Abstract
Practical application of hydrological models requires parameter transfer, both temporally and spatially, to compensate for the lack of data. In this study, the transferability of parameters is evaluated using a lumped hydrological model called the Xinanjiang model to simulate runoff at different spatiotemporal scales in the Jianxi basin in south-east China and its four sub-basins. The functional relationships are built based on the posterior distribution derived by the Differential Evolution Adaptive Metropolis (DREAM) algorithm. The results show that (1) the sensitivity of parameters KE, SM, KI and KG shows obvious temporal characteristics, and the sensitivity of NK and CG shows strong spatial characteristics; (2) most relationships between sensitive parameters and scales are remarkable with goodness-of-fit coefficient higher than 0.9, which has been verified to achieve good performance in the temporal and spatial transfer; (3) the spatial transferability of the model is greatly influenced by the difference between the basin sizes; and (4) those parameters with strong spatial characteristics, such as NK and CG, show obvious impacts on the performance and uncertainty of the model transferred from the larger/smaller to smaller/larger basins.
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References
Bardossy A (2007) Calibration of hydrological model parameters for ungauged catchments. Hydrol Earth Syst Sci 11:703–710. https://doi.org/10.5194/hess-11-703-2007
Bastola S, Murphy C (2013) Sensitivity of the performance of a conceptual rainfall-runoff model to the temporal sampling of calibration data. Hydrol Res 44:484–494. https://doi.org/10.2166/nh.2012.061
Bloschl G, Sivapalan M (1995) Scale issues in hydrological modeling - a review. Hydrol Process 9:251–290. https://doi.org/10.1002/hyp.3360090305
Bruneau P, Gascuelodoux C, Robin P, Merot P, Beven K (1995) Sensitivity to space and time resolution of a hydrological model using digital elevation data. Hydrol Process 9:69–81. https://doi.org/10.1002/hyp.3360090107
Chouaib W, Alila Y, Caldwell PV (2018) Parameter transferability within homogeneous regions and comparisons with predictions from a priori parameters in the eastern United States. J Hydrol 560:24–38. https://doi.org/10.1016/j.jhydrol.2018.03.018
Gupta HV, Sorooshian S, Yapo PO (1998) Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resour Res 34:751–763. https://doi.org/10.1029/97wr03495
Hall JW, Tarantola S, Bates PD, Horritt MS (2005) Distributed sensitivity analysis of flood inundation model calibration. J Hydraul Eng-ASCE 131:117–126. https://doi.org/10.1061/(asce)0733-9429(2005)131:2(117)
Huo J, Liu L (2020) Evaluation method of multiobjective functions’ combination and its application in hydrological model evaluation. Comput Intel Neurosc 2020:8594727–8594723. https://doi.org/10.1155/2020/8594727
Jie MX, Chen H, Xu CY, Zeng Q, Chen J, Kim JS, Guo SL, Guo FQ (2018) Transferability of conceptual hydrological models across temporal resolutions: approach and application. Water Resour Manag 32:1367–1381. https://doi.org/10.1007/s11269-017-1874-4
Kavetski D, Fenicia F, Clark MP (2011) Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: insights from an experimental catchment. Water Resour Res 47:W05501. https://doi.org/10.1029/2010wr009525
Kumar R, Samaniego L, Attinger S (2013) Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations. Water Resour Res 49:360–379. https://doi.org/10.1029/2012wr012195
Liu Z, Guo S, Zhang H, Liu D, Yang G (2016) Comparative study of three updating procedures for real-time flood forecasting. Water Resour Manag 30:2111–2126. https://doi.org/10.1007/s11269-016-1275-0
Liu YR, Li YP, Huang GH, Zhang JL, Fan YR (2017) A Bayesian-based multilevel factorial analysis method for analyzing parameter uncertainty of hydrological model. J Hydrol 553:750–762. https://doi.org/10.1016/j.jhydrol.2017.08.048
McKay MD, Beckman RJ, Conover WJ (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 42:55–61. https://doi.org/10.1080/00401706.1979.10489755
Melsen L, Teuling A, Torfs P, Zappa M, Mizukami N, Clark M, Uijlenhoet R (2016) Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-Alpine basin. Hydrol Earth Syst Sci 20:2207–2226. https://doi.org/10.5194/hess-20-2207-2016
Meng S, Xie X, Yu X (2016) Tracing temporal changes of model parameters in rainfall-runoff modeling via a real-time data assimilation. Water 8:19. https://doi.org/10.3390/w8010019
Merz R, Parajka J, Bloschl G (2009) Scale effects in conceptual hydrological modeling. Water Resour Res 45:W09405. https://doi.org/10.1029/2009WR007872
Nossent J, Elsen P, Bauwens W (2011) Sobol' sensitivity analysis of a complex environmental model. Environ Model Softw 26:1515–1525. https://doi.org/10.1016/j.envsoft.2011.08.010
Perrin C, Oudin L, Andreassian V, Rojas-Serna C, Michel C, Mathevet T (2007) Impact of limited streamflow data on the efficiency and the parameters of rainfall-runoff models. Hydrolog Sci J 52:131–151. https://doi.org/10.1623/hysj.52.1.131
Sobol′ IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simulat 55:271–280. https://doi.org/10.1016/S0378-4754(00)00270-6
Song X, Kong F, Zhan C, Han J, Zhang X, engineering (2013) Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach. Water Sci Eng 6:1–17. https://doi.org/10.3882/j.issn.1674-2370.2013.01.001
Sun WC, Ishidaira H, Bastola S (2012) Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level. Hydrol Process 26:3524–3537. https://doi.org/10.1002/hyp.8429
Tang Y, Reed P, Wagener T, van Werkhoven K (2007) Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation. Hydrol Earth Syst Sci 11:793–817. https://doi.org/10.5194/hess-11-793-2007
Tian Y, Xu Y-P, Zhang X-J (2013) Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang models. Water Resour Manag 27:2871–2888. https://doi.org/10.1007/s11269-013-0321-4
Vrugt JA, ter Braak CJF, Diks CGH, Robinson BA, Hyman JM, Higdon D (2009) Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. Int J Nonlinear Sci Numer Simul 10:273–290. https://doi.org/10.1515/ijnsns.2009.10.3.273
Wan H, Xia J, Zhang L, She D, Xiao Y, Zou L (2015) Sensitivity and interaction analysis based on Sobol’ method and its application in a distributed flood forecasting model. Water 7:2924–2951. https://doi.org/10.3390/w7062924
Wang Y, He B, Takase K (2009) Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge. Hydrolog Sci J 54:886–898. https://doi.org/10.1623/hysj.54.5.886
Xiong L, Wan M, Wei X, Oconnor KM (2009) Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation. Hydrolog Sci J 54:852–871. https://doi.org/10.1623/hysj.54.5.852
Yang X, Magnusson J, Huang S, Beldring S, Xu C-Y (2020) Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions. J Hydrol 582:124357. https://doi.org/10.1016/j.jhydrol.2019.124357
Zahmatkesh Z, Karamouz M, Nazif S (2015) Uncertainty based modeling of rainfall-runoff: combined differential evolution adaptive Metropolis (DREAM) and K-means clustering. Adv Water Resour 83:405–420. https://doi.org/10.1016/j.advwatres.2015.06.012
Zelelew MB, Alfredsen K (2014) Transferability of hydrological model parameter spaces in the estimation of runoff in ungauged catchments. Hydrolog Sci J 59:1470–1490. https://doi.org/10.1080/02626667.2013.838003
Zhang H, Wang B, Liu DL, Zhang M, Feng P, Cheng L, Yu Q, Eamus D (2019) Impacts of future climate change on water resource availability of eastern Australia: a case study of the Manning River basin. J Hydrol 573:49–59. https://doi.org/10.1016/j.jhydrol.2019.03.067
Zhang D, Zhang L, Guan Y, Chen X, Chen X (2012) Sensitivity analysis of Xinanjiang rainfall–runoff model parameters: a case study in Lianghui, Zhejiang province, China. Hydrol Res 43:123–134. https://doi.org/10.2166/nh.2011.131
Zhao R-J (1992) The Xinanjiang model applied in China. J Hydrol 135:371–381. https://doi.org/10.1016/0022-1694(92)90096-E
Zhuo L, Han D, Dai Q, Islam T (2016) Chapter 15 - a comparative study on SMOS and NLDAS-2 soil moistures over a Hydrological Basin—with continental climate. In: Srivastava PK, Petropoulos GP, Kerr YH (eds) Satellite Soil Moisture Retrieval. Elsevier, pp 289–308. doi:https://doi.org/10.1016/B978-0-12-803388-3.00015-2
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This study is supported by the National Natural Science Fund of China (51539009).
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Sheng, S., Chen, H., Guo, FQ. et al. Transferability of a Conceptual Hydrological Model across Different Temporal Scales and Basin Sizes. Water Resour Manage 34, 2953–2968 (2020). https://doi.org/10.1007/s11269-020-02594-5
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DOI: https://doi.org/10.1007/s11269-020-02594-5