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1.
With the availability of spatially distributed data, distributed hydrologic models are increasingly used for simulation of spatially varied hydrologic processes to understand and manage natural and human activities that affect watershed systems. Multi‐objective optimization methods have been applied to calibrate distributed hydrologic models using observed data from multiple sites. As the time consumed by running these complex models is increasing substantially, selecting efficient and effective multi‐objective optimization algorithms is becoming a nontrivial issue. In this study, we evaluated a multi‐algorithm, genetically adaptive multi‐objective method (AMALGAM) for multi‐site calibration of a distributed hydrologic model—Soil and Water Assessment Tool (SWAT), and compared its performance with two widely used evolutionary multi‐objective optimization (EMO) algorithms (i.e. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non‐dominated Sorted Genetic Algorithm II (NSGA‐II)). In order to provide insights into each method's overall performance, these three methods were tested in four watersheds with various characteristics. The test results indicate that the AMALGAM can consistently provide competitive or superior results compared with the other two methods. The multi‐method search framework of AMALGAM, which can flexibly and adaptively utilize multiple optimization algorithms, makes it a promising tool for multi‐site calibration of the distributed SWAT. For practical use of AMALGAM, it is suggested to implement this method in multiple trials with relatively small number of model runs rather than run it once with long iterations. In addition, incorporating different multi‐objective optimization algorithms and multi‐mode search operators into AMALGAM deserves further research. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

2.
A multi‐objective particle swarm optimization (MOPSO) approach is presented for generating Pareto‐optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist‐mutation (EM) operator are introduced. The proposed EM‐MOPSO approach is first tested for few test problems taken from the literature and evaluated with standard performance measures. It is found that the EM‐MOPSO yields efficient solutions in terms of giving a wide spread of solutions with good convergence to true Pareto optimal solutions. On achieving good results for test cases, the approach was applied to a case study of multi‐objective reservoir operation problem, namely the Bhadra reservoir system in India. The solutions of EM‐MOPSOs yield a trade‐off curve/surface, identifying a set of alternatives that define optimal solutions to the problem. Finally, to facilitate easy implementation for the reservoir operator, a simple but effective decision‐making approach was presented. The results obtained show that the proposed approach is a viable alternative to solve multi‐objective water resources and hydrology problems. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

3.
In this study, NSGA‐II is applied to multireservoir system optimization. Here, a four‐dimensional multireservoir system in the Han River basin was formulated. Two objective functions and three cases having different constraint conditions are used to achieve nondominated solutions. NSGA‐II effectively determines these solutions without being subject to any user‐defined penalty function, as it is applied to a multireservoir system optimization having a number of constraints (here, 246), multi‐objectives, and infeasible initial solutions. Most research by multi‐objective genetic algorithms only reveals a trade‐off in the objective function space present, and thus the decision maker must reanalyse this trade‐off relationship in order to obtain information on the decision variable. Contrastingly, this study suggests a method for identifying the best solutions among the nondominated ones by analysing the relation between objective function values and decision variables. Our conclusions demonstrated that NSGA‐II performs well in multireservoir system optimization having multi‐objectives. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

4.
H. S. Kim  S. Lee 《水文研究》2014,28(13):4023-4041
This study aimed to evaluate the effectiveness of the regionalization method on the basis of a combination of a parsimonious model structure and a multi‐objective calibration technique. For this study, 12 gauged catchments in the Republic of Korea were used. The parsimonious model structure, requiring minimal input data, was used to avoid adverse effects arising from model complexity, over‐parameterization and data requirements. The IHACRES rainfall‐runoff model was applied to represent the dynamic response characteristics of catchments in Korea. A multi‐objective approach was adopted to reduce the predictive uncertainty arising from the calibration of a rainfall‐runoff model, by increasing the amount of information retrieved from the available data. The regional relationships (or models) between the model parameters and the catchment attributes were established via a multiple regression approach, incorporating correlation analysis and stepwise regression on linear and logarithmic scales. The impacts of the parameters, calibrated by the multi‐objective approach, on the adequacy of regional relationships were assessed by comparison with impacts obtained by the single‐objective approach. The regional relationships were well defined, despite limited available data. The drainage area, the effective soil depth, the mean catchment slope and the catchment gradient appeared to be the main factors for describing the hydrologic response characteristics in the areas studied. The overall model performance of the regional models based on the multi‐objective approach was good, producing reasonable results for high and low flows and for the overall water balance, simultaneously. The regional models based on the single‐objective approach yielded accurate predictions in high flows but showed limited predictive capability for low flows and the overall water balance. This was due to the optimal model parameter estimates when using a single‐objective measure. The parameters calibrated by the single‐objective approach decreased the predictability of the regional models. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

5.
The principal challenge in the parameterization of storm flow models for agricultural catchments with an artificial drainage network and fields with different degrees of tillage lies in the parsimonious definition of distributed model parameters in a way that reduces the number of calibration parameters to a justifiable minimum. This paper presents a comprehensive case study for the parameter estimation of a distributed storm flow model applied to an agricultural catchment (0.91 km2) in the Mediterranean region. Model parameterization was combined with procedures for multi‐criteria, multi‐storm calibration, where we automatically calibrated three parameters related to flow velocity and infiltration, and compared single and multi‐storm criteria that are based on discharge volume, peak flow, and the Nash–Sutcliffe coefficient. Multi‐storm calibration yielded a set of parameter values for the simulation batch with best multi‐storm overall performance, which are close to the median values in the pre‐calibration of individual storms. Our results suggest that flow velocities and proportionality of the channel infiltration rate do not vary significantly over the course of 11 years. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

6.
A fully automated design methodology based on nonlinear response history analysis is proposed for the optimum seismic design of reinforced concrete (RC) structures. The conventional trial‐and‐error process is replaced by a structural optimization algorithm that serves as a search engine capable of locating the most efficient design in terms of cost and performance. Two variations of the proposed design methodology are introduced. The first approach treats the optimum design problem in a deterministic manner, while in the second variation the optimum design is sought in the framework of a reliability‐based optimization problem. The reliability‐based approach seems to be a more rational procedure since more meaningful design criteria that correlate better with the performance‐based design concept can be adopted. Thus, the practice of using the mean annual frequency of a limit‐state being exceeded to assess the candidate designs is compared with the use of deterministic criteria. Both formulations take into consideration the structural response for a number of limit‐states, from serviceability to collapse prevention. The proposed design procedure is specifically tailored to the design of RC structures, where a preliminary design step of generating tables of concrete sections is introduced. In order to handle the large size of the tables, the concept of multi‐database cascade optimization is implemented. The final design has to comply with the provisions of European design codes. The proposed methodology allows for a significant reduction of the direct construction cost combined with improved control of the seismic performance under earthquake loading. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

7.
The assessment of seismic design codes has been the subject of intensive research work in an effort to reveal weak points that originated from the limitations in predicting with acceptable precision the response of the structures under moderate or severe earthquakes. The objective of this work is to evaluate the European seismic design code, i.e. the Eurocode 8 (EC8), when used for the design of 3D reinforced concrete buildings, versus a performance‐based design (PBD) procedure, in the framework of a multi‐objective optimization concept. The initial construction cost and the maximum interstorey drift for the 10/50 hazard level are the two objectives considered for the formulation of the multi‐objective optimization problem. The solution of such optimization problems is represented by the Pareto front curve which is the geometric locus of all Pareto optimum solutions. Limit‐state fragility curves for selected designs, taken from the Pareto front curves of the EC8 and PBD formulations, are developed for assessing the two seismic design procedures. Through this comparison it was found that a linear analysis in conjunction with the behaviour factor q of EC8 cannot capture the nonlinear behaviour of an RC structure. Consequently the corrected EC8 Pareto front curve, using the nonlinear static procedure, differs significantly with regard to the corresponding Pareto front obtained according to EC8. Furthermore, similar designs, with respect to the initial construction cost, obtained through the EC8 and PBD formulations were found to exhibit different maximum interstorey drift and limit‐state fragility curves. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

8.
Numerous structures have been designed and built without taking earthquake ground motions or outdated seismic design codes into account. In order to improve the seismic performance of existing structures, many retrofit approaches based on performance‐based design have been developed. However, some of these approaches are inapplicable due to structural limitations or because they were developed with the assumption of single‐degree‐of‐freedom, which does not take higher modes into account. To overcome the limitations of these traditional methods, a multi‐performance‐based control design (MPBCD) methodology has been proposed by integrating a decentralized semi‐active control algorithm, magnetorheological dampers, and an advanced multi‐objective optimization method to provide various sets of retrofit control designs to satisfy multiple target performances under multiple seismic intensities without changing structural cross‐section sizes or material properties. This MPBCD method provides engineers with numerous sets of control designs (i.e., control device layouts with control design parameters) to help them select proper control designs to retrofit existing building structures and improve seismic performance. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

9.
H.S. Kim  S. Lee 《水文研究》2014,28(4):2159-2173
The hydrological response characteristics for the catchments in the Republic of Korea are related to a strong seasonality in the rainfall and streamflow distributions with distinct wet and dry seasons. This study aims to improve a model's ability to predict streamflows by minimizing information loss from the available data during the calibration processes. This study assesses calibration techniques incorporating a multi‐objective approach and seasonal calibration. The lumped conceptual rainfall–runoff model IHACRES was applied to selected catchments in Korea. The model was calibrated based on three different methods: the classical approach using a single performance statistic (the single‐objective method), the multi‐objective approach (the multi‐objective method (I)) and the combined approach incorporating multi‐objective and seasonal calibrations (the multi‐objective method (II)). In the multi‐objective approach, the ‘best fit’ models in the calibration period were selected by considering the trade‐offs among multiple statistics. During seasonal calibration, the calibration period was divided into four seasons to investigate whether these calibrated models can improve the model performance with regards to seasonal climate, rainfall and streamflow distributions. The adequacy of the three different calibration methods was assessed through comparison of the variability of model performance in high and low flows and water balance for the entire period and for each seasonal period. The multi‐objective methods yielded more accurate and consistent predictions for high and low flows and water balance simultaneously, compared to the single‐objective method. In particular, the multi‐objective method (II) produces the best modelling capacity to capture the non‐stationary nature of the hydrological response under different climate conditions. The pattern of improvement with the multi‐objective method (II) was generally consistent through the seasons, with the exception of the winter period in the regions partially affected by snow. This exception is due to a potential limitation of the IHACRES model in reflecting the impact of snow on the catchment hydrology. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

10.
Empirically based understanding of streamflow generation dynamics in a montane headwater catchment formed the basis for the development of simple, low‐parameterized, rainfall–runoff models. This study was based in the Girnock catchment in the Cairngorm Mountains of Scotland, where runoff generation is dominated by overland flow from peaty soils in valley bottom areas that are characterized by dynamic expansion and contraction of saturation zones. A stepwise procedure was used to select the level of model complexity that could be supported by field data. This facilitated the assessment of the way the dynamic process representation improved model performance. Model performance was evaluated using a multi‐criteria calibration procedure which applied a time series of hydrochemical tracers as an additional objective function. Flow simulations comparing a static against the dynamic saturation area model (SAM) substantially improved several evaluation criteria. Multi‐criteria evaluation using ensembles of performance measures provided a much more comprehensive assessment of the model performance than single efficiency statistics, which alone, could be misleading. Simulation of conservative source area tracers (Gran alkalinity) as part of the calibration procedure showed that a simple two‐storage model is the minimum complexity needed to capture the dominant processes governing catchment response. Additionally, calibration was improved by the integration of tracers into the flow model, which constrained model uncertainty and improved the hydrodynamics of simulations in a way that plausibly captured the contribution of different source areas to streamflow. This approach contributes to the quest for low‐parameter models that can achieve process‐based simulation of hydrological response. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

11.
The design of floor isolation systems (FISs) for the protection of acceleration sensitive contents is examined considering multiple objectives, all quantified in terms of the probabilistic system performance. The competing objectives considered correspond to (i) maximization of the level of protection offered to the sensitive content (acceleration reduction) and (ii) minimization of the demand for the isolator displacement capacity and, more importantly, for the appropriate clearance to avoid collisions with surrounding objects (floor displacement reduction). Both of these objectives are probabilistically characterized utilizing a versatile, simulation‐based framework for quantifying seismic risk, addressing all important uncertainties related to the seismic hazard and the structural model. FIS performance is assessed through time‐history analysis, allowing for all important sources of nonlinearity to be directly addressed in the design framework. The seismic hazard is described through a stochastic ground motion model. For efficiently performing the multi‐objective optimization, an augmented surrogate modeling methodology is established, considering development of a single metamodel with respect to both the uncertain model parameters and the design variables for the FIS system. This surrogate model is then utilized to simultaneously support the probabilistic risk assessment and the design optimization to provide the Pareto front of dominant designs. Each of these designs establishes a different compromise between the considered risk‐related objectives offering a variety of potential options to the designer. Within the illustrative example, the efficiency of the established framework is exploited to compare three different FIS implementations, whereas the impact of structural uncertainties on the optimal design is also evaluated. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

12.
Establishing a water‐saving planting structure is necessary for the arid, water‐deficient regions of northern China and of the world. Optimizing and adjusting a water‐saving agricultural planting structure is a typical semi‐structured, multi‐level, multi‐objective group decision‐making problem. Therefore, optimization can be best achieved with a swarm intelligence algorithm. We build an optimization model for a water‐saving planting structure with four target functions: (1) maximum total net output, (2) total grain yield, (3) ecological benefits, and (4) water productivity. The decision variable is the yearly seeded area of different crops, and its restrictions are the farmland area, the agricultural water resources, and the needs of the people and other farming‐related industries. Multiple objective particle swarm optimization (MOPSO) is an efficient optimization method, but its main shortcoming is that it can easily fall into a local optimum. Multiple objective chaos particle swarm optimization (MOCPSO) will greatly improve the searching performance of the algorithm by placing chaos technology with the advantages of ergodicity into MOPSO. When MOCPSO is used to solve the multi‐objective optimization model in the middle portion of the Heihe River basin, the results show that MOCPSO has the advantages of a high convergence speed and a tendency not to fall easily into a local optimum. After adopting a water‐saving agricultural planting structure, irrigation water would be reduced by about 7%, which would provide tangible economic, social, and ecological benefits for sustainable agricultural development.  相似文献   

13.
With the recent development of distributed hydrological models, the use of multi‐site observed data to evaluate model performance is becoming more common. Distributed hydrological model have many advantages, and at the same time, it also faces the challenge to calibrate over‐do parameters. As a typical distributed hydrological model, problems also exist in Soil and Water Assessment Tool (SWAT) parameter calibration. In the paper, four different uncertainty approaches – Particle Swarm Optimization (PSO) techniques, Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting algorithm (SUFI‐2) and Parameter Solution (PARASOL) – are taken to a comparative study with the SWAT model applied in Peace River Basin, central Florida. In our study, the observed river discharge data used in SWAT model calibration were collected from the three gauging stations at the main tributary of the Peace River. Behind these approaches, there is a shared philosophy; all methods seek out many parameter set to fit the uncertainties due to the non‐uniqueness in model parameter evaluation. On the basis of the statistical results of four uncertainty methods, difficulty level of each method, the number of runs and theoretical basis, the reasons that affected the accuracy of simulation were analysed and compared. Furthermore, for the four uncertainty method with SWAT model in the study area, the pairwise correlation between parameters and the distributions of model fit summary statistics computed from the sampling over the behavioural parameter and the entire model calibration parameter feasible spaces were identified and examined. It provided additional insight into the relative identifiability of the four uncertainty methods Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

14.
Structural identification based on measured dynamic data is formulated in a multi‐objective context that allows the simultaneous minimization of the various objectives related to the fit between measured and model predicted data. Thus, the need for using arbitrary weighting factors for weighting the relative importance of each objective is eliminated. For conflicting objectives there is no longer one solution but rather a whole set of acceptable compromise solutions, known as Pareto solutions, which are optimal in the sense that they cannot be improved in any objective without causing degradation in at least one other objective. The strength Pareto evolutionary algorithm is used to estimate the set of Pareto optimal structural models and the corresponding Pareto front. The multi‐objective structural identification framework is presented for linear models and measured data consisting of modal frequencies and modeshapes. The applicability of the framework to non‐linear model identification is also addressed. The framework is illustrated by identifying the Pareto optimal models for a scaled laboratory building structure using experimentally obtained modal data. A large variability in the Pareto optimal structural models is observed. It is demonstrated that the structural reliability predictions computed from the identified Pareto optimal models may vary considerably. The proposed methodology can be used to explore the variability in such predictions and provide updated structural safety assessments, taking into consideration all Pareto structural models that are consistent with the measured data. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

15.
Magnetic resonance sounding (MRS) has increasingly become an important method in hydrogeophysics because it allows for estimations of essential hydraulic properties such as porosity and hydraulic conductivity. A resistivity model is required for magnetic resonance sounding modelling and inversion. Therefore, joint interpretation or inversion is favourable to reduce the ambiguities that arise in separate magnetic resonance sounding and vertical electrical sounding (VES) inversions. A new method is suggested for the joint inversion of magnetic resonance sounding and vertical electrical sounding data. A one‐dimensional blocky model with varying layer thicknesses is used for the subsurface discretization. Instead of conventional derivative‐based inversion schemes that are strongly dependent on initial models, a global multi‐objective optimization scheme (a genetic algorithm [GA] in this case) is preferred to examine a set of possible solutions in a predefined search space. Multi‐objective joint optimization avoids the domination of one objective over the other without applying a weighting scheme. The outcome is a group of non‐dominated optimal solutions referred to as the Pareto‐optimal set. Tests conducted using synthetic data show that the multi‐objective joint optimization approximates the joint model parameters within the experimental error level and illustrates the range of trade‐off solutions, which is useful for understanding the consistency and conflicts between two models and objectives. Overall, the Levenberg‐Marquardt inversion of field data measured during a survey on a North Sea island presents similar solutions. However, the multi‐objective genetic algorithm method presents an efficient method for exploring the search space by producing a set of non‐dominated solutions. Borehole data were used to provide a verification of the inversion outcomes and indicate that the suggested genetic algorithm method is complementary for derivative‐based inversions.  相似文献   

16.
A simple phosphorus (P) transfer model of the Welland catchment, UK, is evaluated against multiple objective functions using a Monte Carlo approach that combines calibration, identifiability, sensitivity and uncertainty analysis. The model is based on simple conceptual rainfall‐runoff and river routing components, combined with estimates of the daily non‐point source load derived from annual landuse‐based export coefficients, disaggregated as a function of the runoff. The model has limited data requirements, consistent with data availability, and is parsimoneous with respect to the number of parameters identified through inverse modelling. The best performing parameter sets capture the main aspects of the observed flow and total P (TP) concentrations and provide a suitable basis for a decision‐support tool. However, a trade‐off is evident between matching the observed flow peaks, flow recessions and TP concentrations simultaneously, highlighting some limitations of the model structure and/or calibration data. Model analysis indicates that daily non‐point source load cannot be described as a function of near‐surface runoff and land use alone, but that other influences, including seasonality, are important. However, further model development to improve performance is likely to introduce additional complexity (in terms of parameter numbers), and hence additional problems of parameter identifiability and output uncertainty, which in turn raises issues of the information content of the available data. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

17.
Previous comparison studies on seismic isolation have demonstrated its beneficial and detrimental effects on the structural performance of high‐speed rail bridges during earthquakes. Striking a balance between these 2 competing effects requires proper tuning of the controlling design parameters in the design of the seismic isolation system. This results in a challenging problem for practical design in performance‐based engineering, particularly when the uncertainty in seismic loading needs to be explicitly accounted for. This problem can be tackled using a novel probabilistic performance‐based optimum seismic design (PPBOSD) framework, which has been previously proposed as an extension of the performance‐based earthquake engineering methodology. For this purpose, a parametric probabilistic demand hazard analysis is performed over a grid in the seismic isolator parameter space, using high‐throughput cloud‐computing resources, for a California high‐speed rail (CHSR) prototype bridge. The derived probabilistic structural demand hazard results conditional on a seismic hazard level and unconditional, i.e., accounting for all seismic hazard levels, are used to define 2 families of risk features, respectively. Various risk features are explored as functions of the key isolator parameters and are used to construct probabilistic objective and constraint functions in defining well‐posed optimization problems. These optimization problems are solved using a grid‐based, brute‐force approach as an application of the PPBOSD framework, seeking optimum seismic isolator parameters for the CHSR prototype bridge. This research shows the promising use of seismic isolation for CHSR bridges, as well as the potential of the versatile PPBOSD framework in solving probabilistic performance‐based real‐world design problems.  相似文献   

18.
To improve the efficiency of model fitting, parameter identification techniques have been actively investigated. Recently, the applications of parameter identification migrated from off‐line model fitting to on‐line model updating. The objective of this study is to develop a gradient‐based method for model updating to advance hybrid simulation also called hybrid test. A novel modification of the proposed method, which can reduce the number of design variables to improve the identification efficiency, is illustrated in detail. To investigate the model updating, simulated hybrid tests were conducted with a 5‐story steel frame equipped with buckling‐restrained braces (BRBs) utilized in the shaking table tests conducted in E‐Defense in Japan in 2009. The calibrated analytical model that was verified with the test results can serve as the reference model. In the simulated hybrid tests, the physical BRB substructure is numerically simulated by utilizing a truss element with the 2‐surface model identical to the part of the reference model. Such numerical verification allows simulation of measurement errors for investigation on the performance of the proposed method. Moreover, the feasibility of sharing the identified parameter values, which were obtained from the physical substructure responses, with the relevant numerical models is also verified with the artificial component responses derived from the physical experiments.  相似文献   

19.
A new, adaptive multi‐criteria method for accurate estimation of three‐component three‐dimensional vertical seismic profiling of first breaks is proposed. Initially, we manually pick first breaks for the first gather of the three‐dimensional borehole set and adjust several coefficients to approximate the first breaks wave‐shape parameters. We then predict the first breaks for the next source point using the previous one, assuming the same average velocity. We follow this by calculating an objective function for a moving trace window to minimize it with respect to time shift and slope. This function combines four main properties that characterize first breaks on three‐component borehole data: linear polarization, signal/noise ratio, similarity in wave shapes for close shots and their stability in the time interval after the first break. We then adjust the coefficients by combining current and previous values. This approach uses adaptive parameters to follow smooth wave‐shape changes. Finally, we average the first breaks after they are determined in the overlapping windows. The method utilizes three components to calculate the objective function for the direct compressional wave projection. An adaptive multi‐criteria optimization approach with multi three‐component traces makes this method very robust, even for data contaminated with high noise. An example using actual data demonstrates the stability of this method.  相似文献   

20.
Hydrologic model development and calibration have continued in most cases to focus only on accurately reproducing streamflows. However, complex models, for example, the so‐called physically based models, possess large degrees of freedom that, if not constrained properly, may lead to poor model performance when used for prediction. We argue that constraining a model to represent streamflow, which is an integrated resultant of many factors across the watershed, is necessary but by no means sufficient to develop a high‐fidelity model. To address this problem, we develop a framework to utilize the Gravity Recovery and Climate Experiment's (GRACE) total water storage anomaly data as a supplement to streamflows for model calibration, in a multiobjective setting. The VARS method (Variogram Analysis of Response Surfaces) for global sensitivity analysis is used to understand the model behaviour with respect to streamflow and GRACE data, and the BORG multiobjective optimization method is applied for model calibration. Two subbasins of the Saskatchewan River Basin in Western Canada are used as a case study. Results show that the developed framework is superior to the conventional approach of calibration only to streamflows, even when multiple streamflow‐based error functions are simultaneously minimized. It is shown that a range of (possibly false) system trajectories in state variable space can lead to similar (acceptable) model responses. This observation has significant implications for land‐surface and hydrologic model development and, if not addressed properly, may undermine the credibility of the model in prediction. The framework effectively constrains the model behaviour (by constraining posterior parameter space) and results in more credible representation of hydrology across the watershed.  相似文献   

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