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Landscape- and climate change-induced hydrological alterations in the typically urbanized Beiyun River basin, Beijing, China

  • Yueqiu Zhang
  • Shiliang LiuEmail author
  • Xiaoyun Hou
  • Fangyan Cheng
  • Zhenyao Shen
Original Paper
  • 119 Downloads

Abstract

Landscapes in urbanized regions have experienced considerable changes in recent decades, and had a growing number of negative effects on hydrological processes. However, it is not well understood to what extent the combined and individual landscape and climate factors have altered hydrological processes in such areas. Using Beiyun River in Beijing as a case, we assessed hydrological responses quantitatively based on the Water and Energy Transfer between Soil, Plants and Atmosphere model (WetSpa extension). The results indicated that landscape patterns varied greatly from 2000 to 2012, which was exhibited primarily by the encroachment of built-up land on cropland. The landscape indices selected showed that the landscapes are prone to be disaggregated, fragmented, and complicated due to urbanization. In addition, the WetSpa model is available to simulate daily hydrological processes after calibration with model bias, model confidence and Nash–Sutcliffe efficiency of 18%, 0.71 and 0.84, respectively, and validation with correlation coefficient of 0.81. The model output revealed that the combined effects of landscape pattern change and climate variations increased total runoff and daily active groundwater storage. Among different sub-catchments, the Shahe sub-catchment upstream and Fenggangjianhe sub-catchment downstream had higher discharges, with increasing trends from 2000 to 2012. Compared with period 1 (2000–2005) as the reference, the annual average runoff during period 2 (2006–2011) and period 3 (2012–2016) increased 16.5 mm and 77.5 mm and the daily groundwater storage increased 71.6 mm and 47.3 mm through the combined effects of landscape and climate change. In period 2, the individual climate change had a positive effect on runoff with the contribution rate of 120.6% while landscape variation had a negative effect with the rate of − 20.6%. In period 3, they both had positive effects on runoff with the contribution rates of 93.6% and 6.4%, respectively. This study has practical significance for evaluation of the influence of urbanization on the hydrological processes and future water resource management.

Keywords

Landscape pattern Climate change Hydrological process WetSpa extension Beiyun River 

Abbreviations

PD

Patch density

LPI

Largest Patch Index

AI

Aggregation Index

SPLIT

Splitting Index

SHAPE-AM

Area Weighted Shape Index

ED

Edge density

COHESION

Patch Cohesion Index

SHDI

Shannon’s Diversity Index

Ens

Nash–Sutcliffe efficiency

Re

Relative error

R

Correlation coefficient

Qs

Surface runoff

Qi

Interflow

Qg

Groundwater flow

Notes

Acknowledgements

The research was financially supported by the National Natural Sciences Foundation of China (41530635; 41571173) and National Key Research and Development Project (No. 2016YFC0502103).

References

  1. Aguilera R, Sabater S, Marcé R (2012) In-stream nutrient flux and retention in relation to land use in the Llobregat River Basin. The Llobregat, Springer Berlin, Heidelberg, pp 69–92Google Scholar
  2. Ahiablame L, Sheshukov AY, Rahmani V, Moriasi D (2017) Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin. J Hydrol 551:188–202Google Scholar
  3. Allen RG (2000) Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J Hydrol 229:27–41Google Scholar
  4. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration—guidelines for computing crop water requirements FAO irrigation and drainage paper 56. FAO, RomaGoogle Scholar
  5. Andréassian V (2004) Waters and forests: from historical controversy to scientific debate. J Hydrol 291:1–27Google Scholar
  6. Bartsch WM, Axler RP, Host GE (2015) Evaluating a Great Lakes scale landscape stressor index to assess water quality in the St. Louis River Area of Concern. J Great Lakes Res 41(1):99–110Google Scholar
  7. Brown AE, Zhang L, McMahon TA, Western AW, Vertessy RA (2005) A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. J Hydrol 310:28–61Google Scholar
  8. Chen Y, Pang S, Geng R, Wang X, Bao L (2015) Fluxes of the main contaminant in Beiyun River. Acta Sci Circum 35:2167–2176Google Scholar
  9. Chen J, Theller L, Gitau MW, Engel BA, Harbor JM (2017) Urbanization impacts on surface runoff of the contiguous United States. J Environ Manag 187:470–481Google Scholar
  10. Cuo L, Beyene TK, Voisin N, Su F, Lettenmaier DP, Alberti M, Richey JE (2011) Effects of mid-twenty-first century climate and land cover change on the hydrology of the Puget Sound basin, Washington. Hydrol Process 25:1729–1753Google Scholar
  11. Eum HI, Dibike Y, Prowse T (2016) Comparative evaluation of the effects of climate and land-cover changes on hydrologic responses of the Muskeg River, Alberta, Canada. J Hydrol Reg Stud 8:198–221Google Scholar
  12. Fan M, Shibata H (2015) Simulation of watershed hydrology and stream water quality under land use and climate change scenarios in Teshio River watershed, northern Japan. Ecol Indic 50:79–89Google Scholar
  13. Fiener P, Auerswald K, Oost KV (2011) Spatio-temporal patterns in land use and management affecting surface runoff response of agricultural catchments—A review. Earth Sci Rev 106(1):92–104Google Scholar
  14. Fiquepron JS, Garcia S, Stenger A (2013) Land use impact on water quality: valuing forest services in terms of the water supply sector. J Environ Manage 126:113–121Google Scholar
  15. Gao Y (2014) Analysis of Beiyunhe River heavy rain flood in 2012. Water Sci Eng Technol 5:41–43Google Scholar
  16. Gao YL, Tang JK, Qian J et al (2010) The effects of land use and land cover on water environment: a review. Yellow River 32(12):16–18Google Scholar
  17. Han J, Meng X, Zhou X, Yi B, Liu M, Xiang W (2017) A long-term analysis of urbanization process, landscape change, and carbon sources and sinks: a case study in China’s Yangtze River Delta region. J Clean Prod 141:1040–1050Google Scholar
  18. Jeppesen E, Kronvang B, Meerhoff M, Søndergaard M, Hansen K (2016) Climate change effects on runoff, catchment phosphorus loading and lake ecological state, and potential adaptations. J Environ Qual 38:1930–1941Google Scholar
  19. Ji L, Fu C, Yang B, Liu Y (2013) Analysis of “7. 21” rainstorm flood dispatching in Beiyun River. Beijing Water Aff 1:9–11Google Scholar
  20. Ji DQ, Wen Y, Wei JB, Wu ZF, Liu Q, Cheng J (2015) Relationships between landscape spatial characteristics and surface water quality in the Liu Xi River watershed. Acta Ecol Sinica 35(2):246–253Google Scholar
  21. Kim S, Kim BS, Jun H, Kim HS (2014) Assessment of future water resources and water scarcity considering the factors of climate change and social–environmental change in Han River basin, Korea. Stoch Environ Res Risk Assess 28:1999–2014Google Scholar
  22. Kundu S, Khare D, Mondal A (2017) Individual and combined impacts of future climate and land use changes on the water balance. Ecol Eng 105:42–57Google Scholar
  23. Li Z, Liu W-z, Zhang X-c, Zheng F-l (2009) Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. J Hydrol 377(1–2):35–42Google Scholar
  24. Li G, Xiang X, Tong Y (2013) Impact assessment of urbanization on flood risk in the Yangtze River Delta. Stoch Environ Res Risk Assess 27:1683–1693Google Scholar
  25. Li Y, Li Y, Qureshi S, Kappas M, Hubacek K (2015a) On the relationship between landscape ecological patterns and water quality across gradient zones of rapid urbanization in coastal China. Ecol Model 318:100–108Google Scholar
  26. Li B, Zhou W, Zhao Y, Ju Q, Yu Z, Liang Z, Acharya K (2015b) Using the SPEI to assess recent climate change in the Yarlung Zangbo River Basin, South Tibet. Water 7(10):5474–5486Google Scholar
  27. Li S, Xiong L, Li HY (2016) Attributing runoff changes to climate variability and human activities: uncertainty analysis using four monthly water balance models. Stoch Environ Res Risk Assess 30:251–269Google Scholar
  28. Liu J, Shi Z (2017) Quantifying land-use change impacts on the dynamic evolution of flood vulnerability. Land Use Policy 65:198–210Google Scholar
  29. Liu Y, Smedt F (2004) WetSpa extension, a GIS-based hydrologic model for flood prediction and watershed management. User Manual 388:279–360Google Scholar
  30. Liu YB, Gebremeskel S, Smedt FD, Hoffmann L, Pfister L (2003) A diffusive transport approach for flow routing in GIS-based flood modeling. J Hydrol 283:91–106Google Scholar
  31. Liu S et al (2017) Ecosystem services and landscape change associated with plantation expansion in a tropical rainforest region of Southwest China. Ecol Model 353:129–138Google Scholar
  32. Locatelli L, Mark O, Mikkelsen PS, Nielsen KA, Deletic A, Roldin M, Binning PJ (2017) Hydrologic impact of urbanization with extensive stormwater infiltration. J Hydrol 544:524–537Google Scholar
  33. McGarigal K, Cushman S, Neel MC, Ene E (2002) FRAGSTATS: spatial pattern analysis program for categorical maps. University of Massachusetts, BostonGoogle Scholar
  34. Miller JD, Kim H, Kjeldsen TR, et al (2014) Assessing the impact of urbanization on storm runoff in a peri-urban catchment using historical change in impervious cover. J Hydrol 515:59–70Google Scholar
  35. Mwangi HM, Julich S, Patil SD, McDonald MA, Feger K-H (2016) Relative contribution of land use change and climate variability on discharge of upper Mara River, Kenya. J Hydrol Reg Stud 5:244–260Google Scholar
  36. Napoli M, Massetti L, Orlandini S (2017) Hydrological response to land use and climate changes in a rural hilly basin in Italy. CATENA 157:1–11Google Scholar
  37. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. J Hydrol 10:282–290Google Scholar
  38. NOAA (2016) Global climate change indicators. NOAA-National Centers for Environmental Information, AshevilleGoogle Scholar
  39. Nourani V, Saeidifarzad B (2017) Detection of land use/cover change effect on watershed’s response in generating runoff using computational intelligence approaches. Stoch Environ Res Risk Assess 1:1–17Google Scholar
  40. Ouyang W, Wang W, Hao F, Song K, Wang Y (2010) Pollution characterization of urban stormwater runoff on different underlying surface conditions. China Environ Sci 30:1249–1256Google Scholar
  41. Putro B, Kjeldsen TR, Hutchins MG, Miller J (2016) An empirical investigation of climate and land-use effects on water quantity and quality in two urbanising catchments in the southern United Kingdom. Sci Total Environ 548–549:164–172Google Scholar
  42. Qi X, Liu H, Han H, Chai W, Qu J (2013) A study of metal contamination in sediments in Beisan River System. Acta Sci Circum 33:117–124Google Scholar
  43. Rady RAE-H (2016) Modeling of flow characteristics beneath vertical and inclined sluice gates using artificial neural networks. Ain Shams Eng J 7:917–924Google Scholar
  44. Roberts AD (2016) The effects of current landscape configuration on streamflow within selected small watersheds of the Atlanta metropolitan region. J Hydrol Regional Studies 5:276–292Google Scholar
  45. Schütte S, Schulze RE (2017) Projected impacts of urbanisation on hydrological resource flows: a case study within the uMngeni Catchment, South Africa. J Environ Manag 196:527–543Google Scholar
  46. Shan B, Guan Y, Zhang H (2011) Analysis of the heavy metal pollution features and the assessment situation in the lower reach of the North Canal. J Saf Environ 11:141–145Google Scholar
  47. Sofia G, Roder G, Fontana GD, Tarolli P (2017) Flood dynamics in urbanised landscapes: 100 years of climate and humans’ interaction. Sci Rep 7:40527.  https://doi.org/10.1038/srep40527 Google Scholar
  48. Su S, Xiao R, Jiang Z, Zhang Y (2012) Characterizing landscape pattern and ecosystem service value changes for urbanization impacts at an eco-regional scale. Appl Geogr 34:295–305Google Scholar
  49. Su S, Ma X, Xiao R (2014) Agricultural landscape pattern changes in response to urbanization at ecoregional scale. Ecol Ind 40:10–18Google Scholar
  50. Tomer MD, Schilling KE (2009) A simple approach to distinguish land-use and climate-change effects on watershed hydrology. J Hydrol 376:24–33Google Scholar
  51. Veettil VA, Konapala G, Mishra AK, Li HY (2018) Sensitivity of drought resilience–vulnerability–exposure to hydrologic ratios in contiguous United States. J Hydrol 564:294–306Google Scholar
  52. Verbeiren B, Voorde TVD, Canters F, Binard M, Cornet Y, Batelaan O (2013) Assessing urbanisation effects on rainfall–runoff using a remote sensing supported modelling strategy. Int J Appl Earth Obs Geoinf 21:92–102Google Scholar
  53. Wan WH, Zhao JS, Li HY, Mishra A, Hejazi M, Lu H, Demissie Y, Wang H (2018) A holistic view of water management impacts on future droughts: a global multimodel analysis. J Geophys Res Atmos 123(11):5947–5972Google Scholar
  54. Wang Z, Batelaan O, Smedt FD (1996) A distributed model for water and energy transfer between soil, plants and atmosphere (WetSpa). Phys Chem Earth 21:189–193Google Scholar
  55. Wang C, Fu C, Ji L, Liu Y, Liu Z (2017) Analysis of “7. 20” rainstorm flood dispatching in Beiyun River. Beijing Water Aff 1:39–42Google Scholar
  56. Wu JG, Darrel G, Alexander J, Buyantuyev A, Redman CL (2011) Quantifying spatiotemporal patterns of urbanization: the case of the two fastest growing metropolitan regions in the United States. Ecol Complex 8(1):1–8Google Scholar
  57. Xian W, Shao H, Zhou W (2005) Process of land use/land cover change in the area of middle and lower reach of Jialingjiang River. Prog Geogr 24:114–121Google Scholar
  58. Xiao R, Ouyang Z, Li W, Zhang Z, Gregory TJ (2005) A review of the eco-environmental consequences of urban heat islands. Acta Ecol Sin 25:2055–2060Google Scholar
  59. Xiao R, Su S, Zhang Z, Qi J, Jiang D, Wu J (2013) Dynamics of soil sealing and soil landscape patterns under rapid urbanization. CATENA 109:1–12Google Scholar
  60. Yang C, Yang Z, Nie Y, He X, Hu C (2012a) Adsorption of heavy metals Cu, Pb, Zn over top sediment in North River Canal Chinese. J Environ Eng 6:3438–3442Google Scholar
  61. Yang Z, Nie Y, Hu C (2012b) Migration and transformation of heavy metals at solid/water interface in Beiyunhe River. Chin J Environ Eng 6:3455–3465Google Scholar
  62. Yao L, Wei W, Chen L (2006) How does imperviousness impact the urban rainfall-runoff process under various storm cases? Ecol Ind 60:893–905Google Scholar
  63. Yuan S, Zhang W, Zhang B (2014) Heavy metal contaminant distribution features and the diffusion flux estimation in the sediments of Shahe reservoir, Beijing. J Saf Environ 14:245–249Google Scholar
  64. Zhang Q, Sun P, Jiang T, Tu X, Chen X (2011) Spatio-temporal patterns of hydrological processes and their responses to human activities in the Poyang Lake basin, China. Hydrol Sci J 56:305–318Google Scholar
  65. Zhang A, Zhang C, Fu G, Wang B, Bao Z, Zheng H (2012) Assessments of impacts of climate change and human activities on runoff with SWAT for the Huifa River Basin, Northeast China. Water Resour Manag 26:2199–2217Google Scholar
  66. Zhang Q, Gu X, Singh VP, Chen X (2015) Evaluation of ecological instream flow using multiple ecological indicators with consideration of hydrological alterations. J Hydrol 529:711–722Google Scholar
  67. Zhang Q, Liu J, Singh VP, Gu X, Chen X (2016) Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China. Hydrol Process 30:2562–2576Google Scholar
  68. Zhang Y, Liu S, Cheng F et al (2017) WetSpass-based study of the effects of urbanization on the water balance components at regional and quadrat scales in Beijing, China. Water 10(1):5Google Scholar
  69. Zhao Q, Liu S, Deng L, Dong S, Wang C, Yang Z, Yang J (2012) Landscape change and hydrologic alteration associated with dam construction. Int J Appl Earth Obs Geoinf 16:17–26Google Scholar
  70. Zhou F, Xu Y, Chen Y, Xu C-Y, Gao Y, Du J (2013) Hydrological response to urbanization at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region. J Hydrol 485:113–125Google Scholar
  71. Zhou J, He D, Xie Y (2015) Integrated SWAT model and statistical downscaling for estimating streamflow response to climate change in the Lake Dianchi watershed, China. Stoch Environ Res Risk Assess 29:1193–1210Google Scholar
  72. Zomlot Z, Verbeiren B, Huysmans M, Batelaan O (2015) Spatial distribution of groundwater recharge and base flow: assessment of controlling factors. J Hydrol Reg Stud 4:349–368Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yueqiu Zhang
    • 1
    • 2
  • Shiliang Liu
    • 1
    Email author
  • Xiaoyun Hou
    • 1
  • Fangyan Cheng
    • 1
  • Zhenyao Shen
    • 1
  1. 1.School of Environment, State Key Laboratory of Water Environment SimulationBeijing Normal UniversityBeijingPeople’s Republic of China
  2. 2.Safety Evaluation CenterShenyang Research Institute of Chemical Industry CO., LTDShenyangPeople’s Republic of China

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