Optimal estimation of irrigation schedule – An example of quantifying human interferences to hydrologic processes

Wang, Dingbao; Cai, Ximing

doi:10.1016/j.advwatres.2007.02.006

Cited by 33 publications

(27 citation statements)

References 30 publications

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“…The factors of those methods will become feedback factors that can adaptively adjust the output errors and allow the automatically self-adapting DA system to be available. This approach is similar to those presented in previous studies (Katul et al 1993;Wang and Cai 2007), regarding the coupling of an optimization algorithm with a data assimilation method. More details about the mechanism of the coupled systems will be introduced in section 4 with the experiments of the Lorenz model (Lorenz 1963).…”

Section: Da Systems With a Feedback Mechanismmentioning

confidence: 67%

Evolutionary Algorithm-Based Error Parameterization Methods for Data Assimilation

Bai¹,

Li²

2011

Monthly Weather Review

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The methods of parameterizing model errors have a substantial effect on the accuracy of ensemble data assimilation. After a review of the current error-handling methods, a new blending error parameterization method was designed to combine the advantages of multiplicative inflation and additive inflation. Motivated by evolutionary algorithm concepts that have been developed in the control engineering field for years, the authors propose a new data assimilation method coupled with crossover principles of genetic algorithms based on ensemble transform Kalman filters (ETKFs). The numerical experiments were developed based on the classic nonlinear model (i.e., the Lorenz model). Convex crossover, affine crossover, direction-based crossover, and blending crossover data assimilation systems were consequently designed. When focusing on convex crossover and affine crossover data assimilation problems, the error adjustment factors were investigated with respect to four aspects, which were the initial conditions of the Lorenz model, the number of ensembles, observation covariance, and the observation interval. A new data assimilation system, coupled with genetic algorithms, is proposed to solve the difficult problem of the error adjustment factor search, which is usually performed using trial-and-error methods. The results show that all of the methods can adaptively obtain the best error factors within the constraints of the fitness function.

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Section: Da Systems With a Feedback Mechanismmentioning

confidence: 67%

Evolutionary Algorithm-Based Error Parameterization Methods for Data Assimilation

Bai¹,

Li²

2011

Monthly Weather Review

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“…Irrigation is a major form of human interference in watersheds with crop water stress. The instant applications cause abruptly increase of soil moisture [37]. This study examines the performance of KF and HF, assuming an irrigation application starting at the beginning of the sixth day and lasting for 5 h at an even rate.…”

Section: Performance Of Kf and Hf Under Human Interferences To Hydrolmentioning

confidence: 99%

“…Moreover, if instant human interference is neglected in a simulation model, a large bias of state prediction may occur at the time points when human interference is imposed [15]. In particular, when heavy human interferences exist, the unknown time dependent human inputs may cause non-stationary, unpredictable and biased error to the model forecast [37]. Observation error of streamflow can be non-stationary when river flow is affected by storage regulation [15].…”

Section: Introductionmentioning

confidence: 98%

Robust data assimilation in hydrological modeling – A comparison of Kalman and H-infinity filters

Wang

Cai

2008

Advances in Water Resources

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“…The efficiency of the integer SPSA is comparable to the very fast simulated annealing method. To the best of our knowledge, the paper by Wang and Cai (2007) represents the first application of SPSA to optimal control problems. We will compare three different optimization algorithms for two simple examples where we use inequality constraints in the form of bounds on the control variables.…”

Section: Control Optimization Algorithmsmentioning

confidence: 99%

Data Integration for the Generation of High Resolution Reservoir Models

Reynolds

Oliver

et al. 2009

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The goal of this three-year project was to develop a theoretical basis and practical technology for the integration of geologic, production and time-lapse seismic data in a way that makes best use of the information for reservoir description and reservoir performance predictions. The methodology and practical tools for data integration that were developed in this research project have been incorporated into computational algorithms that are feasible for large scale reservoir simulation models. As the integration of production and seismic data require calibrating geological/geostatistical models to these data sets, the main computational tool is an automatic history matching algorithm. The following specific goals were accomplished during this research. (i) We developed algorithms for calibrating the location of the boundaries of geologic facies and the distribution of rock properties so that production and time-lapse seismic data are honored. (ii) We developed and implemented specific procedures for conditioning reservoir models to time-lapse seismic data. (iii) We developed and implemented algorithms for the characterization of measurement errors which are needed to determine the relative weights of data when conditioning reservoir models to production and time-lapse seismic data by automatic history matching. (iv) We developed and implemented algorithms for the adjustment of relative permeability curves during the history matching process. (v) We developed algorithms for production optimization which accounts for geological uncertainty within the context of closed-loop reservoir management. (vi) To ensure the research results will lead to practical public tools for independent oil companies, as part of the project we built a graphical user interface for the reservoir simulator and history matching software using Visual Basic.iii

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Optimal estimation of irrigation schedule – An example of quantifying human interferences to hydrologic processes

Cited by 33 publications

References 30 publications

Evolutionary Algorithm-Based Error Parameterization Methods for Data Assimilation

Evolutionary Algorithm-Based Error Parameterization Methods for Data Assimilation

Robust data assimilation in hydrological modeling – A comparison of Kalman and H-infinity filters

Data Integration for the Generation of High Resolution Reservoir Models

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