Environmental Modelling & Software, Volume 114

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Elsevier BV
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Introductory overview: Optimization using evolutionary algorithms and other metaheuristics
Holger R. Maier | Saman Razavi | Zoran Kapelan | L. Shawn Matott | J. R. Kasprzyk | Bryan A. Tolson

Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best trade-offs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single- and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.

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A hydrological and water temperature modelling framework to simulate the timing of river freeze-up and ice-cover breakup in large-scale catchments
L. A. Morales-Marín | Palash Sanyal | H. Kadowaki | Zhaoqin Li | Prabin Rokaya | Karl–Erich Lindenschmidt

Abstract Ice phenology, defined as the timing of freeze-up and ice-cover breakup, plays a key role in streamflow regimes in cold-region river catchments. River freeze-up and ice-cover breakup events are controlled by meteorological and hydrological variables. In this study, we present a modelling framework consisting of a physically-based semi-distributed hydrological model and the integration of a 1D stream temperature model that can predict the ice duration in cold region rivers. The hydrological model provides streamflow and hydraulic parameters for the stream temperature model to obtain instream water temperature. The model was successfully applied in the Athabasca River basin in western Canada. Calibration was carried out using the water temperature recorded in the stations at the towns of Hinton, Athabasca and Fort McMurray. Model results show consistent correspondence between simulated freeze-up and breakup dates and the hydrometric station data. In the main tributaries of the basin, freeze-up timing spans from the last week of September to the second week of November and ice-cover breakup occurs from the second week of March to the last week of May. The model presents an application of water temperature and ice phenology simulation which can be incorporated in ice-jam flood forecasting and future climate change studies.

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A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models
Saman Razavi | Hoshin Gupta

Abstract Many applications of global sensitivity analysis (GSA) do not adequately account for the dynamical nature of earth and environmental systems models. Gupta and Razavi (2018) highlight this fact and develop a sensitivity analysis framework from first principles, based on the sensitivity information contained in trajectories of partial derivatives of the dynamical model responses with respect to controlling factors. Here, we extend and generalize that framework to accommodate any GSA philosophy, including derivative-based approaches (such as Morris and DELSA), direct-response-based approaches (such as the variance-based Sobol’, distribution-based PAWN, and higher-moment-based methods), and unifying variogram-based approaches (such as VARS). The framework is implemented within the VARS-TOOL software toolbox and demonstrated using the HBV-SASK model applied to the Oldman Watershed, Canada. This enables a comprehensive multi-variate investigation of the influence of parameters and forcings on different modeled state variables and responses, without the need for observational data regarding those responses.