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Risk-based decision making to evaluate pollutant reduction scenarios

Ebrahim Ahmadisharaf, Brian L. Benham
2019 Science of the Total Environment  
The risk-based framework presented here illustrates a method to propagate watershed model uncertainty and assess performance of alternative pollutant reduction scenarios using existing tools, thereby enabling  ...  Computational watershed models are often used to develop TMDL pollutant reduction scenarios. Uncertainty is inherent in the modeling process.  ...  Acknowledgements We thank Anurag Mishra and Karen Kline for the help with HSPEXP+ and HSPF development in the Woods Creek watershed.  ... 
doi:10.1016/j.scitotenv.2019.135022 pmid:31731127 fatcat:tzylzepimvdl5emw36tp6j3w3q

Stochastic Watershed Water Quality Simulation for TMDL Development - A Case Study in the Newport Bay Watershed1

Yi Zheng, Arturo A. Keller
2008 Journal of the American Water Resources Association  
The Watershed Analysis Risk Management Framework model was used as an example of a complex watershed model.  ...  Our previous studies developed the Management Objectives Constrained Analysis of Uncertainty (MOCAU) approach as an uncertainty analysis technique specifically for watershed water quality models, based  ...  Watershed Analysis Risk Management Framework (WARMF) (e.g., Chen et al., 2004; Keller et al., 2004) , have been increasingly used for TMDL analysis.  ... 
doi:10.1111/j.1752-1688.2008.00232.x fatcat:zw3xl3dikzg5ln6bbzl36kjw5m

What has been accomplished twenty years after the Oreskes et al. (1994) critique? Current state and future perspectives of environmental modeling in the Great Lakes

George B. Arhonditsis, Craig A. Stow, Yerubandi R. Rao, Gurbir Perhar
2014 Journal of Great Lakes research  
Current state and future perspectives of environmental modeling in the Great Lakes "...Complete [model] confirmation is logically precluded by the fallacy of affirming the consequent and by incomplete  ...  Models can only be evaluated in relative terms, and their predictive value is always open to question. The primary value of models is heuristic..." [Oreskes et al. (1994)] http://dx.  ...  The rigorous assessment of uncertainty in model predictions, the optimization of the sampling design of monitoring programs, and the alignment with the policy practice of adaptive management are some of  ... 
doi:10.1016/j.jglr.2014.11.002 fatcat:japd3mrwureihnigjdz3t7yttu

Integration of Bayesian Inference Techniques with Mathematical Modeling [chapter]

G. Arhonditsis, S. Stremilov, A. Gudimov, M. Ramin, W. Zhang
2011 Treatise on Estuarine and Coastal Science  
The Bayesian nature of my work is used: (i) to alleviate problems 23 of spatiotemporal resolution mismatch between watershed and receiving waterbody 24 models; and (ii) to overcome the conceptual or scale  ...  The two eutrophication models are used in conjunction 21 with the SPAtially Referenced Regressions On Watershed attributes (SPARROW) 22 watershed model.  ...  338 various types of models (complex dynamic models, empirical equations, expert 339 judgments) into one coherent framework, while rigorously assessing the uncertainty 340 associated with model structures  ... 
doi:10.1016/b978-0-12-374711-2.00910-4 fatcat:ztvgc5ejsff3ned6sgedmqfrai

Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment)

A.R. Keshtkar, A. Salajegheh, A. Sadoddin, M.G. Allan
2013 Ecological Modelling  
Results for the study area implementation and a suggestion for management are described and discussed. (A.R. Keshtkar). Probabilistic assessment and risk analysis methods are widely used.  ...  Recently, the utilization of graphical models, such as Bayesian networks (BNs), has grown rapidly in natural resources modeling and management under uncertainty and integrated water and catchment management  ...  The authors wish to acknowledge the Forests, Rangelands and Watershed Management Organization for preparing some data of the project. Also, the authors would like to thank E. Shahriari, L.  ... 
doi:10.1016/j.ecolmodel.2013.08.003 fatcat:v642kd7curfr5m4xkwhq2tehle

Regional risk assessment of the Puyallup River Watershed and the evaluation of low impact development in meeting management goals

Eleanor E Hines, Wayne G Landis
2014 Integrated Environmental Assessment and Management  
The model was then evaluated for uncertainty and a sensitivity analysis for the endpoint examined using an entropy reduction analysis.  ...  Risk Assessment as a Management Tool The BN-RRM works well within a management framework.  ... 
doi:10.1002/ieam.1509 pmid:24288344 fatcat:kak37zp4r5axbbzv3ghdkde6s4

The integration of chlorpyrifos acetylcholinesterase inhibition, water temperature and dissolved oxygen concentration into a regional scale multiple stressor risk assessment estimating risk to Chinook salmon in four rivers in Washington State, USA

Wayne G. Landis, Valerie R. Chu, Scarlett E. Graham, Meagan J. Harris, April J. Markiewicz, Chelsea J. Mitchell, Katherine E. Stackelberg, John D. Stark
2019 Integrated Environmental Assessment and Management  
The Bayesian network relative risk model (BN-RRM) was used to conduct this ecological risk assessment and was modified to contain an acetylcholinesterase (AChE) inhibition pathway parameterized using data  ...  Integr Environ Assess Manag 2019;00:1-15. © 2019 SETAC.  ...  Model framework and limitations The intent of the present paper was not to perform a detailed ecological risk assessment of the 4 watersheds.  ... 
doi:10.1002/ieam.4199 pmid:31379044 fatcat:qhn4sd6jsrabfeht24o6r6w3fi

Bayesian Approach for Uncertainty Analysis of an Urban Storm Water Model and Its Application to a Heavily Urbanized Watershed

Misgana K. Muleta, Jonathan McMillan, Geremew G. Amenu, Steven J. Burian
2013 Journal of hydrologic engineering  
This study applies formal Bayesian approach for uncertainty analysis of a widely used storm water management model and illustrates the methodology using a highly urbanized watershed in the Los Angeles  ...  Overall, the UA methodology proved promising for sensitivity analysis, calibration, parameter uncertainty, and total predictive uncer tainty analysis of urban storm water management models.  ...  Conclusions This paper describes an MCMC-based formal, Bayesian method ology for parameter uncertainty and total predictive uncertainty analysis of a widely used urban storm water management model.  ... 
doi:10.1061/(asce)he.1943-5584.0000705 fatcat:fle53fzl7vbfdmuzkltofixnge

Generalized Likelihood Uncertainty Estimation and Markov Chain Monte Carlo Simulation to Prioritize TMDL Pollutant Allocations

Anurag Mishra, Ebrahim Ahmadisharaf, Brian L. Benham, Mary Leigh Wolfe, Scotland C. Leman, Daniel L. Gallagher, Kenneth H. Reckhow, Eric P. Smith
2018 Journal of hydrologic engineering  
This study presents a probabilistic framework that considers both the water quality improvement capability and reliability of alternative total maximum daily load (TMDL) pollutant allocations.  ...  Generalized likelihood uncertainty estimation and Markov chain Monte Carlo techniques were used to assess the relative uncertainty and reliability of two alternative TMDL pollutant allocations that were  ...  Chin (2009) used a stochastic analysis of model residuals for uncertainty analysis of HSPF.  ... 
doi:10.1061/(asce)he.1943-5584.0001720 fatcat:ux4s5wli45e3tkvynx5chi3yli

The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models

Mojtaba Sadegh, Morteza Shakeri Majd, Jairo Hernandez, Ali Torabi Haghighi
2018 Water resources management  
of hydrological model parameters derived in a formal Bayesian framework with a residual-based likelihood function.  ...  Finally, data transformations might offer a greater potential to evaluate model performance and assess specific sections of model behavior, than to calibrate models in a single objective framework.  ...  MATLAB codes for the hydrological models and MCMC algorithm can be obtained from the corresponding author. This is an author-produced, peer-reviewed version of this article.  ... 
doi:10.1007/s11269-018-1908-6 fatcat:erdersay6zcqlfnnqxewulkdlu

Construction of a Bayesian Network for the Assessment of Agri-Environmental Measures – The Case Study of the Venice Lagoon Watershed

Marta Carpani, Carlo Giupponi
2010 Italian Journal of Agronomy  
Two already existing methodologies were combined in a new flexible approach for policy assessment: Conceptual Modelling and Bayesian Networks (BNs).  ...  A methodological framework was designed to assess the effectiveness of agri-environmental policy measures adopted by the Veneto Region to reduce diffuse water pollution of agricultural origin.  ...  Construction of a Bayesian Network for the Assessment of Agri-Environmental Measures -The Case Study of the Venice Lagoon Watershed Marta Carpani 1* , Carlo Giupponi 2 The hierarchical spatial level  ... 
doi:10.4081/ija.2010.265 fatcat:3un33rodtjgo7i7hstsbx5ynka

Partitioning uncertainty in streamflow projections under nonstationary model conditions

Ila Chawla, P.P. Mujumdar
2018 Advances in Water Resources  
To tackle the nonstationarity in the streamflow records, we proposed a hydrologic risk analysis framework for the Xiangxi River, one of the largest tributaries of the Three Gorge Region, China.  ...  The results provided important insights into hydrologic nonstaionarity and uncertainty, and the proposed framework can provide scientific bases for engineering design and risk management in many other  ...  Acknowledgment: This research was supported by the Natural Sciences Foundation (51190095, 51520105013), the 111 Project (B140-08), and Natural Sciences and Engineering Research Council of Canada.  ... 
doi:10.1016/j.advwatres.2017.10.013 fatcat:63dylpkkurc4dcta77lipktxre

Uncertainty analysis for nonpoint source pollution modeling: implications for watershed models

Z. Shen, H. Xie, L. Chen, J. Qiu, Y. Zhong
2014 International Journal of Environmental Science and Technology  
This review focuses on uncertainty issues related to the Soil and Water Assessment Tool (SWAT) model, which is one of the most useful tools for simulating nonpoint source (NPS) pollution processes.  ...  We also compared some widely used uncertainty analysis methods, such as the generalized likelihood uncertainty estimation and first-order error analysis, to provide reliable guidance for the application  ...  Acknowledgments The study was funded by several grants from the National Science Foundation for Distinguished Young Scholars  ... 
doi:10.1007/s13762-014-0598-8 fatcat:w35rivet75hgpitfxncj6377fy

Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

Dongmei Feng, Edward Beighley
2020 Hydrology and Earth System Sciences  
Assessing impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control, and ecosystem conservation practices  ...  The Bayesian model averaging analysis indicates that the probability of such an increase can be up to 85 %.  ...  This modeling framework was also integrated with a Bayesian model averaging (BMA) analysis to assess the performance of different model-forcing-parameter combinations and to provide actionable information  ... 
doi:10.5194/hess-24-2253-2020 fatcat:evg5ckujzja4niglofejq6y7oq

Application of Multimodal Optimization for Uncertainty Estimation of Computationally Expensive Hydrologic Models

Huidae Cho, Francisco Olivera
2014 Journal of water resources planning and management  
The generalized likelihood uncertainty estimation (GLUE) framework has been widely used in hydrologic studies.  ...  However, the extensive random sampling causes a high computational burden that prohibits the efficient application of GLUE to costly distributed hydrologic models such as the soil and water assessment  ...  The authors would like to thank the manuscript reviewers for their valuable comments and suggestions.  ... 
doi:10.1061/(asce)wr.1943-5452.0000330 fatcat:b54a35p7zrenxc3txa5susokvi
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