Impacts of Climate Change on Nitrogen Load and its Control in the Upper Huai River Basin, China

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1 Impacts of Climate Change on Nitrogen Load and its Control in the Upper Huai River Basin, China Xiaoying Yang Department of Environmental Science & Engineering, Fudan University, Shanghai 2016 SWAT Conference

2 Huai River Basin Located in the transition zone between East Asian monsoon humid climate and sub-humid climate. Under substantial influence of large-scale circulation and water vapor transport, and very sensitive to climate change. 2

3 Water Security Issue: Extreme Events Extreme events are frequent in the Huai River Basin. Between 1949 and 2009, there have been 11 serious floods and 13 serious droughts in the Huai River Basin. 3

4 Water Security Issue: Water Pollution Yangtz River Yellow River Pearl River Songhua River Huai River Hai River Liao River Huai River is one of the most seriously polluted rivers in China. Among the monitored river sections, 43.0% fall between category Ⅳ and Ⅴ,15.1% below category V. Chinese Ministry of Environmental Protection 4

5 Huai River Basin Zhumadian City 5

6 Study Region: the Upper Huai River Basin above the Shakou Hydrological Station with a drainage area of 5803km 2 Category III Below Category V

7 Global Climate Change Research Framework Climatic Conditions Other Physical Characteristics Land Use/ Land Cover SWAT Hydrology Component Water Quality Component Pollution Sources Impacts of Climate Change on Nitrogen Load and the Associated Uncertainties Decision Support System for Sustainable Watershed Pollution Control and Water Quality Improvement

8 Data for Hydrological Simulation DEM (1:50,000) Soil (1:1000,000) LULC (1:100,000) Daily weather at the Zhumadian station ( ) Daily and sub-daily precipitation at 28 stations ( ) Daily streamflow at 3 stations ( ) Daily outflow from 3 reservoirs ( ) 8

9 Daily Streamflow Simulation Statistics Daily model estimated 34% of baseflow contribution compared to 58% by the sub-daily model. The baseflow filter program estimated a range of The better performance of the subdaily model could be due to its ability to incorporate the highly concentrated rainstorm events. 9

10 Luzhuang Daily Model Luzhuang Sub-daily Model 10

11 Lixin Daily Model Lixin Sub-daily Model 11

12 Data for Nitrogen Pollution Simulation Annual N emissions from industries along 74 river segments in 2010 Annual N emissions from 6 municipal sewage treatment plants in 2010 Annual N emissions from CAFOs along 74 river segments in 2010 Annual N emissions from SAFOs in 9 counties and 1 district in 2010 Rural population in 9 counties and 1 district in 2010 Monthly TN and NH 4 -N concentrations ( ) 12

13 NSE statistics for Monthly Streamflow and N Load Simulations 13

14 Estimated Amount Simulated by the Sub-daily Model Simulated by the Daily Model 6000 TN Load (Thousand KgN) Monthly TN Load 0 06/01 06/03 06/05 06/07 06/09 06/11 07/01 07/03 07/05 07/07 07/09 07/11 08/01 08/03 08/05 08/07 08/09 08/11 09/01 09/03 09/05 09/07 09/09 09/11 10/01 10/03 10/05 10/07 10/09 10/11 11/01 11/03 11/05 11/07 11/09 11/11 Estimated Amount Simulated by the Sub-daily Model Simulated by the Daily Model 600 NH 4 Load (Thousand KgN) Monthly NH 4 -N Load 0 06/01 06/03 06/05 06/07 06/09 06/11 07/01 07/03 07/05 07/07 07/09 07/11 08/01 08/03 08/05 08/07 08/09 08/11 09/01 09/03 09/05 09/07 09/09 09/11 10/01 10/03 10/05 10/07 10/09 10/11 11/01 11/03 11/05 11/07 11/09 11/11 14

15 Global Climate Change Research Framework 16 GCMs, RCP 45, RCP s, 2080s LARS_WG 100 years of daily series 64 projections 1 baseline scenario Analog Method for Hourly Rainfall Disaggregation Climatic Conditions Other Physical Characteristics SWAT Hydrology Component Water Quality Component Impacts of Climate Change on Nitrogen Load and the Associated Uncertainties Land Use/ Land Cover Pollution Sources Decision Support System for Sustainable Watershed Pollution Control and Water Quality Improvement

16 Projected Change in Precipitation RCP45, 2050s RCP45, 2080s RCP85, 2050s RCP85, 2080s Most GCMs predict increasing monthly rainfall in winter and spring. Projected rainfall changes are more variable in summer and fall. Rainfall projections under the RCP85 scenario are more variable. 20

17 In 2080s, average TN loads in response to all 16 GCMs were predicted to Impacts increase under on TN both Loads emission scenarios. RCP45, 2050s RCP85, 2050s February RCP45, 2080s RCP85, 2080s In 2050s, average TN loads in response to13 and 14 GCMs were 21 predicted to increase under the RCP45 and RCP85 scenarios.

18 In 2080s, average TN loads in response to 14 and 15 GCMs were Impacts on TN Loads predicted to increase under the RCP45 and RCP85 scenarios. RCP45, 2050s RCP85, 2050s May RCP45, 2080s RCP85, 2080s In 2050s, average TN loads in response to 14 GCMs were predicted to increase under both emission scenarios. 22

19 Impacts on TN Loads Average TN loads were predicted to increase only in response to a few GCMs under all four climate change scenarios. RCP45, 2050s RCP85, 2050s August RCP45, 2080s RCP85, 2080s 23

20 In both periods, average TN loads in response to 9 and 12 GCMs were respectively predicted Impacts to increase TN under Loads the RCP45 and RCP 85 scenarios. RCP45, 2050s RCP85, 2050s November RCP45, 2080s RCP85, 2080s 24

21 Global Climate Change Research Framework 16 GCMs, RCP 45, RCP s, 2080s LARS_WG 100 daily series 64 projections 1 baseline scenario Analog Method for Hourly Rainfall Disaggregation Climatic Conditions Other Physical Characteristics SWAT Hydrology Component Water Quality Component Impacts of Climate Change on Nitrogen Load and the Associated Uncertainties Land Use/ Land Cover Pollution Sources Decision Support System for Sustainable Watershed Pollution Control and Water Quality Improvement

22 An Under increase the baseline the average condition, percentage of TN of TN load load reduction was predicted ranged Impacts from in response 3.7% to Pollution 14.1% to all GCMs with Control an under average the Measures of four 7.3%. scenarios, although most increase fell short of 3%. RCP45, 2050s RCP85, 2050s Reducing urea usage by half RCP45, 2080s RCP85, 2080s Farmers apply 750 kg/ha of compound fertilizers and kg/ha of urea for corn, and 750 kg/ha of compound fertilizers and kg/ha of urea for wheat. Reduce the urea application rates by half. 26

23 An Under increase the baseline the average condition, percentage of TN of TN load load reduction was Impacts on Pollution Control Measures predicted ranged from in response 0.6% to 5.7% to all with GCMs an under average the of four 2.9%. scenarios, falling between 3.5% and 5.7%. RCP45, 2050s RCP85, 2050s Constructing vegetative filter strips RCP45, 2080s RCP85, 2080s Assume the establishment of vegetative filter strips in all HRUs with agricultural land. 27

24 Under An decrease the baseline the average condition, percentage of of TN TN load load reduction reduction was predicted Impacts in response on Pollution to all GCMs Control under the Measures ranged from 5.2% to 9.3% with an average of four 7.5%. scenarios, falling between 3.3% and 6.2%. RCP45, 2050s RCP85, 2050s Improve Septic Tank Performance RCP45, 2080s RCP85, 2080s Assume that TN concentration in the septic tank effluent is reduced from 90 mg/l to 45 mg/l. 28

25 Conclusions The Sub-daily SWAT model performs better than the daily model for both hydrological process and N pollution simulations. Climate change tends to have considerably negative impact on future TN loads. There is a high possibility that average monthly TN loads in February, May, and November will increase. Predictions for August TN loads are more variable. Climate change tends to have negative impact on the effectiveness of improving septic tank performance but positive impact on the other two measures. 29

26 Acknowledgement We gratefully acknowledge the financial support provided by Chinese Natural Science Foundation ( ) Chinese Ministry of Education New Faculty Fund ( ) Fudan University Tyndall Center Project (FTC98503B04) UK Royal Society-Chinese Natural Science Foundation Exchange Project ( )