Browse > Article
http://dx.doi.org/10.3741/JKWRA.2021.54.6.365

Estimating time-varying parameters for monthly water balance model using particle filter: assimilation of stream flow data  

Choi, Jeonghyeon (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University)
Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.6, 2021 , pp. 365-379 More about this Journal
Abstract
Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.
Keywords
Data assimilation; Monthly water balance model; Particle filter; Time-varying parameter;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Wang, D., Chen, Y., and Cai, X. (2009). "State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter." Water Resources Research, Vol. 45, No. 11, W11416. doi: 10.1029/2008wr007401   DOI
2 Westra, S., Thyer, M., Leonard, M., Kavetski, D., and Lambert, M. (2014). "A strategy for diagnosing and interpreting hydrological model nonstationarity." Water Resources Research, Vol. 50, No. 6, pp. 5090-5113. doi: 10.1002/2013wr014719   DOI
3 Won, J., Choi, J., Lee, O., and Kim, S. (2020). "Copula-based joint drought index using SPI and EDDI and its application to climate change." Science of Total Environment, Vol. 744, 140701. doi: 10.1016/j.scitotenv.2020.140701   DOI
4 Xiong, L., and Guo, S. (1999). "A two-parameter monthly water balance model and its application." Journal of Hydrology, Vol. 216, No. 1-2, pp. 111-123. doi: 10.1016/s0022-1694(98)00297-2   DOI
5 Xiong, L., and Guo, S. (2012). "Appraisal of Budyko formula in calculating long-term water balance in humid watersheds of southern China." Hydrological Process, Vol. 26, No. 9, pp. 1370-1378. doi: 10.1002/hyp.8273   DOI
6 Xiong, L., Liu, P., Cheng, L., Deng, C., Gui, Z., Zhang, X., and Liu, Y. (2019). "Identifying time-varying hydrological model parameters to improve simulation efficiency by the ensemble Kalman filter: A joint assimilation of streamflow and actual evapotranspiration." Journal of Hydrology, Vol. 568, pp. 758-768. doi: 10.1016/j.jhydrol.2018.11.038   DOI
7 Deng, C., Liu, P., Guo, S., Li, Z., and Wang, D. (2016). "Identification of hydrological model parameter variation using ensemble Kalman filter." Hydrology and Earth System Sciences, Vol. 20, No. 12, pp. 4949-4961. doi: 10.5194/hess-20-4949-2016   DOI
8 Feng, M., Liu, P., Guo, S., Shi, L., Deng, C., and Ming, B. (2017). "Deriving adaptive operating rules of hydropower reservoirs using time-varying parameters generated by the EnKF." Water Resources Research, Vol. 53, No. 8, pp. 6885-6907. doi: 10.1002/2016wr020180.   DOI
9 Guo, S., Wang, J., Xiong, L., Ying, A., and Li, D. (2002). "A macroscale and semidistributed monthly water balance model to predict climate change impacts in China." Journal of Hydrology, Vol. 268, No. 1-4, pp, 1-15. doi: 10.1016/s0022-1694(02)00075-6   DOI
10 Lee, J. (2006). Hydrology. Gumiseogwan.
11 Legesse, D., Vallet-Coulomb, C., and Gasse, F. (2003). "Hydrological response of a catchment to climate and land use changes in Tropical Africa: Case study South Central Ethiopia." Journal of Hydrology, Vol. 275, No. 1-2, pp. 67-85. doi: 10.1016/s0022-1694(03)00019-2   DOI
12 Marshall, L., Sharma, A., and Nott, D. (2006). "Modeling the catchment via mixtures: Issues of model specification and validation." Water Resources Research, Vol. 42, No. 11, W11409. doi: 10.1029/2005wr004613   DOI
13 Moradkhani, H., Hsu, K., Gupta, H., and Sorooshian, S. (2005a). "Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using particle filter." Water Resources Research, Vol. 41, W05012. doi: 10.1029/2004WR003604   DOI
14 Moradkhani, H., Sorooshian, S., Gupta, H., and Houser, P. (2005b). "Dual state-parameter estimation of hydrological models using ensemble Kalman filter." Advances in Water Resources, Vol. 28, No. 2, pp. 135-147. doi: 10.1016/j.advwatres.2004.09.002   DOI
15 Lee, B., and Bae, D. (2011). "Streamflow forecast model on Nakdong river basin." Journal of Korea Water Resources Association, Vol. 50, No. 4, pp. 241-252. doi: 10.3741/JKWRA.2011.44.11.853 (in Korean)   DOI
16 Seibert, J., McDonnell, J., and Woodsmith, R. (2010). "Effects of wildfire on catchment runoff response: A modelling approach to detect changes in snow-dominated forested catchments." Hydrology Research, Vol. 41, No. 5, pp. 378-390. doi 10.2166/nh.2010.036   DOI
17 Brigode, P., Oudin, L., and Perrin, C. (2013). "Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?" Journal of Hydrology, Vol. 476, pp. 410-425. doi: 10.1016/j.jhydrol.2012.11.012   DOI
18 de Vos, N., Rientjes, T., and Gupta, H. (2010). "Diagnostic evaluation of conceptual rainfall-runoff models using temporal clustering. Hydrological Processes, Vol. 24, No. 20, pp. 2840-2850. doi: 10.1002/hyp.7698   DOI
19 Vrugt, J., Gupta, H., Bouten, W., and Sorooshian, S. (2003). "A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters." Water Resources Research, Vol. 39, No. 8. doi: 10.1029/2002WR001642   DOI
20 Abbaszadeh, P., Moradkhani, H., and Yan, H. (2018). "Enhancing hydrologic data assimilation by evolutionary particle filter and Markov chain Monte Carlo." Advances in Water Resources, Vol. 111, pp. 192-204. doi: 10.1016/j.advwatres.2017.11.011   DOI
21 Brown, A., Zhang, L., McMahon, T., Western, A., and Vertessy, R., (2005). "A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation." Journal of Hydrology, Vol. 310 No. 1-4, pp. 28-61. doi: 10.1016/j.jhydrol.2004.12.010   DOI
22 Choi, J., Lee, O., Won, J., and Kim. S. (2020). "Stochastic simple hydrologic partitioning model associated with Markov chain Monte Carlo and ensemble Kalman filter." Journal of Korean Society on Water Environment, Vol. 36, No. 5, pp. 353-363. doi: 10.15681/KSWE.2020.36.5.353 (in Korean)   DOI
23 Dechantcm, M., and Moradkhani, H. (2012). "Examining the effectiveness and robustness of data assimilation methods for calibration and quantification of uncertainty in hydrologic forecasting." Water Resources Research, Vol. 48, W04518. doi: 10.1029/2011WR011011.   DOI
24 Engel, B., Srinivasan, R., Arnold, J., Rewerts, C., and Brown, S. (1993). "Nonpoint-source (NPS) pollution modeling using models integrated with geographic information systems (GIS)." Water Science and Technology, Vol. 28, pp. 685-690. doi: 10.2166/wst.1993.0474   DOI
25 Fan, Y., Huang, G., Baetz, B., Li, Y., Huang, K., Chen, X., and Gao, M. (2017). "Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods." Journal of Hydrology, Vol. 550, pp. 412-426. doi: 10.1016/j.jhydrol.2017.05.010   DOI
26 Gupta, H., Kling, H., Yilmaz, K., and Martinez, G. (2009). "Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling." Journal of Hydrology, Vol. 377, pp. 80-91. doi: 10.1016/j.jhydrol.2009.08.003   DOI
27 Lee, D., Kim, Y., Yu, W., and Lee, G. (2017). "Evaluation on applicability of on/off-line parameter calibration techniques in rainfall-runoff modeling." Journal of Korea Water Resources Association, Vol. 50, No. 4, pp. 241-252. doi: 10.3741/JKWRA.2017.50.4.241 (in Korean)   DOI
28 Cao, Y., Ye, Y., Liang, L., Zhao, H., Jiang, Y., Wang, H., Yi, Z., Shang, Y., and Yan, D. (2019). "A modified particle filter-based data assimilation method for a high-precision 2D hydrodynamic model considering spatial-temporal variability of roughness: Simulation of dam-break flood inundation." Water Resources Research, Vol. 55, pp. 6049-6068. doi: 10.1029/2018WR023568   DOI
29 Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. (2013). "An approach to identify time consistent model parameters: sub-period calibration." Hydrology and Earth System Science, Vol. 17, No. 1, pp. 149-161. doi: 10.5194/hess-17-149-2013   DOI
30 Gordon, N., Salmond, D., and Smith, A. (1993). "Novel approach to nonlinear and non-Gaussian Bayesian state estimation." IEE Proceeding F (Radar and Signal Processing), Vol. 140, pp. 107-113. doi: 10.1049/ip-f-2.1993.0015   DOI
31 Jeremiah, E., Marshall, L., Sisson, S.A., and Sharma, A. (2013). "Specifying a hierarchical mixture of experts for hydrologic modeling: Gating function variable selection." Water Resources Research, Vol. 49, No. 5, pp. 2926-2939. doi: 10.1002/wrcr.20150   DOI
32 Leisenring, M., and Moradkhani, H. (2012). "Analysing the uncertainty of suspended sediment load prediction using sequential data assimilation." Journal of Hydrology, Vol. 468, pp. 268-282. doi: 10.1016/j.jhydrol.2012.08.049   DOI
33 Smith, P., Beven, K., and Tawn, J. (2008). "Detection of structural inadequacy in process-based hydrological models: A particlefiltering approach." Water Resources Research, Vol. 44, No. 1. doi: 10.1029/2006wr005205   DOI
34 Moradkhani, H., DeChant, C., and Sorooshian, S. (2012). "Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method." Water Resources Research, Vol. 48. No. 12, doi: 10.1029/2012wr012144.   DOI
35 Nash, J., and Sutcliffe, J. (1970). "River flow forecasting through conceptual models part I - A discussion of principles." Journal of Hydrology, Vol. 10, pp. 282-290. doi: 10.1016/0022-1694(70)90255-6   DOI
36 Patil, S., and Stieglitz, M. (2015). "Comparing spatial and temporal transferability of hydrological model parameters." Journal of Hydrology, Vol. 525, pp. 409-417. doi: 10.1016/j.jhydrol.2015.04.003   DOI
37 Allen, R., Pereira, L., Raes, D., and Smith, M. (1998). Crop evapotranspiration - Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56, Food and Agriculture Organization of the United Nations, Rome, Italy.
38 Noh, S., Tachikawa, Y., Shiiba, M., and Kim, S. (2011). "Dual state-parameter updating scheme on a conceptual hydrologic model using sequential Monte Carlo filters." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), Vol. 67, pp. I_1-I_6.   DOI
39 Ritter, A., and Munoz-Carpena, R. (2013). "Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments." Journal of Hydrology, Vol. 480, pp. 33-45. doi: 10.1016/j.jhydrol.2012.12.004   DOI
40 Pathiraja, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L., and Moradkhani, H. (2018). "Insights on the impact of systematic model errors on data assimilation performance in changing catchments." Advances in Water Resources, Vol. 113, pp. 202-222. doi: 10.1016/j.advwatres.2017.12.006   DOI
41 Thirel, G., Andreassian, V., Perrin, C., Audouy, J., Berthet, L., Edwards, P. Folton, N., Furusho, C., Kuentz, A., Lerat, J., Lindstrom, G., Martin, E., Mathevet, T., Merz, R., Parajka, J., Ruelland, D., and Vaze, J. (2015). "Hydrology under change: An evaluation protocol to investigate how hydrological models deal with changing catchments." Hydrological Sciences Journal, Vol. 60, No. 7-8, pp. 1184-1199. doi: 10.1080/02626667.2014.967248   DOI
42 Vaze, J., Post, D., Chiew, F., Perraud, J., Viney, N., and Teng, J. (2010). "Climate non-stationarity-validity of calibrated rainfallrunoff models for use in climate change studies." Journal of Hydrology, Vol. 394, No. 3-4, pp. 447-457. doi: 10.1016/j.jhydrol.2010.09.018   DOI
43 Wagener, T., McIntyre, N., Lees, M., Wheater, H., and Gupta, H. (2003). "Towards reduced uncertainty in conceptual rainfallrunoff modelling: dynamic identifiability analysis." Hydrological Process, Vol. 17, No. 2, pp. 455-476. doi: 10.1002/hyp.1135   DOI
44 Vrugt, J., ter Braak, C., Diks, C., and Schoups, G. (2013). "Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications." Advances in Water Resources, Vol. 51, pp. 457-478. doi: 10.1016/j.advwatres.2012.04.002   DOI
45 Clark, M., Rupp, D., Woods, R., Zheng, X., Ibbitt, R., Slater, A., Schmidt, J., and Uddstrom, M. (2008). "Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model." Advances in Water Resources, Vol. 31, No. 10, pp. 1309-1324. doi: 10.1016/j.advwatres.2008.06.005   DOI
46 Merz, R., Parajka, J., and Bloeschl, G. (2011). "Time stability of catchment model parameters: Implications for climate impact analyses." Water Resources Research, Vol. 47, No. 2, W02531. doi: 10.1029/2010wr009505   DOI
47 Choi, D., Yang, J., Chung, G., and Kim, S. (2011). "A conceptual soil water model of catchment water balance: Which hydrologic components are needed to calibrated the model?" Journal of the Korean Society of Civil Engineers, Vol. 31, No. 3B, pp. 211-220. doi: 10.12652/Ksce.2011.31.3B.211 (in Korean)   DOI