Impact of forest cover and conservation agriculture on sediment export: A case study in a montane reserve, south-western China

Science of the Total Environment 702 (2020) 134802

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Impact of forest cover and conservation agriculture on sediment export: A case study in a montane reserve, south-western China Hongxi Liu a,b, Yujun Yi a,⇑, Sergey Blagodatsky c,d, Georg Cadisch c a

State Key Laboratory of Water Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China State Research and Development Center for Watershed Environmental Eco-engineering, Beijing Normal University, Zhuhai 519087, China c Institute of Agricultural Sciences in the Tropics (Hans-Ruthenberg-Institute), University of Hohenheim, Garbenstraße 13, Stuttgart 70599, Germany d Institute of Physicochemical and Biological Problems in Soil Science, Russian Academy of Sciences, 142290 Pushchino, Russia b

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


Relations between forest cover,

conservation agriculture and sediment export was quantified.
Conservation agriculture shifted the turning point of forest cover.
Minimum forest cover and conservation agriculture was recommended to be included in watershed management.

a r t i c l e

i n f o

Article history: Received 7 July 2019 Received in revised form 2 October 2019 Accepted 2 October 2019 Available online 31 October 2019 Editor: Elena Paoletti Keywords: Land-use change impact assessment Soil erosion modelling Minimum forest cover Soil conservation in tropics Watershed management

⇑ Corresponding author. E-mail address: [email protected] (Y. Yi). https://doi.org/10.1016/j.scitotenv.2019.134802 0048-9697/Ó 2019 Elsevier B.V. All rights reserved.

a b s t r a c t Reforestation and agricultural conservation have long been recognized as important in reducing on-site soil loss and off-site sediment export. Quantitative assessment of their effectiveness is critical, and assists cost-benefit analysis and decision-making in land management and landscape planning. We applied a paired watershed approach to monitor 1-year sediment export in two watersheds with forestdominated (reference) and mosaic (target) land use in the Naban River Watershed National Natural Reserve (NRWNNR) in Xishuangbanna, south-western China. Analysis of land-use change in the target watersheds showed decreasing total forest cover (FC) (from 57% to 47%), but increasing FC in steep areas (from 54% to 59%) from 2007 to 2012. A distributed hydrological model (Land-Use Change Impact Assessment, LUCIA) was well calibrated and validated through field data from the two watersheds. Scenarios were created representing different FCs (from 31% to 83%) and agricultural management (asusual and conservation). Simulation results quantified the relation between FC and sediment export as a logarithmic or logit model, indicating at least one turning point of FC, beyond which further forest reduction should significantly increase sediment export. This point was identified in the range between 57% and 61% of the target watershed under as-usual management; it was shifted to 47%–53% by conservation agriculture. Compared with the reference (with 83% FC), conservation agriculture was able to almost fully compensate for increased sediment export by forest reduction to 57% in 2007. However, when forest was reduced further to 47% in 2012, sediment export increased significantly. We concluded that total FC was as important as FC in montane watershed management in steep areas; and crop type conversion, such as rubber to maize in this study, and on-site agriculture management affect more to

2

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

sediment export than agricultural expansion. We recommend conservation agriculture as an efficient tool for reducing sediment export on a watershed scale. Ó 2019 Elsevier B.V. All rights reserved.

1. Introduction Water erosion is one of the most pervasive soil degradation processes, and affects a worldwide area of approximately 1100 million hectares annually (Lal, 2008). It leads to on-site (soil loss) and offsite (sediment export) effects, which ultimately threaten soil health and damage water quality through flooding, pollution and siltation (Ciglasch et al., 2005; dos Reis Oliveira et al., 2018). The degradation of land and water resources by erosion has led to water and soil conservation becoming an important topic for local and national policy makers. Among the most common water and soil conservation techniques (e.g. buffer zone, check dam), reforestation has been widely proved to be efficient in sediment control (Ferreira et al., 2019; Zheng et al., 2008; Quiñonero-Rubio et al., 2016). In particular, FC in steep areas not only prevents on-site hillslope erosion effectively, but more importantly prevents formation of rill or gully erosion, which is potentially a big contributor to channel sediment export (Yan et al., 2013; Selkimäki and González-Olabarria, 2016). Although land conservation policies (e.g. the Water and Soil Conservation Law of China since 1991) strictly limit deforestation and positively encourage reforestation in steep areas (slope > 25°), they pay less attention to total FC. Consequently, spatial land-use trade-offs may result from reforestation in steep areas, while deforestation occurs in less steep areas. This has two outcomes: (1) FC increases in steep areas but total FC decreases; and (2) agriculture expands in watersheds, but not in steep areas. The effects of such land-use change on sediment export are intricate. On one hand, the regional erosion response to land-use change has, in some cases, been proved to be determined more by topographical effects than by land-use change (Bakker et al., 2008). As a result, it is more important to take care erosionprone area (e.g. steep area) with forest than it is to focus on total FC. On the other hand, topography-determined erosion may not apply in a montane watershed, as most areas present steep slopes despite not being in the traditionally defined range of >25°. Moreover, sediment export is not linearly increases with FC reduction (Dedkov and Mozzherin, 1984; Yan et al., 2013). When FC continuously decreases, a turning point exists below which sediment export increases significantly and other water and soil conservations are required to efficiently control sediment export. Agricultural intensification has been widely identified as the major driver of deforestation and erosion exacerbation in montane regions of South-east Asia (Thothong et al., 2011; Park et al., 2010). Moreover, despite good conservation resulting from FC, agricultural expansion is inevitable in most regions owing to the pressures for food security. It is, therefore, necessary to maintain crop yields and conserve soils by proper agricultural management. Conservation agriculture has been proposed and proved to be an efficient approach to meet this goal (Hobbs et al., 2008; Pansak et al., 2008; Tuan et al., 2014). However, focus on conservation agriculture is concerned mostly with on-site soil protection rather than in reducing off-site sediment export. It is noticeable that onsite soil conservation has been shown to be more effective than offsite measures (e.g. buffer zones) in total sediment control (Hessel and Tenge, 2008). Conservation agriculture may, therefore, also be an important tool in sediment export control in watershed management, and perhaps compensate for increased sediment caused by agricultural expansion.

Our study assessed these specific land-use change effects on total sediment export on a watershed scale through a case study in a montane watershed in south-western China, and evaluated the potential of conservation agriculture to reduce total sediment export. We hypothesized that a turning point of FC existed in our watershed; and conservation agriculture could shift the turning point to the lower range. Our specific objectives are (1) qualitatively assess the effects of change in land-use cover (forest, individual types of agriculture) on total sediment yield, and especially how sediment export changed with total FC; and (2) quantitatively evaluate the relation between FC, conservation agriculture and sediment exports by (a) linear proportional change; (b) a logarithmic model, based on the relationship between soil cover and soil loss at the plot scale; and (c) a logit model, whose selection was based on the assumption that agriculture protects soil but is less robust than forest, thus a maximum value of sediment yield exists when forest converts completely to agriculture. 2. Material and methods Because soil erosion is a result of the interaction between climate, terrain properties and human activities (Dai et al., 2009; Ma et al., 2014), it is more feasible to assess sediment export responding to FC change and conservation agriculture application through model simulation by the ‘‘fixing-changing” method (Wang et al., 2009; Tang et al., 2011; Yan et al., 2013). Land-use change was first analyzed from 2007 to 2012 using the land-use/ land-cover (LULC) maps obtained from the LILAC (http://lilac.unihohenheim.de/en/index.php) and SURUMER https://surumer.unihohenheim.de/) projects. We then created different LULC maps with FC from 31% to 82% based on land-use change analysis. A spatial distribution model, LUCIA, was applied to simulate sediment export affected by different types of FC and conservation agriculture. LUCIA was first calibrated and validated by 1-year (2014) field data (discharge and sediment yields) from two paired watersheds. One watershed was selected as a reference (S1) and had 83% forestdominated land cover, and the other was selected as the target watershed (S2); this had a mosaic land cover (Fig. 1). Eight LULC maps (Supplementary Fig. S2) were taken as scenario inputs for a 6-year model run with and without conservation agriculture settings. The model outputs were used for further quantitative analysis. Fig. 2 provided an overview of the working processes, which are described in detail below. 2.1. Study site The study site was located in the Naban River Watershed National Nature Reserve (NRWNNR) in Xishuangbanna, southwestern China. The total protected area is 26,660 ha (22°040 –22°1 70 N, 100°320 –100°440 W) within an elevation range of 539– 2304 m. The annual precipitation is 1100–1600 mm and the mean annual temperature is 18–22 °C. The region has a typical monsoon climate characterized by a distinct rainy season from May to October and a dry season from November to April; 60%–90% of the precipitation is distributed during the rainy season. A digital elevation model (DEM) from an ASTER 30 m satellite data set and stream map from the NRWNNR Ecological Atlas was used to delineate the reserve into seven watersheds with a threshold area of 600 ha (Fig. 1). Two adjacent watersheds (S1 and S2), with similar

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

3

Fig. 1. Land use and sub-watershed delineation of the study site at Naban River Watershed National Nature Reserve (NRWNNR), Xishuangbanna, China, in 2012. The two highlighted sub-watersheds were selected as paired watersheds with forest-dominated and mosaic land uses.

Fig. 2. An overview of the work flow of this study. LULC: land-use land cover.

geological characteristics but different types of land cover, were selected as paired watersheds. According to the 2012 LULC map of NRWNNR, S1 presented a forest-dominant land cover (over 80%); S2 presented a mosaic land cover with 47% forest and 53% agriculture. Quantitative data on environmental characteristics for the two watersheds were collected through the analysis of various types of geoinformation (Table S1), and slope distribution of the two watersheds is shown in Supplementary Fig. S1. A hydrometeorological station was installed at the outlet of each watershed, and each station continuously monitored rainfall, discharge

and sediment export in 2014. The process used to obtain the hydro-meteorological data is described in Section 2.3.2. 2.2. Land-use change analysis LULC maps from 2007 and 2012 were used to analyze land-use change. Because the original 2007 and 2012 LULC maps differed in spatial resolution (30 m resolution for 2007 and 5 m for 2012) and land-use classification, we re-sampled them into a 30-m grid cell and redefined the land-use systems into forest, rubber plantation,

4

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

rice, maize, tea and other types (which included settlements, roads, lakes and open soil). Data on landscape attributes were also collected to help to identify the topographic characteristics of paired watersheds and to run the LUCIA model. These data included a DEM, local drainage direction (LDD) and soil map. A slope map was derived from DEM by GIS-software ArcGIS 10.3.1 and analyzed with the LULC map to derive land-use cover in steep (>25°) and less steep (<25°) areas. 2.3. Modeling process 2.3.1. LUCIA model description The LUCIA model is a process-based, spatially distributed model used to project the impacts of land-cover change and management on agricultural productivity and ecological system services. It is composed of plant (including management), water balance and erosion modules. The plant module (daily time step) is based on the WOrld FOod STudies (WOFOST, Supit, 2003) approach and simulates plant growth, leaf shedding, surface cover changes, etc.; the water balance (hourly time step) module is built on KINEROS 2 (Woolhiser, 1990) and simulates infiltration and runoff production; and the erosion module (hourly time step) follows the steady-state concept proposed by Misra and Rose (1996) and considers rainfall detachment, runoff entrainment at plot soil detachment simulation, and sediment re-entrainment and deposition at watershed sediment export simulation. The literature contains detailed descriptions of all LUCIA modules (Marohn et al., 2013; Lippe et al., 2014; Liu 2018; Yang et al., 2019; Liu et al., 2019). LUCIA has been tested successfully in the tropical mountainous areas of Thailand, Vietnam and China (Lippe et al., 2014; Marohn et al., 2013; Liu et al., 2019; Yang et al., 2019). 2.3.2. Baseline data preparation Spatial maps: LUCIA requires a land-use map, soil type map, area map, DEM and LDD as inputs for spatially explicit simulation. A summary of sources and resolution of spatial input maps appears in Table S2. We assumed little land-use change from 2012 to 2014 in our study site, and took the 2012 LULC map as the model input for 2014 baseline simulation. LULC maps with different FCs were created later, and were based on 2007 LULC and 2014 LULC maps (Section 2.3.4). Vegetation parameters: Plant growth parameters of forest and rubber plantations were calibrated and validated by field investigations in the same study area by Yang et al. (2019). Other landcover types (maize, rice, tea) were taken from the default database provided by LUCIA; these came from the study in Ban Tat, northern Vietnam, by Ayanu et al. (2011). The crop management setting was based on interviews with local people in the study site. Hydro-meteorological data: Discharge and sediment export were continuously monitored at the outlet of both watersheds. We installed an automatic recording station at each outlet. They consisted of a sharp-crested, contracted weir with a V-notch weir and a stilling well, an automatic water-level recorder (Campbell Scientific CS455), an automatic turbidity recorder and a tipping bucket rain gauge (Campbell Scientific TB4). The water-level data were converted to discharge using a control rating curve. Suspended sediments (SS) were collected by taking water samples at timed intervals from 2 min to 1 h depending on turbidity change during storm events. A relationship between SS concentration (g m3) and turbidity (NTU, nephelometric turbidity unit) was established separately for both streams and used to transfer the continuous measured turbidity into SS concentrations. The suspended load was calculated as the product of discharge and SS concentrations. No obvious sedimentation was observed near our monitoring stations, as the sediment texture was dominated by clay, and suspended sediment concentrations fell into the medium range. The

total sediment export in the watershed, therefore, was calculated as the suspended load in the streams. Meteorological information (temperature, radiation, evapotranspiration) was based on 2014 data from Jinghong airport, 23 km away from the watersheds. Rainfall was taken from the field measurement in 2014 from the same study area (Liu et al., 2016). 2.3.3. Model calibration and validation 1 pixel pre-calibration: The vegetation types in the two watersheds included forest, rubber plantation, rice, maize and tea. The coefficient reducing soil detachment (a) is the most influential parameter for the plot scale erosion simulation in LUCIA (Liu et al., 2019). a for rubber plantation was well adjusted by Liu et al. (2019) and taken as 2.5. Other land-use types were run on a 1 pixel model area with an average watershed slope of 23%. The ‘‘pixel model” was roughly adjusted by a based on annual soil loss from the literature, and conservation measures were then tested on major areas of agricultural land (maize, tea, rubber) by use of the management options in LUCIA (Table 1). Watershed scale calibration and validation: The model results (discharge and sediment yields) were first calibrated on watershed S2 by the parameter StreamRatio to best fit field measurement. StreamRatio refers to the ratio of pixel runoff assigned to store in the grid cell; that is, the rest ratio (1-StreamRatio) should go into the stream (Liu, 2018). The spatial map input (land use, area, soil, DEM, LDD) and climate input (rainfall) were then set up at watershed S1 for validation. Other parameters (crop management setting, StreamRatio) remained the same as for watershed S2. The creation of both model calibration and validation was based on an event-based resolution by comparing simulated runoff with measured runoff and total sediment export in the stream. R2, modeling efficiency, coefficient of determination and root mean square error were applied to evaluate model performance (i.e., model goodness of fit, GOF; Loague and Green, 1991). The modeling efficiency (EF) was calculated as:

Pn EF ¼

i¼1 ðOi

P 2  OÞ  ni¼1 ðPi  Oi Þ2 Pn 2 i¼1 ðOi  OÞ

ð1Þ

the coefficient of determination (CD) as:

Pn

CD ¼

2

i¼1 ðOi

 OÞ 2 Pn  i¼1 P i  O

ð2Þ

and root mean square error (RMSE) as:

Pn RMSE ¼

i¼1 ðP i

n

 Oi Þ2

!0:5 

100 O

ð3Þ

where Oi are the observed values, O is the mean of the observed data, Pi are the predicted values and n is the number of samples. EF indicates how well the predicted values correspond to the observed values. A value of 1 means a perfect one-to-one fit. Following the studies of Pansak et al. (2008) and Lippe et al. (2014), an EF threshold of >0.6 was used as the minimum performance criterion during model calibration procedures. CD is a measure of the proportion of the total variance of observed data explained by the predicted data; a value of 1 indicates a perfect prediction fit. We considered CD values between 0.5 and 2 during model calibration and estimation of validation success. RMSE describes the average error of predicted outcomes. The smaller the RMSE, the closer simulated values are to the observed ones; a value of zero indicates a perfect model fit (Bhuyan et al., 2002; Hussein et al., 2007).

5

H. Liu et al. / Science of the Total Environment 702 (2020) 134802 Table 1 Comparison of pixel simulated soil loss with measurement in literature of different land use type. Land use type

Management

Simulated soil loss (t ha1 y1)

Measured soil loss (t ha1 y1)

Reference

Forest

–

0.21

0.05–1.35

Kateb et al. (2013), Li and Shao (2006)

As-usual Rubber Maize Tea

Twice-weeding per year Conventional tillage Residue remaining on the field

2.40 7.25 4.45

2.90 4–7.50 2.00–17.00

Liu et al. (2016) Kateb et al. (2013), Tuan et al. (2014) Kateb et al. (2013), Liski et al. (2003)

Conservation Rubber Maize Tea

Once-weeding per year Straw mulch with 4 t ha1 Straw mulch with 4 t ha1

2.5 1.9

0.83–3.5 0.5–2.9

Liu et al. (2016) Liski et al. (2003) Liski et al. (2003)

2.3.4. Scenario design A ‘‘fixing-changing” method (Wang et al., 2009; Tang et al., 2011; Yan et al., 2013) was used to detect the effect of FC changes and conservation agriculture on sediment export. The calibrated model was first run for each land-use map created by different FC (31%, 38%, 47%, 53%, 57%, 68% and 71%), keeping the DEM, soil data and meteorological data constant, from January 2007 to December 2012. The crop management setting under this run was based on local (as-usual) management following interviews with local people. The second run then changed the crop management setting to conservation agriculture based on the literature (Table 1), keeping other inputs constant. We also ran the 2012 LULC of S1 as a reference. Table 2 shows a summary of 15 scenarios. An explanation of the generation of LULC maps and meteorological data follows. Land-cover map generation: We used seven LULC maps of watershed S2 with FC of 31%, 38%, 47%, 53%, 57%, 68% and 71% for scenario runs. Those showing 57% and 47% FC LULC were original maps from 2007 and 2012. The spatial distribution of forest and agriculture (e.g., steep/flat slope, upstream/downstream) can influence sediment export. To obtain representative LULC maps with

Table 2 Scenario runs to assess the impact of forest cover and conservation agriculture on sediment export. Scenarios No.

Input land use map*

Agricultural management

Rainfall

Calibration Validation

FC47 FC83

As-usual As-usual

1 2 3 4 5 6 7

FC31 FC38 FC47 FC53 FC57 FC68 FC71

8 9 10 11 12 13 14

FC31 FC38 FC47 FC53 FC57 FC68 FC71

As-usual (based on local interviews): Rubber – Twice weeding per year Maize – Conventional tillage Tea – Clean understory by slashing Conservation (based on literature review): Rubber – Once weeding per year Maize – Straw mulch with 4 t ha1 Tea – Straw mulch with 4 t ha1 As-usual

Daily rainfall with hourly intensity from field measurement Daily rainfall from Jinghong airport of year 2007–2012

15

FC83

different FC, creation of the other five maps (31%, 38%, 53%, 68% and 71% FC) was based on LULC maps from 2007 and 2012, and by assuming partial land-use changes. Taking the 71% FC map as an example, we assumed that reforestation only took place from 2007 to 2012; that is, other land-use change (e.g. from forest to agriculture) was excluded. The map with the highest FC (71%) in the greatest area possible was then created. In total, eight LULC maps (including the LULC map of S1 in 2012 as a reference) appear in Supplementary Fig. S2. All maps were processed and exported using GIS-software from ArcGIS 10.3.1, TerrSet and PCRaster4.1.0 (Karssenberg et al., 2010). The land-use component of each LULC map is shown in Fig. S3. Meteorological data: Rainfall may be a more influential factor on sediment yields than land cover (Ma et al., 2014). Because of this we ran each scenario (i.e., LULC map with specific FC) for 6 years (2007– 2012) corresponding to the LULC map years, instead of for 1 year, to include annual rainfall variability. The meteorological data were from Jinghong airport (https://en.tutiempo.net/climate/ws569590.html). We applied the 3-year moving average and Mann– Kendall test to graphically and statistically evaluate rainfall changes in ten recent years (2003–2012). Both indicated no significant change in annual rainfall from 2003 to 2012 (Supplementary Fig. S4 with Mann–Kendall result tau = 0.41, p = 0.12), thus confirming that the rainfall data from 2007 to 2012 were representative. 2.4. Quantification of the relationship between sediment yield, FC and conservation agriculture Simulation results of two scenario groups—scenario 1–7 and reference (scenario 15), and scenario 8–14 and reference—were analyzed separately (Table 2). This enabled us first to quantify the relationship (curve fitting) between FC and sediment export, and then to identify how conservation agriculture was affecting the relation (parameters of the curve). Analysis was made in two steps: (1) the significance of differences in sediment export by varied scenarios was tested by one-way ANOVA. If the difference was significant, the Duncan test was applied for pairwise comparison; and (2) the linear, logarithmic and logit models were fitted by 6year average annual sediment export changing with FC. The linear and logarithmic model are expressed as:

Linearmodel : y ¼ ax þ b

ð4Þ

Logarithmicmodel : y ¼ a  lgðxÞ þ b

ð5Þ

where y is sediment yield and x is FC. The logit model requires at most four parameters with the following formula:

*

FC refers to Forest Cover; number refers to the percentage, e.g. FC31 refers to land-use map with 31% forest cover. Among in total 8 land use maps, FC47 and FC57 are original land use map of S2 watershed in 2012 and 2007, respectively; FC83 is the land use map of S1 watershed in 2012. The other 5 maps (FC31, FC38, FC53, FC68, FC71) were created from land use change analysis from 2007 to 2012 in S2 watershed.

y¼

aebðxcÞ þd 1 þ ebðxcÞ

ð6Þ

To come to convergence with reasonable iteration, we estimated parameters c and d as 1 and 0.5, respectively, by graphically

6

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

observing modeling output. Therefore, the logit model was simplified as:

y¼

aebðx0:5Þ þ1 1 þ ebðx0:5Þ

ð7Þ

where a and b were the parameters we were most concerned with. They determined the maximum sediment yields (a) and sediment decreasing rate with FC increase (b). The Akaike Information Criterion (AIC) was used as the selection criterion among linear, logarithmic and logit models. After obtaining the models between sediment yields and FC under as-usual agriculture (ModAS ) and under conservation agriculture (ModCon ), the differences (Moddiff ) were calculated as:

Moddiff ¼ ModAS  ModCon

ð8Þ

where Moddiff presented reduced sediment yields by applying conservation agriculture. All analyses were performed with the statistical package R version 3.4.3 (http://www.r-project.org/).

3. Results 3.1. Spatial configuration of land-use change in target watershed S2 Fig. 3 shows the spatial configuration of land use and land-use change in the target watershed S2 from 2007 to 2012. To simply the map, we classified land-use change into four categories: (1) no change; (2) reforestation (to other types to forest); (3) agricultural expansion (to other types to agriculture); and (4) agricultural exchange (from one type of agriculture to another type of agriculture) (Supplementary Fig. S5). We observed a clear trend in landuse change, in that reforestation took place mainly in the central parts from 2007 to 2012, where it was concentrated in steep areas; agricultural expansion, meanwhile, occurred mainly in the western and eastern parts (Fig. 3). Maize and tea dominated the land that was newly agricultural and which was mostly converted from forest (Fig. S5). From 2007 to 2012, total FC decreased from 57% to 47%, but FC in steep areas increased from 54% to 59%. By 2012, forest had mostly covered the area of steep slopes that were concentrated in the central part of the watershed (Fig. 3). 3.2. Model simulation results 3.2.1. Model calibration and validation We obtained the parameter of StreamRatio 0.5 for model calibration. A total of 12 out of 15 events, and 13 out of 18 events, were detected by the model for calibration (S2 watershed) and validation (S1 watershed), respectively. LUCIA aimed to simulate a watershed smaller than 3000 ha, and assumed an even rainfall distribution within the watershed. In our study site, rainfall distribution is uneven because of the mountainous topography in both watersheds, and this explains the missing events in model simulation. The 13 detected events in S1 accounted for 85% of total annual sediment yields, and 12 detected events in S2 accounted for 80%. Moreover, both runoff and total sediment export fitted well to field observations. This was demonstrated by an EF of 0.87 and 0.97 for runoff and sediment yield, respectively, in the calibration phase; and of 0.72 and 0.96 for runoff and sediment yield, respectively, in the validation phase (Fig. S6). The same parameterization in watersheds S1 and S2 had good simulation results and indicated that the selected paired watersheds had similar geological characteristics. With good calibration and validation results, the calibrated model parameters were accepted for the scenario simulations.

3.2.2. Simulated sediment export under different scenarios Fig. 4 shows sediment export simulation results for 15 scenarios. In general, sediment export increased with decreasing total FC in the target watershed. Annual sediment export was simulated as 1.83 ± 0.31 to 3.75 ± 0.52 t ha1 with the FC ranging from 71% to 31% in the target watershed under as-usual agricultural management (83% FC representing S1 watershed as reference), and 1.79 ± 0.33 to 3.11 ± 0.90 t ha1 under conservation agriculture. Conservation agriculture significantly decreased sediment export for scenario FC31, but did not significantly decrease sediment for other scenarios (FC38–FC83). However, sediment export in scenarios FC57–FC31 was significantly different to the reference (FC82) under as-usual agricultural management. Scenarios FC47–FC31 showed significant differences to the reference under conservation agriculture. A jump in sediment export was observed from FC53 to FC47 in both as-usual and conservation agricultural management. 3.3. Sediment export affected by forest and maize cover Sediment export showed significant positive correlations with agricultural land covers (maize, rubber, tea and rice), while a significant negative correlation was observed with FC (Table 3). As land covers were highly correlated with each other, a partial correlation analysis was further applied. When FC was taken as the fixed indicator, the partial correlation between sediment export, maize (0.49 with p < 0.05) and rubber (0.49 with p < 0.05) was higher than rice (0.13 with p = 0.38) and tea (0.29 with p = 0.05). Forest and maize cover always showed the highest correlation with sediment export, showing the relation between sediment export, forest and maize cover (Fig. 5). Clearly, sediment export did not increase linearly with decreasing FC. Maize cover divided sediment export into two groups which corresponded well to the jump in FC changing from 47% to 53%. Moreover, with lower maize cover and higher FC, the watershed presented higher resilience to extreme climate conditions (higher precipitation), by showing less volatility in responding to different amounts of yearly rainfall (distance among points under the same FC). 3.4. Relation between FC, conservation agriculture and sediment export We took the average annual sediment export against FC change to reduce the impact of rainfall. Curve fitting between sediment export and FC presented lower AIC for the logarithmic and logit models than for the linear model under as-usual and conservation agriculture (Table 4). If we took the logit model, the parameter was fitted by a = 3.59 and b = 6.33 in Eq. (7) under as-usual agriculture; and by a = 2.79 and b = 5.69 under conservation agriculture (Fig. 6b), namely:

Under as  usual agriculture : sedi ¼

3:61e5:97ðFC0:5Þ þ1 1 þ e5:97ðFC0:5Þ

ð9Þ

Under conservation agriculture : sedicon ¼

2:79e5:69ðFC0:5Þ þ1 1 þ e5:69ðFC0:5Þ

ð10Þ

If we took the logarithmic model, the models under as-usual and conservation agriculture were (Fig. 6b):

Underas  usualagriculture : sedi ¼ 2:46 lg ðFC Þ þ 0:99

ð11Þ

Underconservationagriculture : sedicon ¼ 1:76 lg ðFC Þ þ 1:11

ð12Þ

In all models (linear, logit and logarithmic), conservation agriculture decreased the rate of sediment change by FC (Table 4).

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

7

Fig. 3. Land use and land-use change of target watershed (S2) from 2007 to 2012 in Naban River Watershed National Nature Reserve (NRWNNR), Xishuangbanna, China.

Corresponding to the specific FC, we calculated reduced sediment yields by applying conservation agriculture (Eq. (8); shown as dotted lines in Fig. 6). In our study, watershed land use could be categorized simply into agriculture and forest. Therefore, the dotted line in Fig. 6 indicated efficiency of conservation agriculture in sediment reduction changing with agriculture cover. That is, with decreasing agriculture cover (increasing FC), the effects of conservation agriculture in sediment reduction decreased, but were not linearly proportional to the agricultural cover of the watershed.

4. Discussion 4.1. Spatial configuration of land-use effects on sediment export 4.1.1. Land-use change impact on sediment export from 2007 to 2012 Agricultural expansion and reforestation were both observed in land-use change of the target watershed from 2007 to 2012 (Fig. 3). New rubber plantation regulations were published in Xishuangbanna in August 2007. For the first time they described suitable planting areas, and restricted rubber plantations to areas with

8

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

Fig. 4. Simulated annual sediment export (t ha1 y1) of different scenarios corresponding to different types of forest cover (from 31% to 83%) under as-usual and conservation agricultural management.

Table 3 Correlation and partial correlations between sediment export and each type of land cover change.

Sediment export Forest Rubber Maize Tea Rice Partial correlation Control Forest * **

Sediment export

Forest

Rubber

Maize

Tea

Rice

1

0.85** 1

0.54** 0.81** 1

0.79** 0.52* 0.81** 1

0.86** 0.97** 0.69** 0.97** 1

0.69** 0.79** 0.58* 0.79** 0.76** 1

Variables Sediment export

Forest –

Rubber 0.49**

Maize 0.49**

Tea 0.29*

Rice 0.13

p < 0.01 p < 0.05.

elevations of <950 m and slopes of <25°. They also encouraged the conversion to forest of existing rubber plantations in steep areas. As a result, reforestation on steep slopes (>25°) areas took place in our study site and was mostly realized by converting rubber plantations to forest (Fig. S5). Conversely, maize and tea expanded to the western area. Agricultural expansion is still the major driver of deforestation but has shifted to less steep areas (Fig. 3). The model LUCIA predicted sediment export increasing from 2007 (scenario FC57, 2.22 ± 0.24 t ha1) to 2012 (FC47, 3.29 ± 0.64 t ha1) (Fig. 4). Therefore, despite a growing area of steep slopes being covered by forest (from 54% to 59%), the reduction in total FC (from 57% to 47%) still led to an increase in sediment export.

4.1.2. Role of FC in steep areas and total FC in sediment export control Steep areas are mostly erosion prone because of their high onsite soil loss and the easy formation of rill or gully erosion (Govers, 1992). In our case study, the shifting of forest to steep areas did not lead to a reduction in sediment export for two reasons. (1) The entire target watershed was very steep. The distribution of steepness in the target watershed fits the Gaussian distribution (mean = 19.37, median = 18.42; Fig. S1). >50% of areas fell into the range 10°–30°. The artificial division of steep and not-steep

areas by 25° can scarcely represent erosion-prone areas affected by topography. Indeed, despite maize and tea not expanding to the area (the western part) with slopes of > 25°, these crops did mostly cover slopes of between 10° and 25° (Fig. S5). (2) Forest conversion from rubber plantations. Reforestation took place mainly by converting rubber plantations. Although rubber plantations lead to higher soil loss than does forest (Table 1), soil loss is much lower than in annual crops under as-usual management. Field study also confirmed that rubber plantations, as perennial crops, conserve soil much better than do annual crops, owing to good soil cover and less soil disturbance such as tillage (Liu et al., 2016). Even in a steep area with a slope of 29°, no rill erosion was observed in a rubber plantation (Liu et al., 2018). Steep areas were well protected by forest (54%) and rubber (31%) cover in 2007 and did not act as a major sediment source. Therefore, increased sediment export by decreasing total FC was not compensated for by increasing FC in steep areas.

4.2. Effects of FC on sediment export Curve fitting and an ANOVA test both indicated a turning point below which FC reduction would significantly increase sediment

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

9

Fig. 5. Simulated annual sediment export (t ha1 y1) of all scenarios changing with the corresponding forest and maize cover.

Table 4 Curve fittings for relation between sediment export and forest cover (FC). The Akaike information criterion (AIC) is used to indicate the best fitting model. Equation

Parameters

AIC

a

b

Under as-usual management Linear with Eq. (4) Logarithm with Eq. (5) Logit with Eq. (7)

5.04 2.46 3.59

5.32 0.99 6.33

3.93 3.19 2.96

Under conservation agriculture Linear with Eq. (4) Logarithm with Eq. (5) Logit with Eq. (7)

3.26 1.76 2.79

4.04 1.11 5.69

9.55 8.14 8.39

yield. In our case, this turning point ranged from 57% to 68% under as-usual agricultural management by the ANOVA test and shifted to 47%–57% by conservation agriculture. Indeed, most studies indicated a range (e.g. >70%) instead of the 100% FC as the undisturbed reference when linking land-use change (e.g. deforestation, agricultural expansion) to sediment yield (Dedkov and Mozzherin, 1984; Yan et al., 2013). Initial agricultural expansion was mostly located in flat areas, as in our case, and did not strongly affect total sediment export. Later expansion covered more steep areas and led to strongly increased soil loss at the plot scale, which provided a source of sediment for watershed export. Logit models indicated another turning point below which FC reduction would not significantly increase sediment yield. This point was around 30% (Fig. 6b) and coincided with the Dedkov and Mozzherin (1984) study which defined 30% as threshold, and categorized watersheds below 30% FC as strongly disturbed by agricultural activity, leading to 2.8 times higher sediment yield than undisturbed watersheds (FC > 70%). This turning point may be caused by the deposition process. With increasing plot soil loss by agricultural expansion (forest reduction), transportable sediment by erosion was no longer the limitation, but maximum sediment concentration determined the total sediment export (Bakker et al., 2008). It should be noticed that logarithmic and logit models gave very similar AIC values. Under conservation agriculture, the logarithmic model presented a lower AIC than the logit model (Table 4). In this study, we did not include the range 0%–30% FC

owing to limitations of the applied method in generating different LULC maps. The generality of relationship (logarithm or logit) between FC and sediment yield, especially the existence of the turning point, still needs to be clarified. 4.3. Influences of agricultural type and conservation agriculture in sediment export Sediment export was mostly affected by forest and maize cover change (Fig. 5, Table 3), and this corresponded to the land type producing the lowest (forest) and highest (maize) soil loss (Table 1). Moreover, when FC was fixed, the partial correlation between sediment and rubber turned out to be negative. When total forest remains the same, the rubber plantation becomes the land type producing lowest soil loss. Under this condition, conversion from rubber plantations, one agriculture, to another agriculture (e.g. maize, tea, rice) kept the same FC but still increased sediment export. Cash crops now increasingly dominate agricultural land use, especially in China (Su et al., 2016). Under conditions of soil and water conservation, conversion of agricultural type may contribute more to sediment export change than agricultural expansion in a montane watershed. Although conservation agriculture significantly decreased sediment export only under scenario FC31, compared to as-usual management, it reduced sediment export significantly compared to the reference with FC of 83% (Fig. 4) by shifting the differentiation point to 47%–53%. Simulation of conservation agriculture indicated its efficiency in reducing on-site soil loss (Table 1) and off-site sediment export (Fig. 4), in accordance with field and modeling studies in other regions (Valentin et al., 2008; Zhou et al., 2019; Pansak et al., 2008; Tuan et al., 2014;). Under 2007 land use in the S2 watershed, conservation agriculture was able to almost fully offset increased sediment yield by agricultural expansion (Fig. 4, Table 2). That is, by applying conservation agriculture, it is possible to maintain low sediment yields similar to those under high FC, without sacrificing limited land for reforestation. Therefore, conservation agriculture is an efficient measure for soil conservation at plot and watershed scale. In some cases, it is possible to compensate for the effects of agricultural expansion on sediment export. This was also observed in studies of other watersheds (Valentin et al.,

10

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

Fig. 6. Curve fitting between simulated average annual sediment export (t ha1 y1) and corresponding forest cover under as-usual and conservation agricultural management: (a) curve fitting by logarithmic model; (b) curve fitting by logit model.

2008; Zhou et al., 2019). In the Philippines, when conservation agriculture replaced the traditional mechanized method for cultivating maize (following the slope contour and covered with strips of native grass), sediment export declined from 36.2 Mg ha1 y1 to 0.7 Mg ha1 y1 (Valentin et al., 2008). Although the efficiency of conservation agriculture increases with agricultural expansion/ FC reduction (dotted line in Fig. 6), conservation agriculture could not fully compensate for increasing sediment export when FC decreased to 47% in 2012 compared to the reference (Fig. 4). In summary, sediment export is related to agricultural expansion (total agriculture cover) and types of e converted agriculture. When sediment increase is driven by agricultural expansion, which fits most cases, conservation agriculture represents a good tool in sediment export control and may fully compensate for increased sediment export in some situations.

5. Conclusions Modeling simulations allowed us to assess the relations between FC, conservation agriculture and sediment export. We chose paired watersheds dominated by forest (S1 watershed) and mosaic land cover (S2 watershed), and monitored discharge, sediment export and rainfall for 1 year. The discharge and sediment export of the two watersheds were used to calibrate and validate LUCIA, a spatial distribution hydrological model. After calibration and validation, LUCIA generated good prediction of event-based runoff and total sediment yield in both S1 and S2 watersheds, indicated by the resulting EF of 0.87 and 0.72 for runoff, and 0.97 and 0.96 for sediment export, respectively. Although simplification of uniform rainfall distribution of LUCIA meant that the model did not catch all events, those detected by the model accounted for over 80% of sediment yield within the watersheds. Therefore,

LUCIA was proved to be able to capture major events and successfully mimic sediment yields in the watersheds. The target watershed presented land-use change, with forest shifting to steep areas. Specifically, FC in steep areas increased from 54% to 59%, while total FC reduced from 57% to 47%. The model simulated this land-use change to increase sediment export because over 50% of the study area comprised slopes of 10°–30°. Erosion response in the target watershed is determined by land cover and topography. Under these conditions, both total FC and conservation agriculture are important in sediment control. LULC maps with different FCs ranging from 31% to 81% were tested as scenarios under as-usual and conservation agriculture in the target watershed (S2). Curve fitting quantified the relation between FC and sediment export as a logarithmic or logit model, indicating at least one turning point of FC. An ANOVA test identified one turning point, beyond which further forest reduction should significantly increase sediment yields at watershed level. Under as-usual agricultural management, this threshold was estimated to be in the range 57%–61% of the target watershed (S2). Conservation agriculture shifted the turning point to 47%–53% FC. Under 2007 land use, conservation agriculture can almost fully compensate for increased sediment by agricultural expansion compared to the reference (FC decreased from 83% to 57%); while, in 2012, other management (e.g. buffer zone) is required additional to on-site agricultural conservation when total agriculture cover expanded to 53% (FC decreased to 47%). This was also partly caused by maize expansion from 4% to 17% from 2007 to 2012. Therefore, sediment export related to both total agriculture cover and to specific expanded types of agriculture, emphasizing the powerful effects of on-site soil loss in off-site sediment export. In our case, we suggest a minimum FC of 47%–53%, together with conservation agriculture (i.e. once weeding per year in rubber, straw mulch with

H. Liu et al. / Science of the Total Environment 702 (2020) 134802

4 t ha1 in maize and tea) to efficiently control sediment export and meet the goal of sustainability. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research was financially supported by the National Key Research& Development Program of China (2018YFC0407403), the National Natural Science Foundation of China (51722901), the German Ministry of Education and Research (No. FKZ 01LL0919), the China Postdoctoral Science Foundation (No. 212400201). We would like to thank the Naban River Watershed National Nature Reserve Bureau for their support with the field work, and Elaine Monaghan, BSc(Econ), from Liwen Bianji, Edanz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript. We also greatly thank the two anonymous reviewers for their helpful comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2019.134802. References Ayanu, Y.Z., Nguyen, T.T., Marohn, C., Koellner, T., 2011. Crop production versus surface-water regulation: assessing tradeoffs for land-use scenarios in the Tat Hamlet Watershed, Vietnam. Int. J. Biodivers. Sci., Ecosyst. Servi. Manage. 7 (3), 231–244. Bakker, M.M., Govers, G., van Doorn, A., Quetier, F., Chouvardas, D., Rounsevell, M., 2008. The response of soil erosion and sediment export to land-use change in four areas of Europe: The importance of landscape pattern. Geomophology 98, 213–226. Bhuyan, S.J., Kalita, P.K., Janssen, K.A., Barnes, P.L., 2002. Soil loss predictions with three erosion simulation models. Environ. Model. Softw. 17, 137–146. Ciglasch, H., Amelung, W., Totrakool, S., Kaupenjohann, M., 2005. Water flow patterns and pesticide fluxes in an upland soil in northern Thailand. European Journal of Soil Science 56 (6), 765–777. Dai, S.B., Yang, S.L., Li, M., 2009. The sharp decrease in suspended sediment supply from China’s rivers to the sea: anthropogenic and natural causes. Hydrol. Sci. J. 54 (1), 135–146. Dedkov, A.P., Mozzherin, V.I., 1984. Erosion and sediment yield on the earth. Izdatelsvo Kazanskogo Universiteta. Ferreira, P., van Soesbergen, A., Mulligan, M., Freitas, M., Vale, M.M., 2019. Can forests buffer negative impacts of land-use and climate changes on water ecosystem services? The case of a Brazilian megalopolis. Sci. Total Environ. 685, 248–258. Govers, Gerard, 1992. Relationship between discharge, velocity and flow area for rills eroding loose, non-layered materials. Earth Surf. Proc. Land. 17 (5), 515– 528. Hessel, R., Tenge, A., 2008. A pragmatic approach to modelling soil and water conservation measures with a catchment scale erosion model. Catena 74 (2), 119–126. Hobbs, P.R., Sayre, K., Gupta, R., 2008. The role of conservation agriculture in sustainable agriculture. Phil. Trans. R. Soc. B 363, 543–555. Hussein, J., Yu, B., Ghadiri, H., Rose, C., 2007. Prediction of surface flow hydrology and sediment retention upslope of a vetiver buffer strip. J. Hydrol. 338, 261– 272. Karssenberg, D., Schmitz, O., Salamon, P., de Jong, K., Bierkens, M.F.P., 2010. A software framework for construction of process-based stochastics patiotemporal models and data assimilation. Environ. Modell. Software 25 (4), 489–502. Kateb, H.E., Zhang, H., Zhang, P., Mosandl, R., 2013. Soil erosion and surface runoff on different vegetation covers and slopegradients: Afield experiment in Southern Shaanxi Province, China. Catena 105, 1–10. Lal, R., 2008. The urgency of conserving soil and water to address 21st century issues including global warming. J. Soil Water Conserv. 63, 140A–141A. Li, Y.Y., Shao, M.B., 2006. Change of soil physical properties under long-term natural vegetation restoration in the Loess Plateau of China. J. Arid Environ. 64 (1), 77–96. Lippe, M., Marohn, C., Hilger, T., Dung, N.V., Vien, T.D., Cadisch, G., 2014. Evaluating a spatially-explicit and stream power-driven erosion and sediment deposition model in Northern Vietnam. Catena 120, 134–148.

11

Liski, J., Nissinen, A., Erhard, M., Taskinen, O., 2003. Climatic effects on litter decomposition from arctic tundra to tropical rainforest. Glob. Change Biol. 9 (4), 575–584. Liu, H., Blagodatsky, S., Giese, M., Liu, F., Xu, J., Cadisch, G., 2016. Impact of herbicide application on soil erosion and induced carbon loss in a rubber plantation of Southwest China. Catena 145, 180–192. Liu, H., 2018. Modelling weed management effects on soil erosion in rubber plantations in Southwest China. (PhD thesis). Institute of Agricultural Sciences in the Tropics (Hans-Ruthenberg-Institute), Hohenheim University, Germany, pp. 111–117. Liu, H., Blagodatsky, S., Liu, F., Xu, J., Cadisch, G., 2018. Impact of rubber plantation age on erosive potential studied with USLE model, J. Appl. Water Eng. Res. Liu, H., Yang, X., Blagodatsky, S., Marohn, C., Liu, F., Xu, J., Cadisch, G., 2019. Modelling weed management strategies to control erosion in rubber plantations. Catena 172, 345–355. Loague, K., Green, R.E., 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contam. Hydrol. 7, 51–73. Ma, X., Lu, X.X., van Noordwijk, M., Xu, J.C., 2014. Attribution of climate change, vegetation restoration, and engineering measures to the reduction of suspended sediment in the Kejie catchment, southwest China. Hydrol. Earth Syst. Sci. 18, 1979–1994. Marohn, C., Schreinemachers, P., Quang, D.V., Berger, T., Siripalangkanont, P., Nguyen, T.T., Cadisch, G., 2013. A software coupling approach to assess low-cost soil conservation strategies for highland agriculture in Vietnam. Environ. Modell. Software 45, 116–128. Misra, R.K., Rose, C.W., 1996. Application and sensitivity analysis of process-based erosion model GUEST. Eur. J. Soil Sci. 47, 593–604. dos Reis Oliveira, P.C., Kraak, M.H.S., van der Geest, H.G., Naranjo, S., Verdonschot, P. F.M., 2018. Sediment composition mediated land use effects on lowland streams ecosystems. Sci. Total Environ. 631–632, 459–468. Pansak, W., Hilger, T.H., Dercon, G., Kongkaew, T., Cadisch, G., 2008. Changes in the relationship between soil erosion and N loss pathways after establishing soil conservation systems in uplands in Northeast Thailand. Agric. Ecosyst. Environ. 128, 167–176. Park, J., Duan, L., Kim, B., Mitchell, M.J., Shibata, H., 2010. Potential effects of climate change and variability on watershed biogeochemical processes and water quality in Northeast Asia. Environ. Int. 36 (2), 212–225. Quiñonero-Rubio, J.M., Nadeu, E., Boix-Fayos, C., de Vente, J., 2016. Evaluation of the effectiveness of forest restoration and check-dams to reduce catchment sediment yield. LDD 27 (4), 1018–1031. Selkimäki, M., González-Olabarria, J.R., 2016. Assessing gully erosion occurrence in forest lands in Catalonia (Spain). LDD 28 (2), 616–627. Su, S., Zhou, X., Wan, C., Li, Y., Kong, W., 2016. Land use changes to cash crop plantations: crop types, multilevel determinants and policy implications. Land Use Policy 50, 379–389. Supit, I., 2003. Updated system description of the WOFOST crop growth simulation model as implemented in the crop growth monitoring system applied by the European Commission. Treemail Publishers, Heelsum, The Netherlands. http:// www.treemail.nl/download/treebook7/start.htm. Tang, L.H., Yang, D.W., Hu, H.P., Gao, B., 2011. Detecting the effect of land-use change on streamflow, sediment and nutrient losses by distributed hydrological simulation. J. Hydrol. 409, 172–182. Thothong, W., Huon, S., Janeau, J., Boonsaner, A., de Rouw, A., Planchon, O., Bardoux, G., Parkpian, P., 2011. Impact of land use change and rainfall on sediment and carbon accumulation in a water reservoir of North Thailand. Agric. Ecosyst. Environ. 140, 521–533. Tuan, V.D., Hilger, T., MacDonald, L., Clemens, G., Shiraishi, E., Vien, T.D., Stahr, K., Cadisch, G., 2014. Mitigation potential of soil conservation in maize cropping on steep slopes. Field Crops Res. 156, 91–102. Valentin, F.A.C., Alamban, R., Boosaner, A., Bricquet, J.P., Chaplot, V., de Guzman, T., de Rouw, A., Janeau, J.L., Orange, D., Phachomphonh, K., Phai, Do Duy, Podwojewski, P., Ribolzi, O., Silvera, N., Subagyono, K., Thiebaux, J.P., Toan, Tran Duc, Vadari, T., 2008. Runoff and sediment losses from 27 upland catchments in Southeast Asia: Impact of rapid land use changes and conservation practices. Agri. Ecosys. Environ. 128, 225–238. Wang, G., Xia, J., Chen, J., 2009. Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: A case study of the Chaobai River basin in northern China. Water Resour. Res. 45 (7), 1–12. Woolhiser, D.A., Smith, R.E., Goodrich, D.C. 1990: KINEROS, A kinematic runoff and erosion model: Documentation and user manual. US Department of Agriculture, Agricultural Research Service, ARS-77, 130p. Yan, B., Fang, N.F., Zhang, P.C., Shi, Z.H., 2013. Impacts of land use change on watershed streamflow and sediment yield: an assessment using hydrologic modelling and partial least squares regression. J. Hydrol. 484, 26–37. Yang, X., Blagodatsky, S., Marohn, C., Liu, H., Goblon, R., Xu, J., Cadisch, G., 2019. Climbing the mountain fast but smart: modelling rubber tree growth and latex yield under climate change. For. Ecol. Manage. 439, 55–69. Zheng, H., Chen, F., Ouyang, Z., Tu, N., Xu, W., Wang, X., Miao, H., Li, X., Tian, Y., 2008. Impacts of reforestation approaches on runoff control in the hilly red soil region of Southern China. J. Hydrol. 356, 174–184. Zhou, M., Deng, J., Lin, Y., Belete, M., Wang, K., Comber, A., Huang, L., Gan, M., 2019. Identifying the effects of land use change on sediment export: Integrating sediment source and sediment delivery in the Qiantang River Basin China. Sci. Total Environ. 686, 38–49.