The GrassGro decision support tool: its effectiveness in simulating pasture and animal production and value in determining research priorities

The GrassGro decision support tool combines animal intake and nutrition models, soil moisture and pasture growth models with management rules. GrassGro simulates pasture and animal production using a wide range of pasture species and sheep and cattle enterprises. Data from the Temperate Pasture Sustainability Key Program grazing management sites were used to validate the predictions of GrassGro. The pasture and animal production from a diverse range of sites were successfully simulated. Limitations of GrassGro were identified (parameter sets not available for some pasture species, inability to simulate clumpy swards, rudimentary interspecies competition model) and some improvements were made to its performance (improved species parameter sets and improved modeling of rooting depth). Recommendations are made on priority areas of research to improve GrassGro and on improvements in methodology which could be adopted by future programs like Temperate Pasture Sustainability Key Program.

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A Shoreline Response Program (SRP) Decision Support Tool based on the Geographic Variability in Attenuation and Weathering of Stranded Oil.

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-2021.1.1141234 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Ed Owens ◽

Elliott Taylor ◽

Chunjiang An ◽

Zhi Chen ◽

George Danner ◽

...

Keyword(s):

Decision Support ◽

Web Application ◽

Treatment Options ◽

Critical Role ◽

Treatment Strategies ◽

Decision Support Tool ◽

Data Bases ◽

Support Tool ◽

Fine Grained ◽

Wide Range

ABSTRACT #1141234 The coastal waters of Canada embrace a wide range of physical environments and ecosystems from the warm, sediment-rich waters of the Bay of Fundy to the nutrient-limited cold waters of the high Arctic. This range of biophysical characteristics impacts natural attenuation and weathering processes for oil stranded on shorelines. This study was conducted to: 1) identify and quantify the primary regional parameters that control shoreline oil translocation (removal) processes and pathways and 2) define the effectiveness and environmental consequences of current and potential oiled shoreline treatment strategies and tactics. A specific knowledge gap, here and elsewhere in the world, has been in understanding how the distribution and character of fine-grained sediments affect stranded oil attenuation. Fine-grained sediments (<1mm) can play a critical role in natural or induced (that is, shoreline treatment) oil dispersal. Shoreline sediment samples were collected and analyzed from representative locations on Arctic, Atlantic, and Pacific Ocean beaches to provide a broad geographic characterization of mineral fines at the regional level. This knowledge is the basis for an “Oiled Shoreline Response Program (SRP) Decision Support Tool” to aid spill scientists, students, environmental resource managers, spill responders and the public in understanding the response methods and the ramifications and consequences of their shoreline treatment options without the need to digest technical papers, large reports, or data bases. This MPRI SRP Decision Support Tool is intended to be a dynamic, interactive, multi-layered, geographically and seasonally-based model for shoreline oil spill response decision analyses. A goal of this interactive model is to move away from the traditional static format of learning from explanations in text reports and publications to an interactive tool that encourages its users to explore and fully understand the significance of the different environmental factors outlined in publications and data bases. Recent advances in web technology make this possible. The development of user interface platforms such as React, libraries such as D3, and notebook forms like Observable has created a palette of technologies that together make web application patterns such as Documodels a much more streamlined development process. The power of this medium is to convey a complex subject and to enable a user to grasp keen insights and so understand the consequences of intervention decisions.

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Lessons learned from the implementation of the near real-time S2S4E Decision Support Tool

10.5194/egusphere-egu21-15537 ◽

2021 ◽

Author(s):

Lluís Palma ◽

Andrea Manrique ◽

Llorenç Lledó ◽

Andria Nicodemou ◽

Pierre-Antoine Bretonnière ◽

...

Keyword(s):

Decision Support ◽

Real Time ◽

Climate Models ◽

Decision Support Tool ◽

Lessons Learned ◽

Cloud Infrastructure ◽

Time Service ◽

Support Tool ◽

Wide Range ◽

Prediction Systems

<p>Under the context of the H2020 S2S4E project, industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing during 18 months near real-time subseasonal and seasonal&#160; forecasts tailored to the specific needs of the renewable energy sector. The tool aimed to breach the last mile gap between climate information and the end-user by paying attention to the interaction with agents from the sector, already used to work with weather information, and willing to extend their forecasting horizon by incorporating climate predictions into their daily operations.</p><p>With this purpose, the tool gathered a heterogeneous dataset of seven different essential climate variables and nine energy indicators, providing for each of them bias-adjusted probabilistic information paired with a reference skill metric. To achieve this, data from state-of-the-art prediction systems and reanalysis needed to be downloaded and post-processed, fulfilling a set of quality requirements that ensure the proper functioning of the operational service. During the design, implementation, and testing phases, a wide range of scientific and technical choices had to be made, making clear the difficulties of transferring scientific research to a user-oriented real-time service. A brief showcase will be presented, exemplifying the different tools, methodologies, and best practices applied to the data workflow, together with a case study performed in Oracle&#8217;s cloud infrastructure. We expect that by making a clear description of the process and the problems encountered, we will provide a valuable experience for both, upcoming attempts of similar implementations, and the organizations providing data from climate models and reanalysis.</p>

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Integrated Decision Support Tool for Evaluating Transit and Land-Use Coordination Policies

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1604-04 ◽

1997 ◽

Vol 1604 (1) ◽

pp. 26-31

Author(s):

Thomas D. Fox ◽

William Bowlby

Keyword(s):

Land Use ◽

Decision Support ◽

Air Quality ◽

Decision Support Tool ◽

Quality Analysis ◽

Forecasting Model ◽

Transit Oriented Development ◽

Support Tool ◽

Travel Forecasting ◽

Wide Range

Policy makers in many urban areas have begun to embrace the principles of transit-oriented development as a means to create more livable communities and of light rail transit as a means to address congestion and air-quality problems. A policy-oriented screening tool for applying rigorous technical analyses to transit-oriented development policies to give decision makers meaningful information about a wide range of potential land-use, transit service and financing, and parking management policies is described. The decision support tool (DST) uses a simplified version of the regional travel forecasting model and includes an air-quality analysis module. The DST was validated to the Memphis regional travel forecasting model, and enhancements were incorporated for studying the effects of specific land-use, transit, and parking-policy assumptions. DST provides policy-related output data such as percentage of development in the corridor; transit ridership; annual transit operating cost, revenue, deficit, and cost recovery ratio; annual transit capital cost; and air-quality benefits. A sample application of the model for the Poplar Corridor in Memphis, Tennessee, is presented.

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