Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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Development of Parsimonious Semi-Distributed Hydrologic Partitioning Model Based on Soil Moisture Storages

토양수분 저류 기반의 간결한 준분포형 수문분할모형 개발

  • Choi, Jeonghyeon (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Kim, Ryoungeun (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
  • 최정현 (부경대학교 지구환경시스템과학부 (환경공학전공)) ;
  • 김령은 (부경대학교 지구환경시스템과학부 (환경공학전공)) ;
  • 김상단 (부경대학교 환경공학과)
  • Received : 2020.03.30
  • Accepted : 2020.05.27
  • Published : 2020.05.30
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Abstract

Hydrologic models, as a useful tool for understanding the hydrologic phenomena in the watershed, have become more complex with the increase of computer performance. The hydrologic model, with complex configurations and powerful performance, facilitates a broader understanding of the effects of climate and soil in hydrologic partitioning. However, the more complex the model is, the more effort and time is required to drive the model, and the more parameters it uses, the less accessible to the user and less applicable to the ungauged watershed. Rather, a parsimonious hydrologic model may be effective in hydrologic modeling of the ungauged watershed. Thus, a semi-distributed hydrologic partitioning model was developed with minimal composition and number of parameters to improve applicability. In this study, the validity and performance of the proposed model were confirmed by applying it to the Namgang Dam, Andong Dam, Hapcheon Dam, and Milyang Dam watersheds among the Nakdong River watersheds. From the results of the application, it was confirmed that despite the simple model structure, the hydrologic partitioning process of the watershed can be modeled relatively well through three vertical layers comprising the surface layer, the soil layer, and the aquifer. Additionally, discussions were conducted on antecedent soil moisture conditions widely applied to stormwater estimation using the soil moisture data simulated by the proposed model.

Keywords

References

  1. Ahiablame, L. M., Engel, B. A., and Chaubey, I. (2012). Representation and evaluation of low impact development practices with L-THIA-LID: An example for site planning, Environment and Pollution, 1(2), 1-12.
  2. Ajami, N. K., Gupta, H., Wagener, T., and Sorooshian, S. (2004). Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system, Journal of Hydrology, 298, 112-135. https://doi.org/10.1016/j.jhydrol.2004.03.033
  3. Bardossy, A. (2007). Calibration of hydrological model parameters for ungauged catchments, Hydrology and Earth System Sciences, 11, 703-710. https://doi.org/10.5194/hess-11-703-2007
  4. Beven, K. (2006a). A manifesto for the equifinality thesis, Journal of Hydrology, 320, 18-36. https://doi.org/10.1016/j.jhydrol.2005.07.007
  5. Beven, K. (2006b). Benchmark papers in storm runoff generation, IAHS press, Wallingford, UK.
  6. Bhaduri, B., Harbor, J., Engel, B. A., and Grove, M. (2000). Assessing watershed-scale, long-term hydrologic impacts of land use change using a GIS-NPS model, Environmental Management, 26(6), 643-658. https://doi.org/10.1007/s002670010122
  7. Brocca, L., Melone, F., and Moramarco, T. (2008). On the estimation of antecedent wetness conditions in rainfall-runoff modelling, Hydrological Processes, 22, 629-642. https://doi.org/10.1002/hyp.6629
  8. Brocca, L., Melone, F., Moramarco, T., and Singh, V. P. (2009). Assimilation of observed soil moisture data in storm rainfall-runoff modelling, Journal of Hydrologic Engineering, 14(2), 153-165. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:2(153)
  9. Chapra. S. C. (1997). Surface water-quality modeling, McGraw-Gill, New York, USA.
  10. Chow, V. T., Maidment, D. R., and Mays, L. W. (1988). Applied hydrology, McGraw-Hill, Singapore.
  11. Chung, S. W., Gassman, P. W., Kramer, L. A., Williams, J. R., and Gu, R. R. (1999). Validation of EPIC for two watersheds in southwest Iowa, Journal of Environmental Quality, 28(3), 971-979. https://doi.org/10.2134/jeq1999.00472425002800030030x
  12. De Michele, C. and Salvadori, G. (2002). On the derived flood frequency distribution: analytical formulation and the influence of antecedent soil moisture condition, Journal of Hydrology, 262, 245-258. https://doi.org/10.1016/S0022-1694(02)00025-2
  13. Devi, G. K., Ganasri, B. P., and Dwarakish, G. S. (2015). A review on hydrological models, Aquatic Procedia, 4, 1001-1007. https://doi.org/10.1016/j.aqpro.2015.02.126
  14. Eckhardt, K. and Arnold, J. G. (2001). Automatic calibration of a distributed catchment model, Journal of Hydrology, 251, 103-109. https://doi.org/10.1016/S0022-1694(01)00429-2
  15. Green, C. H., Tomer, M. D., Di Luzio, M., and Arnold, J. G. (2006). Hydrologic evaluation of the soil and water assessment tool for a lager tile-drained watershed in Iowa, Transactions of the American Society of Agricultural and Biological Engineers, 49(2), 413-422.
  16. Grove, M., Harbor, J., Engel, B. A., and Muthukrinan, S. (2001). Impacts of urbanization on surface hydrology. Little Eagle Creek, Indiana, and Analysis of L-THIA model sensitivity to data resolution, Physical Geography, 22, 135-153. https://doi.org/10.1080/02723646.2001.10642734
  17. Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, Z. F. (2009). Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modeling, Journal of Hydrology, 377, 80-91. https://doi.org/10.1016/j.jhydrol.2009.08.003
  18. Jeon, J. H., Lim, K. J., and Engel, B. A. (2014). Regional calibration of SCS-CN L-THIA model: Application for ungauged basins, Water, 6(5), 1339-1359. https://doi.org/10.3390/w6051339
  19. Kim, J., Lim, K. J., Park, Y., Heo, S., Park, J., Ahn, J., Kim, K., and Choi, J. (2007). The effect of slope-based curve number adjustment on direct runoff estimation by L-THIA, Journal of Korean Society on Water Environment, 23(6), 897-995. [Korean Literature]
  20. Kim, J., Park, Y., Jeon, J. H., Engel, B. A., Ahn, J., Park, Y. K., Kim, K., Choi, J., and Lim, K. J. (2007). Evaluation of L-THIA WWW direct runoff estimation with AMC adjustment, Journal of Korean Society on Water Environment, 23(4), 474-481. [Korean Literature]
  21. Kim, Y., Engel, B. A., Lim, K. J., Larson, V., and Duncan, B. (2002). Runoff impacts of land-use change in Indian River Lagoon watershed, Journal of Hydrologic Engineering, 7(3), 245-251. https://doi.org/10.1061/(ASCE)1084-0699(2002)7:3(245)
  22. Leroy, J. D. (2004). Modeling lake level variations using L-THIA in the Lake Maxinkuckee watershed, Master's Thesis, Purdue University, West Lafayette, Indiana.
  23. Lim, K. J., Engel, B. A., Kim, Y. S., Choi, J., and Kim, K. (2003). L-THIA/NPS to assess the impacts of urbanization on estimated runoff and NPS pollution, Journal of the Korean Society of Agricultural Engineers, 45(4), 78-88. [Korean Literature]
  24. Lim, K. J., Engel, B. A., Muthukrishnan, S., and Harbor, J. (2006). Effects of initial abstraction and urbanization on estimated runoff using CN technology, Journal of the American Water Resources Association, 42(3), 629-643. https://doi.org/10.1111/j.1752-1688.2006.tb04481.x
  25. Lim, K. J., Engel B. A., Tang, Z., Choi, J., Kim, K., Muthukrishnan, S., and Tripathy, D. (2005). Automated web GIS-based hydrograph analysis tool, WHAT, Journal of the American Water Resource Association, 41(6), 1407-1416. https://doi.org/10.1111/j.1752-1688.2005.tb03808.x
  26. Michel, C., Andreassian, V., and Perrin, C. (2005). Soil conservation service curve number method: how to mend a wrong soil moisture accounting procedure?, Water Resource Research, 41, 1-6.
  27. Moon, G. W., Yoo, J. Y., and Kim, T. W. (2004). Comparing calculation techniques for effective rainfalls using NRCS-CN method: Focused on introducing weighted average and slope-based CN, Journal of the Korean Society of Civil Engineers, 34(4), 1171-1180. [Korean Literature] https://doi.org/10.12652/Ksce.2014.34.4.1171
  28. Nash, J. E. and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models: Part 1 - A discussion of principles, Journal of Hydrology, 10, 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  29. National Institute of Environmental Research (NIER). (2014). A research on control targets and strategies for impervious surface management, R&D Final Report. [Korean Literature].
  30. Norbiato, D., Borga, M., Degli Esposti, S., Gaume, E., and Anquetin, S. (2008). Flash flood warning based on rainfall thresholds and soil moisture conditions: an assessment for gauged and ungauged basins, Journal of Hydrology, 362, 274-290. https://doi.org/10.1016/j.jhydrol.2008.08.023
  31. Patil, S. D. and Stieglitz, M. (2015). Comparing spatial and temporal transferability of hydrological model parameters, Journal of Hydrology, 525, 409-417. https://doi.org/10.1016/j.jhydrol.2015.04.003
  32. Ponce, V. M. and Hawkins, H. (1996). Runoff curve number: Has it reached maturity?, Journal of Hydrologic Engineering, 1(1), 11-19. https://doi.org/10.1061/(ASCE)1084-0699(1996)1:1(11)
  33. Ryu, J., Kim, E., Han, M., Kim, Y. S., Kum, D., Lim, K. J., and Park, B. K. (2014). Enhancement of estimation method on the land T-P pollutant load in TMDL using L-THIA, Journal of Korean Society Environmental Engineering, 36(3), 162-171. [Korean Literature] https://doi.org/10.4491/KSEE.2014.36.3.162
  34. Shi, W., Huang, M., Gongadze, K., and Wu, L. (2017). A modified SCS-CN method incorporating storm duration and antecedent soil moisture estimation for runoff prediction, Water Resource Management, 31, 1713-1727. https://doi.org/10.1007/s11269-017-1610-0
  35. Shoemaker, L., Lahlou, M., Bryer, M., Kumar, D., and Kratt, K. (1997). Compendium of tools for watershed assessment and TMDL development, U. S. EPA Office of Water, Washington DC, USA.
  36. Singh, P. K., Mishra, S. K., Berndtsson, R., Jain, M. K., and Pandey, R. P. (2015). Development of a modified SMA based MSCS-CN model for runoff estimation, Water Resource Management, 29, 4111-4127. https://doi.org/10.1007/s11269-015-1048-1
  37. Sivapalan, M. (2003). Prediction in ungauged basins: a grand challenge for theoretical hydrology, Hydrological Processes, 17, 3163-3170. https://doi.org/10.1002/hyp.5155
  38. Tang, Z., Engel, B. A., Lim, K. J., and Pijanowski, B. C. (2005). Minimizing the impact of urbanization on long term runoff, Journal of the American Water Resources Association, 41(6), 1347-1359. https://doi.org/10.1111/j.1752-1688.2005.tb03804.x
  39. Tang, Z., Engel, B. A., Pijanowski, B. C., and Lim, K. J. (2005). Forecasting land use change and its environmental impact at a watershed scale, Journal of Environmental Management, 76(1), 35-45. https://doi.org/10.1016/j.jenvman.2005.01.006
  40. Tramblay, Y., Bouvier, C., Martin, C., Didon-Lescot, J. F., Todorovik, D., and Domergue, J. M. (2010). Assessment of initial soil moisture condition for event-based rainfall-runoff modelling, Journal of Hydrology, 387, 176-187. https://doi.org/10.1016/j.jhydrol.2010.04.006
  41. U. S. Department of Agriculture-Soil Conservation Service (USDA-SCS). (1985). National engineering handbook, Section 4. Hydrology, U. S. Department of Agriculture Soil Conservation Service, Washington, DC, USA.
  42. United States Environmental Protection Agency (U. S. EPA). (2015). Storm water management model user's manual Version 5.1, U. S. Environmental Protection Agency, Washington, DC, USA.
  43. United States Environmental Protection Agency (U. S. EPA). (2016). Storm water management model reference manual, Volume I. Hydrology, U. S. Environmental Protection Agency, Washington, DC, USA.
  44. Wagener, T. and Gupta, H. V. (2005). Model identification for hydrological forecasting under uncertainty, Stochastic Environmental Research and Risk Assessment, 19, 378-387. https://doi.org/10.1007/s00477-005-0006-5
  45. Winsemius, H. C., Schaefili, B., Montanari, A., and Savenije, H. H. G. (2009). On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information, Water Resources Research, 45, W12422. https://doi.org/10.1029/2009WR007706
  46. Young, A. (2006). Stream flow simulation within UK ungauged catchments using a daily rainfall-runoff model, Journal of Hydrology, 320, 155-172. https://doi.org/10.1016/j.jhydrol.2005.07.017
  47. Zhang, Z., Wagener, T., Reed, P., and Bhushan, R. (2008). Reducing uncertainty in predictions in ungauged basins by combining hydrologic indices regionalization and multiobjective optimization, Water Resources Research, 44(12), W00B04.