DOI QR코드

DOI QR Code

Assessing Climate Change Impacts on Hydrology and Water Quality using SWAT Model in the Mankyung Watershed

SWAT 모형을 이용한 기후변화에 따른 만경강 유역에서의 수문 및 수질 영향 평가

  • Kim, Dong-Hyeon (Department of Rural Construction Engineering, Chonbuk National University) ;
  • Hwang, Syewoon (Department of Agricultural Engineering, Institute of Agriculture and Life Science, Gyeongsang National University) ;
  • Jang, Taeil (Department of Rural Construction Engineering, Chonbuk National University) ;
  • So, Hyunchul (Department of Rural Construction Engineering, Chonbuk National University)
  • Received : 2018.09.20
  • Accepted : 2018.10.11
  • Published : 2018.11.30

Abstract

The objective of this study was to estimate the climate change impact on water quantity and quality to Saemanguem watershed using SWAT (Soil and water assessment tool) model. The SWAT model was calibrated and validated using observed data from 2008 to 2017 for the study watershed. The $R^2$ (Determination coefficient), RMSE (Root mean square error), and NSE (Nash-sutcliffe efficiency coefficient) were used to evaluate the model performance. RCP scenario data were produced from 10 GCM (General circulation model) and all relevant grid data including the major observation points (Gusan, Jeonju, Buan, Jeongeup) were extracted. The systematic error evaluation of the GCM model outputs was performed as well. They showed various variations based on analysis of future climate change effects. In future periods, the MIROC5 model showed the maximum values and the CMCC-CM model presented the minimum values in the climate data. Increasing rainfall amount was from 180mm to 250mm and increasing temperature value ranged from 1.7 to $5.9^{\circ}C$, respectively, compared with the baseline (2006~2017) in 10 GCM model outputs. The future 2030s and 2070s runoff showed increasing rate of 16~29% under future climate data. The future rate of change for T-N (Total nitrogen) and T-P (Total phosphorus) loads presented from -26 to +0.13% and from +5 to 47%, respectively. The hydrologic cycle and water quality from the Saemanguem headwater were very sensitive to projected climate change scenarios so that GCM model should be carefully selected for the purpose of use and the tendency analysis of GCM model are needed if necessary.

Keywords

References

  1. Arabi, M., J. R. Frankenberger, B. A. Engel, and J. G. Arnold, 2007. Representation of agricultural conservation practices with SWAT. Hydrological Processes 22(16): 3042-3055. doi:10.1002/hyp.6890.
  2. Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Williams, 1998. Large area hydrologic modeling and assessment part I: model development. Journal of American Water Resources Association 34(1): 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
  3. Chaemiso, S. E., A. Abebe, and S. M. Pingale, 2016. Assessment of the impact of climate change on surface hydrological processes using SWAT: a case study of Omo-Gibe river basin, Ethiopia. Journal of the Modeling Earth Systems and Environment 2(4): 1-15. doi: 10.1007/s40808-016-0257-9.
  4. Cho, J., S. Hwang, G. Go, K. Y. Kim, and J. Kim, 2015. Assessing the climate change impact on agricultural reservoirs using the SWAT model and CMIP5 GCMs. Journal of the Korean Society of Agricultural Engineers 57(5): 1-12 (in Korean). doi:10.5389/KSAE.2015.57.5.001.
  5. Chung, U., J. Cho, and E. J. Lee, 2015. Evaluation of agro-climatic index using multi-model ensemble downscaled climate prediction of CMIP5. Journal of the Korean Agricultural and Forest Meteorology 17(2): 108-125 (in Korean). doi:10.5532/KJAFM.2015.17.2.108.
  6. Do, Y. and G. kim, 2018. Analysis of the change of dam inflow and evapotranspiration in the Soyanggang dam basin according to the AR5 climate change scenarios. Journal of the Korean Society of Agricultural Engineers 60(1): 89-99 (in Korean). doi:10.5389/KSAE.2018.60.1.089.
  7. Han, J. H., D. J. Lee, B. Kang, S. W. Chung, W. S. Jang, K. J. Lim, and J. Kim, 2017. Potential impacts of future extreme storm events on streamflow and sediment in Soyang-dam watershed. Journal of Korean Society on Water Environment 33(2): 160-169 (in Korean). doi: 10.15681/KSWE.2017.33.2.160.
  8. Her, Y., I. Chaubey, J. Frankenberger, and J. Jeong, 2017. Implications of spatial and temporal variations in effects of conservation practices on water management strategies. Journal of the Agricultural Water Management 180: 252-266. doi:10.1016/j.agwat.2016.07.004.
  9. Hwang, S., 2012. Utility of gridded observations for statistical bias-correction of climate model outputs and its hydrologic implication over west central florida. Journal of the Korean Society of Agricultural Engineers 54(5): 91-102 (in Korean). doi:10.5389/KSAE.2012.54.5.091.
  10. Hwang, S., J. Cho, and K. S. Yoon, 2018. Assessing the skills of CMIP5 GCMs in reproducing spatial climatology of precipitation over the coastal area in East Asia. Journal of Korea Water Resources Association 51(8): 629-642 (in Korean). doi:10.3741/JKWRA.2018.51.8.629.
  11. Hwang, S., Y. Her, and S. J. Chang, 2013. Uncertainty in regional climate change impact assessment using biascorrection technique for future climate scenarios. Journal of the Korean Society of Agricultural Engineers 55(4): 95-106 (in Korean). doi:10.5389/KSAE.2013.55.4.095.
  12. Green, C. H., M. D. Tomer, M. Di Luzio, and J. G. Arnold, 2006. Hydrologic evaluation of the soil and Water assessment tool for a large tile-drained watershed in Iowa. American Society of Agricultural and Biological Engineers 49(2): 413-422.
  13. Jang, S. S. and S. J. Kim, 2017. Assessment of climate change impact on highland agricultural watershed hydrologic cycle and water quality under RCP scenarios using SWAT. Journal of the Korean Society of Agricultural Engineers 59(3): 41-50 (in Korean). doi: 10.5389/KSAE.2017.59.3.041.
  14. Jang, S. S. and S. J. Kim, 2018. Assessment of climate change impact on best management practices of highland agricultural watershed under RCP scenarios using SWAT. Journal of the Korean Society of Agricultural Engineers 60(4): 123-132 (in Korean). doi:10.5389/KSAE.2018.60.4.123.
  15. Jang, S. S., S. R. Ahn, H. K. Joh, and S. J. Kim, 2015. Assessment of climate change impact on Imha-dam watershed hydrologic cycle under RCP scenarios. Journal of the Korean Association of Geographic Information Studies 18(1): 156-169 (in Korean). doi:10.11108/kagis.2015.18.1.156.
  16. Jang. Y., J. Park, and D. Seo, 2018. Estimation of flow rate and pollutant loading changes of the Yo-cheon basin under AR5 climate change scenarios using SWAT. Journal of Korean Society of Water and Wastewater 32(3): 221-233 (in Korean). doi:10.11001/jksww.2018.32.3.221.
  17. Jung, C. G., J. W. Moon, C. H. Jang, and D. R. Lee, 2013. Assessment of climate change impacts on hydrology and snowmelt by applying RCP scenarios using SWAT model for Hanriver watersheds. Journal of the Korean Society of Agricultural Engineers 55(5): 37-48 (in Korean). doi: 10.5389/KSAE.2013.55.5.037.
  18. Kim, B. K. and K. S. Kim, 2011. Assessment of climate change impacton water quality simulation in the basin of Heuk stream. Journal of the Korean Society of Environmental Technology 12(2): 112-117 (in Korean).
  19. Kim, D. H. and S. M. Kim, 2017. Estimation of inflow into Namgang dam according to climate change using SWAT model. Journal of the Korean Society of Agricultural Engineers 59(6): 9-18 (in Korean). doi: 10.5389/KSAE.2017.59.6.009.
  20. Kim, E. J., B. K. Park, Y. S. Kim, D. H. Rhew, and K. W. Jung, 2015. A study on development of management targets and evaluation of target achievement for non-point source pollution management in Saemangeum watershed. Journal of Korean Society of Environmental Engineers 37(8): 480-491 (in Korean). doi:10.4491/KSEE.2015.37.8.480.
  21. Kim, S. M., Y. R. Yu, and Y. K. Park, 2017. Estimation of non-point source load by agricultural drainage system in Mankyung river basin. Journal of the Korean Society of Environmental Technology 18(4): 391-400 (in Korean).
  22. Kim, S. M., Y. K. Park, C. H. Won, and M. H. Kim, 2016. Analysis of scenarios for environmental instream flow considering water quality in Saemangeum watershed. Journal of the Korean Society of Environmental Engineers 38(3): 117-127 (in Korean). doi:10.4491/KSEE.2016.38.3.117.
  23. Lee, J. K. and Y. O. Kim, 2012. Selecting climate change scenarios reflecting uncertainties. Journal of the Korean Metheorological Society 22(2): 149-161 (in Korean). doi: 10.14191/Atmos.2012.22.2.149.
  24. Lee, Y. J., S. R. Ahn, B. Kang, and S. J. Kim, 2008. Assessment of future climate and land use change on hydrology and stream water quality of Anseongcheon watershed using SWAT model (II). Journal of the Korean Society of Civil Engineers 28(6B): 665-673 (in Korean).
  25. Mehdi, B., K. Schulz, R. Ludwig, F. Ferber, and B. Lehner, 2018. Evaluating the importance of non-unique behavioural parameter sets on surface water quality variables under climate change conditions in a mesoscale agricultural watershed. Journal of The Water Resour Manage 32(2): 619-639.
  26. Narsimlu, B., A. K. Gosain, and B. R. Chahar, 2013. Assessment of future climate change impacts on water resources of upper Sind river basin, India using SWAT model. Journal of The Water Resource Management 27(10): 3647-3662. doi:10.1007/s11269-013-0371-7.
  27. Nash, J. E. and J. E. Sutcliffe, 1970. River flow forecasting though conceptual models: Part I, A discussion of principles. Journal of Hydrology 10(3): 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  28. Navarro, E. M., H. E. Andersen, A. Nielsen, H. Thodsen, and D. Trolle, 2017. The impact of the objective function in multi-site and multi-variable calibration of the SWAT model. Journal of The Environmental Modelling & Software 93: 255-267. doi:10.1016/j.envsoft.2017.03.018.
  29. Neitsch, S. L., J. G. Arnold, J. R. Kiniry, and J. R. Williams, 2011. Soil and Water Assessment Tool Theoretical Documentation Version 2009. Grassland, Soil and Water Research Laboratory, Agricultural Research Service Blackland Research Center, Texas 1-565.
  30. Park, J. Y., H. Jung, C. H. Jang, and S. Kim, 2014. Assessing climate change impact on hydrological components of Yongdam dam watershed using RCP emission scenarios and SWAT model. Journal of the Korean Society of Agricultural Engineers 56 (3): 19-29 (in Korean). doi:10.5389/KSAE.2014.56.3.019.
  31. Park, J., J. Cho, E. J. Lee, and I. Jung, 2017. Evaluation of reference evapotranspiration in South Korea according to CMIP5 GCMs and estimation methods. Journal of The Korean Society of Rural Planning 23(4): 153-168 (in Korean). doi:10.7851/Ksrp.2017.23.4.153.
  32. Park, J. Y., M. S. Lee, Y. J. Lee, and S. J. Kim, 2008. The analysis of future land use change impact on hydrology and water quality using SWAT model. Journal of the Korean Society of Civil Engineers 28(2B): 187-197 (in Korean).
  33. Park, M. H., H. L. Cho, and B. K. Koo, 2015. Nn evalution of climate change effects on pollution loads of the Hwangryong river watershed in Korea. Journal of the Korean Water Resource Association 48(3): 185-196 (in Korean). doi:10.3741/JKWRA.2015.48.3.185.
  34. Seo, D. and J. Kim, 2016. Reduction of pollutant concentrations in urban stormwater runoff by settling. Journal of the Society of Environmental engineers 38(4): 210-218 (in Korean). doi:10.4491/KSEE.2016.38.4.210.
  35. Seong. C., S. Hwang, C. Oh, and J. Cho, 2017. Developing surface water quality modeling framework considering spatial resolution of pollutant load estimation for Saemangeum using HSPF. Journal of the Korean Society of Agricultural Engineers 59(3): 83-96 (in Korean). doi: 10.5389/KSAE.2017.59.3.083.
  36. Shin, Y. and H. Jung, 2015. Assessing uncertainty in future climate change in Northeast Asia using multiple CMIP5 GCMs with four RCP scenarios. Journal of Korea Environmental Impact Assessment 24(3): 205-216 (in Korean). doi:10.14249/eia.2015.24.3.205.
  37. Yasin, H. Q. and R. S. Clemente, 2014. Application of SWAT model for hydrologic and water quality modeling in Thachin river basin, Thailand. Arabian Journal for Science and Engineering 39(3): 1671-1684. doi:10.1007/s13369-013-0770-3.
  38. Yoo, S. H., T. Kim, S. H. Lee, and J. Y. Choi, 2015. Trend analysis of projected climate data based on CMIP5 GCMs for climate change impact assessment on agricultural water resources. Journal of the Korean Society of Agricultural Engineers 57(5): 69-80 (in Korean). doi:10.5389/KSAE.2015.57.5.069.
  39. Zhang, Y., J. Xia, J. Chen, and M. Zhang, 2011. Water quantity and quality optimization modeling of dams operation based on SWAT in Wenyu river catchment, China. Environmental Monitoring and Assessment 173(1-4): 409-430. doi:10.1007/s10661-010-1396-5.
  40. Zhang, Y., J. Xia, Q. Shao, and X. Zhai, 2011. Water quantity and quality simulation by improved SWAT in highly regulated Huai river basin of China. Stochastic Environmental Research and Risk Assessment 27(1): 11-27. doi:10.1007/s00477-011-0546-9.