Modelling spatial distribution of surface runoff and sediment yield in a Chinese river basin without continuous sediment monitoring

Figures & data

Fig. 1 (a) Location of the study area within the provinces Jiangxi (J) and Anhui (A) in southeastern China; (b) location of the three climate stations (1: Anqing, 2: Jingdezhen, 3: Meilin/Tunxi), and (c) topography (DEM: 90 m SRTM, Jarvis et al. 2008) and river network in the Changjiang River basin, sampling sites (i.e. sub-basin outlets) and the Tankou gauge at the outlet.

Table 1 Relative land-use cover in the Changjiang catchment (using a supervised multispectral classification approach based on Landsat TM data of the year 2006; Schmalz et al. 2012).

Land-use class	Proportion of study area (%)
Mixed forest	70.0
Arable land	16.7
Tea	10.8
Rangeland	2.0
Settlements	0.3
Water	0.2

Table 2 Data input necessary to depict water and sediment fluxes in the basin.

Data used for the SWAT model	Data source and details
Digital elevation model	SRTM 90 m, Jarvis et al. (2008)
Stream network	Derived from SRTM-DEM with ArcMap, manually corrected (comparison to Google Earth)
Soil map	IIASA Harmonized World Soil Data Base, 800 m, FAO/IIASA/ISRIC/ISSCAS/JRC (2009)
Physical soil parameters	IIASA, ISRIC
Land-use map	90 m, Schmalz et al. (2012)
Vegetation parameters	SWAT database (Neitsch et al. 2011)
Crop management	Farmer interviews (Song 2011) and literature research concerning the dominant crops tea (Ruan et al. 2002), rice (Pan and Shi 2002, Rice Knowledge Bank) and cabbage (AELF, Lattauschke 2006)
Climate data (precipitation, temperature, humidity, wind, solar radiation)	Daily climate data 1960–2010 from the stations Jingdezhen 58527, Meilin/Tunxi 58531, Anqing 58424 (CMA 2011).
Discharge data for calibration	Tankou gauging station (Jiangxi Province): daily discharge data 1981–2010 (Jiangxi Hydrological Bureau 2011)
Suspended sediment concentrations	Field measurements (see Section 2.1)

Sources

AELF (2011).

IIASA: Harmonized World Soil Database. http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4 [Accessed 25 June 2010].

ISRIC (2010).

Rice Knowledge Bank: http://www.knowledgebank.irri.org/rice.htm [Accessed 29 July 2011].

Table 3 Variables used for the calibration of streamflow at Tankou gauging station and their initial and final values. ‘Fixed’ means to have set a fixed value according to literature research or expert knowledge, ‘calculated’ variables are explained with the corresponding procedure in the text, ‘calibrated’ means that the variable was calibrated by manual adjustment and using SWAT-CUP (see text).

Variable name	Definition (Unit)	Initial value	Final value	Input file	Procedure
PET method	Method for calculating potential evapotranspiration		Penman-Monteith	BSN	Fixed
EPCO	Plant evaporation compensation factor	1	0.99	BSN	Calibrated
ESCO	soil evaporation compensation factor	0.95	0.01	BSN	Calibrated
Surlag	Surface runoff lag coefficient	4	0.6	BSN	Calibrated
Alpha-bf	Baseflow alpha factor (d)	0.048	0.0524	GW	Calculated
GW_DELAY	Groundwater delay time (d)	31	44	GW	Calculated
GW-Revap	Groundwater revap coefficient	0.02	0.19	GW	Calibrated
Revapmn	Threshold depth of water in the shallow aquifer for revap to occur (mm)	1	1.875	GW	Calibrated
Rchrg_Dp	Groundwater recharge to deep aquifer (fraction)	0.05	0	GW	Fixed
Alpha-BNK	Baseflow alpha factor for bank storage (d)	0	0.64	RTE	Calibrated
CH_K2	Effective hydraulic conductivity in main channel alluvium (mm h^-1)	0	50	RTE	Fixed based on tables in Arnold et al. (2011)
CH_N2	Manning’s n for the main channel	0.014	0.075	RTE	Fixed based on tables in Arnold et al. (2011)
CANMX FRST	Maximum canopy index	0	20	HRU	Fixed after literature research
CANMX TEA		0	5
CANMX RNGE, RICE, CABG		0	1
CN2 for different land uses	Initial SCS runoff curve number for moisture condition II	31–92; values vary according to land-use class	FRST 57	MGT	Fixed according to Neitsch et al. (2010)
			RICE 86
			CABG 80
			TEA 69
			RNGE 58
			URBN 84
			WATR 92
Mgt operations for different land uses	Agricultural management operations	default	Specific values vary according to crop	MGT	Fixed according to Song (2011) and literature research
GSI FRST	Maximum stomatal conductance at high solar radiation and low vapour pressure deficit (m s^-1)	0.002	0.006	crop.dat	Fixed after literature research
BLAI FRST	Maximum potential leaf area index	5	7	crop.dat	Fixed after literature research
Point source discharge (flow, NO3 and min P)	Estimated waste water entries: water flow, N and P (see text)	0	Specific for each sub-basin	rec*.dat	Calculated for 50 sub-basins

Table 4 Variables used for the parameterization of sediment and their initial and final values.

Variable name	Definition (Unit)	Initial value	Final value	Input file
CH_BED_D50	D50 median particle size diameter of channel bed sediment (µm)	500	2000	RTE
PRF	Peak rate adjustment factor for sediment routing in the main channel	1	0.5	BSN
FILTERW	Width of edge-of-field filter strip (m)	0	5	MGT
USLE_C for Cabbage in arable land class	Minimum value of USLE C factor for water erosion applicable to the land cover/plant	0.2	0.05	Crop.dat
USLE_P for CABG	USLE equation support practice factor	1	0.3	MGT
USLE_P for RICE		1	0.1
USLE_P for TEAT		1	0.5

Table 5 Studies analysing surface runoff depending on land use.

Reference	Catchment or plots	Method, studied land use, period	Results
El Kateb et al. (2013)	Shaanxi Province (hilly topo-graphy, yellow-brown soil)	Field experiments, 33 erosion plots (7 m^2), five vegetation covers (e.g. tea, forest) and three levels of slope gradient, 15 July–30 October 2008	Runoff was associated with rainfall intensity, vegetation cover and slope gradients
			Ranking: forest < grassland < farmland
			No significant difference between the two forest types (mean: 17–35 m^3 ha^-1), even with different slopes
			Tea plantation: 83 m^3 ha^-1 (slight slope)–190 m^3 ha^-1 (steep slope)
			Proportions of generated runoff (%): 6.6% (moderate slope)–24.5% (steep slope) at tea plantation and 10.7% (moderate slope)−13.0% (slight slope) at the forest (both types), with a rainfall intensity ≤5 mm h^-1; 13.6% (slight slope)–30.5% (steep slope) at the tea plantation and 2.4% (slight slope)–2.9% (steep slope) at the forest (both types), with a rainfall intensity >5 mm h^-1
			For the rainfall event with the highest intensity of 97.5 mm h^-1: highest runoff at tea plantation (104.9 m^3 ha^-1), followed by agriculture (maize, without slopes >30°), then grassland, and lastly forest
Hill and Peart (1998)	Southern China	Review of plot studies, forest, tea, rice	Forest and woodland: 13–99 mm
			Rubber/tea: 13–206 mm
			Upland rice: 3395 mm
			Cultivation on slopes: 1104 mm
			Water yields from cultivated land mostly two orders of magnitude higher than from forest
			Ranking: forest ± woodland < scrub ± grass < tree crops and bare soil < cultivated slopes
Lee et al. (2010)	Korean mountainous area (10 and 17% slope)	Field experiments, rainfall event with 280 mm, Chinese cabbage	286–1167 m^3 ha^-1 (comparing conventional tillage, reduced tillage and reduced tillage with rye mulching)
			Runoff as % of total rainfall amount: 30.6–41.7% (conventional tillage), 15.3–32.9% (reduced tillage), 10.2–21.3% (reduced tillage with rye mulching)
			Conclusion: reduced tillage lowered runoff, increasing surface coverage by rye mulching reduces raindrop impacts, and reduces water flow through rills
Liang et al. (2013)	Taihu Lake area	Rice	Using AWD (alternate wetting and drying) irrigation as a water saving practice minimized surface runoff
			Reduced floodwater depths in the rice fields helped to buffer against runoff events after heavy storms
			Consequently, the number of runoff events and total volume surface runoff was significantly lower
Lu et al. (2004)	Seven plots in Zheijiang Province (red soil region)	Plot study, forest, tea	Differences in runoff mainly determined by vegetation coverage and plant characteristics the deciduous broad-leaf forest showed high adaptability to the red soils and was effective in reducing surface runoff
			Extensive cultivated tea trees had limited growth, so limited water and soil conservation
			Sparse coniferous forest resulted in high runoff (much less than on bare soil)
			Coefficients of runoff: broad-leaf forest < mixed forest < grass land < citrus orchard < tea brush < sparse coniferous forest < bare soil
Wu et al. (2011)	Watershed of Qingshan Lake, Zhejiang Province (yellow red soils)	Field experiments, Chinese cabbage (5 growth stages), 10 simulated rainfalls	Runoff occurrence time decreased with the growth of Chinese cabbage because of the increasing vegetation coverage ratio
			Runoff volumes at seedling and rosette stages were both higher than those at folding stages
			Larger vegetation coverage ratios speeded up the runoff occurrence but did not increase the runoff volumes significantly (growing plant roots hold up surface runoff and leaves intercept rainfall through its special folded formation)
			Folding stages had larger vegetation cover than seedling and rosette stages, which could slow down the surface runoff speed

Table 6 Studies analysing the spatial distribution of sediment yield between sub-basins. See Fig. 2 for catchment locations.

Reference	Catchment	Study aim and period	Method	Results
1. Bieger et al. (2013)	Xiangxi Catchment Three Gorges area of the Hubei Province	Average annual sediment yield, 1981–1993	SWAT model	1987: 0.03–2.56 t ha^-1 year^-1
				2007: soil erosion (47.7%) and sediment loads (41.9%) decreased. Reason: forested area increased by 3.6 % since 1987, large areas of cropland were replaced with orange orchards
2. Li et al. (2010)	Liao watershed of the Poyang Lake area, Jiangxi Province (59% forest)	Annual soil erosion and sediment yield (rain data 1971–2000)	USLE and GIS	Spatial distribution of soil erosion: varies from nil (flat and well-covered areas) to more than 500 t ha-1 year^-1 (mountainous areas with sparse vegetation)
				Average soil erosion: 18.2 t ha^-1 year^-1 (SD: 109.3 t ha^-1 year^-1)
				Erosion rate: 39.5% of the watershed under tolerant erosion, while the rest of the area with erosion to different extents
3. Lu et al. (2003)	Upper Yangtze basin (loess area)	Long-term mean sediment yield, 1957–1987	Interpolation and a mapping approach	Mean: 4.97 t ha^-1 year^-1 (SD 5.19 t ha^-1 year^-1)
				Highest value reached: 31.18 t ha^-1 year^-1
4. Lu et al. (2011)	Poyang Lake basin	Soil erosion for 1990 and 2000	Remote sensing, GIS and USLE	Average soil erosion: 11 t ha^-1 year^-1 (slight)
				Eroded areas mainly located in the Ganjiang and Xinjiang headstreams and in middle reaches and headwaters of the Fuhe and Xiushui Rivers
5. Ouyang et al. (2010)	Longliu watershed, at conjunction of Qinghai–Tibet Plateau and Loess Plateau. Study area is the main part of the Upper Yellow River watershed	Annual soil erosion, simulations of 1977, 1996, 2000 and 2006	SWAT model	Main soil erosion from agricultural land, relatively slight soil erosion in forests and on grassland in steep mountain areas
				Averaged soil erosion loads for the 4-year study period: 9, 10, 7 and 8 t ha^-1 year^-1
				Highest soil erosion loads in the four single years: 160, 81, 85 and 67 t ha^-1 year^-1
6. Shen et al. (2010)	Zhangjiachong watershed (1.62 km^2), Three Gorges Reservoir area	Annual mean soil erosion rate distribution, 2004–2007	Plot measurements, WEPP model	Annual mean soil erosion: 0.2–3.8 t ha^-1 year^-1
				Most serious erosion occurred in regions with intense agricultural activities and where most crops were planted on soil dike terraces
				Least eroded areas were rice paddy fields at low-gradient slopes
7. Wu and Chen (2012)	East River basin (tributary of Pearl River; 25 325 km^2) in southern China	Annual average sediment yield, 1973–1988	SWAT model	Annual average sediment yield: 40.3 t ha^-1 year^-1
				Higher soil erosion rates related to high percentage of agricultural land (~37%)
				Erosion is quite sensitive to soil properties and slope
8. Yan et al. (2011)	Eentire Yangtze basin, and Poyang Lake sub-basin (158 677 km^2)	Sediment yield, 23 years	Based on the sediment monitoring network	Entire Yangtze basin: up to 30 t ha^-1 year^-1
				Poyang Lake sub-basin: 0.75–6.5 t ha^-1 year^-1
				(35 stations; 23 years)

Table 7 Studies analysing the influence of land use on the spatial distribution of sediment yields. See Fig. 2 for catchment locations.

Reference	Catchment	Study aim and period	Method and studied land use	Results
9. Bieger et al. (2012)	Xiangxi Catchment (Three Gorges Area)	Mean annual sediment yield, 1985–1993	SWAT model, crops	48 t ha^-1 year^-1from agricultural areas
10. Chen et al. (2012)	Taiwan	Annual average soil erosion at rice fields (different management), 2005–2007	Experimental study, rice	0.74–4.15 t ha^-1 year^-1
11. El Kateb et al. (2013)	Shaanxi Province	Soil loss, 15 July–30 October 2008	Erosion plots, forest and tea	Highest soil loss: maize and tea
				Tea plantations at slopes >30° were most susceptible to erosion
				Tea plantations generated significantly higher soil loss (1.991–17.552 t ha^-1 year^-1) than forest lands (0.009–0.057 t ha^-1 year^-1) at all the three slope gradients
				Negligible soil losses were generated by forests
				The proportion of the generated soil loss was 7.4–63.5% for tea and 0.13–1.47% for forest
				Forest (cork-oak and pine) did not produce any soil loss until (compared to tea cultivation and grassland) on average, around four times higher runoff was generated
12. Hill and Peart (1998)	Southern China	Mean erosion rates	Review, forest and crops	Forest and woodland: 0.05 t ha^-1 year^-1, cultivated slopes: 62.4 t ha^-1 year^-1
				From cultivated land mostly about three orders of magnitude higher than from forest
13. Huang et al. (2010)	Red soil region at the Ecological Benefit Monitoring Station of the Yangtze River Protection Forest Project, in Hunan Province	Mean annual soil erosion at conventional sloping farmland (maize, peanut, sweet potato, potato, and pumpkin) with three different reforestation types, 2002–2007	Field experiments, forest and crops	Conventional sloping farmland (1.084 t ha^-1 year^-1) > natural succession type forest (0.066 t ha^-1 year^-1) > artificial economic forest (0.059 t ha^-1 year^-1) > pine secondary forest (0.041 t ha^-1 year^-1)
14. Lee et al. (2010)	Korean mountainous area	Soil loss under Chinese cabbage, 280 mm rainfall event	Experimental results, cabbage	Conventional tillage plots: 4.1–11.5 t ha^-1
				Reduced tillage plots: 2.2–2.4 t ha^-1
				Reduced tillage with rye mulching: 0.2–1.3 t ha^-1
				Reduced tillage reduced soil erosion; reduced tillage with rye mulching on soil surface doubled the effect of reducing soil erosion
15. Long et al. (2006)	Zhongjiang, a typical agricultural county of Sichuan Province	Soil erosion, maps of year 2000	GIS spatial analysis, crops	Most serious soil erosion occurred on agricultural land with a slope of 10–25°
				Both farmland and permanent crops were affected by soil erosion, with almost the same soil erosion for corresponding slope conditions
16. RRC AP (2001)	Sri Lanka	Soil erosion in tea plantations	Report; review, tea	0.33 and 250 t ha^-1 year^-1
17. Shen et al. (2010)	Three Gorges Reservoir Area	Soil erosion, sediment loads, 2004–2007	Plot experiments, tea	Tea trees: high soil erosion in the first year; soil stability enhanced gradually with growth
				Hedgerows in the plots reduced sediment loads (mean annual): 39.6%
18. Wang et al. (2013)	Le’an (Raohe basin)	Suspended sediment, June 2010–July 2011, 17 water samples bi-monthly	Suspended sediment monitoring, forest and crops	Suspended sediment concentrations: 0.2–74.4 (average: 9.99 mg L^-1)
				Increasing trend from upstream to downstream
				Northeast area (80% forests and 12% cultivated land): 0.2–40.8 mg L^-1 (average: 6.92 mg L^-1): low, no significant variability
				Southwest area (52% forests and 40% cultivated land): 2.2–74.36 mg L^-1 (average 14.39 mg L^-1): high, varies considerably
				Forests contribute to reduce soil loss and sediment concentration, while arable land and related activities cause considerable soil loss during rainfall events
19. Wu and Chen (2012)	East River Basin	Annual sediment yield, 8-year period	SWAT model, forest and crops	Highest: 409 t ha^-1 year^-1 from the agricultural areas, but for the whole basin 24.9 t ha^-1 year^-1
				105.5 t ha^-1 year^-1 from agricultural land, only 0.2 t ha^-1 year^-1 from forests
20. Zhang et al. (2011a)	Mountainous woodlands in China	Average annual soil loss	USLE, forest	0.0382 t ha^-1 year^-1
				about 99.89% of Chinese woodland area in the slight erosion class, whereas it only resulted in about 41.97% of soil loss under woodland area, and 58.03% of soil loss occurred under high erosion potential zone (>5 t ha^-1 year^-1)
21. Zhang et al. (2011b)	Shangshe catchment of the Dabie Mountains of the Anhui province	Annual soil losses from different land-use types (pine forest, Chinese fir forest, bamboo forest, and tea garden), 2000–2007	Field plot observations, integral equation method, forest and tea	Tea garden: <0.10 t ha^-1, except in 2001 (0.55 t ha^-1), mainly due to the grass cutting and tending
				Chinese fir forest, pine forest and bamboo forest: <0.10 t ha^-1, except: pine forest in 2005 (0.109 t ha^-1)
				bush changed to bare land: soil loss rates of up to 350 t ha^-1
				when vegetation coverage increased to 70 and 80% (natural regeneration), amount of soil erosion decreased significantly to around 3 t ha^-1

Fig. 2 Locations of study areas used for validating sediment model results. Numbers are related to Tables 6 and 7 (missing numbers: 3: Upper Yangtze basin, 8: entire Yangtze basin, 12: southern China, 14: Korean area, 16: area in Sri Lanka, 20: mountainous woodlands). Black circle represents location of study area.

Table 8 Model performance for daily and monthly streamflow during calibration and validation periods.

	Calibration 2001–2010	Validation 1986–2000	Calibration 2001–2010	Validation 1986–2000
Time step	Daily	Daily	Monthly	Monthly
NSE	0.56	0.51	0.87	0.79
r^2	0.56	0.52	0.88	0.85
Ave error	−2.69	−7.79	−81.81	−237.13
PBIAS	5.75	12.02	5.75	12.02

Fig. 3 Precipitation, and hydrograph of observed and simulated daily streamflow at Tankou station during the calibration period (2001–2010) (CMA 2011; Jiangxi Hydrological Bureau 2011).

Table 9 Correlation (r²) between measured precipitation (PCP) and measured streamflow (Q) for the entire simulation period 1986–2010 on daily and monthly basis.

	Q Tankou
	vs PCP Jingdezhen	vs PCP Tunxi	vs PCP Anqing
Daily basis
Entire data set	0.17	0.24	0.07
Monsoon period	0.17	0.26	0.06
Low-flow period	0.09	0.10	0.04
3 days	0.28	0.39	0.18
3 consecutive days
Entire data set	0.28	0.39	0.18
Monthly basis
Entire data set	0.70	0.79	0.59
Monsoon period	0.57	0.73	0.47
Low-flow period	0.49	0.58	0.37

Fig. 4 Flow components within the sub-basin at the basin outlet (site SUB45): values averaged over 10 years (2001–2010, calibration period). Note SUR_Q, surface runoff; LAT_Q, lateral flow; GW_Q, groundwater flow.

Table 10 Simulated surface runoff (SURQ; average annual values, 2001–2010) (left) for the entire basin depending on land use (LU) and (right) in HRUs of two example sub-basins with different land use and the same soil (LU-Soil). n is the number of HRUs within the selected sub-basin.

LU	SURQ (mm)	SUB	LU-Soil	SURQ mean (mm)	n
Forest	164.82	19	FRST-ACu3	165.50	5
Tea	327.27		TEAT-ACu3	329.19	5
Cabbage	615.87		CABG-ACu3	619.51	4
Rice	702.00		RICE-ACu3	704.61	1
		45	FRST-ACu3	165.84	5
			TEAT-ACu3	335.06	4
			CABG-ACu3	621.98	4
			RICE-ACu3	710.06	1

Fig. 5 Flow components—GWQ: groundwater flow; LatQ: lateral flow; SurQ: surface runoff—within all sub-basins, represented as pies (%), as well as sediment yield, SYLD (t ha^-1), represented as background colour of sub-basins. Annual values averaged over 10 years (2001–2010, calibration period).

Table 11 Measured total suspended solids (TSS) at 12 detailed sites in two field campaigns in October 2010 and February/March 2011.

SWAT sub-basin	Date	TSS (mg L^-1)	Date	TSS (mg L^-1)
SUB05	19 October 2010	0.80	10 March 2011	0.22
SUB06	11 October 2010	0.40	11 March 2011	0.30
SUB19	26 October 2010	0.73	28 February 2011	0.51
SUB23	21 October 2010	0.95	1 March 2011	0.34
SUB07	9 October 2010	0.53	27 February 2011	0.51
SUB16	12 October 2010	5.40	26 February 2011	0.50
SUB17	16 October 2010	0.60	4 March 2011	0.41
SUB15	8 October 2010	0.55	9 March 2011	0.35
SUB18	10 October 2010	0.50	25 February 2011	0.73
SUB25	18 October 2010	1.67	14 March 2011	0.81
SUB43	17 October 2010	0.87	12 March 2011	0.34
SUB45	20 October 2010	0.20	13 March 2011	0.34
SUB18*	14 October 2010*	47.53
SUB25*	14 October 2010*	121.80

*Note: Two additional samples were taken after a heavy rain event.

Fig. 6 Daily dynamics of the simulated sediment yield within the sub-basin at the basin outlet (SUB45) over 10 years (2001–2010, calibration period), as well as precipitation (CMA 2011). See Fig. 2 for catchment locations.

Fig. 7 Seasonal dynamics of the simulated sediment yield within the sub-basin at the basin outlet (site SUB45): values are the monthly sum of sediment yield averaged over 10 years (2001–2010, calibration period). See Fig. 2 for catchment locations.

Table 12 Simulated sediment yield (SYLD; average annual values, 2001–2010) (left) for the entire basin depending on land use (LU) and (right) in HRUs of two example sub-basins with different land use and same soil (LU-Soil). n is the number of HRUs within the selected sub-basin.

LU	SYLD (t ha^-1)	SUB	LU-Soil	SYLD (t ha^-1)	n
Forest	0.18	19	FRST-ACu3	0.13	5
Tea	0.10		TEAT-ACu3	0.09	5
Cabbage	37.48		CABG-ACu3	32.66	4
Rice	2.23		RICE-ACu3	1.56	1
		45	FRST-ACu3	0.13	5
			TEAT-ACu3	0.08	4
			CABG-ACu3	26.11	4
			RICE-ACu3	1.27	1

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