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Results for “Environmental Modelling and Software”

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At least 19 recordsLinked to original sources
USGS2012

Identifying the decision to be supported: a review of papers from environmental modelling and software

Two of the basic tenets of decision support system efforts are to help identify and structure the decisions to be supported, and to then provide analysis in how those decisions might be best made. One example from wetland management would be that wildlife biologists must decide when to draw down water levels to optimise aquatic invertebrates as food for breeding ducks. Once such a decision is identified, a system or tool to help them make that decision in the face of current and projected climate conditions could be developed. We examined a random sample of 100 papers published from 2001-2011 in Environmental Modelling and Software that used the phrase “decision support system” or “decision support tool”, and which are characteristic of different sectors. In our review, 41% of the systems and tools related to the water resources sector, 34% were related to agriculture, and 22% to the conservation of fish, wildlife, and protected area management. Only 60% of the papers were deemed to be reporting on DSS. This was based on the papers reviewed not having directly identified a specific decision to be supported. We also report on the techniques that were used to identify the decisions, such as formal survey, focus group, expert opinion, or sole judgment of the author(s). The primary underlying modelling system, e.g., expert system, agent based model, Bayesian belief network, geographical information system (GIS), and the like was categorised next. Finally, since decision support typically should target some aspect of unstructured decisions, we subjectively determined to what degree this was the case. In only 23% of the papers reviewed, did the system appear to tackle unstructured decisions. This knowledge should be useful in helping workers in the field develop more effective systems and tools, especially by being exposed to the approaches in different, but related, disciplines. We propose that a standard blueprint for reporting on DSS be developed for consideration by journal editors to aid them in filtering papers that use the term, “decision support”.

Leipzig↗
USGS2008

The Importance of Uncertainty and Sensitivity Analysis in Process-based Models of Carbon and Nitrogen Cycling in Terrestrial Ecosystems with Particular Emphasis on Forest Ecosystems — Selected Papers from a Workshop Organized by the International Society for Ecological Modelling (ISEM) at the Third Biennal Meeting of the International Environmental Modelling and Software Society (IEMSS) in Burlington, Vermont, USA, August 9-13, 2006

Many process-based models of carbon (C) and nitrogen (N) cycles have been developed for terrestrial ecosystems, including forest ecosystems. They address many basic issues of ecosystems structure and functioning, such as the role of internal feedback in ecosystem dynamics. The critical factor in these phenomena is scale, as these processes operate at scales from the minute (e.g. particulate pollution impacts on trees and other organisms) to the global (e.g. climate change). Research efforts remain important to improve the capability of such models to better represent the dynamics of terrestrial ecosystems, including the C, nutrient, (e.g. N) and water cycles. Existing models are sufficiently well advanced to help decision makers develop sustainable management policies and planning of terrestrial ecosystems, as they make realistic predictions when used appropriately. However, decision makers must be aware of their limitations by having the opportunity to evaluate the uncertainty associated with process-based models ( Smith and Heath, 2001 and Allen et al., 2004 ). The variation in scale of issues currently being addressed by modelling efforts makes the evaluation of uncertainty a daunting task.

Ecological Modelling↗
USGS2012

Approaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization

The TSPROC ( T ime S eries PROC essor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetic operations, and generation of seasonal and annual statistics and hydrologic indices. TSPROC can also be used to generate some of the key input files required to perform parameter optimization by means of the PEST ( P arameter EST imation) computer software. Through the use of TSPROC, the objective function for use in the model-calibration process can be focused on specific components of a hydrograph.

Techniques and Methods↗
USGS2015

Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models

The PEST++ Version 1 object-oriented parameter estimation code is here extended to Version 3 to incorporate additional algorithms and tools to further improve support for large and complex environmental modeling problems. PEST++ Version 3 includes the Gauss-Marquardt-Levenberg (GML) algorithm for nonlinear parameter estimation, Tikhonov regularization, integrated linear-based uncertainty quantification, options of integrated TCP/IP based parallel run management or external independent run management by use of a Version 2 update of the GENIE Version 1 software code, and utilities for global sensitivity analyses. The Version 3 code design is consistent with PEST++ Version 1 and continues to be designed to lower the barriers of entry for users as well as developers while providing efficient and optimized algorithms capable of accommodating large, highly parameterized inverse problems. As such, this effort continues the original focus of (1) implementing the most popular and powerful features of the PEST software suite in a fashion that is easy for novice or experienced modelers to use and (2) developing a software framework that is easy to extend. The PEST++ Version 3 software suite can be compiled for Microsoft Windows®4 and Linux®5 operating systems; the source code is available in a Microsoft Visual Studio®6 2013 solution; Linux Makefiles are also provided. PEST++ Version 3 continues to build a foundation for an open-source framework capable of producing robust and efficient parameter estimation tools for large environmental models.

Techniques and Methods↗
USGS2022

Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services

Social Values for Ecosystem Services (SolVES) version 4.0 is a fully open-source, GIS-based tool designed to aid in the creation of quantitative, spatially explicit models of the nonmonetary values attributed to cultural ecosystem services, such as aesthetics and recreation, specifically to facilitate their incorporation into larger ecosystem service assessments. Newly redeveloped for QGIS, SolVES can be applied in a wide variety of biophysical and social contexts including mountain, forest, coastal, riparian, agricultural, and urban settings worldwide. Redeveloping SolVES for an open-source platform was intended to expand its user base by eliminating the cost of proprietary GIS software licenses and to remove the impact of proprietary software changes on SolVES development. Providing additional options would enable users to delineate relevant stakeholder groups to better assess how differing preferences impact the intensity and spatial distribution of perceived social values.

Environmental Modelling & Software↗
USGS2022

A scalable model-independent iterative data assimilation tool for sequential and batch estimation of high dimensional model parameters and states

Ensemble-based data assimilation (DA) methods have displayed strong potential to improve model state and parameter estimation across several disciplines due to their computational efficiency, scalability, and ability to estimate uncertainty in the dynamic states and the parameters. However, a barrier to adoption of ensemble DA methods remains. Namely, there is currently a lack of available tools that enable efficient and scalable DA in a non-intrusive fashion and that support implementation flexibility. This paper presents an open-source software tool (PESTPP-DA) that implements a range of data assimilation methods—Ensemble Kalman filter, Ensemble Kalman Smoother and Ensemble Smoother—using the widely known PEST model-interface protocols, to interact with any model. Two iterative solutions can be used for nonlinear and/or non-Gaussian assimilation problems. To demonstrate the broad range of PESTPP-DA applications, two synthetic case studies are presented: (1) the Lorenz model and (2) a groundwater pumping test in the presence of a non-Gaussian hydraulic conductivity field.

Environmental Modelling & Software↗
USGS2022

The MODFLOW Application Programming Interface for simulationcontrol and software interoperability

The MODFLOW API allows other programs to control MODFLOW and interactively change variables without having to modify the source code. The MODFLOW API is based on the Basic Model Interface (BMI), which is a set of conventions that define how to initialize a simulation, update the model state by advancing in time, and finalize the run. For many existing MODFLOW coupling applications, the information provided to MODFLOW must be updated multiple times in a time step. As this capability to modify variables within a time step is not defined by the BMI, an extension to BMI was developed. This eXtended Model Interface is part of the MODFLOW API and allows such a tight coupling to other models. Examples are included for a variety of use cases, including new flexibility for users to develop custom packages without modifying the MODFLOW source code and coupling MODFLOW with other models and optimization libraries.

Environmental Modelling & Software↗
USGS2021

Supporting cost-effective watershed management strategies for Chesapeake Bay using a modeling and optimization framework

Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program's (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States' largest estuary . The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >30,000 solutions organized by objective, nutrient control level, and for ~200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework's current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies.

Chesapeake Bay watershed↗
USGS2024

An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management

Wildfires are increasing in size and severity across much of the western United States, exposing vulnerable wildland-urban interfaces to post-fire hazards. The Mediterranean chaparral region of Northern California contains many high sloping watersheds prone to hazardous post-fire flood events and identifying watersheds at high risk of soil loss and debris flows is a priority for post-fire response and management. Uncrewed Aerial Systems (UAS; aka drones) offer post-fire management teams the ability to quickly mobilize and survey burned areas with very high-resolution imagery (∼1 cm), facilitating emergency management and post-fire hazard assessment. However, adoption of this technology by hazard response teams may be hindered by complicated workflows for UAS data acquisition, image processing and analysis. We present an open-source workflow using mature Geographic Information Systems (GIS) software and Python packages in a Jupyter Notebook environment that guides users through classification of true-color UAS imagery to generate high resolution burn severity maps which can then be scaled across larger watersheds using Sentinel-2 normalized burn ratio (NBR) images. Soil burn severity classifications using a weighted brightness (WB) image and Char Index (CI) generated from UAS imagery were validated with in-situ data and random stratified points, resulting in the CI having the highest overall accuracy of 87.5%. CI also displayed a marginally stronger relationship over the WB with the post-fire Sentinel-2 NBR, R 2 = 0.79 and R 2 = 0.78 respectively. Our methods offer the unique opportunity to standardize GIS workflows, promoting replication through transparency, while improving the user's understanding of scientific GIS functionality.

California↗
USGS2019

The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States

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.

Conterminous United States↗
USGS2019

Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities

The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow , and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors’ collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and uncertainty management.

Environmental Modelling & Software↗
USGS2019

A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay Case Study

Nutrient-reduction efforts have been undertaken in recent decades to mitigate the impacts of eutrophication in coastal and estuarine systems worldwide. To track progress in response to one of these efforts we use Generalized Additive Models (GAMs) to evaluate a diverse suite of water quality constituents over a 32-year period in the Chesapeake Bay, an estuary on the east coast of the United States. Model development included selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporating hydrologic variability via either river flow or salinity, and using interventions to model method or laboratory changes suspected to impact data. This approach, transferable to other systems, allows for evaluation of water quality data in a statistically rigorous way, while being suitable for application to many sites and variables. This enables consistent generation of annual updates, while providing a tool for developing insights to a range of management- and research-focused questions.

Chesapeake Bay↗
USGS2020

Building a landslide hazard indicator with machine learning and land surface models

The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.

Oregon, Washington↗
USGS2020

Channel cross-section analysis for automated stream head identification

Headwater streams account for more than half of the streams in the United States by length. The substantial occurrence and susceptibility to change of headwater streams makes regular updating of related maps vital to the accuracy of associated analysis and display. Here we present work testing new methods of completely automated remote headwater stream identification using metrics derived from channel Digital Elevation Model (DEM) cross-sections. A jump in standard deviation of curvature (sK) is found to correlate with the presence of stream heads. Field and remotely validated stream and channel initiation points from 4 diverse study areas in North Carolina as well as a simulated surface are used to test the sK findings. The sK value within individual catchments equal to 0.5*Tukey's upper inner fence is found to be a reliable threshold for identifying the upslope extent of channels in varied landscapes.

Environmental Modelling & Software↗
USGS2021

An attention U-Net model for detection of fine-scale hydrologic streamlines

Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains.

North Carolina↗
USGS2021

Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses

Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often requiring that practitioners have theoretical and practical understanding of several advanced topics. Worse, implementing these important analyses can take substantial time and effort. This additional effort is often cited as justification for postponing, or even forgoing, these analyses. Herein, we present scripting tools to facilitate the efficient and repeatable construction of high-dimensional, geostatistical-based PEST interfaces, including uncertainty analyses. As demonstrated, these tools can be applied with minimal effort to a model with varied temporal and spatial discretization. Ultimately, these tools can enable low-cost access to valuable decision-support analyses earlier and more frequently during the modeling workflow.

Environmental Modelling & Software↗
USGS2021

What's left before participatory modeling can fully support real-world environmental planning processes: A case study review

In environmental participatory modeling (PM), both computer and non-computer-based modeling techniques are used to aid participatory problem description, solution, and decision-making actions in environmental contexts. Although many PM case studies have been published, few efforts have sought to systematically describe and understand dominant PM processes or establish best practices for PM. As a first step, we have reviewed a random sample of environmental PM case study articles (n = 60) using a novel PM process evaluation instrument. We found that significant work likely remains for PM to fully support participatory and integrated planning processes. While PM reports systematically address knowledge integration and learning, they often neglect the facilitation of a multi-value perspective within a democratic process , and the integration across organizations within a governance system. If not reported, we suspect these aspects are also neglected in practice. We conclude with key research and practice issues for improving PM as an approach for real-world participatory planning and governance.

Environmental Modelling & Software↗
USGS2021

LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.

Environmental Modelling & Software↗