Climate change , vegetation restoration and engineering as a 1 : 2 : 1 explanation for reduction of suspended sediment in southwest China

Introduction Conclusions References


Introduction
Sediment transport in rivers can be a symptom of erosion problems, but it also increases with landslides, riverbank instability, and disturbances such as (road) construction and mining activities.Dam construction negatively impacts the level (Verbist et al., 2010).Walling and Fang (2003)  on annual sediment loads, 4.8 % (7 rivers) had an increased load, 49.3 % (70 rivers) were stable and 46.9 % (68 rivers) had a decreased load.Liu et al. (2008) similarly classified the 10 major rivers in China and found seven with decreasing sediment and stable runoff, one with decreasing sediment and runoff, and two with significant decreases in sediment and runoff.Dai et al. (2009) reported that the decadal suspended sediment flux decreased by 70.2 % from 1.81 Gt yr −1 for 1954-1963 to 0.54 Gt yr −1 for 1996-2005 in nine major rivers in China.Soil erosion is caused by the interaction between climate (especially rainfall intensity, amount and distribution), terrain properties and human activities (Dai et al., 2009), and results in a major loss of natural capital (Pimentel, 2006).Vegetation restoration (i.e.tree planting, grass establishment and ecological restoration measures) and engineering measures (i.e.terrace and silt check dams) are commonly employed for erosion control in China (Huang and Zhang, 2004).The relative contribution of these measures is a debated issue and may depend on local context.
Check dams were identified as the most effective short-term measure for reducing coarse sediment entering the Yellow River riverbank (Ran et al., 2008;Xiang-Zhou et al., 2004;Xu et al., 2013).Reservoirs intercept most of the suspended sediments and override any effect of erosion reduction (Rijsberman and Wolman, 1985).Over the past 20 yr, vegetation-based soil and water conservation has had a negligible effect on the Pearl River (Wu et al., 2012), contributing only 9.2 % to reduction of sediment load in the Miyun reservoir, Beijing (Tang et al., 2011); and less than 15 % reduction to the Three Gorges Reservoir (Ming et al., 2009) and Yangtze River (Dai et al., 2008).However, Wang et al. (2007b) found that soil conservation measures were responsible for 40 % to the total sediment load decrease in the Yellow River basin.The relationship between forest cover and soil erosion is complex (Ran et al., 2013) as the litter layer and understory vegetation, which exert primary control, varies with forest, vegetation type and management (Hairiah et al., 2006).Mixed forest may be the most effective for controlling erosion (Men, 2011).But none of these studies have considered the impacts of climatic variation and change, which likely interact with the roles of vegetation.Introduction

Conclusions References
Tables Figures

Back Close
Full According to soil conservation zone planning, the Kejie watershed is a key protection area in Yunnan province.In light of the alternative solutions for controlling sediment loading in streams, it is important to understand the role of the vegetation restoration and engineering measures previously undertaken for erosion control.In recent decades there has been little change in average annual runoff but a dramatic decrease in annual sediment yield.As there is currently no widely accepted method for attributing the decline of sediment yield to land use and land cover change, engineering efforts and climate change, we set out to explore a modeling approach in which land cover effects, climate and engineering impacts can be separated by recombination.
The objective of this article is to use the data available for the Kejie watershed to (1) detect the change trend of suspend sediment yield at the outlet of the watershed; (2) explore the potential driving factors that led to the decrease of suspended sediment load in the watershed; (3) quantify the contribution of vegetation restoration to the decrease in sediment load, using a distributed hydrological model (SWAT) based on five time periods land use maps; (4) quantify the relationship between forest cover and soil erosion modulus in the watershed; (5) tease out the climate effect by switching rainfall records and (6) identify the effects of engineering based on the difference between modeled (in the absence of engineering) and measured sediment loads.

Description of the watershed
The Kejie watershed in western Yunnan Province is an upstream watershed in the Salween Basin and has a total area of 1755 km 2 (Fig. 1).The Donghe River, a major tributary of the upper Salween River, is the main watercourse and runs for 95.2 km with an average slope of 11 0 (ranging from 1 to 88 0 ).The watershed provides environmental goods and services to Baoshan Prefecture in Yunnan and to Myanmar and Thailand downstream.The climate is sub-tropical in the valley and temperate in mountain areas.
The mean annual precipitation is 994.5 mm with a recorded maximum of 1368.Ma et al. (2009a).Administratively, the Kejie watershed covers most of Longyang County and small parts of Shidian and Changning County, all in the Baoshan Prefecture.Baoshan is considered to be a key watershed protection area (Fig. 1b).While 34.2 % of the total area in Yunnan province is classified as sensitive to soil erosion, 37 % of Baoshan prefecture, and 49 % of Longyang county were classified as such in 2004 (Ma et al., 2009b).Of the total erosion-sensitive area in Longyang County, 76.8 % was classified as medium erosion prone, 18.5 % as slightly erosion prone, and 4.7 % as high-risk erosion area.
Landslides and small-scale mud-rock flows happen frequently, with heavy damages to property.Great efforts have been made by the central and local government in China to combat soil erosion since 1980's.
In recent decades, annual sediment yield has varied between 14.7 × 10 4 t (2005; 0.84 t ha −1 yr −1 ) and 495.1 × 10 4 t (1985; 28.2 t ha −1 yr −1 ) with an average of 173.0 × 10 4 t (9.86 t ha −1 yr −1 ).The main soil type is red.The natural vegetation of semi-moist broadleaved forest disappeared many decades ago and has been replaced by conifer with a mix of alder (Alnus nepalensis) and other broadleaved species.
There is a middle-sized reservoir with a capacity of 5.850 × 10 4 m 3 , namely Beimiaoshuiku (BSK), located in up-stream Donghe River, which was built in 1958 and has been operational since 1959 (Fig. 1c).As the reservoir was operational before the start of our study period (1965) and its management has not undergone major change, its main effect in this study was as a constant sediment trap for its upstream area.Introduction

Conclusions References
Tables Figures

Back Close
Full 3 Materials and methods

Hydro-meteorological datasets
Three meteorological stations with long-term records  in or adjacent to the Kejie watershed, and one with short-term records (1998)(1999)(2000)(2001)(2002) in the Xizhuang subwatershed, were available.The daily value of six parameters were collected, namely rainfall, maximum temperature, minimum temperature, wind speed, relative humidity, and sunshine hours.In addition, two rainfall stations with long-term daily rainfall data in the Kejie watershed , and seven rainfall stations with short-term daily rainfall data in the Xizhuang sub-watershed (1998)(1999)(2000)(2001)(2002), were available.One hydrological station with long-term daily discharge and suspended sediment data is located at the outlet of the Kejie watershed .One middle-size reservoir with longterm daily outflow readings is situated in the upper reaches of the Donghe River in the Kejie watershed .All hydro-meteorological datasets were provided by the Baoshan Department of Hydrology and Meteorology (Fig. 1c, Table 1).

Land use maps
The soil map, digital elevation model (DEM) and vegetation/crop parameters were discussed by Ma et al. (2009a).

Change trend detection
The areal annual rainfall was calculated on the basis of annual rainfall from five rainfall stations with long-term observed data using the Inverse Distance Weighted (IDW) method of ESRI ArcGIS9.3.The annual runoff and SSY was summarized on the basis of the daily values from the outlet of the Kejie watershed.
A non-parametric test, a Mann-Kendall test (Mann, 1945;Kendall, 1975) was performed alongside the parametric t-test in the monotonic change trend detection for long-term records (van Belle and Hughes, 1984); this has been extensively used with environmental time series (Burn et al., 2004;Ma et al., 2009a).Here we used it to identify the trend of annual rainfall, rainfall erosivity (R-factor), runoff and SSY.Tests used the Kendall package in the R statistical analysis software (Team, 2008).
Piecewise-regression model is an effective tool to model abrupt thresholds.In a "broken-stick" model, two or more lines are joined at unknown point(s), called "breakpoint(s)" (Toms and Lesperance, 2003) and are widely used to identify ecological thresholds (Oswald et al., 2011;Zhang et al., 2013).A simple model with two straight lines joined sharply at the breakpoint, appropriate when there is an abrupt transition, was selected in this study and implemented in R (Team, 2008), with the following equations: Where y 1 is the annual suspended sediment yield, t i is the corresponding year, α is the turn-point (year), and β 0 , β 1 and β 2 are regression coefficients.ε is the residual of the fit.Before the turning point the slope of the line is β 1 , afterwards it is β 1 + β 2 .

Model selection and description
We used the SWAT model calibrated and validated with the Kejie watershed water balanced data (Ma et al., 2009a)  ment yield (SSY) under 5 different land use maps.The SWAT model, predicting longterm impacts of land use on water, sediment and agricultural chemical yield in large complex watersheds with varying soils, land-use and management conditions (Arnold and Fohrer, 2005), is widely used to simulate the SSY (Betrie et al., 2011;Cai et al., 2012;Oeurng et al., 2011;Qiu et al., 2012).For erosion, it uses the Modified Universal Soil Loss Equation (MUSLE) developed by Williams and Berndt (Williams and Berndt, 1977).
In SWAT, a watershed is divided into multiple sub-basins, which are further subdivided into Hydrologic Response Units (HRU) consisting of uniform land-cover, soil, and slope that drain directly to the sub-basin's channel.The hydrological modeling component in SWAT was discussed in Ma et al. (2009a).Details of MUSLE equation factors can be found in Neitsch et al. (2011).The channel sediment routing equation uses a modification of Bagnold's sediment transport equation (Bagnold, 1977).Sediment deposition in the channel is based on stream power (Williams, 1980) and fall velocity related to particle size.Channel degradation is adjusted using USLE soil erodibility and channel cover factors (Arnold et al., 1995).
Calibration of sediment yield was carried out manually through trial and error until satisfactory results were obtained.Based on previous studies (Betrie et al., 2011;Cai et al., 2012;Oeurng et al., 2011;Qiu et al., 2012), nine parameters are often considered when simulating sediment yield in SWAT, namely USLE-C (land cover factor in USLE equation), USLE_P (practice factor in USLE equation), SPCO (linear parameter for calculating the maximum amount of sediment that can be re-entrained during channel sediment routing), SPEXP (Exponent parameter for calculating sediment re-entrained in channel sediment routing), PRF (peak rate adjustment factor for sediment routing in the main channel), CH_COV (Channel cover factor), CH_Erod (channel erodibility factor), ADJ_PKR (peak rate adjustment factor for sediment routing in sub-basin).Introduction

Conclusions References
Tables Figures

Back Close
Full

Model setup and evaluation
The ArcSWAT model version 2009.93.7b was run in an ArcGIS 9.3 interface, with basic parameters as described by Ma et al. (2009a).Because observed sediment data was not complete in 1967, and the data in 1970 was missing, the simulation period of 1965-1970 was treated as a "warming up" period for the model.Monthly SSY records from 1971-1980 were split into two segments, 1971-1975 and 1976-1980, in order to calibrate and subsequently validate sediment-relative parameters.
In term of evaluation of the performance of the model, three indexes were used, as before, the NSE (Nash-Sutcliff efficiency), PBIAS (percentage bias), and RSR (ratio of the root mean square error to the standard deviation of measured data) (Moriasi et al., 2007).Details can be found in Ma et al. (2009a).

Recombining climate change and land cover change
The calibrated and validated model was used to distinguish the effect of climate change, vegetation change and engineering measures on suspended sediment yield, by recombining climate and land cover data (Tang et al., 2011;Ma et al., 2009b).The effect of change in engineering measures was estimated from the difference between the observed and simulated SSY.

Conclusions References
Tables Figures

Back Close
Full Per decade the SSY was related to annual runoff, but the relationship as a whole shifted (Fig. 3).When compared at any given runoff rate, annual SSY increased from 1970's to 1980's and subsequently declined.
A piecewise-regression model identified the breakpoint in recorded annual SSY to be 1985 (Fig. 4).Over the period 1971-1985, an increase was observed with a correlation coefficient of 0.29, which was not statistically significant (at α = 0.05); there was a decrease from 1986-2010 and a statistically significant correlation coefficient of 0.80 (α = 0.05) was recorded.A similar pattern of sediment yield change was observed at Yichang station, Yangtze River (Dai et al., 2009).
Since 1970s, many hillsides with vegetation had been converted to terraced fields to meet the food needs of an increasing population (Zhang et al., 1999).Road construction and other infrastructure development exacerbated soil erosion during this period.
In the 1980's China made a transition from a central planning economy to a market economy.Measures were taken to rectify this issue; steep slopes were reforested and soil and water conservation programs including ecological restoration, were rolled out.
Engineering measures (terrace improvement, silt check dam) also contributed to the decrease of sediment yield in the watershed.Other human activities, i.e. sand mining in the river (extraction of riverbed sediment) and riverbank protection, may have contributed to the decrease in sediment yield.
Using the breakpoint identified, the study period was divided into two periods: 1971- a similar temperature increase caused glacier recession and permafrost degradation in the headwaters of the Himalayas and the Tibetan Plateau.This occurred alongside an increase in sediment loads.The elevation of the Kejie watershed ranges from 963-3076 m a.s.l. and there is some snow cover on the mountaintop, but no glacier or permafrost.The influence of temperature on water yield and associated suspended sediment is likely negligible.
While the observed declining trend in annual rainfall in the watershed over 1971-2010 was not statistically significant, inter-annual variability ranged from −32.4 % (2009) to 25.1 % (2001) (Fig. 5a).A similar change trend was observed in the annual R-factor, although this exhibited greater variability (−45.5 % (2009) to 41.3 % ( 2001)) (Fig. 5b).As it is a main driving factor of soil erosion, the high variability in annual R-factor causes high variability in the predicted soil erosion and assessments are required, at least, a decadal basis.

Land use and land cover change
Land-cover estimates for the five observation points are summarized in Table 3.The cover fractions of forest, cropland and settlement increased by 33.9, 5.0 and 2 % respectively, while the area of grassland and barren land declined by 24.6 and 15.9 % respectively from 1974 to 2009, with small variations in what was identified as open water.
The increase of forest in the Kejie watershed (from 21.9 to 55.8 %) can be directly attributed to the forest policy of the central government in China.Aerial seeding of reforestation started in 1987, and followed by two large-scale conservation programs, Introduction

Conclusions References
Tables Figures

Back Close
Full namely the Natural Forest Protection Program (NFPP), and the Grain for Green project (GGP) (which was originally called the Sloping Land Conversion Program), were implemented in China in the past decade.The NFPP was introduced in 1998 to rehabilitate and develop natural forests (Zhang et al., 2000).GGP started in 1999 and it aimed to restore the landscape by paying farmers to plant trees rather than crops (Wang et al., 2007a).The forest cover in China as a whole increased from 16.6 to 18.2 % in 2005, and the goal is to reach 26 % by 2050 (Wang et al., 2007a).Yunnan province was the priority for NFPP and was also in the priority areas of GGP.In the Kejie watershed, the forest cover increased from 37.3 to 55.8 % between 2001 and 2009.The increase ratio of forest cover from 2001 to 2009 was higher than for the period of 1974-2001.

Soil and water conservation programs
The first soil and water conservation program in Yunnan Province started in 1989 as part of a National Key Soil Conservation Project supported by the central government of China (Wei et al., 2011).After that, several soil and water conservation programs were launched, such as the Yangtze River treatment project, the Pearl River treatment project and the Treasury bond projects supported by the central government (Ma et al., 2009b).Ecological measures and engineering measures have been undertaken in these programs.On the basis of the local inventory data, five soil and water conservation projects (ranging in area between 20.36 and 27.87 km 2 ) were implemented in the Kejie watershed from 2000 to 2005.On the basis of the local inventory data, soil erosion area was reduced by 40-81 % (Fig. 6).Table 4 lists the contribution to the soil loss reduction from different measures.Generally speaking, the contribution from ecological measures was around 50 %.The value was with high uncertainty as the assessment method was somewhat subjective, and lacked details of the routing processes of sediment from the plot to watershed level.Introduction

Conclusions References
Tables Figures

Back Close
Full

Soil erosion control measures taken in the process of the new constructing projects:
Several construction projects were implemented in the watershed during the previous decades, which lead to soil wasting (Table 5).From 2004 to 2010, a total of 1907.3 × 10 4 m 3 waste soil from construction sites was treated properly, as a key measure to prevent soil and water losses.
Sand mining near the riverbank: Several sand mining plants take sand from the Donghe River.It was difficult to quantify the amount of sand taken as the plants lacked a license and operated irregularly.
Riverbank protection: In order to prevent and eliminate the flood disaster, the riverbanks of the Donghe in the upper-stream were reconstructed with concrete during the past decades.These engineering measures could not be directly represented in the SWAT modeling.

Model calibration and validation
The watershed was divided into 45 sub-basins while the number of HRUs varied depending on the land cover map (353 for the 1974 map) in the SWAT model.The calibration attempts showed that the most sensitive parameters for sediment modeling in the Kejie watershed were USLE_P, SPCON, ADJ_PKR and CH_EROD.These parameters were adjusted from the initial estimates to fit the model simulations with the observed monthly sediment data over 1971-1975 (Fig. 7a).The calibrated parameters were validated from 1976 to 1980 (Fig. 7b).The calibrated values of the parameters are listed in Table 6.NSE, RSR and PBIAS indicators were 0.73, 0.52, and −4.6 % respectively.For the validation period, the simulated and observed monthly values resulted in a satisfactory model with NSE, RSR and PBIAS 0.77, 0.48, and −9.3 %, respectively.The model slightly under estimated the monthly suspended sediment.According to the criteria suggested by Moriasi et al. (2007), the performance of sediment simulation ranged from good to very good.Introduction

Conclusions References
Tables Figures

Back Close
Full The observed change (Measured2 -Measured1) was partitioned using simulation results for various combinations of land cover (L) and climate (C) as follows: The 1974 land-cover map was deemed to represent the land condition for the period 1970-1985; the 1991 land-cover map stood for 1986-1998, the 2001 map stood for 1999-2002, and the 2009 map stood for 2008-2010.Three scenarios were defined to represent different combinations of land use and climate condition, namely Simulat-edL1C1, SimulatedL1C2 and SimulatedL2C2.SimulatedL1C1 was the baseline simulation for period1; SimulatedL1C2 was used to predict a "business as usual" scenario with the land cover kept constant into period 2; And SimulatedL2C2 was used to provide a counterfactual of what might happen in period 2 without engineering measures, but with actual land cover for 1986 to 2010.Table 7 lists the mean annual SSY over period1 and period2.
From period 1 to 2, the mean annual SSY decreased by 40 % (95.6 × 10 4 t yr −1 ) under the joint impacts of human activities and climate change (Table 8); the value decreased by 10 % (23.9 × 10 4 t yr −1 ) under the impact of climate change; the value decreased by 18 % (44.1 × 10 4 t yr −1 ) under the impact of land cover change, and by 10 % (23.8 × 10 4 t yr −1 ) under the impact of other engineering measures.The bias of the observed and simulated values was within 2 %, which indicated that the simulations were acceptable.Introduction

Conclusions References
Tables Figures

Back Close
Full The decrease in the mean annual SSY from period 1 to 2 was 46 % attributable to land cover change, 25 % to climate change (a milder rainfall regime), and 25 % to other engineering measures.The bias from model simulation accounted for 4 % of the observed change (Table 8).Although the trend in rainfall is not statistically different from what can be expected for a no-change hypothesis, the predicted sediment yield is more sensitive to rainfall change than water discharge (Lu et al., 2013).
A comparison of the contributions of climate change and human activities (reforestation, ecological restoration measures, engineering measures, sand mining, and river-bank protection) showed that human activities were a governing factor for river sediment delivery, coinciding with the findings of Dai et al. (2009) in China.This work showed that reforestation and ecological restoration played a dominant role in controlling soil erosion.

Effect of ecological restoration on suspended sediment yield
To further explore the effect of vegetation restoration on SSY, 6 sub-basins with similar land cover and land use change trends were selected from the watershed (Fig. 8a).
The total area was 421.8 km 2 with sub-basin size ranging from 35.8 km 2 to 133.1 km 2 .The soil and water conservation programs described in Fig. 6 at the Ajiadahe, Binmawahe, Longwangmiao, Santaizihe and Wadudahe were located in sub-basin 1, 25, 2, 7, and 17 respectively.We assumed that the land cover in the watershed changed from the 1974 land use to the land use of 1991, 2001, 2006 and 2009 in the period 1 (1971-1985) respectively.The average annual soil erosion modulus (1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982)(1983)(1984)(1985) from six sub-basins under five land use maps was simulated using the calibrated and validated SWAT model.When the land cover changed from the 1974 map to the 2009 map, the reduction in the soil erosion modulus ranged between 15.56 and 34.58 t ha −1 a Introduction

Conclusions References
Tables Figures

Back Close
Full

Conclusions
The data show a larger contribution of land use/cover change to the reduction in suspended sediment yield, relative to engineering and other human activities than the majority of previous studies elsewhere in China.The sharp decrease in sediment yield from 1985, although assisted by a milder rainfall regime, was mostly due to the effects of more than 10 yr of reforestation (forest cover increasing from 21.9 to 55.8 %) and soil and water conservation programs.Since 1985, the health and stability of the river ecosystem has significantly improved.Introduction

Conclusions References
Tables Figures

Back Close
Full  Full  Full  Full  Full found that among 145 rivers in a global dataset Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Five land use maps of the Kejie watershed were used to analyze land use change in the past decades.Maps for 1974, 1991, 2001 and 2006 were classified by Ma et al. (2009a); an additional map for 2009 was obtained from the Baoshan Department of Forestry, based on a SPOT 5 image from 2009.As the classification of this 2009 map was more detailed than the previous maps, map units were combined to match the earlier legend.Discussion Paper | Discussion Paper | Discussion Paper | from 1971 to 1979 to simulate the suspended sedi-Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1985 (1) and1986-2010 (2).From period 1 to 2, the mean annual rainfall, R-factor and runoff decreased by 3.8 %, 5.5 % and 10.8 % respectively, and mean annual SSY decreased by 39.7 %.The magnitudes of change in runoff and sediment were influenced by climate change, vegetation measures and other soil and water conservation measuresMa et al. (2009a) described a monotonic increasing trend in the average annual temperature of 0.41• C (10 yr) −1 for the period since 1965.According toLu et al. (2010), Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .
Fig. 1.Location map of the Kejie watershed, Southwest China, with current erosion classification of the province (B) and location of weather and rainfall stations plus catchment outflow (C).

Table 1 .
Characteristics of hydro-meteorological stations in the Kejie watershed.

Table 2 .
Mann-Kendall trend tests of P, R-factor, Runoff and SS in Kejie watershed; Tau is the Mann-Kendall rank correlation coefficient.

Table 7 .
Simulated and measured annual suspended sediment yield in the Kejie watershed.

Table 8 .
Contribution of vegetation restoration, climate change and other engineering measures to suspended sediment between period 1 and 2 in the Kejie watershed.