Evaluating the impacts of farmers’ behaviors on a hypothetical agricultural water market based on double auction

Agricultural water markets are considered effective instruments to mitigate the impacts of water scarcity and to increase crop production. However, previous studies have limited understanding of how farmers’ behaviors affect the performance of water markets. This study develops an agent-based model to explicitly incorporate farmers’ behaviors, namely irrigation behavior (represented by farmers’ sensitivity to soil water deficit k) and bidding behavior (represented by farmers’ rent seeking l and learning rate b), in a hypothetical water market based on a double auction. The model is applied to the Guadalupe River Basin in Texas to simulate a hypothetical agricultural water market under various hydrological conditions. It is found that the joint impacts of the behavioral parameters on the water market are strong and complex. In particular, among the three behavioral parameters, k affects the water market potential and its impacts on the performance of the water market are significant under most scenarios. The impacts of l or b on the performance of the water market depend on the other two parameters. The water market could significantly increase crop production only when the following conditions are satisfied: (1) k is small and (2) l is small and/or b is large. The first condition requires efficient irrigation scheduling, and the second requires well-developed water market institutions that provide incentives to bid true valuation of water permits

Real-Time Water Decision Support Services For Droughts

Through application of computational methods and an integrated information system, real-time data and river modeling systems can help decision makers identify more effective actions for management practice. The purpose of this study is to develop a real-time decision support model to recommend optimal curtailments during water shortages for decision makers. To enable ease of use and re-use, the workflows (i.e., analysis and model steps) of the real-time decision support model are published as Web services delivered through an internet browser, including model inputs, a published workflow service, and visualized outputs. The model consists of two major components: the real-time river flow prediction system and the optimization model. The RAPID model, which is a river routing model developed at University of Texas Austin for parallel computation of river discharge, is applied to predict real-time river flow rates. The workflow of the RAPID model has been built and published as a Web application that allows non-technical users to remotely execute the model and visualize results as a service through a simple Web interface. An optimization model is being developed to provide real-time water withdrawal decision support using the RAPID output and the clustering particle swarm optimization algorithm (CPSO) and genetic algorithm methods. The model is being tested using historical drought data from 2011 in the Upper Guadalupe River Basin in Texas. The objective of the optimization is to assist the Texas Commission on Environmental Quality (TCEQ) in minimizing the total daily curtailment hours of all permit holders, with constraints on user seniority and ecological river flow. The optimization model workflows is linked to the RAPID model workflow to provide real-time water decision support services. Finally, visualization of the output using Bing-map and WorldWide Telescope helps decision makers predict outcomes from alternative weather or policy scenarios

Community-based metadata integration for environmental research

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732The ability to aggregate information about environmental data and analysis processes across tools and services and across projects provides a powerful capability for discovering resources and coordinating projects and a means to convey the rich, community-scale context of data. In this paper, we summarize the science and engineering use cases motivating the metadata and provenance infrastructure of the Environmental Cyberinfrastructure Demonstrator (ECID) Cyberenvironment project at the National Center for Supercomputing Applications (NCSA) and discuss the requirements driving our system design. The user-level metadata and provenance capabilities being developed within ECID are described and we summarize the team’s experiences in building them, and show how our experience can inform the continuing development and refinement of collaborative environmental science environments

Communitybased Metadata Integration for Environmental Research

ABSTRACT The ability to aggregate information about environmental data and analysis processes across tools and services and across projects provides a powerful capability for discovering resources and coordinating projects and a means to convey the rich, community-scale context of data. In this paper, we summarize the science and engineering use cases motivating the metadata and provenance infrastructure of the Environmental Cyberinfrastructure Demonstrator (ECID) Cyberenvironment project at the National Center for Supercomputing Applications (NCSA) and discuss the requirements driving our system design. The user-level metadata and provenance capabilities being developed within ECID are described and we summarize the team's experiences in building them, and show how our experience can inform the continuing development and refinement of collaborative environmental science environments

Standing together for reproducibility in large-scale computing: report on reproducibility@XSEDE

This is the final report on reproducibility@xsede, a one-day workshop held in conjunction with XSEDE14, the annual conference of the Extreme Science and Engineering Discovery Environment (XSEDE). The workshop's discussion-oriented agenda focused on reproducibility in large-scale computational research. Two important themes capture the spirit of the workshop submissions and discussions: (1) organizational stakeholders, especially supercomputer centers, are in a unique position to promote, enable, and support reproducible research; and (2) individual researchers should conduct each experiment as though someone will replicate that experiment. Participants documented numerous issues, questions, technologies, practices, and potentially promising initiatives emerging from the discussion, but also highlighted four areas of particular interest to XSEDE: (1) documentation and training that promotes reproducible research; (2) system-level tools that provide build- and run-time information at the level of the individual job; (3) the need to model best practices in research collaborations involving XSEDE staff; and (4) continued work on gateways and related technologies. In addition, an intriguing question emerged from the day's interactions: would there be value in establishing an annual award for excellence in reproducible research? Overvie

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state‐of‐the‐art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water and natural hazard management are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined

A New Framework for Adaptive Sampling and Analysis During Long- Term Monitoring and Remedial Action Management

The Argonne team has gathered available data on monitoring wells and measured hydraulic heads from the Argonne 317/319 site and sent it to UIUC. Xiaodong Li, a research assistant supported by the project, has reviewed the data and is beginning to fit spatiotemporal statistical models to it. Another research assistant, Yonas Demissie, has gotten the site's Modflow model working and is developing a transport model that will be used to generate artificial data. Abhishek Singh, a third research assistant supported by the project, has performed a literature review on inverse modeling and is receiving training on the software that will be used in this project (D2K). He has also created two models of user preferences and successfully implemented them with an interactive genetic algorithm on test functions. Meghna Babbar, the fourth research assistant supported by the project, has created an interactive genetic algorithm code and initial user interface in D2K. Gayathri Gopalakrishnan, the last research assistant who is partially supported by the project, has collected and analyzed data from the phytoremediation systems at the 317/319 site. She has found good correlations between concentrations in the ground water and in branches of the trees, which indicates excellent promise for using the trees as cost-effective long-term monitoring of the contaminants

A New Framework for Adaptive Sampling and Analysis During Long-Term Monitoring and Remedial Action Management

The Argonne team has gathered available data on monitoring wells and measured hydraulic heads from the Argonne 317/319 site and sent it to UIUC. Xiaodong Li, a research assistant supported by the project, has reviewed the data and has fit initial spatiotemporal statistical models to it. Another research assistant, Yonas Demissie, has completed generation of the artificial data that will be used for model development and testing. In order to generate the artificial data a detailed groundwater flow and contaminant transport model was developed based upon characteristics of the 317/319 site. The model covers a multi-year time horizon that includes both before and after planting of the trees. As described in the proposal, the artificial data is created by adding ''measurement'' error to the ''true'' value from the numerical model. To date, only simple white noise error models have been considered. He is now reviewing the literature and beginning to develop a hierarchical modeling approach for the artificial data. Abhishek Singh, a third research assistant supported by the project, is implementing learning models for learning users preferences in an interactive genetic algorithm for solving the inverse problem. Meghna Babbar, the fourth research assistant supported by the project, has been improving the user interface for the interactive genetic algorithm and preparing a long-term monitoring design problem for testing the approach. Gayathri Gopalakrishnan, the last research assistant who is partially supported by the project, has collected substantial data from the 317/319 phytoremediation site at Argonne and has begun learning approaches for modeling these data

Evaluating the impacts of farmers’ behaviors on a hypothetical agricultural water market based on double auction

Agricultural water markets are considered effective instruments to mitigate the impacts of water scarcity and to increase crop production. However, previous studies have limited understanding of how farmers’ behaviors affect the performance of water markets. This study develops an agent-based model to explicitly incorporate farmers’ behaviors, namely irrigation behavior (represented by farmers’ sensitivity to soil water deficit k) and bidding behavior (represented by farmers’ rent seeking l and learning rate b), in a hypothetical water market based on a double auction. The model is applied to the Guadalupe River Basin in Texas to simulate a hypothetical agricultural water market under various hydrological conditions. It is found that the joint impacts of the behavioral parameters on the water market are strong and complex. In particular, among the three behavioral parameters, k affects the water market potential and its impacts on the performance of the water market are significant under most scenarios. The impacts of l or b on the performance of the water market depend on the other two parameters. The water market could significantly increase crop production only when the following conditions are satisfied: (1) k is small and (2) l is small and/or b is large. The first condition requires efficient irrigation scheduling, and the second requires well-developed water market institutions that provide incentives to bid true valuation of water permits