DOI QR코드

DOI QR Code

토지이용변화에 따른 미계측 유역의 기저유출량 산정 및 평가

Estimation and assessment of baseflow at an ungauged watershed according to landuse change

  • 이지민 (국립강원대학교 지역건설공학과) ;
  • 신용철 (APEC 기후센터) ;
  • 박윤식 (국립강원대학교 지역건설공학과) ;
  • 금동혁 (국립강원대학교 지역건설공학과) ;
  • 임경재 (국립강원대학교 지역건설공학과) ;
  • 이승오 (홍익대학교 토목공학과) ;
  • 김형수 (인하대학교 토목공학과) ;
  • 정영훈 (국립강원대학교 환경연구소)
  • Lee, Ji Min (Regional Infrastructure Engineering, Kangwon National University) ;
  • Shin, Yongchun (APEC climate center) ;
  • Park, Youn Shik (Regional Infrastructure Engineering, Kangwon National University) ;
  • Kum, Donghyuk (Regional Infrastructure Engineering, Kangwon National University) ;
  • Lim, Kyoung Jae (Regional Infrastructure Engineering, Kangwon National University) ;
  • Lee, Seung Oh (School of Urban and Civil Engineering, Hongik University) ;
  • Kim, Hungsoo (Department of Civil Engineering, Inha university) ;
  • Jung, Younghun (Environmental Research Center, Kangwon National University)
  • 투고 : 2014.08.29
  • 심사 : 2014.09.29
  • 발행 : 2014.11.30

초록

기후변화와 도시화는 기저유출이 하천유량에 미치는 계절별 특성에 변동성을 초래한다. 이러한 기저유출의 변동성은 수생태의 혼란을 유발할 뿐만 아니라 불안정한 수자원 관리를 초래할 수 있다. 토지이용변화는 직접유출과 기저유출에 영향을 주며, 결과적으로 다른 수문순환 요소들에게 미치게 된다. 일반적으로 기저유출은 관측된 하천유량을 통해 산정되지만, 모델링의 유량 예측을 통해서 미계측 유역의 기저유출량 산정에 유용하게 사용 될 수 있다. 따라서, 본 연구의 목적은 1) RECESS 통해 alpha factor를 산정한 후, SWAT 모형에 적용하여 보정 예측을 향상시키고, WHAT 시스템을 미계측 유역의 적용하여 기저유출을 분석하며, 3) 토지이용변화에 따른 기저유출 특성을 평가하는 것이다. 이러한 목적으로 미계측 지역인 갑천 유역에 Period 1(1990-1996)과 Period 2(2000-2006)로 설정하여 적용하였다. RECESS를 통해 alpha factor를 산정한 후, SWAT 모형 보정에 적용한 결과는 유량예측의 정확성을 향상시키고, 기저유출의 특성인 감수부분도 반영되었다. 두기간 사이의 연평균 기저유출을 비교했을 때 토지이용변화는 연평균 기저유출량에 큰 영향을 미치지 않는 것으로 나타났다. 그러나 두기간 사이의 계절별 기저유출의 분포를 비교했을 때 토지이용변화는 기저유출의 계절별 특성에서 큰 상이성을 초래했다. 따라서 토지이용변화로 인한 갑천 유역의 유량 및 기저유출의 변동성은 금강 본류로 전달되기 때문에 계절별 변화에 따라 전략적으로 분석 및 관리가 필요하다.

Baseflow gives a significant contribution to stream function in the regions where climatic characteristics are seasonally distinct. In this regard, variable baseflow can make it difficult to maintain a stable water supply, as well as causing disruption to the stream ecosystem. Changes in land use can affect both the direct flow and baseflow of a stream, and consequently, most other components of the hydrologic cycle. Baseflow estimation depends on the observed streamflow in gauge watersheds, but accurate predictions of streamflow through modeling can be useful in determining baseflow data for ungauged watersheds. Accordingly, the objectives of this study are to 1) improve predictions of SWAT by applying the alpha factor estimated using RECESS for calibration; 2) estimate baseflow in an ungauged watershed using the WHAT system; and 3) evaluate the effects of changes in land use on baseflow characteristics. These objectives were implemented in the Gapcheon watershed, as an ungauged watershed in South Korea. The results show that the alpha factor estimated using RECESS in SWAT calibration improves the prediction for streamflow, and, in particular, recessions in the baseflow. Also, the changes in land use in the Gapcheon watershed leads to no significant difference in annual baseflow between comparable periods, regardless of precipitation, but does lead to differences in the seasonal characteristics observed for the temporal distribution of baseflow. Therefore, the Guem River, into which the stream from the Gapcheon watershed flows, requires strategic seasonal variability predictions of baseflow due to changes in land use within the region.

키워드

참고문헌

  1. Abbaspour, KC, Yang, J, Maximov, I, Siber, R, Bonger, K, Mieleitner, J, Zobrist, J and Srinivasan, R (2007). Modelling Hydrology and Water Quality in the Pre-alpine Thur Watershed using SWAT, J. of Hydrology, 333(2), pp. 413-430. https://doi.org/10.1016/j.jhydrol.2006.09.014
  2. Ahiablame, L, Chaubey, I, Engel, B, Cherkauer, K and Merwade, V (2013). Estimation of annual baseflow at ungauged sites in Indiana USA, J. of Hydrology, 476(7), pp. 13-27. https://doi.org/10.1016/j.jhydrol.2012.10.002
  3. Arnold, JG and Allen, PM (1999). Validation of Automated Methods for Estimating Baseflow and Groundwater Recharge From Stream Flow Records, J. of American Water Resources Association, 35(2), pp. 411-424. https://doi.org/10.1111/j.1752-1688.1999.tb03599.x
  4. Arnold, JG, Allen, PM and Bernhardt, GA (1993). A Comprehensive surface-groundwater flow model, J. of hydrology, 142(1), pp. 47-69. https://doi.org/10.1016/0022-1694(93)90004-S
  5. Bako, MD and Owoade, A (1988). Field application of a numerical method for the derivation of baseflow recession constant, J. of Hydrological processes, 2(4), pp. 331-336. https://doi.org/10.1002/hyp.3360020404
  6. Beven, KJ and Binley, AM (1992). The future of distributed models: model calibration and uncertainty prediction, J. of Hydrological Processes, 6(3), pp. 279-298. https://doi.org/10.1002/hyp.3360060305
  7. Bieger, K, Hormaan, G and Fohrer, N (2013). Detailed spatial analysis of the plausibility of surface runoff and sediment yields at HRU level in a mountainous watershed in China, 2013 International SWAT Confrerence, Toulouse, France, http://swat.tamu.edu/media/77467/j11_bieger.pdf.
  8. Bonuma, NB, Rossi, CG, Arnold, JG, Reichert, JM, Minella, JP, Allen, PM and Volk, M (2012). Simulating landscape sediment transport capacity by using a modified SWAT model, J. of Environmental Quality, doi:10.2134/jeq2012.0217.
  9. Chaplot, V (2005). Impact of DEM mesh size and soil map scale on SWAT runoff, sediment, and NO3-N loads predictions, J. of Hydrology, 312(1), pp. 207-222. https://doi.org/10.1016/j.jhydrol.2005.02.017
  10. Chapman, TA (1999). Comparison of algorithms for stream flow recession and baseflow separation, J. of Hydrological Processes, 13, pp. 701-704. https://doi.org/10.1002/(SICI)1099-1085(19990415)13:5<701::AID-HYP774>3.0.CO;2-2
  11. Chiu, MC and Kuo, MH (2012). Application of R/K selection to macro invertebrate responses to extreme floods, J. of Ecological Entomology, 37(2), pp. 145-154. https://doi.org/10.1111/j.1365-2311.2012.01346.x
  12. Chow, VT, Maidment, DR and Mays, LW (1988). Applied Hydrology, McGraw-Hill Series in water resources and Environmental Engineering, New York.
  13. De, Moel, H and Aerts, JCJH (2011). Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates, J. of Natural Hazards, 58(1), pp. 407-425. https://doi.org/10.1007/s11069-010-9675-6
  14. Eberhart, R and Kennedy, JA (1995). New optimizer using particle swarm theory. Micro Machine and Human Science, 1995, MHS'95, Proceedings of the Sixth International Symposium on, pp. 39-43.
  15. Eckhardt, KA (2008). Comparison of baseflow indices, which were calculated with seven different baseflow separation methods, J. of hydrology, 352(1-2), pp. 168-173. https://doi.org/10.1016/j.jhydrol.2008.01.005
  16. Eckhardt, K (2005). How to construct recursive digital filters for baseflow separation, J. of Hydrological Processes, 19(2), pp. 507-515. https://doi.org/10.1002/hyp.5675
  17. Ferguson, G and Gleeson, T (2012). Vulnerability of coastal aquifers to groundwater use and climate change, J. of Nature Climate Change, 2, pp. 342-345. https://doi.org/10.1038/nclimate1413
  18. Gao, YZ, Giese, M, Han, XG, Wang, DL, Zhou, ZY, Brueck, H, Lin, S and Taube, F (2009). Land use and drought interactively affect interspecific competition and species diversity at the local scale in a semiarid steppe ecosystem, J. of Ecological research, 24(3), pp. 627-635. https://doi.org/10.1007/s11284-008-0532-y
  19. Gitau, MW and Chaubey, I (2010). Regionalization of SWAT model parameters for use in ungauged watersheds, J. of Water, 2(4), pp. 849-871. https://doi.org/10.3390/w2040849
  20. Hall, FR (1968). Base-Flow Recessions-A Review, J. of Water Resources Research, 4, pp. 973-983. https://doi.org/10.1029/WR004i005p00973
  21. Hamel. P, Daly, E and Fletcher, TD (2013). Source-control stormwater management for mitigating the impacts of urbanization on baseflow: A review, J. of Hydrology, 485(2), pp. 201-211. https://doi.org/10.1016/j.jhydrol.2013.01.001
  22. Jeong, J, Kannan, N, Arnold, J, Glick, R, Gosselink, L, Srinivasan, R and Harmel, D (2011). Development of Sub daily Erosion and Sediment Transport Models in SWAT, Trans. ASABE, 54, pp. 1685-1691. https://doi.org/10.13031/2013.39841
  23. Kim, SJ, Kwon, HJ, Park, G and Lee, MS (2005). Assessment of land-use impact on streamflow via a grid-based modelling approach including paddy fields, J. of Hydrological processes, 2005, 19(19), 3801-3817. https://doi.org/10.1002/hyp.5982
  24. Kuczera, G and Parent, E (1998). Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, J. of Hydrology, 211(1-4), pp. 69-85. https://doi.org/10.1016/S0022-1694(98)00198-X
  25. Kulandaiswamy, VC and Seetharaman, SA (1969). Note on Barnes' method of hydrograph separation, J. of Hydrology, 9, pp. 222-229. https://doi.org/10.1016/0022-1694(69)90080-8
  26. Li, R and Merchant, JW (2013). Modeling vulnerability of groundwater to pollution under future scenarios of climate change and biofuels-related land use change: A case study in North Dakota, USA, J. of Science of the Total Environment, 447(1), pp. 32-45. https://doi.org/10.1016/j.scitotenv.2013.01.011
  27. Lim, KJ, Engel, BA, Tang, Z, Choi, J, Kim, KS, Muthukrishnan, S and Tripathy, D (2005). Automated Web Gis based hydrograph analysis tool, WHAT1, J. of American Water Resources Association, 41, pp. 1407-1416. https://doi.org/10.1111/j.1752-1688.2005.tb03808.x
  28. Lim, KJ, Park, YS, Kim, J, Shin, YC, Kim, NW, Kim, SJ, Jeon, JH, Engel, BA (2010). Development of genetic algorithm-based optimization module in WHAT system for hydrograph analysis and model application, J. of Computers & Geosciences, 36(7), pp. 936-944. https://doi.org/10.1016/j.cageo.2010.01.004
  29. Malhi, Y, Aragao, LE, Galbraith, D, Huntingford, C, Fisher, R, Zelazowski, P, Sitch, S, McSweeney, C and Meir, P (2009). Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest, J. of Proceedings of the National Academy of Sciences, 106(49), pp. 20610-20615. https://doi.org/10.1073/pnas.0804619106
  30. Maneta, MP, Torres, MDO, Wallender, WW, Vosti, S, Howitt, R, Rodrigues, L, Bassoi, LH and Panday, S (2009). A spatially distributed hydro economic model to assess the effects of drought on land use, farm profits, and agricultural employment, J. of Water Resources Research, 45(11), W11412.
  31. Mehta, VK, Haden, VR, Joyce, BA, Purkey, DR and Jackson, LE (2013). Irrigation demand and supply, given projections of climate and land-use change, in Yolo County, California, J. of Agricultural Water Management, 117(31), pp. 70-82. https://doi.org/10.1016/j.agwat.2012.10.021
  32. Nathan, RJ and McMahon, TA (1990). Evaluation of automated techniques for base flow and recession analyses, J. of Water Resources Research, 26(7), pp. 1465-1473.
  33. Neitsch, SL, Arnold, JG, Kiniry, JR, Williams, JR and King, KW (2005). Soil and water assessment tool: theoretical documentation, version 2005, Texas, USA.
  34. Ogden, FL, Raj, PN, Downer CW and Zahner, JA (2011). Relative importance of impervious area, drainage density, width function, and subsurface storm drainage on flood runoff from an urbanized catchment, J. of Water Resources Research, 47(12), W12503.
  35. Price, K (2011). Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: a review, J. of Geography, 35, pp. 465-492.
  36. Rostamian, R, Jaleh, A, Afyuni, M, Mousavi, SF, Heidarpour, M, Jalalian, A and Abbaspour, KC (2008). Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran, J. of Hydrological sciences, 53(5), pp. 977-988. https://doi.org/10.1623/hysj.53.5.977
  37. Samuel, J, Coulibaly, P and Metcalfe, RA (2012). Identification of rainfall-runoff model for improved baseflow estimation in ungauged basins, J. of Hydrological Processes, 26(3), pp. 356-366. https://doi.org/10.1002/hyp.8133
  38. Santhi, C, Allen, PM, Muttiah, RS, Arnold, JG and Tuppad, P (2008). Regional estimation of base flow for the conterminous United States by hydrologic landscape regions, J. of Hydrology, 351(1), pp. 139-153. https://doi.org/10.1016/j.jhydrol.2007.12.018
  39. Shuster, WD, Bonta, J, Thurston, H, Warnemuende, E and Smith, DR (2005). Impacts of impervious surface on watershed hydrology: A review, J. of Urban Water, 2(4), pp. 263-275. https://doi.org/10.1080/15730620500386529
  40. Singh, A, Imtiyaz, M, Isaac, RK and Denis, DM (2012). Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India, J. of Agricultural Water Management, 104, pp. 113-120. https://doi.org/10.1016/j.agwat.2011.12.005
  41. Sophocleous, M (2002). Interactions between groundwater and surface water: the state of the science, J. of Hydrogeology, 10(1), pp. 52-67. https://doi.org/10.1007/s10040-001-0170-8
  42. Szilagyi, J and Parlange, MB (1998). Baseflow separation based on analytical solutions of the Boussinesq equation, J. of Hydrology, 204(1), pp. 251-260. https://doi.org/10.1016/S0022-1694(97)00132-7
  43. Tallaksen, LM (1995). A review of baseflow recession analysis, J. of hydrology, 165(1), pp. 349-370. https://doi.org/10.1016/0022-1694(94)02540-R
  44. Van Griensven, A and Meixner, T (2006). Methods to quantify and identify the sources of uncertainty for river basin water quality models, J. of Water Science & Technology, 53(1), pp. 51-59.
  45. Wang, L, Lyons, J, Kanehl, P and Bannerman, R (2001). Impacts of urbanization on stream habitat and fish across multiple spatial scales, J. of Environmental Management, 28(2), pp. 255-266.
  46. Wilcox, BP and Huang, Y (2010). Woody plant encroachment paradox: Rivers rebound as degraded grasslands convert to woodlands, J. of Geophysical Research Letters, 37(7) pp 1-5.