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  • A comparison of methods for discretizing continuous variables in Bayesian Networks
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-07-17
    Tomas Beuzen, Lucy Marshall, Kristen D. Splinter

    Bayesian Networks (BNs) are an increasingly popular method for modelling environmental systems. The discretization of continuous variables is often required to use BNs. There are three main methods of discretization; manual, unsupervised, and supervised. Here, we compare and demonstrate each approach with a BN that predicts coastal erosion. Results reveal that supervised discretization methods produced BNs of the highest average predictive skill (73.8%), followed by manual discretization (69.0%) and unsupervised discretization (64.8%). However, each method has specific advantages that may make them more suitable for particular applications. Manual methods can produce physical meaningful BNs, which is favorable in environmental modelling. Supervised methods can autonomously and optimally discretize variables and may be preferred when predictive skill is a modelling priority. Unsupervised methods are computationally simple and versatile. The optimal discretization scheme should consider both the performance and practicality of the scheme.

    更新日期:2018-07-18
  • Web-based tool compilation of analytical equations for groundwater management applications
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-07-17
    Jana Glass, Ramandeep Jain, Ralf Junghanns, Jana Sallwey, Thomas Fichtner, Catalin Stefan

    The INOWAS platform provides a compilation of free web-based tools for groundwater management. All tools are running on a web server and can be accessed via standard web browsers. The analytical equations implemented enable the assessment of saltwater intrusion induced by pumping or sea level rise, the calculation of travel time through unconfined aquifers and the evaluation of pumping-induced river drawdown. The groundwater mounding calculator can be used to estimate the rise of groundwater levels underneath infiltration basins. To determine the contaminant concentration downgradient of a constant source, an analytical tool solving the advection-dispersion equation can be utilized. All tools are incorporated into a decision support environment. The user is provided with detailed online support that contains the theoretical background of the tools, possible applications and examples.

    更新日期:2018-07-18
  • Downscaling of climate model output for Alaskan stakeholders
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-04-30
    John E. Walsh, Uma S. Bhatt, Jeremy S. Littell, Matthew Leonawicz, Michael Lindgren, Thomas A. Kurkowski, Peter A. Bieniek, Richard Thoman, Stephen Gray, T. Scott Rupp

    The paper summarizes an end-to-end activity connecting the global climate modeling enterprise with users of climate information in Alaska. The effort included retrieval of the requisite observational datasets and model output, a model evaluation and selection procedure, the actual downscaling by the delta method with its inherent bias-adjustment, and the provision of products to a range of users through visualization software that empowers users to explore the downscaled output and its sensitivities. An additional software tool enables users to examine skill metrics and relative rankings of 21 global models for Alaska and six other domains in the Northern Hemisphere. The downscaled temperatures and precipitation are made available as calendar-month decadal means under three different greenhouse forcing scenarios through 2100 for more than 4000 communities in Alaska and western Canada. The visualization package displays the uncertainties inherent in the multi-model ensemble projections. These uncertainties are often larger than the projected changes.

    更新日期:2018-07-14
  • A semantic multi-criteria approach to evaluate different types of energy generation technologies
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-04-30
    Miriam Martínez-García, Aida Valls, Antonio Moreno, Arantza Aldea

    Multi-Criteria Decision Aid methods are used to find the best option from a set of alternatives when multiple and conflicting criteria have to be optimized simultaneously. The evaluation of the suitability or risk of each alternative is usually performed by assigning a numerical value. However, sometimes the data required to measure a criterion may be found in the form of semantic values such as tags. This paper proposes a methodology to calculate the strength of an outranking relation for a pair of alternatives using semantic criteria following the principles of ELECTRE-III (i.e. by means of concordance and discordance indices). The preferences about semantic data are represented in an ontology by means of objective and subjective functions. The paper explains how this new methodology was applied to analyse different electricity generation technologies using environmental and economic criteria. Two scenarios are tested to show how semantic criteria may influence the final decision.

    更新日期:2018-07-14
  • An open-source Python implementation of California's hydroeconomic optimization model
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-07-12
    Mustafa S. Dogan, Max A. Fefer, Jonathan D. Herman, Quinn J. Hart, Justin R. Merz, Josue Medellín-Azuara, Jay R. Lund

    This short communication describes a new open-source implementation of the CALVIN model (CALifornia Value Integrated Network), a large-scale network flow optimization model of California's water supply system. The model is cross-platform, uses common data formats, and connects to several freely available linear programming solvers. Given inputs including hydrology, urban/agricultural demand curves, and variable operating costs, the model minimizes the systemwide cost of water scarcity and operations including surface and groundwater reservoirs, wastewater reuse, desalination, environmental flow requirements, and hydropower. Key outputs include water shortage costs and marginal economic values of water and infrastructure capacity. We benchmark the scalability of different solvers up to roughly 5 million decision variables, using shared-memory parallelization on a high performance computing cluster. Runtimes are reduced by two orders of magnitude relative to the original model when no initial solution is provided, in addition to the benefits such as accessibility and transparency that come with an open-source platform. While this model is specific to California, the data and model structure are separated, so a similar framework could be used in any system where water allocation has been formulated as a network flow problem.

    更新日期:2018-07-12
  • A simplified approach to produce probabilistic hydrological model predictions
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-07-06
    David McInerney, Mark Thyer, Dmitri Kavetski, Bree Bennett, Julien Lerat, Matthew Gibbs, George Kuczera

    Probabilistic predictions from hydrological models, including rainfall-runoff models, provide valuable information for water and environmental resource risk management. However, traditional “deterministic” usage of rainfall-runoff models remains prevalent in practical applications, in many cases because probabilistic predictions are perceived to be difficult to generate. This paper introduces a simplified approach for hydrological model inference and prediction that bridges the practical gap between “deterministic” and “probabilistic” techniques. This approach combines the Least Squares (LS) technique for calibrating hydrological model parameters with a simple method-of-moments (MoM) estimator of error model parameters (here, the variance and lag-1 autocorrelation of residual errors). A case study using two conceptual hydrological models shows that the LS-MoM approach achieves probabilistic predictions with similar predictive performance to classical maximum-likelihood and Bayesian approaches, but is simpler to implement using common hydrological software and has a lower computational cost. A public web-app to help users implement the simplified approach is available.

    更新日期:2018-07-08
  • An integrated modeling framework for crop and biofuel systems using the DSSAT and GREET models
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-07-07
    Ryan Anderson, Deepak Keshwani, Ashu Guru, Haishun Yang, Suat Irmak, Jeyamkondan Subbiah

    As global demand for food, energy, and water resources continues to increase, decision-makers in these sectors must find sustainable ways to produce and provide for the growing population. While many models have been created to aid in decision-making in these systems, there is a lack of robust integrated models that enable an understanding of the interconnections of these systems. This study develops a modeling framework that explores the connections of the corn and ethanol systems, two major food and energy resources. A crop modeling tool (DSSAT) and a biofuel life cycle assessment tool (GREET) are connected using a service-oriented architecture programming approach. A Python program is written to connect these two models and run scenario analyses to assess environmental impacts of the integrated system. This paper explores the impact of decisions such as fertilizer use and plant population on environmental effects of greenhouse gases, energy use, and water in the integrated system.

    更新日期:2018-07-08
  • 更新日期:2018-06-28
  • An improved nightlight-based method for modeling urban CO2 emissions
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-06-22
    Ji Han, Xing Meng, Hanwei Liang, Zhi Cao, Liang Dong, Cheng Huang

    An accurate modeling of urban CO2 emissions is important for understanding the dynamics of carbon cycle and for designing low-carbon policies. We develop an improved nightlight-based method to model urban CO2 emissions and investigate their spatiotemporal patterns. Differing from the previous methods, in processing the pre-modeling data, we bring forward the existing CO2 inventories from national and provincial levels to city level, and correct the saturation and blooming problems of nightlight. In modeling the correlation between nightlight and statistically accounted CO2 emissions, we highlight a panel-data regression analysis that considers the spatiotemporal heterogeneity across cities and over time simultaneously. Eleven cities in Yangtze River Delta of China were selected for a case study testing our method. The internal and external validations have proven the predominance of our proposed method for capturing the nightlight-CO2 correlation, and for describing the spatial distribution and heterogeneity of urban CO2 emissions.

    更新日期:2018-06-22
  • A generalised approach for identifying influential data in hydrological modelling
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-06-22
    David P. Wright, Mark Thyer, Seth Westra, Benjamin Renard, David McInerney

    Influence diagnostics are used to identify data points that have a disproportionate impact on model parameters, performance and/or predictions, providing valuable information for use in model calibration. Regression-theory influence diagnostics identify influential data by combining the leverage and the standardised residuals, and are computationally more efficient than case-deletion approaches. This study evaluates the performance of a range of regression-theory influence diagnostics on ten case studies with a variety of model structures and inference scenarios including: nonlinear model response, heteroscedastic residual errors, data uncertainty and Bayesian priors. A new technique is developed, generalised Cook's distance, that is able to accurately identify the same influential data as standard case deletion approaches (Spearman rank correlation: 0.93–1.00) at a fraction of the computational cost (<1%). This is because generalised Cook's distance uses a generalised leverage formulation which outperforms linear and nonlinear leverage formulations because it has less restrictive assumptions. Generalised Cook's distance has the potential to enable influential data to be efficiently identified on a wide variety of hydrological and environmental modelling problems.

    更新日期:2018-06-22
  • A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-06-21
    Jeremy T. White

    An open-source, scalable and model-independent (non-intrusive) implementation of an iterative ensemble smoother has been developed to alleviate the computational burden associated with history-matching and uncertainty quantification of real-world-scale environmental models that have very high dimensional parameter spaces. The tool, named pestpp-ies, implements the ensemble-smoother form of the popular Gauss-Levenberg-Marquardt algorithm, uses the pest model-interface protocols and includes a built-in parallel run manager, multiple lambda testing and model run failure tolerance. As a demonstration of its capabilities, pestpp-ies is applied to a synthetic groundwater model with thousands of parameters and to a real-world groundwater flow and transport model with tens of thousands of parameters. pestpp-ies is shown to efficiently and effectively condition parameters in both cases and can provide means to estimate posterior forecast uncertainty when the forecasts depend on large numbers of parameters.

    更新日期:2018-06-22
  • An empirical workflow to integrate uncertainty and sensitivity analysis to evaluate agent-based simulation outputs
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-06-18
    Carolina G. Abreu, Celia G. Ralha

    This paper presents an empirical study comparing different uncertainty analysis (UA) and sensitivity analysis (SA) methods focussing their usefulness for the output analysis of land use/land cover change (LUCC) agent-based models (ABMs). As a result, a workflow to integrate UA and SA is presented to evaluate ABMs outputs. We developed a baseline scenario and performed a comprehensive investigation of the impacts that differences in sample sizes, sample techniques, and SA methods may have on the model output. The analysis is done in the context of a particular agent-based simulator with a LUCC model in a Brazilian Cerrado case study. The experiments indicate that there are known challenges to be overcome by the use of statistical methods. Even though the presented analysis was done over a particular simulator, we intend to contribute to the community that understands the importance of statistical validation techniques to improve the level of confidence in agent-based simulation outputs.

    更新日期:2018-06-18
  • Application and evaluation of two model fusion approaches to obtain ambient air pollutant concentrations at a fine spatial resolution (250m) in Atlanta
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-06-14
    Josephine T. Bates, Audrey Flak Pennington, Xinxin Zhai, Mariel D. Friberg, Francesca Metcalf, Lyndsey Darrow, Matthew Strickland, James Mulholland, Armistead Russell

    Epidemiologic studies rely on accurately characterizing spatiotemporal variation in air pollutant concentrations. This work presents two model fusion approaches that use publicly available chemical transport simulations, dispersion model simulations, and observations to estimate air pollutant concentrations at a neighborhood-level spatial resolution while incorporating comprehensive chemistry and emissions sources. The first method is additive and the alternative method is multiplicative. These approaches are applied to Atlanta, GA at a 250 m grid resolution to obtain daily 24-hr averaged PM2.5 and 1-hr max CO and NOx concentrations during the years 2003–2008 for use in health studies. The modeled concentrations provide comprehensive estimates with steep spatial gradients near roadways, secondary formation and loss, and effects of regional sources that can influence daily variation in ambient pollutant concentrations. Results show high temporal and spatial correlation and low biases across monitors, providing accurate pollutant concentration estimates for epidemiologic analyses.

    更新日期:2018-06-14
  • Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-06-13
    Marc Jaxa-Rozen, Jan Kwakkel

    Complex environmental models typically require global sensitivity analysis (GSA) to account for non-linearities and parametric interactions. However, variance-based GSA is highly computationally expensive. While different screening methods can estimate GSA results, these techniques typically impose restrictions on sampling methods and input types. As an alternative, this work evaluates two decision tree-based methods to approximate GSA results: random forests, and Extra-Trees. These techniques are applicable with common sampling methods, and continuous or categorical inputs. The tree-based methods are compared to reference Sobol GSA and Morris screening techniques, for three cases: an Ishigami-Homma function, a H1N1 pandemic model, and the CDICE integrated assessment model. The Extra-Trees algorithm performs favorably compared to Morris elementary effects, accurately approximating the relative importance of Sobol total effect indices. Furthermore, Extra-Trees can estimate variable interaction importances using a pairwise permutation measure. As such, this approach could offer a user-friendly option for screening in models with inputs of mixed types.

    更新日期:2018-06-14
  • Assessment of watershed health, vulnerability and resilience for determining protection and restoration Priorities
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-04-04
    So-Ra Ahn, Seong-Joon Kim

    The watershed health, vulnerability and recovery potential for determining protection and restoration priorities were assessed for the Han River basin (34,148 km2) in South Korea. Six components, including the watershed landscape, stream geomorphology, hydrology, water quality, aquatic habitat conditions, and biological conditions, were used to evaluate the watershed health (basin natural capacity). The Soil and Water Assessment Tool (SWAT) was utilized to examine the hydrology and water quality components in the study basin, which includes 237 sub-watersheds, three multipurpose dams, one hydroelectric dam, and three multifunction weirs. Four components, namely, the future impervious area, future climate conditions, future water use, and recent land cover changes were used to evaluate the watershed vulnerability to artificial stressors. We determined the protection and restoration priorities by evaluating and comparing the health and vulnerability of each sub-watershed. Sixty-seven sub-watersheds out of 237 were classified to have restoration priorities with high recovery potentials.

    更新日期:2018-06-03
  • A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-06-07
    Daniel Jato-Espino, Nora Sillanpää, Susanne M. Charlesworth, Jorge Rodriguez-Hernandez

    Urban drainage is being affected by Climate Change, whose effects are likely to alter the intensity of rainfall events and result in variations in peak discharges and runoff volumes which stationary-based designs might not be capable of dealing with. Therefore, there is a need to have an accurate and reliable means to model the response of urban catchments under extreme precipitation events produced by Climate Change. This research aimed at optimizing the stormwater modelling of urban catchments using Design of Experiments (DOE), in order to identify the parameters that most influenced their discharge and simulate their response to severe storms events projected for Representative Concentration Pathways (RCPs) using a statistics-based Climate Change methodology. The application of this approach to an urban catchment located in Espoo (southern Finland) demonstrated its capability to optimize the calibration of stormwater simulations and provide robust models for the prediction of extreme precipitation under Climate Change.

    更新日期:2018-06-03
  • KarstMod: A modelling platform for rainfall - discharge analysis and modelling dedicated to karst systems
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-06-16
    N. Mazzilli, V. Guinot, H. Jourde, N. Lecoq, D. Labat, B. Arfib, C. Baudement, C. Danquigny, L. Dal Soglio, D. Bertin

    We propose an adjustable modelling platform (KarstMod) for both the simulation of spring discharge at karst outlets and analysis of the hydrodynamics of the compartments considered in the model. KarstMod provides a modular, user-friendly modelling environment for educational, research and operational purposes. It can reproduce the structure of most conceptual lumped models of karst systems in the literature. The modularity of the platform allows to compare different hydrosystems within the same methodological approach. To promote good modelling practices, the platform provides a variety of graphs and tools that facilitate improved understanding and insights in the behaviour of the models, and that detect possible flaws in structure and parameterization. The model and users manual are freely downloadable from the SNO Karst website (www.sokarst.org).

    更新日期:2018-06-03
  • Comparing the effects of dynamic versus static representations of land use change in hydrologic impact assessments
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-06-30
    Paul D. Wagner, S. Murty Bhallamudi, Balaji Narasimhan, Shamita Kumar, Nicola Fohrer, Peter Fiener

    Representations of land use change in hydrologic impact assessment studies mostly rely on static land use information of two points in time, even though the availability of dense time series of land use data allows for the incorporation of dynamic land use changes. We compare the hydrologic impacts of dynamic land use change assessments to those of static land use change assessments. These effects are illustrated with the help of two land use scenarios applied to a hydrologic model of a rapidly developing meso-scale (2036 km2) catchment upstream of Pune, India. The results show that a linear dynamic land use development could be better approximated with the static approach than a non-linear development. An analysis of the impact of the frequency of land use updates indicates that the prediction of non-linear land use change impacts already improves substantially when frequent land use information every five to nine years is used.

    更新日期:2018-06-03
  • Land use change and ecosystem services in mountainous watersheds: Predicting the consequences of environmental policies with cellular automata and hydrological modeling
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-07-01
    Ilkwon Kim, Sebastian Arnhold, Sora Ahn, Quang Bao Le, Seong Joon Kim, Soo Jin Park, Thomas Koellner

    Integrated model systems that simulate land use and land cover change (LUCC) and associated changes of ecosystem services (ES) are increasingly important for supporting policies and management decisions. However, only few model frameworks exist that consider policy options as drivers of future LUCC and ES simultaneously. We present a modeling procedure that predicts policy-induced LUCC and ES through a combination of cellular automata (CA) and the Soil and Water Assessment Tool (SWAT). We employed this procedure to assess the efficiency of alternative policy instruments including direct payments and command-and-control regulations in mountain watersheds of South Korea. Our approach successfully captures spatial patterns of LUCC, hydrological processes, and the associated gains and losses in ES as well as potential negative externalities (“leakage” effects). Our modeling procedure provides an informative and robust basis for the development of decision support systems for mountain watersheds, where water provision and regulation are of particular concern.

    更新日期:2018-06-03
  • Development of a data model to facilitate rapid watershed delineation
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-07-22
    Scott Haag, Ali Shokoufandeh

    An efficient model to store and retrieve watershed boundaries using graph-theoretic approaches is proposed. Our approach utilizes three algorithms and accepts as input standard Digital Elevation Model DEM derived flow direction grids (D8) or stream reach catchment boundaries and outputs derived watershed boundaries. This technique was applied to the ≈ 36,000 km2 Delaware River Watershed using the National Hydrography Plus Version 2 Dataset. This technique is showed to provide significant reductions in processing (98–99%), storage (81–82%), and retrieval complexity (95–96%) for polygons and (80–86%) for nodes when compared to existing and hypothetical data models that are used to create watershed boundaries.

    更新日期:2018-06-03
  • Evaluating the impact of climate change on fluvial flood risk in a mixed-used watershed
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-08-31
    Xin Xu, Yu-Chen Wang, Margaret Kalcic, Rebecca Logsdon Muenich, Y.C. Ethan Yang, Donald Scavia

    Predicting flood risk is important for climate change adaptation. We quantify fluvial flood risk due to changing climate in a mixed-use watershed in Michigan, USA. We apply two approaches to project future climate change: an ensemble of temperature and precipitation perturbations on the historical record and an ensemble of global and regional climate models. We incorporate climate projections into the Soil and Water Assessment Tool (SWAT) to estimate daily streamflow, then quantify flood risk using indices related to flood probability, duration, magnitude, and frequency. Results indicate rising temperatures may counteract small increases in precipitation, likely due to increased evapotranspiration. Climate model data without bias correction used in SWAT produced reasonable future streamflow changes—similar to the perturbation of historical climate—therefore retaining the predicted change in the flood frequency distribution. This work advances the application of climate models in SWAT for flood risk evaluation at watershed scales.

    更新日期:2018-06-03
  • Field-scale evaluation of pesticide uptake into runoff using a mixing cell and a non-uniform uptake model
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-09-22
    Dirk F. Young, Meridith M. Fry

    The uptake of soil-applied pesticide by runoff was evaluated at the field scale (0.06–3.6 ha) by examining data from previous field studies and using two popular chemical-uptake models: a uniform mixing cell, in which runoff mixes uniformly to a set depth, and a non-uniform uptake model, where chemical uptake decreases exponentially with soil depth. Both uptake models were implemented through the field-scale Pesticide Root Zone Model (PRZM5), which includes an update for adjusting runoff uptake parameters. For the nonuniform model, we conceptualized runoff as a distribution of subsurface flow, which assisted in revealing that a large amount (81%) of runoff must bypass soil interaction to adequately simulate the field data, with the remaining runoff acting over about a 3-cm depth. Similarly, the mixing cell required a large amount (84%) of runoff bypass for proper simulation, with the remaining portion acting over a 0.75-cm depth.

    更新日期:2018-06-03
  • Developing a hydrological simulation tool to design bioretention in a watershed
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-11-21
    Sang-Soo Baek, Mayzonee Ligaray, Jong-Pyo Park, Hyun-Suk Shin, Yongsung Kwon, Joseph T. Brascher, Kyung Hwa Cho

    Continuous urbanization has negatively impacted the ecological and hydrological environments at the global, regional, and local scales. This issue was addressed by developing Low Impact Development (LID) practices to deliver better hydrologic function and improve the environmental, economic, social and cultural outcomes. This study developed a modeling software to simulate and optimize bioretentions among LID in a given watershed. The model calculated a detailed soil infiltration process in bioretention with hydrological conditions (e.g. unsaturated and saturated soil) and hydraulic facilities (e.g. riser and underdrain) and also generated an optimized plan using Flow Duration Curve (FDC). The optimization result from the simulation demonstrated that the location and size of bioretention, as well as the soil texture, are important elements for an efficient bioretention. We hope that this developed software will aid in establishing effective LID strategies for improving urban water sustainability and management.

    更新日期:2018-06-03
  • Simulating seasonal variability of phytoplankton in stream water using the modified SWAT model
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-11-22
    JongCheol Pyo, Yakov A. Pachepsky, Minjeong Kim, Sang-Soo Baek, Hyuk Lee, YoonKyung Cha, Yongeun Park, Kyung Hwa Cho

    The ability to simulate algal systems is critical for watershed-scale models. The objective of this study was to develop and evaluate a modified algal module that simulates the dynamics of three major algal groups (cyanobacteria, green algae, and diatoms) in a stream using variables available in the Soil and Water Assessment Tool. The proposed module 1) models the dynamics of the three algal groups while accounting for nutrients from algal die-off and 2) has temperature multipliers that consider the effect of temperature changes on kinetic rates. Data to test the module were collected from a forest-dominated watershed over two years. The modified module was efficient in predicting seasonal variations in algal group biomass and simulated the regeneration of nutrients after algal die-off. This module will be useful in predicting the dynamics of the three studied algal groups and evaluating the best management practices for algal blooms in watersheds.

    更新日期:2018-06-03
  • A generic framework to analyse the spatiotemporal variations of water quality data on a catchment scale
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-11-22
    Qinli Yang, Miklas Scholz, Junming Shao, Guoqing Wang, Xiaofang Liu

    Most spatiotemporal studies treat spatial and temporal analysis separately. However, spatial and temporal changes occur simultaneously and are correlated. In this study, we propose a generic framework to simultaneously analyse the spatial and temporal variations of water quality on a catchment scale. Specifically, we analyse the heterogeneity of temporal evolution of water quality data among different sampling sites, and the heterogeneity of spatial distribution of water quality data over different sampling times, respectively, by integrating the techniques of normalized mutual information, dynamic time wrapping and cluster analysis. To bring deep insight into the spatiotemporal variations, inter-change and intra-change are further defined and distinguished, respectively. Taking the Fuxi River catchment as a case study, results indicate that the proposed framework is intuitive and efficient. Beyond this, the generic framework can be expanded for other catchments and various environmental data.

    更新日期:2018-06-03
  • Improving the catchment scale wetland modeling using remotely sensed data
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-11-22
    S. Lee, I.-Y. Yeo, M.W. Lang, G.W. McCarty, A.M. Sadeghi, A. Sharifi, H. Jin, Y. Liu

    This study presents an integrated wetland-watershed modeling approach that capitalizes on inundation maps and geospatial data to improve spatial prediction of wetland inundation and assess its prediction uncertainty. We outline problems commonly arising from data preparation and parameterization used to simulate wetlands within a (semi-) distributed watershed model. We demonstrate how wetland inundation can be better captured by the wetland parameters developed from remotely sensed data. We then emphasize assessing model prediction using inundation maps derived from remotely sensed data. This integrated modeling approach is tested using the Soil and Water Assessment Tool (SWAT) with an improved riparian wetlands (RWs) extension, for an agricultural watershed in the Mid-Atlantic Coastal Plain, US. This study illustrates how spatially distributed information is necessary to predict inundation of wetlands and hydrologic function at the local landscape scale, where monitoring and conservation decision making take place.

    更新日期:2018-06-03
  • Model-based analysis of the relationship between macroinvertebrate traits and environmental river conditions
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-12-06
    Marie Anne Eurie Forio, Peter L.M. Goethals, Koen Lock, Victor Asio, Marlito Bande, Olivier Thas

    Aquatic macroinvertebrates, 18 physical-chemical water characteristics and 30 hydromorphological variables were assessed at 85 locations on Leyte island, Philippines. Biological traits derived from literature were linked to the biological samples based on four different trait estimation methods. These data were used to determine the relation with river characteristics using negative binomial regression. At least five feeding habit modalities were associated with conductivity, velocity, pH, temperature, ammonium-N concentrations, and sediment. The various methods of estimating trait abundance differ in determined major patterns and ecological implications. Therefore, the estimation method used should be explicitly described in trait-related papers to avoid misinterpretation. Trait abundance-environment relationships can be linear or non-linear and therefore a careful selection of the functional relationship should be performed. The process of extracting knowledge from data is of paramount importance as relevant ecological insights were extracted providing insights on flow, wastewater and nutrient management in the rivers.

    更新日期:2018-06-03
  • Data-driven rainfall/runoff modelling based on a neuro-fuzzy inference system
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-12-06
    N. Bartoletti, F. Casagli, S. Marsili-Libelli, A. Nardi, L. Palandri
    更新日期:2018-06-03
  • Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-12-08
    Ll. Corominas, M. Garrido-Baserba, K. Villez, G. Olsson, U. Cortés, M. Poch

    The aim of this paper is to describe the state-of-the art computer-based techniques for data analysis to improve operation of wastewater treatment plants. A comprehensive review of peer-reviewed papers shows that European researchers have led academic computer-based method development during the last two decades. The most cited techniques are artificial neural networks, principal component analysis, fuzzy logic, clustering, independent component analysis and partial least squares regression. Even though there has been progress on techniques related to the development of environmental decision support systems, knowledge discovery and management, the research sector is still far from delivering systems that smoothly integrate several types of knowledge and different methods of reasoning. Several limitations that currently prevent the application of computer-based techniques in practice are highlighted.

    更新日期:2018-06-03
  • Imbalanced classification techniques for monsoon forecasting based on a new climatic time series
    Environ. Model. Softw. (IF 4.177) Pub Date : 2017-12-13
    A. Troncoso, P. Ribera, G. Asencio-Cortés, I. Vega, D. Gallego

    Monsoons have been widely studied in the literature due to their climatic impact related to precipitation and temperature over different regions around the world. In this work, data mining techniques, namely imbalanced classification techniques, are proposed in order to check the capability of climate indices to capture and forecast the evolution of the Western North Pacific Summer Monsoon. Thus, the main goal is to predict if the monsoon will be an extreme monsoon for a temporal horizon of a month. Firstly, a new monthly index of the monsoon related to its intensity has been generated. Later, the problem of forecasting has been transformed into a binary imbalanced classification problem and a set of representative techniques, such as models based on trees, models based on rules, black box models and ensemble techniques, are applied to obtain the forecasts. From the results obtained, it can be concluded that the methodology proposed here reports promising results according to the quality measures evaluated and predicts extreme monsoons for a temporal horizon of a month with a high accuracy.

    更新日期:2018-06-03
  • 更新日期:2018-06-03
  • Inverse modelling of snow depths
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-02-09
    Uwe Schlink, Daniel Hertel

    Operational snow forecasting models contain parameters for which site-specific values are often unknown. As an improvement a Bayesian procedure is suggested that estimates, from past observations, site-specific parameters with confidence intervals. It turned out that simultaneous estimation of all parameters was most accurate. From 2.5 years of daily snow depth observations the estimates were for snow albedo 0.94, 0.89, and 0.56, for snow emissivity 0.88, 0.92, and 0.99, and for snow density ( g / c m ³ ) 0.14, 0.05, and 0.11 at the German weather stations Wasserkuppe, Erfurt-Weimar, and Artern, respectively. Using estimated site-specific parameters, ex post snow depth forecasts achieved an index of agreement IA = 0.4–0.8 with past observations; IA = 0.3–0.8 for a 51-years period. They outperformed the precision of predictions based on default parameter values (0.1 < IA<0.3). The developed inverse approach is recommended for parameter estimation and snow forecasting at sub-alpine stations with more or less urban impact and for application in education.

    更新日期:2018-06-03
  • Environmental data stream mining through a case-based stochastic learning approach
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-02-16
    Fernando Orduña Cabrera, Miquel Sànchez-Marrè

    Environmental data stream mining is an open challenge for Data Science. Common methods used are static because they analyze a static set of data, and provide static data-driven models. Environmental systems are dynamic and generate a continuous data stream. Dynamic methods coping with the temporal nature of data must be provided in Data Science. Our proposal is to model each environmental information unit, timely generated, as a new case/experience in a Case-Based Reasoning (CBR) system. This contribution aims to incrementally build and manage a Dynamic Adaptive Case Library (DACL). In this paper, a stochastic method for the learning of new cases and management of prototypes to create and manage the DACL in an incremental way is introduced. This stochastic method works with two main moments. An evaluation of the method has been carried using a data stream of air quality of the city of Obregon, Sonora. México, with good results. In addition, other datasets have been mined to ensure the generality of the approach.

    更新日期:2018-06-03
  • Modelling background air pollution exposure in urban environments: Implications for epidemiological research
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-02-26
    Álvaro Gómez-Losada, José Carlos M. Pires, Rafael Pino-Mejías

    Background pollution represents the lowest levels of ambient air pollution to which the population is chronically exposed, but few studies have focused on thoroughly characterizing this regime. This study uses clustering statistical techniques as a modelling approach to characterize this pollution regime while deriving reliable information to be used as estimates of exposure in epidemiological studies. The background levels of four key pollutants in five urban areas of Andalusia (Spain) were characterized over an 11-year period (2005–2015) using four widely-known clustering methods. For each pollutant data set, the first (lowest) cluster representative of the background regime was studied using finite mixture models, agglomerative hierarchical clustering, hidden Markov models (hmm) and k-means. Clustering method hmm outperforms the rest of the techniques used, providing important estimates of exposures related to background pollution as its mean, acuteness and time incidence values in the ambient air for all the air pollutants and sites studied.

    更新日期:2018-06-03
  • Hybrid SOM+k-Means clustering to improve planning, operation and management in water distribution systems
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-03-08
    Bruno Brentan, Gustavo Meirelles, Edevar Luvizotto, Joaquin Izquierdo

    With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes. In the three cases, this hybrid technique produces excellent results.

    更新日期:2018-06-03
  • Creating extreme weather time series through a quantile regression ensemble
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-03-21
    Manuel Herrera, Alfonso P. Ramallo-González, Matthew Eames, Aida A. Ferreira, David A. Coley

    Heat waves give rise to order of magnitude higher mortality rates than other weather-related natural disasters. Unfortunately both the severity and amplitude of heat waves are predicted to increase worldwide as a consequence of climate change. Hence, meteorological services have a growing need to identify such periods in order to set alerts, whilst researchers and industry need representative future heat waves to study risk. This paper introduces a new location-specific mortality risk focused definition of heat waves and a new mathematical framework for the creation of time series that represents them. It focuses on identifying periods when temperatures are high during the day and night, as this coincidence is strongly linked to mortality. The approach is tested using observed data from Brazil and the UK. Comparisons with previous methods demonstrate that this new approach represents a major advance that can be adopted worldwide by governments, researchers and industry.

    更新日期:2018-06-03
  • 更新日期:2018-05-27
  • Optimisation of the two-dimensional hydraulic model LISFOOD-FP for CPU architecture
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-05-22
    Jeffrey Neal, Toby Dunne, Chris Sampson, Andrew Smith, Paul Bates

    Flood inundation models are increasingly used for a wide variety of river and coastal management applications. Nevertheless, the computational effort to run these models remains a substantial constraint on their application. In this study four developments to the LISFLOOD-FP 2D flood inundation model have been documented that: 1) refine the parallelisation of the model; 2) reduce the computational burden of dry cells; 3) reduce the data movements between CPU and RAM; and 4) vectorise the core numerical solver. The value of each of these developments in terms of compute time and parallel efficiency was tested on 12 test cases. For realistic test cases, improvements in single core performance of between 4.2x and 8.4x were achieved, which when combined with parallelisation on 16 cores resulted in computation times 34-60x shorter than previous LISFLOOD-FP models on one core. Results were compared to a sample of commercial models for context.

    更新日期:2018-05-23
  • Metamodel-assisted analysis of an integrated model composition: an example using linked surface water – groundwater models.
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-05-22
    Vasileios Christelis, Andrew G. Hughes

    Integrated modelling is a promising approach to simulate processes operating within complex environmental systems. It is possible, however, that this integration may lead to computationally expensive compositions. In order to retain the process fidelity without loss of accuracy, the use of Kriging metamodels is proposed to perform Monte Carlo simulation and sensitivity analysis, in lieu of compositions developed using the model linking standard OpenMI. Results from the Monte Carlo simulation showed that the metamodels were in a good agreement with the original responses. However, metamodels provided a less accurate approximation of the original output distribution for the composition which involved a stronger non-linear behaviour. The fast runtimes of the metamodels allowed for increased computational budgets leading to an accurate screening of the important parameters for an Elementary Effects Test. Overall, Kriging metamodels provided significant computational savings without compromising the quality of the outcomes, even using small training data sets.

    更新日期:2018-05-23
  • A Cloud-Based Flood Warning System for Forecasting Impacts to Transportation Infrastructure Systems
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-05-22
    Mohamed M. Morsy, Jonathan L. Goodall, Gina L. O'Neil, Jeffrey M. Sadler, Daniel Voce, Gamal Hassan, Chris Huxley

    The ability to quickly and accurately forecast flooding is increasingly important as extreme weather events become more common. This work focuses on designing a cloud-based real-time modeling system for supporting decision makers in assessing flood risk. The system, built using Amazon Web Services (AWS), automates access and pre-processing of forecast data, execution of a computationally expensive high-resolution 2D hydrodynamic model, Two-dimensional Unsteady Flow (TUFLOW), and map-based visualization of model outputs. A graphical processing unit (GPU) version of TUFLOW was used resulting in an 80x execution time speed-up compared to the central processing unit (CPU) version. The system is designed to run automatically to produce near real-time results and consume minimal computational resources until triggered by an extreme weather event. In the study, the design is applied to a case study in coastal Virginia to predict flooding and inform decision makers regarding transportation infrastructure during extreme weather events.

    更新日期:2018-05-23
  • Effects of the pre-processing algorithms in fault diagnosis of wind turbines
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-05-21
    Pere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó, Jordi Solé-Casals

    The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy.

    更新日期:2018-05-21
  • The influence of knowledge in the design of a recommender system to facilitate industrial symbiosis markets
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-05-07
    Guido van Capelleveen, Chintan Amrit, Devrim Murat Yazan, Henk Zijm

    Industrial symbiosis aims to stimulate or enhance cooperation between industrial firms to utilize industrial waste streams from other industries and to share related knowledge, in order to achieve sustainable production. Recommenders can support industries through the identification of item opportunities in waste marketplaces, enhancing activities that may lead to the development of an active waste exchange network. To build effective recommendation, we study the role of knowledge in the design of a recommender that suggests waste materials to be used in process industries. This paper compares the performance of a knowledge based input-output recommender with a recommender based on association rules. The two recommenders are evaluated with real-world data collected through deploying surveys in a workshop setting. Our research shows that many data challenges arise when creating recommendations from explicit knowledge and suggests that techniques based on the concept of implicit knowledge may be preferable in the design of an industrial symbiosis recommender.

    更新日期:2018-05-07
  • Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-05-04
    Laura Ríos-Pena, Thomas Kneib, Carmen Cadarso-Suárez, Nadja Klein, Manuel Marey-Pérez

    When studying the empirical phenomenon of wildfires, we can distinguish between the occurrence at a specific location and time and the burnt area measured. This study proposes using structured additive regression models based on zero-one-inflated beta distribution for studying wildfire occurrence and burnt area simultaneously. Beta distribution affords a convenient way of studying the percentage of burnt area in cases where such percentages are bounded away from zero and one. Inflation with zeros and ones enables observations without wildfires or with 100% burnt areas to be treated as special cases. Structured additive regression allows one to include a variety of covariates, while simultaneously exploring spatial and temporal correlations. Our inferences are based on an efficient Markov chain Monte Carlo simulation algorithm utilizing iteratively weighted least squares approximations as proposal densities. Application of the proposed methodology to a large wildfire database covering Galicia (Spain) provides essential information for improved wildfire management.

    更新日期:2018-05-05
  • Downscaling of climate model output for Alaskan stakeholders
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-04-30
    John E. Walsh, Uma S. Bhatt, Jeremy S. Littell, Matthew Leonawicz, Michael Lindgren, Thomas A. Kurkowski, Peter A. Bieniek, Richard Thoman, Stephen Gray, T. Scott Rupp

    The paper summarizes an end-to-end activity connecting the global climate modeling enterprise with users of climate information in Alaska. The effort included retrieval of the requisite observational datasets and model output, a model evaluation and selection procedure, the actual downscaling by the delta method with its inherent bias-adjustment, and the provision of products to a range of users through visualization software that empowers users to explore the downscaled output and its sensitivities. An additional software tool enables users to examine skill metrics and relative rankings of 21 global models for Alaska and six other domains in the Northern Hemisphere. The downscaled temperatures and precipitation are made available as calendar-month decadal means under three different greenhouse forcing scenarios through 2100 for more than 4000 communities in Alaska and western Canada. The visualization package displays the uncertainties inherent in the multi-model ensemble projections. These uncertainties are often larger than the projected changes.

    更新日期:2018-04-30
  • A semantic multi-criteria approach to evaluate different types of energy generation technologies
    Environ. Model. Softw. (IF 4.177) Pub Date : 2018-04-30
    Miriam Martínez-García, Aida Valls, Antonio Moreno, Arantza Aldea

    Multi-Criteria Decision Aid methods are used to find the best option from a set of alternatives when multiple and conflicting criteria have to be optimized simultaneously. The evaluation of the suitability or risk of each alternative is usually performed by assigning a numerical value. However, sometimes the data required to measure a criterion may be found in the form of semantic values such as tags. This paper proposes a methodology to calculate the strength of an outranking relation for a pair of alternatives using semantic criteria following the principles of ELECTRE-III (i.e. by means of concordance and discordance indices). The preferences about semantic data are represented in an ontology by means of objective and subjective functions. The paper explains how this new methodology was applied to analyse different electricity generation technologies using environmental and economic criteria. Two scenarios are tested to show how semantic criteria may influence the final decision.

    更新日期:2018-04-30
Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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