To provide support for resource management decision making, computational modeling workflows in environmental simulation need to be efficient, reproducible, and robust with regard to the risk of unwanted outcomes. Unfortunately, each of these three attributes is difficult to achieve in practice; aspirations to simultaneously achieve all of them are truly lofty. Too often, modeling analyses are inefficient, the workflow is largely opaque and unknown, and the important simulated outcomes lack the context of uncertainty and/or risk.
This Research Topic calls for papers that demonstrate rapid, reproducible and/or robust modeling through worked examples and software tools (a preference for open source). The worked examples should demonstrate how the researcher aspired to be rapid, reproducible, and robust; we are interested in the process and approach as much as the results. We hope that, among lessons learned and results presented, the articles in this Research Topic provide a starting point from which other practitioners and researchers can build. Descriptions of how the modeling analysis results were/are used to inform decision making should be discussed. We particularly welcome descriptions of trials and tribulations:What was difficult? What didn’t work? How were these issues overcome?
Software tools may include techniques to automate modeling workflow elements or increase efficiency, reproducibility, robustness of decision-support modeling elements. This includes frameworks to build models from original data in flexible ways that may enable hypothesis testing in the form of changing discretization, process representation, and other modeling decisions efficiently and transparently. To that end, real-world demonstrations of multi-model/Bayesian-model selection/comparison, as well as examples of how model structural error can be accommodated would be exciting contributions. Innovative demonstrations of uncertainty analysis, data assimilation, and management optimization under uncertainty/robust optimization at scale in the decision-support context are also of keen interest, as are demonstrations of how machine-learning-based environmental decision support analyses can be rapid, reproducible and robust at scale.
Topic Editor Jeremy White is employed by Intera, Inc., an environmental modeling consultancy. Topic Editor Catherine Moore is employed by GNS Science, an environmental modeling consultancy. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
To provide support for resource management decision making, computational modeling workflows in environmental simulation need to be efficient, reproducible, and robust with regard to the risk of unwanted outcomes. Unfortunately, each of these three attributes is difficult to achieve in practice; aspirations to simultaneously achieve all of them are truly lofty. Too often, modeling analyses are inefficient, the workflow is largely opaque and unknown, and the important simulated outcomes lack the context of uncertainty and/or risk.
This Research Topic calls for papers that demonstrate rapid, reproducible and/or robust modeling through worked examples and software tools (a preference for open source). The worked examples should demonstrate how the researcher aspired to be rapid, reproducible, and robust; we are interested in the process and approach as much as the results. We hope that, among lessons learned and results presented, the articles in this Research Topic provide a starting point from which other practitioners and researchers can build. Descriptions of how the modeling analysis results were/are used to inform decision making should be discussed. We particularly welcome descriptions of trials and tribulations:What was difficult? What didn’t work? How were these issues overcome?
Software tools may include techniques to automate modeling workflow elements or increase efficiency, reproducibility, robustness of decision-support modeling elements. This includes frameworks to build models from original data in flexible ways that may enable hypothesis testing in the form of changing discretization, process representation, and other modeling decisions efficiently and transparently. To that end, real-world demonstrations of multi-model/Bayesian-model selection/comparison, as well as examples of how model structural error can be accommodated would be exciting contributions. Innovative demonstrations of uncertainty analysis, data assimilation, and management optimization under uncertainty/robust optimization at scale in the decision-support context are also of keen interest, as are demonstrations of how machine-learning-based environmental decision support analyses can be rapid, reproducible and robust at scale.
Topic Editor Jeremy White is employed by Intera, Inc., an environmental modeling consultancy. Topic Editor Catherine Moore is employed by GNS Science, an environmental modeling consultancy. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
