Uncertainty and risk in water quality modelling and management

The case is presented for increasing attention to the evaluation of uncertainty in water quality modelling practice, and for this evaluation to be extended to risk management applications. A framework for risk-based modelling of water quality is outlined and presented as a potentially valuable component of a broader risk assessment methodology. Technical considerations for the successful implementation of the modelling framework are discussed. The primary arguments presented are as follows. (1) For a large number of practical applications, deterministic use of complex water quality models is not supported by the available data and/or human resources, and is not warranted by the limited information contained in the results. Modelling tools should be flexible enough to be employed at levels of complexities which suit the modelling task, data and available resources. (2) Monte Carlo simulation has largely untapped potential for the evaluation of model performance, estimation of model uncertainty and identification of factors (including pollution sources, environmental influences and ill-defined objectives) contributing to the risk of failing water quality objectives. (3) For practical application of Monte Carlo methods, attention needs to be given to numerical efficiency, and for successful communication of results, effective interfaces are required. A risk-based modelling tool developed by the authors is introduced.

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River water quality modelling and simulation based on Markov Chain Monte Carlo computation and Bayesian inference model

African Journal of Science Technology Innovation and Development ◽

10.1080/20421338.2019.1692460 ◽

2020 ◽

Vol 12 (6) ◽

pp. 771-785 ◽

Cited By ~ 1

Author(s):

Mrunmayee Manjari Sahoo ◽

Kanhu Charan Patra

Keyword(s):

Water Quality ◽

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Bayesian Inference ◽

Water Quality Modelling ◽

Inference Model ◽

Monte Carlo Computation ◽

Simulation Based ◽

River Water Quality Modelling

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Monte Carlo based calibration and uncertainty analysis of a coupled plant growth and hydrological model

Biogeosciences Discussions ◽

10.5194/bgd-10-19509-2013 ◽

2013 ◽

Vol 10 (12) ◽

pp. 19509-19540 ◽

Cited By ~ 4

Author(s):

T. Houska ◽

S. Multsch ◽

P. Kraft ◽

H.-G. Frede ◽

L. Breuer

Keyword(s):

Monte Carlo ◽

Plant Growth ◽

Cross Validation ◽

Model Performance ◽

Coupled Model ◽

Radiation Use Efficiency ◽

Retention Curve ◽

Modelling Framework ◽

Use Efficiency ◽

Radiation Use

Abstract. Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures, e.g. by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow a more detailed analysis of the dynamic behaviour of the soil–plant interface. We used the Python programming language to couple two of such high process oriented independent models and to calibrate both models simultaneously. The Catchment Modelling Framework (CMF) simulated soil hydrology based on the Richards equation and the van-Genuchten–Mualem retention curve. CMF was coupled with the Plant growth Modelling Framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation. The Monte Carlo based Generalised Likelihood Uncertainty Estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions to it. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 106 model runs randomly drawn from an equally distributed parameter space. Three objective functions were used to evaluate the model performance, i.e. coefficient of determination (R2), bias and model efficiency according to Nash Sutcliffe (NSE). The model was applied to three sites with different management in Muencheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matters of roots, storages, stems and leaves. Best parameter sets resulted in NSE of 0.57 for the simulation of soil moisture across all three sites. The shape parameter of the retention curve n was highly constrained whilst other parameters of the retention curve showed a large equifinality. The root and storage dry matter observations were predicted with a NSE of 0.94, a low bias of −58.2 kg ha−1 and a high R2 of 0.98. Dry matters of stem and leaves were predicted with less, but still high accuracy (NSE = 0.79, bias = 221.7 kg ha−1, R2 = 0.87). We attribute this slightly poorer model performance to missing leaf senescence which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use-efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need of including agricultural management options in the coupled model.

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An integrated water quality modelling framework for reporting on Great Barrier Reef catchments

Piantadosi, J., Anderssen, R.S. and Boland J. (eds) MODSIM2013, 20th International Congress on Modelling and Simulation ◽

10.36334/modsim.2013.l21.ellis ◽

2013 ◽

Keyword(s):

Water Quality ◽

Great Barrier Reef ◽

Barrier Reef ◽

Water Quality Modelling ◽

Modelling Framework

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Spatial Statistical Approaches to Water Quality Modelling

10.15760/etd.7383 ◽

2000 ◽

Author(s):

Janardan Mainali

Keyword(s):

Water Quality ◽

Water Quality Modelling ◽

Statistical Approaches

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Water Quality Forecasting in Shrimp Cultures based on Monte Carlo Tree Search

2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST) ◽

10.1109/iceast52143.2021.9426250 ◽

2021 ◽

Author(s):

Dhanachai Pinitsava ◽

Piyaporn Surinlert ◽

Worapan Kusakunniran

Keyword(s):

Water Quality ◽

Monte Carlo ◽

Tree Search ◽

Monte Carlo Tree Search ◽

Water Quality Forecasting

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Comparing Wetland Ecosystems Service Provision under Different Management Approaches: Two Cases Study of Tianfu Wetland and Nansha Wetland in China

Sustainability ◽

10.3390/su13168710 ◽

2021 ◽

Vol 13 (16) ◽

pp. 8710

Author(s):

Yuchao Zhang ◽

Steven Loiselle ◽

Yimo Zhang ◽

Qian Wang ◽

Xia Sun ◽

...

Keyword(s):

Water Quality ◽

Ecosystem Services ◽

Wetland Management ◽

Nutrient Retention ◽

Limited Information ◽

Urban Centers ◽

Wetland Ecosystems ◽

Fundamental Information ◽

Management Approaches ◽

Study Sites

The largest blue-green infrastructures in industrialized, urbanized and developed regions in China are often multiuse wetlands, located just outside growing urban centers. These areas have multiple development pressures while providing environmental, economic, and social benefits to the local and regional populations. Given the limited information available about the tradeoffs in ecosystem services with respect to competing wetland uses, wetland managers and provincial decision makers face challenges in regulating the use of these important landscapes. In the present study, measurements made by citizen scientists were used to support a comparative study of water quality and wetland functions in two large multiuse wetlands, comparing areas of natural wetland vegetation, tourism-based wetland management and wetland agriculture. The study sites, the Nansha and Tianfu wetlands, are located in two of the most urbanized areas of China: the lower Yangtze River and Pearl River catchments, respectively. Our results indicated that the capacity of wetlands to mitigate water quality is closely related to the quality of the surrounding waters and hydrological conditions. Agricultural areas in both wetlands provided the lowest sediment and nutrient retention. The results show that the delivery of supporting ecosystem services is strongly influenced by the location and use of the wetland. Furthermore, we show that citizen scientist-acquired data can provide fundamental information on quantifying these ecosystem services, providing needed information to wetland park managers and provincial wetland administrators.

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Hydrodynamics and water quality modelling in a regulated river segment: application on the instream flow definition

Ecological Modelling ◽

10.1016/j.ecolmodel.2003.07.009 ◽

2004 ◽

Vol 173 (2-3) ◽

pp. 197-218 ◽

Cited By ~ 19

Author(s):

Luis Filipe Gomes Lopes ◽

José S.Antunes Do Carmo ◽

Rui Manuel Vitor Cortes ◽

Daniel Oliveira

Keyword(s):

Water Quality ◽

Regulated River ◽

Instream Flow ◽

Water Quality Modelling

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Multivariate Error Covariance Estimates by Monte Carlo Simulation for Assimilation Studies in the Pacific Ocean

Monthly Weather Review ◽

10.1175/mwr2984.1 ◽

2005 ◽

Vol 133 (8) ◽

pp. 2310-2334 ◽

Cited By ~ 18

Author(s):

Anna Borovikov ◽

Michele M. Rienecker ◽

Christian L. Keppenne ◽

Gregory C. Johnson

Keyword(s):

Monte Carlo ◽

Data Assimilation ◽

Forecast Error ◽

Independent Set ◽

Velocity Fields ◽

Optimal Interpolation ◽

Model Errors ◽

Error Statistics ◽

Practical Applications ◽

Monte Carlo Techniques

Abstract One of the most difficult aspects of ocean-state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model–observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross covariances between different model variables used. Here a comparison is made between a univariate optimal interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature profiles. In the UOI case only temperature is updated using a Gaussian covariance function. In the MvOI, salinity, zonal, and meridional velocities as well as temperature are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimate of the forecast error statistics is made by Monte Carlo techniques from an ensemble of model forecasts. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross covariances between the fields of different physical variables constituting the model-state vector, at the same time incorporating the model’s dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere–Ocean array have been assimilated in this study. To investigate the efficacy of the multivariate scheme, two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity, and temperature. For reference, a control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when the multivariate correction is used, as is evident from the analyses of the rms differences between these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating water masses with properties close to the observed, while the UOI fails to maintain the temperature and salinity structure.

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Comparable Encryption Scheme over Encrypted Cloud Data in Internet of Everything

Security and Communication Networks ◽

10.1155/2017/6430850 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11

Author(s):

Qian Meng ◽

Jianfeng Ma ◽

Kefei Chen ◽

Yinbin Miao ◽

Tengfei Yang

Keyword(s):

Model Performance ◽

Data Integrity ◽

Cloud Service ◽

Third Party ◽

Encryption Scheme ◽

Cloud Data ◽

Practical Applications ◽

Internet Of Everything ◽

Window Method ◽

Comparable Encryption

User authentication has been widely deployed to prevent unauthorized access in the new era of Internet of Everything (IOE). When user passes the legal authentication, he/she can do series of operations in database. We mainly concern issues of data security and comparable queries over ciphertexts in IOE. In traditional database, a Short Comparable Encryption (SCE) scheme has been widely used by authorized users to conduct comparable queries over ciphertexts, but existing SCE schemes still incur high storage and computational overhead as well as economic burden. In this paper, we first propose a basic Short Comparable Encryption scheme based on sliding window method (SCESW), which can significantly reduce computational and storage burden as well as enhance work efficiency. Unfortunately, as the cloud service provider is a semitrusted third party, public auditing mechanism needs to be furnished to protect data integrity. To further protect data integrity and reduce management overhead, we present an enhanced SCESW scheme based on position-aware Merkle tree, namely, PT-SCESW. Security analysis proves that PT-SCESW and SCESW schemes can guarantee completeness and weak indistinguishability in standard model. Performance evaluation indicates that PT-SCESW scheme is efficient and feasible in practical applications, especially for smarter and smaller computing devices in IOE.

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