercept eroded sediment from the upper slope, thus conserving soil fertility (Poudel et al., 1999; Ng et al., 2008; Sun et al., 2008). The objective of this study was to evaluate the soil erosion of traditional slope land farming and the conservation measures. In the Three Gorges Reservoir Area, a number of experimental plot sets have been constructed to collect field data. However, these plots are expensive to construct and maintain. Computer simulation models are increasingly popular for simulating soil loss and to quantify the processes of detachment, transport and deposition of eroded soil. It would be useful to identify a soil erosion model that would be applicable for this area, which can be used in evaluation of the effects of different practices on soil erosion and to select the best practices. The Zhangjiachong Watershed was selected as the study area where experimental plots were constructed. The Water Erosion Prediction Project (WEPP) model is a physicallybased model capable of modeling runoff and soil loss for a wide range of environmental conditions (Foster et al., 1987; Nearing et al., 1989; Flanagan and Nearing, 1995). Its main advantage is that it can be extrapolated to conditions for which field testing is not feasible. It is intended for use on small agricultural watersheds in which the sediment yield at the outlet is significantly influenced by hill slope and channel processes, and has a recommended maximum field size of 2.6 km2
Z. Shen et al. / Catena 81 (2010) 104–112
(Foster et al., 1987). Despite this limitation, WEPP capabilities have been successfully applied to watersheds with bigger sizes (N2.6 km2) (Amore et al., 2004; Baigorria and Romero, 2007; Pandey et al., 2008). Shen et al. (2009) compared the modeling performances of WEPP and the Soil and Water Assessment Tool (SWAT) for the Zhangjiachong Watershed, and the results indicated that WEPP provided better predictions than SWAT for both runoff and sediment yields. Therefore, the WEPP model was selected to evaluate the effects of soil erosion and soil conservation measures in this study. 2. Method and materials
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Table 1 The experimental plots' details. Plot Number
Conservation type
1 2 3 4 5 6 7 8 9 10
Stone dike terraces — peanut Soil dike terraces — orange trees Soil dike terraces with hedgerows — orange trees Soil dike terraces with hedgerows — peanut Slope land with hedgerows — tea trees Slope land with hedgerows — orange trees Slope land — peanut Slope land — tea trees Slope land — orange trees Sloping wild grassland
2.1. Study area The Zhangjiachong Watershed covers an area of 1.62 km2 in the southwest of Zigui County in the Three Gorges Reservoir Area (Fig. 1). This region has an annual mean temperature of 18 °C, an annual mean precipitation of 1439 mm, and an elevation of 148–530 m. The mean slope of the watershed area is 22.0% and maximum slope of some hilly parts is 71.6%. The main land uses are soil dike terraces — 51.9%, stone dike terraces — 22.0%, paddy field — 14.1%, and mixed forest — 10.0%. Ten experimental plots were constructed in the Zhangjiachong Watershed, each of size 2 m × 10 m, with slope of 46.6%. The borders of the plots were built with concrete. Below each plot was a concrete tank to collect surface runoff and sediment from the plot during rainfall events. Of the ten plots, one with sloping wild grassland was set as the reference. Three types of plants were planted in the plots, including peanut, orange trees, and tea trees (Table 1). The orange and tea trees were one year old when planted. The two conservation measures were terraces and hedgerows. The hedgerow used in this study was a shrub, Amorpha fruticosa. 2.2. Model description The WEPP watershed model is a continuous simulation computer program that predicts sediment yield and deposition from overland flow on hill slopes, sediment yield and deposition from concentrated flow in small channels, and sediment deposition in impoundments. It computes spatial and temporal distributions of sediment yield and deposition, and provides explicit estimates of when and where in a watershed or on a hill slope that erosion occurs so that conservation measures can be selected to effectively control sediment yield (Flanagan and Nearing, 1995). The Green and Ampt equation modified by Mein and Larson (1973) and Chu (1978) was implemented in the model to estimate
infiltration. Before the model computes runoff it adjusts the amount of excess rainfall by depression storage. The surface runoff is routed using two procedures: a semi-analytical solution of the kinematic wave equation is used for a single event, and an approximate method is used for the continuous mode (Stone et al., 1995). The model uses a steady-state, sediment continuity equation to estimate net detachment and deposition (Flanagan and Nearing, 2000). dG = dx = Df + Di
ð1Þ
where G is sediment load (kg s− 1 m− 1), x represents distance downslope (m), Df is rill erosion rate (kg s− 1 m− 2) and Di is inter-rill sediment delivery to the rill (kg s− 1 m− 2). Di is considered independent of x, and always N0. Df is N0 for detachment and b0 for deposition. For model calculations, both Df and Di are computed on a per rill area basis, thus G is solved on a per unit rill width basis. After computations, sediment yield is expressed as sediment yield per unit land area. The simulated values were evaluated by visual inspection of the figures of observed and simulated values, and also by certain statistical criteria for goodness-of-fit, including the deviation (Re), the coefficient of determination (R2) and model efficiency values (ENS). Re of runoff and sediment values is given by the following equation (Yen, 1993): Re = ðPi −Oi Þ = Oi × 100%
ð2Þ
where Oi and Pi are the ith pair of the observed and simulated values. The smaller the |Re| the better the model result.
Fig. 1. The location and land use of the Zhangjiachong Watershed.
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Table 2 Details of the five selected rainfall events. No.
Date
Precipitation (mm)
Duration time (h)
Mean intensity (mm h− 1)
Max intensity (mm h− 1)
1 2 3 4 5
2005-07-09 2005-08-28 2006-06-22 2006-06-29 2006-07-17
158.9 23.9 49.9 36.2 26.1
33 30 36 6.5 22
4.82 0.80 1.39 5.57 1.19
70.5 3.8 120 120 7.5
R2 is calculated for each relationship, using the following formula (Legates and McCabe, 1999):
2
"
R =
n
∑
i=1
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi!#2 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n – – –2 –2 ∑ Pi − P Oi − O Pi − P = ð3Þ ∑ Oi − O i=1
i=1
where Ō is the mean of observed values, P̄ is the mean of simulated values, and n is the total number of paired values. ENS is expressed as (Nash and Sutcliffe, 1970): n
2
n
ENS = 1− ∑ ðOi −Pi Þ = ∑ i=1
i=1
– 2 Oi − O
ð4Þ
The range of ENS is from −∞ to 1, with 1 indicating a perfect fit. 2.3. Data The construction of the experimental plots was completed in April 2004, hence there were only observed data since then. Watershed surface runoff and sediment were measured for years 2004–2007. For every rainfall event the stream flow was measured. A water level gauge was installed at the watershed outlet to provide water levels and the flow velocity measured at the same site. The spillway was rectangular-shaped so that discharge could be well estimated. Since the sediment concentration was relatively low for non-rainfall days, the sediment flow samples were collected only for rainy days. The sediment concentration was obtained by filtration and evaporation methods. Water and eroded sediment from the plots were collected in the concrete tank after rainfall events. They were mixed uniformly to estimate the mean sediment concentration. The surface runoff was calculated according to the water level of the tank. The sediment yield was calculated by multiplying water volume in the tank and the mean sediment concentration. The model inputs include daily precipitation, temperature, solar radiation, dew points, wind speed, management, soil, slope, and land use. Rainfall data were collected at the Zigui Soil and Water Conservation Experiment Station. The other required climate data were collected at the Zigui County Weather Station. Management and soil input files were built from field investigations. The digital elevation model (DEM) and slope data were developed from the watershed contour map (at 3-m intervals). Land use information was obtained from interpretation of IRS P6 LISS-4 (5-m resolution) satellite imagery, based on field investigation.
Fig. 2. The annual soil erosion rates for the ten plots.
2.4. Modeling scheme The WEPP model was used to simulate the monthly stream flow and sediment yield of the watershed, the annual runoff and soil erosion of the plots, and the rainfall event runoff and soil erosion of the watershed and the plots for 2004–2007. Sensitivity analysis was performed for 2004 and 2005 by changing the value of a parameter within an acceptable range and observing the stream flow and sediment yield output. Effective hydraulic conductivity was found to be the most sensitive parameter for runoff, and baseline inter-rill erodibility, effective hydraulic conductivity, rill erodibility and critical hydraulic shear stress values were most sensitive for soil erosion. The parameter that produced the maximum sensitivity was adjusted first, followed by the other parameters. Once the model was calibrated, WEPP was run with the calibrated parameters, and the stream flow and sediment yield values simulated for the validation period. All the calibrated parameter values were within the physically meaningful value ranges. Five rainfall events (Table 2) were selected, with a range of mean rainfall intensity of 0.80–5.57 mm h− 1, and the runoff and soil erosion simulated. The five events were selected because there were relatively complete records of their runoff and soil erosion, and all could have resulted in soil erosion. The model parameters set for the previous simulation were used. To further investigate the effects of traditional slope land farming on soil erosion, Plots 7–10 (slope land with peanut, tea trees, orange trees and wild grassland, respectively) were selected and their runoff and soil erosion rates modeled by WEPP, using Plot 10 as the reference. 3. Results and discussion 3.1. Effects of conservation measures The soil erosion rates were analyzed at the annual and rainfall event scale. At the rainfall event level, 21 rainfall events were uniformly selected from four years when sediment yield was measured for the plots (Table 3).
Table 3 Dates and precipitations for the 21 rainfall events. Date Precipitation (mm) Date Precipitation (mm) Date Precipitation (mm)
2004-6-7 35.4 2005-5-24 65.3 2006-5-2 123.3
2004-6-21 38.6 2005-6-11 21.3 2006-6-22 49.9
2004-7-2 18 2005-7-10 158.7 2006-6-29 36.2
2004-7-11 125.3 2005-8-1 84.1 2006-7-7 45.4
2004-7-79 59.2 2005-8-4 68.4 2006-7-29 43.3
2004-8-7 150.7 2005-8-22 154.2 2006-9-4 115.3
2004-8-23 55.4 2005-8-29 32.5 2007-3-3 43.5
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3.1.1. Annual soil erosion rates The soil erosion rates for the ten plots were calculated based on the four years' observations of rainfall events (Fig. 2). The soil erosion rate was calculated from January to December except during 2004 May– December only, since the plots were not constructed until May 2004. The precipitation for the first four months of every year was relatively small, and so the soil erosion rate for 2004 was close to the real value of the whole year. The soil erosion rates decreased over time for most plots, except for Plots 4, 5, 8 and 10. When comparing 2007 with 2004, the values declined greatly, with the reduction from 74.7% to 100%. The reason that soil erosion rates decreased was that a gradual process stabilized soil and plants in the plots. During the construction of the plots there was great disturbance of soil in this process. The orange and tea trees needed a long period to stabilize soil. Therefore, soil erosion in 2004 was relatively high. As time passed, the plants grew, and soil stability was gradually enhanced. 3.1.2. Comparison of effects for different conservation measures 3.1.2.1. Hedgerows versus slope land. On an annual basis, Plot 5 that was planted with tea trees and hedgerows had less sediment load than Plot 8 (no hedgerows) (Fig. 2). The mean annual reduction rate was about 39.6%. However, Plot 6 that was planted with orange trees and hedgerows had no obvious control effects when compared with Plot 9 (no hedgerows) (Fig. 2); the soil erosion was greater in Plot 6 than Plot 9 in 2005 and 2006. For the event comparison, the result of soil erosion rates for planting tea trees on slope land with and without hedgerows (Plots 5 and 8,
Fig. 3. Soil erosion rate for planting with and without hedgerows.
107
respectively) is shown in Fig. 3(a). Fifteen of the 21 rainfall events showed that planting tea trees on slope land with hedgerows was beneficial for controlling soil erosion. For other events (Nos. 1, 2, 4, 9, 11, and 19), there were opposite results. The reason may be that event Nos. 1, 2 and 4 were at the beginning of plot experiments, when hedgerows were small. Because there was bare land around hedgerows, they possibly had little effect at early stages. The result of soil erosion rates for planting orange trees on slope land with and without hedgerows (Plots 6 and 9, respectively) is shown in Fig. 3(b). Of the 21 rainfall events, 13 showed that planting orange trees was beneficial to controlling soil erosion, the other eight events had opposite results. The results indicate that hedgerows were beneficial in controlling soil erosion, similar to some previous studies (Jin et al., 2008; Knapen et al., 2008; Gomez et al., 2009).
3.1.2.2. Terraces versus slope land. On average there were good control effects on soil erosion of planting peanuts on stone dike terraces compared with slope land farming (Plots 1 and 7, respectively). The mean annual sediment reduction rate of terracing was 23.2%. On the contrary, there were little effects on soil erosion while planting orange trees on soil dike terraces (Plot 2) compared with slope land (Plot 9). The soil erosion rates of soil dike terraces were even greater than that of slope land. The soil erosion rates for planting peanuts on stone dike terraces and slope land (Plots 1 and 7, respectively) are compared in Fig. 4(a) on a rainfall event basis. Twelve of the 21 rainfall events showed that
Fig. 4. Soil erosion rate for stone dike terraces and slope land.
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Fig. 5. Observed and simulated stream flow for the calibration and validation periods.
planting peanuts on stone dike terraces was beneficial in controlling soil erosion. For the other nine events, there were opposite results. The result of soil erosion rates for planting orange trees on soil dike terraces and slope land (Plots 2 and 9, respectively) is shown in Fig. 4(b). Only five of 21 rainfall events showed that planting orange trees on soil dike terraces was beneficial in controlling soil erosion in the plots. For the other 16 events, there were opposite results. The stone dike terraces were better than soil dike terraces at controlling soil erosion in this study. There are two possible reasons. One is that the height of soil dikes was insufficient due to the relatively short length of the plots. Thus, compared with slope land, the soil dikes increased the area of bare land and could not effectively slow the surface flow. Therefore, more soil was eroded. The second reason is that peanuts were better at covering the soil surface than orange trees. Similar results were found in Sichuan Basin, China, where there was a better soil conservation effect from planting crops than trees on terraces (Zhang et al., 2008). 3.1.2.3. Hedgerows versus terraces. The above comparisons show that hedgerows had the best control effect on soil erosion, followed by stone dike terraces, and the soil dike terraces had the least effect. Table 4 Statistical analysis of observed and simulated stream flows. Periods
Calibration Validation
Mean stream flow (m3/s) Observed
Simulated
0.0435 0.0341
0.0475 0.0379
Fig. 6. Observed and simulated sediment yield for the calibration and validation periods.
In some studies, however, terraces are effective in soil conservation (Gachene et al., 1997; Wakindiki and Ben-Hur, 2002; Lue et al., 2009). Terraces can reduce the steepness and length of slopes, deactivating the natural slope–length function of the erosion process. Therefore, they increase the infiltration rate associated with a decrease in the velocity, quantity and energy of overland flow, thus reducing soil erosion. However, soil conservation is only one aspect of planting, and socioeconomic factors and the design of the conservation measures also need to be considered (Abu Hammad and Borresen, 2006; Cao et al., 2007) . The soil erosion rates estimated from plot experiments were used to assess the effects of conservation measures and to calibrate and validate the WEPP model for the study area. However, there are many methodological problems concerning to plot experiments, including the boundary effects, the appropriate number of replicated plots, and the uncertainty of data in the spatial or temporal context (Wendt et al., 1986; Nearing et al., 1999; Ollesch and Vacca, 2002; Parsons et al., 2006; Wainwright et al., 2008). Due to financial and resource constraints, the plots were not replicated in this study area. To
Table 5 Statistical analysis of observed and simulated sediment yield.
Re
R2
ENS
Periods
9.2% 11.1%
0.937 0.976
0.864 0.835
Calibration Validation
Mean sediment yield (t) Observed
Simulated
35.95 36.75
38.10 35.31
Re
R2
ENS
6.0% -3.9%
0.945 0.982
0.847 0.828
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3.2. WEPP modeling
overall simulations of monthly mean sediment were satisfactory and so used for further analysis. Pandey et al. (2008) also obtained satisfactory simulations of runoff and soil loss in a small Indian watershed using WEPP, but Raclot and Albergel (2006) got poor erosion simulation results for a Mediterranean watershed due to the seasonal effects. Therefore, the suitability of WEPP should be examined when using it in new regions.
3.2.1. The monthly erosion simulation of watershed The simulated monthly mean stream flow values of the WEPP model for the calibration and validation periods were compared with observed values (Fig. 5). The simulated peak values matched consistently well with the measured peak values for stream flow in all years (Fig. 5). Reasonably high ENS values for the calibration and validation periods (0.864 and 0.835, respectively) showed satisfactory performance of the model (Table 4). The Re values between observed and simulated monthly mean values were 9.2 and 11.1% for the whole calibration and validation periods, respectively. These results along with other criteria indicate satisfactory overall simulation of monthly mean stream flow by WEPP. The simulated monthly mean sediment yield values of the WEPP model for the calibration and validation periods were compared with observed values (Fig. 6). The simulated peak values matched consistently well with the measured peak values for sediment in all years (Fig. 6). Reasonably high ENS values for the calibration and validation periods (0.847 and 0.828, respectively) along with other criteria showed that the model performed satisfactorily (Table 5). The
3.2.2. The annual erosion simulation of plots The observed and simulated annual runoff and soil erosion rates were compared (Fig. 7). The annual runoff and soil erosion rates were simulated reasonably well. The mean |Re| = 11.1 and 18.5% for runoff and soil erosion, respectively; and respective means of ENS = 0.787 and 0.929. Vegetation is important in reducing runoff and soil loss. It can add litter fall to the ground surface, thereby increasing soil organic matter and improving soil structure, which can increase soil infiltration capacity and decrease erodibility. The effectiveness of plants in improving soil quality and controlling runoff and soil loss depends on variations in plant morphology and architecture (Bochet et al., 1998; Gyssels and Poesen, 2003; Casermeiro et al., 2004; Xu et al., 2009). However, in this study, no conventional plant effectively decreased soil loss compared with sloping wild grassland. Among the three plants, peanuts had the most severe soil erosion; for three of the four years, the soil erosion rates were greater than the sloping wild grassland. The WEPP model reflected the real situation very well
avoid or minimize problems associated with non-replication, the plots were constructed and managed identically including layout, dimensions, material, location and personnel. Therefore, the effects of different conservation techniques were comparable in this study.
Fig. 7. Annual runoff and soil erosion rate simulations of traditional slope land farming.
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ENS = 0.960 and 0.964. Similar to the above annual situation, no conventional plant effectively decreased soil loss compared with sloping wild grassland, and the WEPP model simulated these successfully. Bhuyan et al. (2002) compared the model performances of WEPP, the Erosion Productivity Impact Calculator (EPIC), the Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS), and their results indicated that WEPP predictions were better than those by the other two models in most of the cases. De la Rosa et al. (2005) used the SIDASS model (an adaptation of the WEPP model) to model the runoff coefficient and the inter-rill soil erosion, and their results showed that the model replicated the natural runoff and soil erosion responses very well.
3.2.5. Soil erosion distribution over the watershed The above simulation showed that the WEPP model adequately simulated soil erosion of the Zhangjiachong Watershed. Using the same parameter set, the annual mean soil erosion rate distribution over the watershed was modeled (Fig. 10), which ranged from 20 to 380 t km− 2. The rate was similar with that of a nearby watershed in the same county (220–940 t km− 2) (Ng et al., 2008), but much lower than those of the Loess Plateau in the northwest China (1700–6300 t km− 2) (Li et al., 2003) and of Sichuan Province in the upper Yangtze River Basin (2415 t km− 2) (He et al., 2007). The most seriously eroded area was the region in the northwest within this watershed. Field investigation showed that the topography of this region varied greatly. There were many agricultural activities in this region, and most crops were planted on soil dike terraces, which had the poorest soil conservation effect in the former analysis. All these reasons made the region the most seriously eroded. The least eroded area of the watershed was the central region, where there were many agricultural activities, such as paddy rice. However, this region is almost the lowest part of the watershed and has low-gradient slopes, and soil erosion was not intense. The soil erosion rate simulation by WEPP was in accordance with the real situation. Fig. 8. Runoff and soil erosion simulations of the five rainfall events on the watershed.
3.3. Addressing future research and implementation efforts (Fig. 7), with the trends of the four plots very similar for observed and simulated results. 3.2.3. The rainfall events erosion simulation of watershed The observed and simulated watershed runoff and sediment yield for the five rainfall events were compared (Fig. 8). Comparing the observed and simulated values showed that the model simulated well with high R2 and ENS. Ramsankaran et al. (2009) also obtained satisfactory rainfall events simulation results of soil erosion at the watershed level using the WEPP model. The rainfall characteristics determine erosivity, an important factor in the cause–effect relationship in soil erosion. This relationship has been studied many times (Hastings et al., 2005; Marques et al., 2007; Dunkerley, 2008; Machado et al., 2008). The contribution of less intense but longer events was also significant. Abu Hammad et al. (2006) showed that runoff and soil erosion were both likely in rain events in which Rt30 (30 min mean rainfall rate) N 4 mm h− 1, a quite low rain rate threshold. In the present study, there was obvious soil erosion only in four rainfall events. Though event No. 5 had similar characteristics to No. 2, there was no soil loss monitored at the watershed level. WEPP successfully simulated these processes at the rainfall event step (Fig. 8). 3.2.4. The rainfall events erosion simulation of plots WEPP simulations of runoff and soil erosion were performed for Plots 7–10 for the five rainfall events (Fig. 9). The mean |Re| = 18.7 and 23.7% for runoff and soil erosion, respectively; and respective mean
Uncertainty is inevitable in any system and is likely to cause confusion in decision-making. Like other models, the WEPP simulations also have uncertainties in terms of input data, parameters and model structure. The input data may cause uncertainty because the weather data, the observed runoff and sediment yield data, land use information map, soil type map, and DEM cannot be absolutely accurate. The uncertainty attributed to the parameters can be explained by the fact that the calibrated parameter values may not adequately capture the temporal and spatial variability of the watershed. As for the model structure, it may cause uncertainty because there are a set of assumptions and simplifications in developing the equations and algorithms for the model. All these uncertainties should be assessed when using the modeling results to make better decisions in future. Most runoff and soil erosion occur in April–September and this period is critical for flood control in the Three Gorges Reservoir Area. According to the operation plan of the Three Gorges Reservoir, the water surface elevation is kept the lowest during this period (around 150 m for April–May and 145 m for June–September). The vast soil loss may threaten the safety of the Three Gorges Dam. Meanwhile, the eroded soil carries large amounts of nutrients and pesticides, which may pose a danger to the aquatic ecology. However, the conservation effects of the measures mentioned above are limited. Therefore, additional measures, such as filter strips, wetlands, grassed waterway, etc., are also needed to prevent the soil and contaminants into the reservoir during this period. Consequently, the research on their combined effects is necessary.
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Fig. 9. Rainfall events runoff and soil erosion simulations of four plots.
4. Conclusions Ten experimental plots were constructed to evaluate the effect of soil conservation in the Zhangjiachong Watershed. The soil conservation measures were analyzed by field investigation data. Hedgerows had the best control effect on soil erosion, followed by the stone dike terraces, and the soil dike terraces had the least. Soil conservation measures must be selected according to several factors, such as soil types, topographical conditions, hydrological processes, land uses and land management practices.
The application of WEPP for modeling runoff and soil erosion of the watershed and plots showed that it could be used adequately in this area. The annual mean soil erosion rate distribution over the watershed was modeled and indicated that the most seriously eroded area was the northwest. Therefore, control measures should be taken for this region. Plot experiments can provide the actual soil erosion situation, but are expensive. WEPP modeling performed well in this area, and combined with plot experiments can help with decision-making in selecting soil conservation practices.
Fig. 10. The simulated soil erosion distribution over the Zhangjiachong Watershed.
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