Top

Water Resources Management

Published in:

22-11-2023

A Framework for Algorithmic Improvement to Mitigate the Effects of Equifinality in the Calibration of High-dimensional Parameters for Hydrological Models

Authors: Xiaoyang Tang, Deshan Tang, Fulin Zhang

Published in: Water Resources Management | Issue 1/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

When performing hydrological model parameter calibration, equifinality inevitably reduces the simulation and prediction ability of hydrological models. To lessen the impact of equifinality, a novel algorithmic improvement framework is proposed in this paper. This framework allows the parameters to be searched hierarchically in order of sensitivity size and shrinks the original ranges of the parameters before the final search. The shuffled complex evolution (SCE_UA) algorithm, which is the most popular method for addressing hydrological model calibration issues, is improved using this new framework yielding HSRS_SCE algorithm, which stands for the SCE_UA algorithm with hierarchical search (HS) and range shrinkage (RS). A 26-dimensional parametric calibration problem is constructed and solved in this study utilizing 12 schemes based on the HSRS_SCE algorithm with various parameters and a control scheme based on the SCE_UA algorithm. The results show that the HSRS_SCE algorithm can not only produce calibrated parameter results significantly superior to those of the traditional scheme (p < 0.05) but also produce objective function values 26.1% better than those of the traditional scheme and reduce the search time through parallel computing.

previous article Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models

next article Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Arsenault R, Poulin A, Côté P, Brissette F (2014) Comparison of stochastic optimization algorithms in hydrological model calibration. J Hydrol Eng 19:1374–1384. https://doi.org/10.1061/(asce)he.1943-5584.0000938CrossRef

Berezowski T, Nossent J, Chormaåski J, Batelaan O (2015) Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model. Hydrol Earth Syst Sci 19:1887–1904. https://doi.org/10.5194/hess-19-1887-2015CrossRef

Beven K (1993) Prophecy, reality and uncertainty in distributed hydrological modelling. Adv Water Resour 16:41–51. https://doi.org/10.1016/0309-1708(93)90028-ECrossRef

Duan QY, Gupta VK, Sorooshian S (1993) Shuffled Complex Evolution Approach for effective and efficient global minimization. J Optim Theory Appl 76:501–521CrossRef

Griensven AV, Meixner T, Grunwald S et al (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 324:10–23. https://doi.org/10.1016/j.jhydrol.2005.09.008CrossRef

Her Y, Chaubey I (2015) Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty. Hydrol Process 29:4220–4237. https://doi.org/10.1002/hyp.10487CrossRef

Ivanov VY, Vivoni ER, Bras RL, Entekhabi D (2004) Preserving high-resolution surface and rainfall data in operational-scale basin hydrology: a fully-distributed physically-based approach. J Hydrol 298:80–111. https://doi.org/10.1016/j.jhydrol.2004.03.041CrossRef

Jahandideh-Tehrani M, Bozorg-Haddad O, Loáiciga HA (2020) Application of particle swarm optimization to water management: an introduction and overview. Environ Monit Assess 192–281. https://doi.org/10.1007/s10661-020-8228-z

Kang T, Lee S (2014) Modification of the SCE-UA to include constraints by embedding an adaptive penalty function and application: application Approach. Water Resour Manag 28:2145–2159. https://doi.org/10.1007/s11269-014-0602-6CrossRef

Khakbaz B, Imam B, Hsu K, Sorooshian S (2012) From lumped to distributed via semi-distributed: calibration strategies for semi-distributed hydrologic models. J Hydrol 418–419:61–77. https://doi.org/10.1016/j.jhydrol.2009.02.021CrossRef

Ko A, Mascaro G, Vivoni ER (2019) Strategies to improve and evaluate physics-based hyperresolution hydrologic simulations at regional basin scales. Water Resour Res 55:1129–1152. https://doi.org/10.1029/2018WR023521CrossRef

Mascaro G, Vivoni ER, Méndez-Barroso LA (2015) Hyperresolution hydrologic modeling in a regional watershed and its interpretation using empirical orthogonal functions. Adv Water Resour 83:190–206. https://doi.org/10.1016/j.advwatres.2015.05.023CrossRef

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/10.1080/00401706.2000.10485979CrossRef

Pandey BK, Khare D, Kawasaki A, Mishra PK (2019) Climate change impact assessment on blue and green water by coupling of representative CMIP5 climate models with physical based hydrological model. Water Resour Manag 33:141–158. https://doi.org/10.1007/s11269-018-2093-3CrossRef

Reshma T, Reddy KV, Pratap D et al (2015) Optimization of calibration parameters for an event based watershed model using genetic algorithm. Water Resour Manag 29:4589–4606. https://doi.org/10.1007/s11269-015-1077-9CrossRef

Schoups G, Vrugt JA (2010) A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-gaussian errors. Water Resour Res 46:1–17. https://doi.org/10.1029/2009WR008933CrossRef

Shamir E, Meko DM, Graham NE, Georgakakos KP (2007) Hydrologic model framework for water resources planning in the Santa Cruz River, Southern Arizona. J Am Water Resour Assoc 43:1155–1170. https://doi.org/10.1111/j.1752-1688.2007.00095.xCrossRef

Sun N, Yan H, Wigmosta MS et al (2019) Regional Snow parameters Estimation for large-domain hydrological applications in the Western United States. J Geophys Res Atmos 124:5296–5313. https://doi.org/10.1029/2018JD030140CrossRef

Sun R, Hernández F, Liang X, Yuan H (2020) A calibration framework for high-resolution hydrological models using a multiresolution and heterogeneous strategy. Water Resour Res 56:1–26. https://doi.org/10.1029/2019WR026541CrossRef

Tang Y, Reed P, Wagener T (2006) How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrol Earth Syst Sci 10:289–307. https://doi.org/10.5194/hess-10-289-2006CrossRef

Tolson BA, Shoemaker CA (2007) Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resour Res 43:1–16. https://doi.org/10.1029/2005WR004723CrossRef

Vema VK, Sudheer KP (2020) Towards quick parameter estimation of hydrological models with large number of computational units. J Hydrol 587. https://doi.org/10.1016/j.jhydrol.2020.124983

Vivoni ER, Entekhabi D, Bras RL, Ivanov VY (2007) Controls on runoff generation and scale-dependence in a distributed hydrologic model. Hydrol Earth Syst Sci 11:1683–1701. https://doi.org/10.5194/hess-11-1683-2007CrossRef

Vrugt JA, Braak CJF, ter, Gupta HV, Robinson BA (2009) Equifinality of formal (DREAM) and informal (GLUE) bayesian approaches in hydrologic modeling? Stoch Environ Res Risk Assess 23:1011–1026. https://doi.org/10.1007/s00477-008-0274-yCrossRef

Wu Q, Liu S, Cai Y et al (2017) Improvement of hydrological model calibration by selecting multiple parameter ranges. Hydrol Earth Syst Sci 21:393–407. https://doi.org/10.5194/hess-21-393-2017CrossRef

Wu L, Liu X, Chen J et al (2022) Overcoming equifinality: time-varying analysis of sensitivity and identifiability of SWAT runoff and sediment parameters in an arid and semiarid watershed. Environ Sci Pollut Res 29:31631–31645. https://doi.org/10.1007/s11356-022-18573-9CrossRef

Yuan W, Liu M, Wan F (2019) Calculation of critical rainfall for small-watershed flash floods based on the HEC-HMS hydrological model. Water Resour Manag 33:2555–2575. https://doi.org/10.1007/s11269-019-02257-0CrossRef

Title: A Framework for Algorithmic Improvement to Mitigate the Effects of Equifinality in the Calibration of High-dimensional Parameters for Hydrological Models
Authors: Xiaoyang Tang
Deshan Tang
Fulin Zhang
Publication date: 22-11-2023
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 1/2024
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-023-03667-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 1/2024

Multi-Objective Optimization and Coordination of Power Generation, Ecological Needs, and Carbon Emissions in Reservoir Operation

Multi-Risk Interaction Analysis of Cascade Hydropower Stations Based on System Dynamics Simulation

Analogy Between SCS-CN and Muskingum Methods

Prediction of Irrigation Water Quality Indices Using Random Committee, Discretization Regression, REPTree, and Additive Regression

A Systematic Review of Methods for Investigating Climate Change Impacts on Water-Energy-Food Nexus

Different Infiltration Methods for Swat Model Seasonal Calibration of Flow and Sediment Production