Development of a participatory Green Infrastructure design, visualization and evaluation system in a cloud supported jupyter notebook computing environment Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-13 Lorne Leonard, Brian Miles, Bardia Heidari, Laurence Lin, Anthony M. Castronova, Barbara Minsker, Jong Lee, Charles Scaife, Lawrence E. Band
Steady-state distributed modeling of dissolved oxygen in data-poor, sewage dominated river systems using drainage networks Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-12 Marco Pilotti, Steven C. Chapra, Giulia Valerio
The determination of water quality along a river drainage network is of paramount importance for many environmental applications. Whereas several codes have been proposed to deal with this problem along a single reach of a river, no simple solution has been proposed for a complex river network. In this contribution, a methodology is presented for preliminary screening of river water quality at the watershed-scale and at steady state. The solution process exploits the information contained within the watershed's space-filling drainage network (SFDN), that is automatically computed by processing the raster map of the watershed's elevations. To this end, a visiting algorithm for non-binary trees is coupled to a Newton-Raphson method that locally solves the system of nonlinear water-quality mass balances. We believe that the resulting framework will be especially valuable for initial screening analyses in data-poor watersheds. A free code called Q2T that implements the described methods is provided.
Applying big data computational tools to simulate land transformations: The LTM-cluster framework Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-10 H. Omrani
This study introduces a novel framework for land change simulation that combines the traditional Land Transformation Model (LTM) with new big data clustering tools for the purposes of conducting land change simulations of large areas (e.g., continental scale) and over multiple time steps. This new framework, called “LTM-cluster”, subsets massive land use datasets which are presented to the artificial neural network-based LTM. LTM-cluster uses the k-means clustering algorithm implemented within the Spark high-performance compute environment (HPC). To illustrate the framework, we apply it to three case studies in the United States which vary in simulation extents, cell size, time intervals, number of inputs, and quantity of urban change. Findings indicate consistent and substantial improvements in accuracy performance for all three case studies compared to the traditional LTM model implemented without input clustering. Specifically, the percent correct match, the area under the operating characteristics curve, and the error rate improved on average of 9%, 11%, and 4%, respectively with LTM-cluster compared to a non-clustering technique. These results confirm that LTM-cluster has high reliability when handling large datasets. Future studies should expand on the framework by exploring other clustering methods and algorithms.
Prognostics of forest recovery with r.recovery GRASS-GIS module: An open-source forest growth simulation model based on the diffusive-logistic equation Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-10 L.A. Richit, C. Bonatto, R.V. da Silva, J.M.V. Grzybowski
We present an open-source computational tool for the 2D simulation of the Diffusive-Logistic Growth (DLG) model. The r.recovery module offers a complete environment for the simulation of forestry regeneration in conservation areas and includes a built-in tool for calibration and validation of the model parameters through the use of standard and freely available satellite imagery. It was implemented as an add-on to the GRASS software, a largely applied open-source Geographic Information System (GIS). To illustrate its application, we present a complete case study of forest regeneration carried out in the Espigão Alto State Park (EASP), Brazil, from which we assess typical values of forest diffusion and growth rate parameters, along with the prognostics of forest density status for the coming decades. We observe that the r.recovery tool can be advantageously applied by forestry managers and policy-makers as a form of acquiring technical and scientifically-based information for strategy development and decision-making.
Modelling agro-environmental variables under data availability limitations and scenario managements in an alluvial region of the north China plain Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-04 Kiril Manevski, Christen D. Børgesen, Xiaoxin Li, Mathias N. Andersen, Xiying Zhang, Yanjun Shen, Chunsheng Hu
Controlling rainwater storage as a system: An opportunity to reduce urban flood peaks for rare, long duration storms Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-01 M. Di Matteo, R. Liang, H.R. Maier, M.A. Thyer, A.R. Simpson, G.C. Dandy, B. Ernst
Globally, urban infill is stressing existing stormwater systems, necessitating costly infrastructure upgrades. Although household rainwater tanks provide significant distributed storage, they have virtually no impact on reducing peak flows for rare, long duration events. This study introduces an innovative “smart systems” approach to operating tanks to overcome this limitation. Smart tanks are operated as systems and tank opening/closing is optimised to reduce peak flows. To evaluate the proposed approach, we develop a simulation-optimization model by coupling SWMM with a multi-objective genetic algorithm. The results for a two allotment case study show a consistent reduction in peak flows for a 24 h, 1 in 100-year storm for a range of rainfall patterns and tank sizes. For example, a system of 10 kL smart tanks reduced peak flows by 39%–48% compared with the same sized retention tanks. This smart systems approach provides an opportunity to reduce the cost of stormwater infrastructure.
The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-01 R.S. Regan, K.E. Juracek, L.E. Hay, S.L. Markstrom, R.J. Viger, J.M. Driscoll, J.H. LaFontaine, P.A. Norton
The ability to effectively manage water resources to meet present and future human and environmental needs is essential. Such an ability necessitates a comprehensive understanding of hydrologic processes that affect streamflow at a watershed scale. In the United States, water-resources management at scales ranging from local to national can benefit from a nationally consistent, process-based watershed modeling capability to provide the requisite understanding. The National Hydrologic Model (NHM) infrastructure, which was developed by the U.S. Geological Survey to support coordinated, comprehensive, and consistent hydrologic modeling at multiple scales for the conterminous United States, provides this essential capability. NHM-based applications provide information to enable more effective water-resources planning and management, fill knowledge gaps in ungaged areas, and support basic scientific inquiry. In the future, as process algorithms and data sets improve, the NHM infrastructure will continue to evolve to better support the nation's water-resources research and management needs.
Uncertainty in the parameterization of sediment build-up and wash-off processes in the simulation of sediment transport in urban areas Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-01 Angela Gorgoglione, Fabián A. Bombardelli, Bruno J.L. Pitton, Lorence R. Oki, Darren L. Haver, Thomas M. Young
Drainage-system management relies on results from urban stormwater models; errors in these models may have serious implications. Inaccurate field data or overly simplified models may cause the complex response of an urban basin to a rainfall event to be inadequately represented. Before undertaking expensive studies to gather and analyze additional data, it is reasonable to understand what enhancement in model performance would result if individual uncertainties could be decreased. This paper uses data collected during field monitoring campaigns to calibrate and validate a hydrologic and sediment-transport model within the Storm Water Management Model (SWMM). Solid accumulation and disappearance rates were identified as factors generating the highest model sensitivity. A generalized evaluation matrix is presented that considers both the uncertainty in input variables and the associated sensitivity in model response to inform model performance expectations and guide investments in model improvement toward actions with maximum benefit.
An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model Environ. Model. Softw. (IF 4.177) Pub Date : 2018-10-01 Maurizio Bagnara, Ramiro Silveyra Gonzalez, Stefan Reifenberg, Jörg Steinkamp, Thomas Hickler, Christian Werner, Carsten F. Dormann, Florian Hartig
Dynamic global vegetation models (DGVMs) are of crucial importance for understanding and predicting vegetation, carbon, nitrogen and water dynamics of ecosystems in response to climate change. Their complexity, however, creates challenges for model analysis and data integration. A solution is to interface DGVMs with established statistical computing environments. Here we introduce rLPJGUESS, an R-package that couples the widely used DGVM LPJ-GUESS with the R environment for statistical computing, making existing R-packages and functions readily available to perform complex analyses with this model.We demonstrate the advantages of this framework by using rLPJGUESS to perform several otherwise laborious tasks: first, a set of single simulations, followed by global and local sensitivity analyses, a Bayesian calibration with a Markov-Chain Monte Carlo (MCMC) algorithm, and a predictive simulation with multiple climate scenarios. Our example highlights the opportunities of interfacing existing models in earth and environmental sciences with state-of-the-art computing environments such as R.
Evaluating gullying effects on modeling erosive responses at basin scale Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-28 A. Millares, M. Díez-Minguito, A. Moñino
The objective of this research was to assess the effect of gullying on the erosive response at the basin scale by modeling. For this purpose, a distributed hydrological model, which includes erosion by raindrop, rilling and gullying, was configured and applied in a Mediterranean basin in southern Spain. The results show a range of parameters close to those provided by the literature and good agreement with field measurements. However, the simulations indicate that the rill erodibility parameter at the surface is overestimated by as much as 70% to compensate for gullying processes. Other parameters, such as the subsoil erodibility of the soil profile, play an important role in the erosive response of the basin. A geomorphological threshold for sediment yield that relates density of gullies and erodibility parameters was found. These responses are especially relevant in semiarid environments where the intense pulses of precipitation have important effects on landscape evolution.
Virtual globe-based three-dimensional dynamic visualization method of gas diffusion Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-25 Ruozhen Cheng, Jing Chen, Ming Cao
Three-dimensional dynamic visualization of the long range gas diffusion contributes to disaster forecasting and assessment. Existing three-dimensional visualization methods, which calculate the gas concentration without considering the terrain and display the gas concentration in the local range by isosurfaces, cannot meet the demand for intensive calculation and dynamic visualization of gas diffusion over a wide range. This study proposes a virtual globe-based three-dimensional dynamic visualization method of the long range gas diffusion, consisting of a voxel-based multi-scale data model, a terrain-dependent multi-scale concentration field calculation method using graphics processing units (GPU), and a GPU-based multi-scale spherical ray-casting algorithm. The method is then applied to the long range natural gas diffusion and is evaluated according to the efficiency of the concentration calculation and dynamic visualization. The results show that the proposed method can achieve dynamic visualization of the long range gas diffusion above 30 frames per second.
Analytical modeling of hyporheic flow for in-stream bedforms: Perturbation method and implementation Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-25 Sven Frei, Morvarid Azizian, Stanley B. Grant, Vitaly A. Zlotnik, Daniel Toundykov
Hyporheic flow and nutrient turnover in hyporheic systems are strongly influenced by in-stream bedforms. An accurate representation of topographical variations of the stream-streambed interface is therefore essential in analytical models in order to represent the couplings between hydrological and biogeochemical processes correctly. The classical Toth approach replaces the streambed surface topography by a flat surface which is identical to a truncation of the original physical flow domain into a rectangle. This simplification can lead to biased estimates of hyporheic flow and nutrient cycling within hyporheic systems. We present an alternative analytical modeling approach for solving hyporheic problems without domain truncation that explicitly accounts for topographical variations of the streambed. The presented approach is based on the application of perturbation theory. Applications of the method to hyporheic systems, ranging from the centimeter-scale of rippled bedforms to riffle structures of 10 m and larger scale, indicate a high accuracy of the approach.
An R-based open framework for reproducible climate data access and post-processing Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-22 M. Iturbide, J. Bedia, S. Herrera, J. Baño-Medina, J. Fernández, M.D. Frías, R. Manzanas, D. San-Martín, E. Cimadevilla, A.S. Cofiño, J.M. Gutiérrez
Climate-driven sectoral applications commonly require different types of climate data (e.g. observations, reanalysis, climate change projections) from different providers. Data access, harmonization and post-processing (e.g. bias correction) are time-consuming error-prone tasks requiring different specialized software tools at each stage of the data workflow, thus hindering reproducibility. Here we introduce climate4R, an R-based climate services oriented framework tailored to the needs of the vulnerability and impact assessment community that integrates in the same computing environment harmonized data access, post-processing, visualization and a provenance metadata model for traceability and reproducibility of results. climate4R allows accessing local and remote (OPeNDAP) data sources, such as the Santander User Data Gateway (UDG), a THREDDS-based service including a wide catalogue of popular datasets (e.g. ERA-Interim, CORDEX, etc.). This provides a unique comprehensive open framework for end-to-end sectoral reproducible applications. All the packages, data and documentation for reproducing the experiments in this paper are available from http://www.meteo.unican.es/climate4R.
Map based discovery of hydrologic data in the HydroShare collaboration environment Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-23 Zhaokun Xue, Alva Couch, David Tarboton
Data discovery refers to the process of locating pre-existing data for use in new research. In the HydroShare collaboration environment for water science, there are more than twenty kinds of data that can be discovered, including data from specific sites on the globe, data corresponding to regions on the globe, and data with no geospatial meaning, such as laboratory experiment results. This paper discusses lessons learned in building a data discovery system for HydroShare. This was a surprisingly difficult problem; default behaviors of software components were unacceptable, use cases suggested conflicting approaches, and crafting a geographic view of a large number of candidate resources was subject to the limits imposed by web browsers, existing software capabilities, human perception, and software performance. The resulting software was a complex melding of user needs, software capabilities, and performance requirements.
Advances in Bayesian network modelling: Integration of modelling technologies Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-23 Bruce G. Marcot, Trent D. Penman
Bayesian network (BN) modeling is a rapidly advancing field. Here we explore new methods by which BN model development and application are being joined with other tools and model frameworks. Advances include improving areas of Bayesian classifiers and machine-learning algorithms for model structuring and parameterization, and development of time-dynamic models. Increasingly, BN models are being integrated with: management decision networks; structural equation modeling of causal networks; Bayesian neural networks; combined discrete and continuous variables; object-oriented and agent-based models; state-and-transition models; geographic information systems; quantum probability; and other fields. Integrated BNs (IBNs) are becoming useful tools in risk analysis, risk management, and decision science for resource planning and environmental management. In the near future, IBNs may become self-structuring, self-learning systems fed by real-time monitoring data. Such advances may make model validation difficult, and may question model credibility, particularly if based on uncertain sources of knowledge systems and big data.
Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-24 Stelian Curceac, Camille Ternynck, Taha B.M.J. Ouarda, Fateh Chebana, Sophie Dabo Niang
Air temperature is a significant meteorological variable that affects social activities and economic sectors. In this paper, a non-parametric and a parametric approach are used to forecast hourly air temperature up to 24 h in advance. The former is a regression model in the Functional Data Analysis framework. The nonlinear regression operator is estimated using a kernel function. The smoothing parameter is obtained by a cross-validation procedure and used for the selection of the optimal number of closest curves. The other method applied is a Seasonal Autoregressive Moving Average (SARMA) model, the order of which is determined by the Bayesian Information Criterion. The obtained forecasts are combined using weights calculated based on the forecast errors. The results show that SARMA has a better performance for the first 6 forecasted hours, after which the Non-Parametric Functional Data Analysis (NPFDA) model provides superior results. Forecast pooling improves the accuracy of the forecasts.
Gamified online survey to elicit citizens’ preferences and enhance learning for environmental decisions Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-24 Alice H. Aubert, Judit Lienert
Sensitivity analysis of spatio-temporal models describing nitrogen transfers, transformations and losses at the landscape scale Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-21 Jordi Ferrer Savall, Damien Franqueville, Pierre Barbillon, Cyril Benhamou, Patrick Durand, Marie-Luce Taupin, Hervé Monod, Jean-Louis Drouet
Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to determine the appropriate spatial and temporal resolution of models and to reduce the number of factors to be measured or estimated accurately. Comprehensive spatial and temporal sensitivity analyses were applied to the NitroScape model, a deterministic spatially distributed model describing nitrogen transfers and transformations in rural landscapes. Simulations were led on a theoretical landscape that represented five years of intensive farm management and covering an area of 3 km2. Cluster analyses were applied to summarize the results of the sensitivity analysis on the ensemble of model outputs. The methodology we applied is useful to synthesize sensitivity analyses of models with multiple space-time input and output variables and could be ported to other models than NitroScape.
Testing an environmental flow-based decision support tool: Evaluating the fish model in the Murray Flow Assessment Tool Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-20 Rebecca E. Lester, Carmel A. Pollino, Courtney R. Cummings
Robust environmental decision support tools are critical to maximise the ecological benefit of management decisions. However the models that underpin these rarely undergo rigorous evaluation. Here, we evaluated components of a scenario-based habitat suitability model, the Murray Flow Assessment Tool, by correlating model outputs against fish monitoring data collected since its development. Overall, we detected a low correlation between habitat suitability scores for fish and fish assemblages during low-flow conditions, including when lags in fish response to hydrological inputs were introduced. Scores specific to fish functional groups were also poorly correlated with data for those groups. Finally, model outcomes were highly sensitive to methods used to combine both individual indices and weightings for each component. Thus, we recommend using constant weightings, simple and consist combination methods and reconsidering the number of fish functional groups as simplifications to this model and in the development of similar habitat suitability models elsewhere.
A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-19 Chiara Magliozzi, Gianpaolo Coro, Robert Grabowski, Aaron I. Packman, Stefan Krause
Quantifying uncertainties in EO-based ecosystem service assessments Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-19 Ana Stritih, Peter Bebi, Adrienne Grêt-Regamey
Ecosystem service (ES) assessments are widely promoted as a tool to support decision-makers in ecosystem management, and the mapping of ES is increasingly supported by the spatial data on ecosystem properties provided by Earth Observation (EO). However, ES assessments are often associated with high levels of uncertainty, which affects their credibility. We demonstrate how different types of information on ES (including EO data, process models, and expert knowledge) can be integrated in a Bayesian Network, where the associated uncertainties are quantified. The probabilistic approach is used to map the provision and demand of avalanche protection, an important regulating service in mountain regions, and to identify the key sources of uncertainty. The model outputs show high uncertainties, mainly due to uncertainties in process modelling. Our results demonstrate that the potential of EO to improve the accuracy of ES assessments cannot be fully utilized without an improved understanding of ecosystem processes.
Many-objective portfolio optimization approach for stormwater management project selection encouraging decision maker buy-in Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-19 M. Di Matteo, H.R. Maier, G.C. Dandy
Although formal simulation-optimization approaches have been shown to be able to identify near-optimal outcomes for a range of stormwater management problems, stakeholder acceptance of these solutions can be problematic, especially if there is a lack of familiarity with the optimization processes and simulation model used to arrive at these solutions. To address this problem, a portfolio optimization problem formulation is introduced that allows stormwater best management practices (BMPs) to be evaluated by stakeholders before the portfolio selection process. This enables the search space to be constrained before the BMP optimization process, ensuring that model results are transparent and only represent solutions that are trusted by experienced practitioners. This has the effect of reducing reliance on simulation-optimization involving complex stormwater simulation models, and increasing buy-in to the optimization results. The portfolio optimization formulation is applied to a catchment management problem in Australia, using a typical many-objective optimization approach including visualization techniques.
A collaborative analysis framework for distributed gridded environmental data Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-19 Hao Xu, Sha Li, Yuqi Bai, Wenhao Dong, Wenyu Huang, Shiming Xu, Yanluan Lin, Bin Wang, Fanghua Wu, Xiaoge Xin, Li Zhang, Zaizhi Wang, Tongwen Wu
As the amount of environmental data expands exponentially worldwide, researchers are challenged to efficiently analyze data maintained in multiple data centers. Because distributed data access, server-side analysis, multinode collaboration, and extensible analytic functions are still research gaps in this field, this paper introduces a collaborative analysis framework for gridded environmental data, i.e. CAFE. Multiple CAFE nodes can collaborate to perform complex data analysis. Analytic functions are performed near where data are stored. A web-based user interface allows researchers to search for data of interest, submit analytic tasks, check the status of tasks, visualize the analysis results, and download the resulting data files. CAFE facilitates overall research efficiency by dramatically lowering the amount of data that must be transmitted from data centers to researchers for analysis. The results of this study may lead to the further development of collaborative computing paradigm for environmental data analysis.
Design and development of a web-based interface for the Agricultural Policy Environmental eXtender (APEX) model Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-18 Qingyu Feng, Bernard A. Engel, Dennis C. Flanagan, Chi-hua Huang, Haw Yen, Lei Yang
The diffuse nature of nonpoint source (NPS) pollution as well as that of the effectiveness of best management practices (BMP) to control NPS pollution necessitates BMP evaluation at the field scale. In this study, a web interface was developed for application of the Agricultural Policy Environmental eXtender (APEX) model at the field scale. The interface contains background databases for field location, soil, agricultural management and climate across the contiguous United States. Users can specify properties to run the model for an individual field and compare the results under various land management and conservation practice choices. A case study was conducted to demonstrate the capability of the web interface for simulating various land management scenarios. This tool can help provide on-site information for NPS pollution management related policies and serve as a communication tool among scientists, engineers, and stakeholders.
Adsorption characteristics of layered soil as delay barrier of some organic contaminants: Experimental and numerical modeling Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-17 Sana Dardouri, Jalila Sghaier
To minimize the contaminant migration in the soil, the use of two and three-layer capillary barriers is proposed as an efficient solution. Indeed, the soil can be used as a physicochemical filter for some pollutants. To discuss the effectiveness of layered soil in the reduction of organic pollutant transport, an experimental and numerical model was developed in this study.The adsorption capacity of the cationic dye, which used as an example of adsorbed organic pollutant, in a soil (sand, clay, silty soil) has been studied in batch and fixed bed column. It was found that the concentration at the column outlet doesn't exceed 4.5% of the initial concentration after 214 days. Due to the presence of a smectite clay layer and three layer capillary barriers, the transport of dye was insignificant because of the high adsorption capacity of clay and silty soil. The experimental results were also confronted with a numerical simulation. A finite element analysis model was employed in this study to predict the coupled process of variably saturated soils by the contaminants transported in runoff.Experimental and numerical results confirm that the water velocity and the kinetic adsorption are inversely proportional through the two and three layer barrier system.
A knowledge modelling framework for intelligent environmental decision support systems and its application to some environmental problems Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-15 Mihaela Oprea
Environmental processes are highly complex and their understanding involves the analysis of various quantitative and qualitative parameters (physical, chemical, geographical etc), which are more or less correlated. Appropriate environmental knowledge can deal with this complexity in a tractable way. Such knowledge is essential for solving particular environmental problems. Generating valuable environmental knowledge is a challenging research topic, especially for environmental data science, as efficient knowledge can lie behind data. Integrated environmental modelling uses a holistic view and can provide a possible better solution to environmental problems understanding. The paper presents a knowledge modelling framework for intelligent environmental decision support systems (IEDSS) by following such a holistic perspective. Thus, the proposed framework integrates an ontological approach and two data analysis approaches (data mining and Bayesian networks), which are applied for the generation of a knowledge base that is used by an IEDSS for decision making. The application of the framework is illustrated on three case studies from different environmental domains: (1) water (river resource management, river water pollution analysis), (2) air (air pollution analysis, ozone prediction), and (3) soil (soil pollution analysis).
Global Sensitivity Analysis for High-Dimensional Problems: How tObjectively Group Factors and Measure Robustness and Convergence while Reducing Computational Cost Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-10 Razi Sheikholeslami, Saman Razavi, Hoshin V. Gupta, William Becker, Amin Haghnegahdar
Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) thigh-dimensional models because very large numbers of model runs are typically required tachieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA thigh-dimensional models. We alsprovide a new measure of robustness that assesses GSA stability and convergence. For twmodels, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust tsampling variability, while converging with a much smaller number of model runs.
Determining the initial spatial extent of an environmental impact assessment with a probabilistic screening methodology Environ. Model. Softw. (IF 4.177) Pub Date : 2018-09-01 Luk J.M. Peeters, Daniel E. Pagendam, Russell S. Crosbie, Praveen K. Rachakonda, Warrick R. Dawes, Lei Gao, Steve P. Marvanek, YongQiang Zhang, Tim R. McVicar
A crucial decision in defining the scope of an environmental impact assessment is to delineate the initial assessment area. We developed a probabilistic methodology to determine this area, which starts by identifying a key environmental variable, maximum acceptable change and acceptable probability of exceeding that threshold.The exceedance probability is determined with a limits of acceptability rejection sampling of informed prior parameter distributions. A qualitative uncertainty analysis, a formal and systematic discussion of the main assumptions and model choices, is complemented with global sensitivity analysis of the model results to identify the major sources of uncertainty and provide guidance for further research and data collection.For the case study on coal development in the Gloucester Basin (NSW, Australia), the initial assessment extent is unlikely to extend more than 5 km from the edge of the planned coal mines. The major source of uncertainty is the planned mine water production rate.
Geospatial uncertainty modeling using Stacked Gaussian Processes Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-30 Kareem Abdelfatah, Junshu Bao, Gabriel Terejanu
A network of independently trained Gaussian processes (StackedGP) is introduced to obtain predictions of geospatial quantities of interest (model outputs) with quantified uncertainties. The uncertain nature of model outputs is due to model inadequacy, parametric uncertainty, and measurement noise. StackedGP framework supports component-based modeling in environmental science, enhances predictions of quantities of interest through a cascade of intermediate predictions usually addressed by cokriging, and propagates uncertainties through emulated dynamical systems driven by uncertain forcing variables. By using analytical first and second-order moments of a Gaussian process with uncertain inputs using squared exponential and polynomial kernels, approximated expectations of model outputs that require an arbitrary composition of functions can be obtained. The performance of the proposed nonparametric stacked model in model composition and cascading predictions is measured in a wildfire and mineral resource problem using real data, and its application to time-series prediction is demonstrated in a 2D puff advection problem.
A new rapid watershed delineation algorithm for 2D flow direction grids Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-30 Scott Haag, Bahareh Shakibajahromi, Ali Shokoufandeh
In this paper we propose an algorithm for retrieving an arbitrary watershed boundary from a 2D Flow Direction Grid. The proposed algorithm and associated data model provides geometric speed increases in watershed boundary retrieval while keeping storage constraints linear in comparison to existing techniques. The algorithm called Watershed Marching Algorithm (WMA) relies on an existing data structure, the modified nested set model, originally described by Celko and applied to hydrodynamic models by Haag and Shokoufandeh in 2017. In contrast to existing algorithms that scale proportionally to the area of the underlying region, the complexity of the WMA algorithm is proportional to the boundary length. Results for a group of tested watersheds (n = 14,718) in the ≈ 36,000 km2 Delaware River Watershed show a reduction of between 0 and 99% in computational complexity using a 30 m DEM vs. existing techniques.
Inside the Black Box: Understanding key drivers of global emission scenarios Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-30 Jonathan Koomey, Zachary Schmidt, Holmes Hummel, John Weyant
Technology and policy implications of global energy and emissions scenarios can be difficult to analyze because underlying assumptions and drivers of scenarios are rarely made explicit. This article documents methods for standardizing emissions scenario results that can be applied to virtually any scenario, enabling more meaningful comparisons among scenarios than has been possible in the past.This approach uses charts showing the dynamics and effects of emission drivers, mitigation technologies, and policies. Applying these methods will enable the policy and research communities to better understand the implications of scenarios, and help analysts learn more rapidly. As a matter of good practice, modelers should create decompositions like the ones put forth in this article, policy makers should request them, and funders of scenario analysis and sponsors of model comparisons should support the application and further development of such tools.
Repeated discrete choices in geographical agent based models with an application to fisheries Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-30 Ernesto Carrella, Richard M. Bailey, Jens Koed Madsen
Most geographical agent-based models simulate agents through custom-made decision-making algorithms. This makes it difficult to assess which results are general and which are contingent on the algorithm's details. We present a set of general algorithms, applicable in any agent-based model for choosing repeatedly from a set of alternatives. We showcase each in the same fishery agent-based model and rank their performance under various scenarios. While complicated algorithms tend to perform better, too much sophistication lowers performance. Further, while some algorithms perform well under all scenarios, others are optimal only in specific circumstances. It is therefore impossible to produce a single, unequivocal performance ranking even for simple general algorithms. We advocate then a heuristic zoo approach where multiple algorithms are implemented in the same model; this allows us to identify its best algorithm and test sensitivity to misspecifications of the decision-making component.
Introduction of the GAM model for regional low-flow frequency analysis at ungauged basins and comparison with commonly used approaches Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-29 T.B.M.J. Ouarda, C. Charron, Y. Hundecha, A. St-Hilaire, F. Chebana
Generalized Additive Models (GAMs) are introduced in this study for the regional estimation of low-flow characteristics at ungauged basins and compared to other approaches commonly used for this purpose. GAMs provide more flexibility in the shape of the relationships between the response and explanatory variables in comparison to classical models such as multiple linear regression (MLR). Homogeneous regions are defined here using the methods of hierarchical cluster analysis, canonical correlation analysis and region of influence. GAMs and MLR are then used within the delineated regions and also for the whole study area. In addition, a spatial interpolation method is also tested. The different models are applied for the regional estimation of summer and winter low-flow quantiles at stations in Quebec, Canada. Results show that for a given regional delineation method, GAMs provide improved performances compared to MLR.
Coupling land-use change and hydrologic models for quantification of catchment ecosystem services Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-29 S.G. Yalew, T. Pilz, C. Schweitzer, S. Liersch, J. van der Kwast, A. van Griensven, M.L. Mul, C. Dickens, P. van der Zaag
Representation of land-use and hydrologic interactions in respective models has traditionally been problematic. The use of static land-use in most hydrologic models or that of the use of simple hydrologic proxies in land-use change models call for more integrated approaches. The objective of this study is to assess whether dynamic feedback between land-use change and hydrology can (1) improve model performances, and/or (2) produce a more realistic quantification of ecosystem services. To test this, we coupled a land-use change model and a hydrologic mode. First, the land-use change and the hydrologic models were separately developed and calibrated. Then, the two models were dynamically coupled to exchange data at yearly time-steps. The approach is applied to a catchment in South Africa. Performance of coupled models when compared to the uncoupled models were marginal, but the coupled models excelled at the quantification of catchment ecosystem services more robustly.
Semantic knowledge network inference across a range of stakeholders and communities of practice Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-29 Kostas Alexandridis, Shion Takemura, Alex Webb, Barbara Lausche, Jim Culter, Tetsu Sato
This paper provides empirical and experimental assessments of thematic knowledge discourses based on two case studies in the US Virgin Islands and Florida. We utilize a latent semantic indexing analysis over natural language corpus to classify and categorize knowledge categories. We computed TF*IDF scores and associated co-occurrence Jaccard similarity scores to construct semantic knowledge networks. Using network analysis, we computed structural metrics over four composite groups: neighbor-based, centrality, equivalence and position. The analysis show that structural network characteristics of environmental knowledge can exponentially predict associations between knowledge categories. We show that connectivity play a critical role on acquisition, representation, and diffusion patterns of knowledge within local communities. We provide evidence of a global prevalence of a shared knowledge core. We show that core social-ecological attributes of knowledge follow scale-free, power law distributions and stable, equilibrium network structures. We identify two distinct models of bidirectional translation: a bottom-up and a top-down.
A toolbox for the optimal design of run-of-river hydropower plants Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-28 Veysel Yildiz, Jasper A. Vrugt
Integrating GLEAMS sedimentation into RZWQM for pesticide sorbed sediment runoff modeling Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-27 Christopher DeMars, Yu Zhan, Huajin Chen, Phil Heilman, Minghua Zhang
Sediment transport from agricultural fields to native waterways is a significant pollution vector, not just for the bulk sediment, but also for additional mass of pesticides traveling offsite that are sorbed to soil particles. Existing field scale models that track plant growth as well as the fate and transport of applied pesticides lack an integrated sediment transport component. This study sought to address this lack of available modeling tools for researchers and regulators by integrating the sediment and surface flow components of Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) model into the mature Root Zone Water Quality Model (RZWQM) to create a derivative model named RZWQM-Sed. Previous research identified RZWQM as a quick running, agricultural field scale model that accurately estimated offsite transport of solutes. Unlike other well performing field scale agricultural models, the full source code of RZWQM was available for modification and extension. However, RZWQM lacked a sediment component and thus could not measure all pollutants moving offsite. GLEAMS sedimentation was chosen for integration due to its well documented history, compatibility with the RZWQM codebase, and source code availability. Sensitivity analysis of the RZWQM runoff variables showed that the residual water content, saturated water content, and bubbling pressure from the Brooks-Corey equation had the highest influence for RZWQM followed by the saturated hydraulic conductivity and the non-Brooks-Corey bubbling pressure. Analysis of GLEAMS variables showed the most significant variables are the USLE parameters (cfact, pfact, ksoil) and Manning's N. The latter variable only showed sensitivity at very high surface roughness while the USLE parameters had a linear relationship over the entire domain. The integrated model was calibrated and validated using multiple real-world datasets spanning ranges of space and time. The final model performed well in the primary task of predicting the mass of sorbed chemicals in the tailwater (Nash-Sutcliffe coefficient > 0.3).
Development and improvement of the simulation of woody bioenergy crops in the Soil and Water Assessment Tool (SWAT) Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-27 Tian Guo, Bernard A. Engel, Gang Shao, Jeffrey G. Arnold, Raghavan Srinivasan, James R. Kiniry
Quantifying Populus growth and the impacts on hydrology and water quality are important should it be widely planted. Soil and Water Assessment Tool (SWAT) tree growth algorithms and parameters for hybrid poplar in Midwestern US and cottonwood in Southern US were improved. Tree growth representation led to SWAT2012 code changes including a new leaf area parameter (TREED), new leaf area index algorithm, and leaf biomass algorithm. Simulated hybrid poplar LAI and aboveground woody biomass (PBIAS: 34 - 5%, NSE: 0.51–0.99, and R2: 0.72–0.99), and cottonwood aboveground biomass, runoff, sediment, and nitrate-N (PBIAS: 39 - 11%, NSE: 0.86–0.99, and R2: 0.93–0.99) from the modified SWAT were satisfactory. Improved algorithms, and parameter values and potential ranges for Populus were reasonable. Thus, the modified SWAT can be used for Populus biofeedstock production modeling and hydrologic and water quality response to its growth.
Tools and methods in participatory modeling: Selecting the right tool for the job Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-27 Alexey Voinov, Karen Jenni, Steven Gray, Nagesh Kolagani, Pierre D. Glynn, Pierre Bommel, Christina Prell, Moira Zellner, Michael Paolisso, Rebecca Jordan, Eleanor Sterling, Laura Schmitt Olabisi, Philippe J. Giabbanelli, Zhanli Sun, Christophe Le Page, Sondoss Elsawah, Todd K. BenDor, Klaus Hubacek, Alex Smajgl
Various tools and methods are used in participatory modelling, at different stages of the process and for different purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We offer a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justification or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant effect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based on expert opinion and a survey of modelers engaged in participatory processes, we offer practical guidelines to improve decisions about method selection at different stages of the participatory modeling process.
Assessing the performance of a physically-based soil moisture module integrated within the Soil and Water Assessment Tool Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-27 Junyu Qi, Xuesong Zhang, Gregory W. McCarty, Ali M. Sadeghi, Michael H. Cosh, Xubin Zeng, Feng Gao, Craig S.T. Daughtry, Chengquan Huang, Megan W. Lang, Jeffrey G. Arnold
A Richards-equation-based soil moisture module was developed and integrated within the Soil and Water Assessment Tool (SWAT). Four years of daily soil moisture measurements from 10 monitoring stations at three depths (i.e., 5, 10, and 50 cm) in the Choptank River watershed, Maryland, were used to test the module performance. Results show that, as compared with the original SWAT soil moisture module, the Richards-equation-based soil moisture module improved R2 from 0.12 to 0.45 and reduced soil moisture simulation bias (mean[simulation] – mean[measurement]) from −0.10 to −0.02 (m3 m−3), averaged across the 10 stations at soil surface layer (i.e., 5 cm depth). Noticeable improvements were also observed for deeper soil layers, and for both dry and wet periods. Notably, the soil moisture coupling strength between different soil layers was substantially improved with the new module. The enhanced SWAT model is expected to better inform soil water and irrigation management.
Implementation of stochastic multi attribute analysis (SMAA) in comparative environmental assessments Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-27 Valentina Prado, Reinout Heijungs
The selection of an alternative based on the results of a comparative environmental assessment such as life cycle assessment (LCA), environmental input-output analysis (EIOA) or integrated assessment modeling (IAM) is challenging because most of the times there is no single best option. Most comparative cases contain trade-offs between environmental criteria, uncertainty in the performances and multiple diverse values from decision makers. To circumvent these challenges, a method from decision analysis, namely stochastic multi attribute analysis (SMAA), has been proposed instead. SMAA performs aggregation that is partially compensatory (hence, closer to a strong sustainability perspective), incorporates performance uncertainty in the assessment, is free from external normalization references and allows for uncertainties in decision maker preferences. This paper presents a thorough introduction of SMAA for environmental decision-support, provides the mathematical fundamentals and offers an Excel platform for easy implementation and access.
Embedding co-production and addressing uncertainty in watershed modeling decision-support tools: Successes and challenges Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-26 Bradley L. Barnhart, Heather E. Golden, Joseph R. Kasprzyk, James J. Pauer, Chas E. Jones, Keith A. Sawicz, Nahal Hoghooghi, Michelle Simon, Robert B. McKane, Paul M. Mayer, Amy N. Piscopo, Darren L. Ficklin, Jonathan J. Halama, Paul B. Pettus, Brenda Rashleigh
Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders – a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss successful outcomes and challenges associated with embedding various forms of co-production into each stage of the conceptual model. We also emphasize the “3 Cs” (i.e., characterization, calculation, communication) of uncertainty and provide evidence-based suggestions for their incorporation in the watershed modeling DST development process. We conclude by presenting a list of best practices derived from current literature for achieving effective and robust watershed modeling decision-support tools.
A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-20 Álvaro Gómez-Losada, G. Asencio–Cortés, F. Martínez–Álvarez, J.C. Riquelme
A software tool for simulating contaminant transport and remedial effectiveness in sediment environments Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-18 Xiaolong Shen, David Lampert, Stephen Ogle, Danny Reible
Sediments have often acted as sinks for contaminants that possess strong affinity for solids near historical pollution sources. Mathematical models describing the evolution of contaminant concentrations in sediment environments provide a scientific basis for decision support and remediation design. Herein, novel software (CapSim) is introduced including processes relevant to natural attenuation and in-situ treatment and containment (capping). The tool has been used as a basis for remedial design at a number of sites throughout the United States. CapSim is built on the concept of an arbitrary number of layers that each exhibit traditional porous media transport processes including sorption (linear and non-linear, transient or local equilibrium), advection, diffusion, dispersion, multicomponent linked reactions and, critically, processes specific to the sediment-water interface including bioturbation of both solids and porewater, deposition, consolidation, and interaction with the overlying surface water. A summary of recent applications and selected simulations of key features are presented.
Mobile device application for monitoring cyanobacteria harmful algal blooms using Sentinel-3 satellite Ocean and Land Colour Instruments Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-18 Blake A. Schaeffer, Sean W. Bailey, Robyn N. Conmy, Michael Galvin, Amber R. Ignatius, John M. Johnston, Darryl J. Keith, Ross S. Lunetta, Rajbir Parmar, Richard P. Stumpf, Erin A. Urquhart, P. Jeremy Werdell, Kurt Wolfe
Cyanobacterial harmful algal blooms (cyanoHAB) cause human and ecological health problems in lakes worldwide. The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management and for targeted deployment of water quality monitoring resources. Software platforms that permit timely, useful, and cost-effective delivery of information from satellites are required to help managers respond to cyanoHABs. The Cyanobacteria Assessment Network (CyAN) mobile device application (app) uses data from the European Space Agency Copernicus Sentinel-3 satellite Ocean and Land Colour Instrument (OLCI) in near real-time to make initial water quality assessments and quickly alert managers to potential problems and emerging threats related to cyanobacteria. App functionality and satellite data were validated with 25 state health advisories issued in 2017. The CyAN app provides water quality managers with a user-friendly platform that reduces the complexities associated with accessing satellite data to allow fast, efficient, initial assessments across lakes.
Modelling hydrology and water quality in a mixed land use catchment and eutrophic lake: Effects of nutrient load reductions and climate change Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-13 Wang Me, David P. Hamilton, Christopher G. McBride, Jonathan M. Abell, Brendan J. Hicks
The objective of this study was to combine a catchment model with a one–dimensional lake water quality model to simulate the trophic state of a eutrophic shallow lake in response to nutrient load reductions and climate change. The catchment and lake models gave satisfactory performance in simulating observed data, indicating that the key processes that affect nutrient loads and lake trophic status were adequately represented. Simulating removal of nutrients by reducing fertiliser applied to farmland or irrigated wastewater had minor effects on nutrient concentrations in the lake, but simulations using a projected climate for 2090 showed a major impact on nutrients and water quality. This overarching effect indicated that polymictic lakes may be particularly vulnerable to eutrophication associated with climate change due to increased internal nutrient loading, which will lead to a biological response of increased algal biomass, while changes in external loads will have lesser relative impact.
Input data processing tools for the integrated hydrologic model GSFLOW Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-09 Murphy A. Gardner, Charles G. Morton, Justin L. Huntington, Richard G. Niswonger, Wesley R. Henson
Integrated hydrologic modeling (IHM) encompasses a vast number of processes and specifications, variable in time and space, and development of models can be arduous. Model input construction techniques have not been formalized or made easily reproducible. Creating the input files for integrated hydrologic models requires complex GIS processing of raster and vector datasets from various sources. Developing stream network topology that is consistent with the model grid-scale digital elevation model (DEM) is important for robust simulation of surface water and groundwater exchanges. Distribution of meteorological data over the model domain is difficult in complex terrain at the model-grid scale, but is necessary for realistic simulations. As model development requires extensive GIS and computer programming expertise, the use of IHMs has mostly been limited to research groups with available financial, human, and technical resources. Here we present a series of open-source Python scripts that are combined with ESRI ArcGIS to provide a formalized technique for the parameterization and development of inputs for the readily available IHM called GSFLOW. This Python toolkit automates many of the necessary and laborious processes of parameterization, including stream network development, land coverages, and meteorological distribution over the model domain. The final products of the toolkit are PRMS ready Parameter Files, along with several input parameters for a MODFLOW model, including input for the Streamflow Routing Package. A demonstration of the toolkit is provided to illustrate its capabilities.
Landscaping for road traffic noise abatement: model validation Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-09 Timothy Van Renterghem, Dick Botteldooren
Deliberately changing terrain undulation and ground characteristics (“acoustical landscaping”) is an potential noise abatement solution near roads. However, there is hardly any research regarding the validity of sound propagation models to predict its effectiveness. Long-term continuous sound pressure level measurements near a complex road traffic and sound propagation case were performed. Three types of modeling approaches were validated, covering the full spectrum of available techniques. A two-dimensional full-wave technique (the finite-difference time-domain method, FDTD), but also an advanced engineering model (the Harmonoise point-to-point model), provide accurate transmission loss predictions, both in 1/3 octave bands and for total A-weighted sound pressure levels. Two common and widely used semi-empirical engineering methods (ISO9613-2 and CNOSSOS) yield rather inaccurate results, notwithstanding the short propagation distance. The sensitivity to input data was assessed by modelling various scenarios with the FDTD method. Detailed ground effect modelling was shown to be of main importance.
Variance based sensitivity analysis of 1D and 2D hydraulic models: An experimental urban flood case Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-09 S. Chen, P.-A. Garambois, P. Finaud-Guyot, G. Dellinger, R. Mosé, A. Terfous, A. Ghenaim
This paper explores spatial sensitivities of 1D and 2D shallow water (SW) models of branched urban flood flows, based on an experimental data-set and a variance decomposition method for various combination of uncertainty sources. General sensitivity patterns of SW model for subcritical flows show that: simulated water height variance is explained upstream by inflow discharge and roughness whereas downstream it is fully explained by downstream water height, influence of lateral inflows propagates in both directions. High sensitivities to roughness can be local in space thus identifying the strongest hydraulic controls for orienting calibration efforts is needed. 2D sensitivities show: the filtering effect of branched network topography on inflow, the zone of influence of boundary conditions, the role of large streets as global flow pattern separators, the difference in roughness sensitivity patterns with 1D. This sensitivity analysis of SW models could be a base for studying flow path and uncertainty propagation.
Do salt marshes survive sea level rise? Modelling wave action, morphodynamics and vegetation dynamics Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-10 Ü.S.N. Best, M. van der Wegen, J. Dijkstra, P.W.J.M. Willemsen, B.W. Borsje, Dano Roelvink
Modeling the extent of surface water floods in rural areas: Lessons learned from the application of various uncalibrated models Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-07 Daniel B. Bernet, Andreas Zischg, Volker Prasuhn, Rolf Weingartner
Surface water floods (SWFs) do not only increasingly threaten cities, but also affect rural areas. So far, little research has been dedicated to the prediction of SWFs in rural environments, although in practice the process is already being considered in deterministic flood hazard assessments. To test the validity of such assessments, we select four raster-based models with differing complexity and evaluate whether they reliably predict inundated areas by SWF in rural areas. The uncalibrated models are first applied to four artificial surfaces and second, to eight case studies covering manifold geographical and meteorological settings. For the case studies, the models' prediction skills are assessed based on inundated areas inferred from various sources. The models’ performance is rather low for all case studies, which highlights the necessity for calibration and/or validation of such models. Moreover, the case studies provide more general conclusions concerning the modeling of SWFs in rural areas.
A reduced-complexity shoreline change model combining longshore and cross-shore processes: the LX-Shore model Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-04 Arthur Robinet, Déborah Idier, Bruno Castelle, Vincent Marieu
A reduced-complexity numerical model, LX-Shore, is developed to simulate shoreline evolution along wave-dominated sandy coasts. The model can handle any sandy shoreline geometries (e.g. sand spits, islands), including non-erodible areas such as coastal defenses and headlands, and is coupled with a spectral wave model to cope with complex nearshore wave fields. Shoreline change is primarily driven by the gradients in total longshore sediment transport and by the cross-shore transport owing to variability in incident wave energy. Application to academic cases and a real coast highlights the potential of LX-Shore to simulate shoreline change on timescales from hours (storm) to decades with low computational cost. LX-Shore opens new perspectives in terms of knowledge on the primary mechanisms locally driving shoreline change and for ensemble-based simulations of future shoreline evolution.
An R package to estimate daily meteorological data and downscaling climate models over landscapes Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-04 Miquel De Cáceres, Nicolas Martin-StPaul, Marco Turco, Antoine Cabon, Victor Granda
High-resolution meteorological data are necessary to understand and predict climate-driven impacts on the structure and function of terrestrial ecosystems. However, the spatial resolution of climate reanalysis data and climate model outputs is often too coarse for studies at local/landscape scales. Additionally, climate model projections usually contain important biases, requiring the application of statistical corrections. Here we present ‘meteoland’, an R package that integrates several tools to facilitate the estimation of daily weather over landscapes, both under current and future conditions. The package contains functions: (1) to interpolate daily weather including topographic effects; and (2) to correct the biases of a given weather series (e.g., climate model outputs). We illustrate and validate the functions of the package using weather station data from Catalonia (NE Spain), re-analysis data and climate model outputs for a specific county. We conclude with a discussion of current limitations and potential improvements of the package.
Extending coupled hydrological-hydraulic model chains with a surrogate model for the estimation of flood losses Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-04 Andreas Paul Zischg, Guido Felder, Markus Mosimann, Veronika Röthlisberger, Rolf Weingartner
A comprehensive graphical modeling platform designed for integrated hydrological simulation Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-04 Yong Tian, Yi Zheng, Feng Han, Chunmiao Zheng, Xin Li
This study developed a comprehensive graphical data processing and modeling system named Visual HEIFLOW (VHF) for integrated surface water-groundwater modeling. Its distinctive features include the following. First, VHF uses a generic multivariable-space-time data cube model, which enables the system to efficiently handle large time-series datasets over a large spatial domain. Second, VHF streamlines the entire modeling procedure from data preparation at the very beginning to visualization and analysis of modeling results in a uniform environment. Third, VHF allows updating the land use input at user-specified time points without manual intervention and therefore allows a direct simulation of the hydrological effects of changing land use. The applicability and versatility of VHF were demonstrated in a case study in the Heihe River Basin, the second largest inland river basin in China. The case study also demonstrated that VFH facilitates process understanding and supports management decision-making.
Modelling pesticides leaching in cropping systems: Effect of uncertainties in climate, agricultural practices, soil and pesticide properties Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-03 Sabine-Karen Lammoglia, François Brun, Thibaud Quemar, Julien Moeys, Enrique Barriuso, Benoît Gabrielle, Laure Mamy
Modelling of pesticide leaching is paramount to managing the environmental risks associated with the chemical protection of crops, but it involves large uncertainties in relation to climate agricultural practices, soil and pesticide properties. We used Latin Hypercube Sampling to estimate the contribution of these input factors with the STICS-MACRO model in the context of a 400 km2 catchment in France, and two herbicides applied to maize: bentazone and S-metolachlor. For both herbicides, the most influential input factors on modelling of pesticide leaching were the inter-annual variability of climate, the pesticide adsorption coefficient and the soil boundary hydraulic conductivity, followed by the pesticide degradation half-life and the rainfall spatial variability. This work helps to identify the factors requiring greater accuracy to ensure better pesticide risk assessment and to improve environmental management and decision-making processes by quantifying the probability and reliability of prediction of pesticide concentrations in groundwater with STICS-MACRO.
A Bayesian total uncertainty analysis framework for assessment of management practices using watershed models Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-03 Ali Tasdighi, Mazdak Arabi, Daren Harmel, Daniel Line
A Bayesian total uncertainty analysis framework is presented to assess the model estimates of the effectiveness of watershed management practices in reducing nonpoint source (NPS) pollution. The framework entails a two-stage procedure. First, various sources of modeling uncertainties are characterized during the period before implementing Best Management Practices (BMPs). Second, the effectiveness of BMPs are probabilistically quantified during the post-BMP period. The framework was used to assess the uncertainties in effectiveness of two BMPs in reducing daily total nitrogen (TN) loads in a 54 ha agricultural watershed in North Carolina using the SWAT model. The results indicated that the modeling uncertainties in quantifying the effectiveness of selected BMPs were relatively large. Assessment of measured data uncertainty revealed that higher errors were observed in simulating TN loads during high flow events. The results of this study have important implications for decision-making under uncertainty when models are used for water quality simulation.
Distribution-based sensitivity analysis from a generic input-output sample Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-03 Francesca Pianosi, Thorsten Wagener
In a previous paper we introduced a distribution-based method for Global Sensitivity Analysis (GSA), called PAWN, which uses cumulative distribution functions of model outputs to assess their sensitivity to the model's uncertain input factors. Over the last three years, PAWN has been employed in the environmental modelling field as a useful alternative or complement to more established variance-based methods. However, a major limitation of PAWN up to now was the need for a tailored sampling strategy to approximate the sensitivity indices. Furthermore, this strategy required three tuning parameters whose optimal choice was rather unclear. In this paper, we present an alternative approximation procedure that tackles both issues and makes PAWN applicable to a generic sample of inputs and outputs while requiring only one tuning parameter. The new implementation therefore allows the user to estimate PAWN indices as complementary metrics in multi-method GSA applications without additional computational cost.
A fully hydrodynamic urban flood modelling system representing buildings, green space and interventions Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-02 V. Glenis, V. Kutija, C.G. Kilsby
City Catchment Analysis Tool – CityCAT-is a novel software system for rapid assessment of combined pluvial and fluvial flood risk using a unique combination of: efficient software architecture throughout and especially in the numerical part; use of standard, readily available data sets; efficient algorithms for grid generation; and robust and accurate solutions of the flow equations. It is based on advanced software architecture and accurate solutions for complex free-surface flow over the terrain distinguishing between permeable and impermeable surfaces and taking into account effects of man-made features such as buildings as obstacles to flow. The software is firstly rigorously validated with demanding test cases based on analytical solutions and laboratory studies. Then the unique capability for assessment of the effectiveness of specific flood alleviation interventions across large urban domains, such as roof storage on buildings or introduction of permeable surfaces, is demonstrated.
Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks Environ. Model. Softw. (IF 4.177) Pub Date : 2018-08-02 David P. Callaghan, Tom E. Baldock, Behnam Shabani, Peter J. Mumby
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