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http://dx.doi.org/10.17663/JWR.2020.22.1.15

The Selection of Optimal Distributions for Distributed Hydrological Models using Multi-criteria Calibration Techniques  

Kim, Yonsoo (Smart City Institute, Daumsoft, Inc.)
Kim, Taegyun (Landscape Architecture, Gyeongnam National University of science and Technology)
Publication Information
Journal of Wetlands Research / v.22, no.1, 2020 , pp. 15-23 More about this Journal
Abstract
The purpose of this study is to investigate how the degree of distribution influences the calibration of snow and runoff in distributed hydrological models using a multi-criteria calibration method. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-National Weather Service (NWS) is employed to estimate optimized parameter sets. We have 3 scenarios depended on the model complexity for estimating best parameter sets: Lumped, Semi-Distributed, and Fully-Distributed. For the case study, the Durango River Basin, Colorado is selected as a study basin to consider both snow and water balance components. This study basin is in the mountainous western U.S. area and consists of 108 Hydrologic Rainfall Analysis Project (HRAP) grid cells. 5 and 13 parameters of snow and water balance models are calibrated with the Multi-Objective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm. Model calibration and validation are conducted on 4km HRAP grids with 5 years (2001-2005) meteorological data and observations. Through case study, we show that snow and streamflow simulations are improved with multiple criteria calibrations without considering model complexity. In particular, we confirm that semi- and fully distributed models are better performances than those of lumped model. In case of lumped model, the Root Mean Square Error (RMSE) values improve by 35% on snow average and 42% on runoff from a priori parameter set through multi-criteria calibrations. On the other hand, the RMSE values are improved by 40% and 43% for snow and runoff on semi- and fully-distributed models.
Keywords
Distributed hydrologic model; Multi-criteria calibration; MOSCEM; Model complexity; HL-RDHM; Snow melting;
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1 Burnash, R. J. C., 1995, The NWS river forecast system - catchment modeling. In: Singh, V.P. (Ed.), Computer Models of Watershed Hydrology." Water Resources Publications, Littleton, Colorado, USA, pp. 311-366
2 Das, T., Bardossy, A., Zehe, E., and He, Y. (2008) "Comparison of Conceptual Model Performance using Different Representations of Spatial Variability, J. Hydrol., 356, pp. 106-118   DOI
3 Entin, J., Robock, A., Vinnikov, K.Y., Qiu, S., Zabelin, V., Liu, S., Namkhai, A., and Adyasuren, T. (1999) "Evaluation of Global Soil Wetness Project soil moisture simulations." J. Meteorolo. Soc., Japan, 77, 183-198.   DOI
4 Gupta, H. V., Bastidas, L. A., Sorooshian, S., Shuttleworth, W. J., and Yang, Z. L. (1999) "Parameter estimation of a land surface scheme using multicriteria methods." Journal of Geophysical Research, 104, D16, pp. 19,491-19,503   DOI
5 Ajami, K. N., 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, pp. 112-135   DOI
6 Anderson, E. A. (1973) "National Weather Service River Forecast System-snow accumulation and ablation model." NOAA Technical Memorandum NWS HYDRO-17, 217
7 Reed, S., Koren, V., Smith, M., Zhang, Z., Moreda, F., Seo, D.-J., and DMIP Participants (2004) "Overall distributed model intercomparison project results." Journal of Hydrology, 298, pp. 27-60   DOI
8 Isenstein, M., E., Wi, S., Yang, E. C. Y., and Brown, C. (2015) "Calibration of a Distributed Hydrologic Model using Streamflow and Remote Sensing Snow Data." World Environmental and Water Resources Congress, ASCE 2015
9 Kim and Kim(2019), An Optimization of distributed Hydrologic Model using Multi-Objective Optimization Method, Journal of Wetlands Research, Vol. 21, No. 1, pp. 001-008
10 Rajib, A., M., Merwade, V., and Yu, Z. (2016) "Multi-objective Calibration of a Hydrologic Model using Spatially Distributed Remotely Sensed/In-situ Soil Moisture." Journal of Hydrology, 536, pp. 192-207   DOI
11 Anderson, E. A. (2006) "Snow accumulation and ablation model: NWSRFS (National Weather Service River Forecast System) Snow17 Snow Model." in User Manual for Release 81
12 Vrugt, J. A., Gupta, H. V., Bastidas, L., Bouten, W., and Sorooshian, S. (2003b) "Effective and efficient algorithm for multi-objective optimization of hydrologic models." Water Resources Research, 39, 1214, doi:10.1029/2002WR001746
13 Reed, S., Maidment, D. R. (1999) "Coordinate transformations for using NEXRAD data in GIS-based hydrologic modeling." Journal of Hydrologic Engineering, 4, pp. 174-183   DOI
14 Robock, A., Luo, L., Wood, E.F., Wen, F., Mictell, K.E., Houser, P.R., Schaake, J.C., Lohmann, D., Cosgrove, B., Sheffield, J., Duan, Q., Higgins, R.W., Pinker, E.T., Tarpley, J.D., Basara, J.B., Crawford, K.C. (2003) "Evaluation of the North American Land Data Assimilation System over the southern Great Plains during warm season." J. Geophys. Res. 108 (D22), 8846. http://dx.doi.org/10.1029/2002JD003245.
15 Smith, M., Seo, D.-J., Koren, V., Reed, S. M., Zhang, Z., Duan, Q., Moreda, F., and Cong, S. (2004) "The distributed model intercomparison project (DMIP): Motivation and experiment design." Journal of Hydrology, 298, pp. 4-26   DOI
16 Srinivasan, G., Robock, A., Entin, J.K., Luo, L., Vinnikov, K.Ya., and Viterbo, P. (2000) "Soil moisture simulations in revised AMIP models." J. Geophys. Res., 105 (D21), 26635-26644.   DOI
17 Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorroshian, S. (2003a) "A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters." Water Resources Research, 39(8), 1201, doi:10.1029/2002WR001642
18 Zhang, Y., Z. Zhang, S. Reed, and Koren, V. (2011) "An enhanced and automated approach for deriving a priori SAC-SMA parameters from the soil survey geographic dataset." Computers & Geosciences, 37, pp. 219-231   DOI