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  • Creating extreme weather time series through a quantile regression ensemble ☆
    Environ. Model. Softw. (IF 4.404) 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.

  • Hybrid SOM+k-Means clustering to improve planning, operation and management in water distribution systems
    Environ. Model. Softw. (IF 4.404) Pub Date : 2018-03-08
    Bruno Brentan, Gustavo Meirelles, Edevar Luvizotto Jr., 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.

  • Modelling background air pollution exposure in urban environments: Implications for epidemiological research
    Environ. Model. Softw. (IF 4.404) 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.

  • Environmental data stream mining through a case-based stochastic learning approach
    Environ. Model. Softw. (IF 4.404) 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.

  • Inverse modelling of snow depths
    Environ. Model. Softw. (IF 4.404) 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/cm³ 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:2018-01-10
  • Imbalanced classification techniques for monsoon forecasting based on a new climatic time series
    Environ. Model. Softw. (IF 4.404) 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.

  • Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques
    Environ. Model. Softw. (IF 4.404) 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.

  • Model-based analysis of the relationship between macroinvertebrate traits and environmental river conditions
    Environ. Model. Softw. (IF 4.404) 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.

  • Data-driven rainfall/runoff modelling based on a neuro-fuzzy inference system
    Environ. Model. Softw. (IF 4.404) Pub Date : 2017-12-06
    N. Bartoletti, F. Casagli, S. Marsili-Libelli, A. Nardi, L. Palandri
  • Improving the catchment scale wetland modeling using remotely sensed data
    Environ. Model. Softw. (IF 4.404) 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.

  • Simulating seasonal variability of phytoplankton in stream water using the modified SWAT model
    Environ. Model. Softw. (IF 4.404) 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.

  • A generic framework to analyse the spatiotemporal variations of water quality data on a catchment scale
    Environ. Model. Softw. (IF 4.404) 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.

  • Developing a hydrological simulation tool to design bioretention in a watershed
    Environ. Model. Softw. (IF 4.404) 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.

  • Field-scale evaluation of pesticide uptake into runoff using a mixing cell and a non-uniform uptake model
    Environ. Model. Softw. (IF 4.404) 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.

  • Evaluating the impact of climate change on fluvial flood risk in a mixed-used watershed
    Environ. Model. Softw. (IF 4.404) 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.

  • Development of a data model to facilitate rapid watershed delineation
    Environ. Model. Softw. (IF 4.404) 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.

  • 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 4.404) 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.

  • Comparing the effects of dynamic versus static representations of land use change in hydrologic impact assessments
    Environ. Model. Softw. (IF 4.404) 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.

  • KarstMod: A modelling platform for rainfall - discharge analysis and modelling dedicated to karst systems
    Environ. Model. Softw. (IF 4.404) 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).

  • A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events
    Environ. Model. Softw. (IF 4.404) 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.

  • Assessment of watershed health, vulnerability and resilience for determining protection and restoration Priorities
    Environ. Model. Softw. (IF 4.404) 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.

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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