Effects of land-use and climate change on sediment and nutrient retention in Guizhou, China

Figures & data

Figure 1. Study area.

Figure 2. Land-use maps for 1995 and 2015.

Figure 3. Scenario framework.

Table 1. Changes in average sediment retention from 1995 to 2015 in Guizhou Province.

Indicators	1995 t/ha	2015 t/ha	Change rate %
Sediment exported	17.71	14.94	−15.64
Sediment retained	769.38	656.92	−14.62

Figure 4. Spatial pattern of changes in exported sediment (a) and retained sediment (b) at the watershed scale from 1995 to 2015 in Guizhou Province.

Table 2. Changes in average nutrient retention from 1995 to 2015 in Guizhou Province.

Indicators	1995 kg/ha	2015 kg/ha	Change rate %
Nitrogen retention	3.63	3.48	−4.13
Phosphorus retention	1.36	1.21	−11.03

Figure 5. Spatial pattern of changes in nitrogen retention (a) and phosphorus retention (b) at a watershed scale from 1995 to 2015 in Guizhou Province.

Table 3. Changes in average sediment retention from 1995 to 2015 under different scenarios in Guizhou Province.

Indicators	1995 t/ha	2015 under Scenario A t/ha	2015 under Scenario B t/ha	Change rate from 1995 to 2015 under scenario A %	Change rate from 1995 to 2015 under scenario B %
Sediment exported	17.71	15.04	17.58	−15.08	−0.73
Sediment retained	769.38	656.82	769.81	−14.63	0.06

Note: Scenario A was defined as climate change with no land-use change from 1995 to 2015. Scenario B was defined as land-use change with no climate change from 1995 to 2015.

Figure 6. Changes in the spatial pattern of exported sediment (a) and retained sediment (b) at a watershed scale from 1995 to 2015 under different scenarios in Guizhou Province.

Table 4. Changes in average nutrient retention from 1995 to 2015 under different scenarios in Guizhou Province.

Indicators	1995 kg/ha	2015 under Scenario A kg/ha	2015 under Scenario B kg/ha	Change rate from 1995 to 2015 under scenario A %	Change rate from 1995 to 2015 under scenario B %
Nitrogen retention	3.63	3.64	3.47	0.28	−4.41
Phosphorus retention	1.36	1.37	1.21	0.74	−11.03

Note: Scenario A was defined as climate change with no land-use change from 1995 to 2015. Scenario B was defined as land-use change with no climate change from 1995 to 2015.

Figure 7. Spatial pattern of nitrogen retention (a) and phosphorus retention (b) at a watershed scale from 1995 to 2015 under different scenarios in Guizhou Province.

Figure 8. Sensitivity analysis of parameters in the InVEST sediment retention model.

Figure 9. Sensitivity analysis of parameters in the InVEST nutrient retention model.

Table 5. Changes of land-use and climate from 1995 to 2015 in Guizhou Province.

Categories	Quantitative change	Change rate
Forestland (km^2)	197	0.83%
Shrub land (km^2)	−1202	−1.67%
Grassland (km^2)	−616	−1.97%
Dry land (km^2)	944	2.82%
Paddy fields (km^2)	−434	−2.96%
Built-up land (km^2)	1011	187.57%
Water body (km^2)	113	28.11%
Unused land (km^2)	−13	−26.00%
Rainfall (mm)	−25	−2.08%
Rainfall erosivity (MJ mm/ha/h/yr)	−203	−6.90%

Table A1. Data sources for InVEST sediment retention and nutrient retention model.

Data	Description
Digital elevation model (DEM)	ASTER GDEM data with a 30 m resolution (NASA, Houston, TX, USA).
Land-use	Spatial resolution at 30 m × 30 m. Land-use categories were divided into forestland, shrub land, grassland, farmland, built-up land, water body, and unused land.
Climate	Contained daily average temperature, maximum temperature, minimum temperature, average relative humidity, average wind speed, sunshine hours, and precipitation.
Soil types	Contained 13 soil types (i. e., Yellow earths, Limestone soils, Litho soils, Paddy soils, Red earths, Purplish soils, Yellow-brown earths, Mountain meadow soils, Brown earths, Alluvial soils, Bog soils, Fluvo-aquic soils and Skeletol soils.
Soil erodibility	Calculated wtih the EPIC (Erosion-productivity impact calculator) model (Williams, Renard, and Dyke 1983).$\mathrm{K}=\left\{0.2+0.3exp\left[-0.0256SAN⟨1-SIL/100⟩\right]\right\}\ast {\left[\frac{SIL}{⟨CLA+SIL⟩}\right]}^{0.3}\ast \left\{1-\frac{0.25C}{\left[C+exp\left(3.72-2.95C\right)\right]}\right\}\ast \left\{1-0.75SN1/\left[SN1+\mathrm{e}\mathrm{x}\mathrm{p}\left(-5.51+22.95SN1\right)\right]\right\}$Where K denotes the soil erodibility, SAN denotes the sand proportion, SIL denotes the silt proportion, CLA denotes the clay proportion, and C denotes the organic matter proportion, SN1 = 1− SAN/100.
Rainfall erosivity	Calculated by Zhang and Fu (2003).$\mathrm{R}=0.849\times \alpha \sum _{j=1}^k{\left(X_J\right)}^{\beta }-29.651$$\beta =0.8363+18.44P_{d12}^{-1}+24.455P_{d12}^{-1}$$\alpha =21.586{\beta }^{-7.1891}$Where R denotes rainfall erosivity [MJ·mm/(ha·h)], while Xj denotes erosive rainfall on day j. Erosive rainfall is rainfall ≥ 12 mm, otherwise it is calculated as zero. Pd12 denotes daily average rainfall ≥12 mm.

Table A2. Parameters of InVEST sediment retention and nutrient retention model.

Land-use types	Forest-land	Shrub land	Grassl-and	Dry land	Paddy fields	Built-up land	Water body	Unused land
Crop-management	0.001	0.010	0.300	0.800	0.005	0.000	0.000	0.000
Support practice	1.0	1.0	1.0	0.5	0.9	0.0	0.0	1.0
Output coefficients of Nitrogen	1.800	2.100	3.100	7.710	0.450	11.000	0.001	14.900
Output coefficients of Phosphorus	0.011	0.028	0.100	4.680	0.030	0.240	0.001	0.510
Removal efficiency/%	70	60	48	25	55	5	5	5

Williams, J. R., K. G. Renard, and P. T. Dyke. 1983. “EPIC: A New Method for Assessing Erosion’s Effect on Soil Productivity.” Journal of Soil and Water Conservation 38: 381–383.

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Zhang, W., and J. Fu. 2003. “Rain Erositivity Estimation under Different Rainfall Amount.” Recourses Science 25: 35–41.

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