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Response of Non-point Source Pollution Loads to Land Use Change under Different Precipitation Scenarios from a Future Perspective

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Abstract

To optimize the management of non-point source (NPS) pollution in a changing environment, the cellular automata (CA) and Hydrological Simulation Program-Fortran (HSPF) models were used to study the response mechanism of NPS pollution loads to land use change and different precipitation scenarios. Taking the Dongjiang River Basin as a case study, the land use situation and its spatial distribution patterns in 2020, 2030 and 2050 were predicted by the logistic regression-based CA model. The trends of the NPS pollution loads under different land use and precipitation scenarios were quantitatively evaluated. The results show that the total accuracy of the land use change simulated by the CA model was 81%. Both the HSPF model and the CA model were highly applicable to this basin. Precipitation is proven to be the main driving force of NPS pollution. From 2020 to 2050, the annual load, average monthly load, maximum and minimum monthly load of the BOD and TP show an upward trend. TN shows a slight downward trend, which is related to the reduction in cultivated land area and the use of nitrogen fertilizer. In view of the future trend of NPS pollution, the basin should continue to control TN pollution and focus on strengthening BOD and TP control to achieve high-quality management of the water environment.

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References

  • Aryal SK, Ashbolt S, Mclntosh BS, Petrone KP, Maheepala S, Chowdhury RK, Gardener T, Gardiner R (2016) Assessing and mitigating the hydrological impacts of urbanisation in semi-urban catchments using the storm water management model. Water Resour Manag 30:5437–5454

    Article  Google Scholar 

  • Cai YP, Rong QQ, Yang ZF, Yue WC, Tan Q (2018) An export coefficient based inexact fuzzy bi-level multi-objective programming model for the management of agricultural nonpoint source pollution under uncertainty. J Hydrol 557:713–725

    Article  Google Scholar 

  • Corwin DL, Loague K, Ellworth TR (1998) GIS-based modeling of non-point source pollutants in the vadose zone. J. Soil Water Conserv 53:34–38

    Google Scholar 

  • Chen L, Xu JJ, Wang GB, Liu HB, Zhai LM, Li S, Sun C, Shen ZY (2018) Influence of rainfall data scarcity on non-point source pollution prediction: implications for physically based models. J Hydrol 562:1–16

    Article  Google Scholar 

  • Ding J, Jiang Y, Fu L, Liu Q, Peng QZ, Kang MY (2015) Impacts of land use on surface water quality in a subtropical river basin: a case study of the Dongjiang River basin, southeastern China. Water 7:4427–4445

    Article  Google Scholar 

  • Gounaridis D, Chorianopoulos I, Koukoulas S (2018) Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr 90:134–144

    Article  Google Scholar 

  • General land use plan of Shenzhen city (2009) Guangdong Province, China (2006–2020), Shenzhen Urban Planning and Land Resources Committee

  • IPCC (2007) Summary for policymakers of climate change: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  • Jahanishakib F, Mirkarimi SH, Salmanmahiny A, Poodat F (2018) Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting. Environ. Monit. Assess 190:332

    Article  Google Scholar 

  • Keefer L, Markus M, Getahun E (2017) Monitoring and modeling of Nutrient & Sediment Loads for varying climate & landscapes. Building resiliency in the face of risk. Illinois State Water Survey, Champaign

    Google Scholar 

  • Kim S, Seo DJ, Riazi H, Shin C (2014) Improving water quality forecasting via data assimilation-application of maximum likelihood ensemble filter to HSPF. J.Hydrol 519:2797–2809

    Article  Google Scholar 

  • Lee SB, Yoon CG, Jung KW, Hwang HS (2010) Comparative evaluation of runoff and water quality using HSPF and SWMM. Water Sci Technol 62:1401–1409

    Article  Google Scholar 

  • Lai CG, Shao QX, Chen XH, Wang ZL, Zhou XW, Yang B, Zhang LL (2016) Flood risk zoning using a rule mining based on ant colony algorithm. J Hydrol 542:268–260

    Article  Google Scholar 

  • Lai C, Chen X, Wang Z, Yu H, Bai X (2020) Flood risk assessment and regionalization from past and future perspectives at basin scale. Risk Anal 40:1399–1417. https://doi.org/10.1111/risa.13493

    Article  Google Scholar 

  • Lv L, Peng QZ, Liao JY, Jiang Y, Kang MY (2013) Fluctuation and trends in precipitation and runoff in the Dongjiang River basin over 50 years. Res Sci 35:514–520

    Google Scholar 

  • Li X, Gar-Onyeh A (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16:323–343

    Article  Google Scholar 

  • Lu Q, Chang NB, Joyce J, Chen AS, Savic DA, Djordjevic S, Fu GT (2018) Exploring the potential climate change impact on urban growth in London by a cellular automata-based Markov chain model. Comput. Environ. Urban Syst 68:121–132

    Article  Google Scholar 

  • Lin JY, Chen TL, Han QZ (2018) Simulating and predicting the impacts of light rail transit systems on urban land use by using cellular automata: a case study of Dongguan, China. Sustainability 10:1293

    Article  Google Scholar 

  • Liu J, Kuang W, Zhang Z, Xu X, Qin Y, Ning J, Zhou W, Zhang S, Li R, Yan C, Wu S, Shi X, Jiang N, Yu D, Pan X, Chi W (2014) Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J Geogr Sci 24(2):195–210

    Article  Google Scholar 

  • Ma GM; Wang YY; He LH; Liang HJ; Su BD; Hu Y; Wang G (2017) Study on non-point sources pollution loading of nitrogen and phosphorus in Ashi River Basin. IOP Conf. Ser.: Earth Environ. Sci 69: 012033

  • Ongley ED, Zhang XL, Tao Y (2010) Current status of agricultural and rural non-point source pollution assessment in China. Environ Pollut 158:1159–1168

    Article  Google Scholar 

  • Ouyang W, Huang HB, Hao FH, Shan YS, Guo BB (2012) Evaluating spatial interaction of soil property with non-point source pollution at watershed scale: the phosphorus indicator in Northeast China. Sci Total Environ 432:412–421

    Article  Google Scholar 

  • Ouyang Y, Parajuli PB, Feng G, Leininger TD, Wan YS, Dash P (2018) Application of climate assessment tool (CAT) to estimate climate variability impacts on nutrient loading from local watersheds. J Hydrol 563:363–371

    Article  Google Scholar 

  • Owens LB, Shipitalo MJ, Bonta JV (2008) Water quality response time to pasture management changes in small and large watersheds. J Soil Water Conserv 63:292–299

    Article  Google Scholar 

  • Ribarova, I., Ninov, P., Cooper, D. (2008) Modeling nutrient pollution during a first flood event using HSPF software: Iskar River case study, Bulgaria Ecol Model 211: 241–246

  • Rodrigues V, Estrany J, Ranzini M, Cicco VMJ, Martín-Benito T, Hedo J, Lucas-Borja ME (2018) Effects of land use and seasonality on stream water quality in a small tropical catchment: the headwater of Córrego Água Limpa, São Paulo (Brazil). Sci Total Environ 622-623:1553–1561

    Article  Google Scholar 

  • Stern M, Flint L, Minear J, Flint A, Wright S (2016) Characterizing changes in streamflow and sediment supply in the Sacramento River Basin, California, using Hydrological Simulation Program-FORTRAN(HSPF). Water 10:432

    Article  Google Scholar 

  • Shen ZY, Zhong YC, Huang Q, Chen L (2015) Identifying non-point source priority management areas in watersheds with multiple functional zones. Water Res 68:563–571

    Article  Google Scholar 

  • USEPA (2003) National management measures to control non-point pollution from agriculture. EPA-841-B-03-004. United States Environmental Protection Agency, Washington, DC

  • Vivoni ER, Richards KT (2005) Integrated use of GIS-based field sampling and modeling for hydrologic and water quality studies. J Hydroinf 7:235–250

    Article  Google Scholar 

  • Vrebos D, Beauchard O, Meire P (2017) The impact of land use and spatial mediated processes on the water quality in a river system. Sci Total Environ 601-602:365–373

    Article  Google Scholar 

  • Wang HL, Wu ZN, Hu CH (2015) A comprehensive study of the effect of input data on hydrology and non-point source pollution modeling. Water Resour Manag 29:1505–1521

    Article  Google Scholar 

  • Wang Z, Xie PW, Lai C, Chen X, Wu X, Zeng Z, Li J (2017) Spatiotemporal variability of reference evapotranspiration and contributing climatic factors in China during 1961-2013. J Hydrol 544:97–108

    Article  Google Scholar 

  • Wang ZW, Yang ST, Zhao CS, Bai J, Lou HZ, Chen K, Wu LN, Dong GT, Zhou QW (2016) Assessment of non-point source Total phosphorus pollution from different land use and soil types in a mid-high latitude region of China. Water 8:505

    Article  Google Scholar 

  • Wang ZL, Chen JC, Lai CG, Zhong RD, Chen XH, Yu HJ (2018) Hydrologic assessment of the TMPA 3B42-V7 product in a typical alpine and gorge region: the Lancang River basin, China. Hydrol Res 49(6):2002–2015

  • Zhai XY, Zhang YY, Wang XL, Xia Y, Liang T (2014) Non-point source pollution modelling using soil and water assessment tool and its parameter sensitivity analysis in Xin’anjiang catchment. China Hydrol Process 28:1627–1640

    Article  Google Scholar 

  • Zema DA, Denisi P, Ruiz EVT, Gómez JA (2016) Evaluation of surface runoff prediction by AnnAGNPS model in a large Mediterranean watershed covered by olive groves. Land Degrad 27:811–822

    Article  Google Scholar 

  • Zhou Y, Zhang Q, Li K, Chen XH (2012) Hydrological effects of water reservoirs on hydrological processes: complexity evaluations based on the multi-scale entropy analysis. Hydrol Process 26:3253–3262. https://doi.org/10.1002/hyp.8406

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the National Natural Science Foundation of China (Grant Nos. 51509040, 51709127 and 51509127), the National Key R&D Program of China (2017YFC0405900), the Natural Science Foundation of Guangdong Province, China (Grant No. 2017A030310172), and the Open Project Program of Chongqing Key Laboratory of Karst Environment (Grant No. Cqk 201702).

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Correspondence to Peng Wang.

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Bai, X., Shen, W., Wang, P. et al. Response of Non-point Source Pollution Loads to Land Use Change under Different Precipitation Scenarios from a Future Perspective. Water Resour Manage 34, 3987–4002 (2020). https://doi.org/10.1007/s11269-020-02626-0

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