Recently, Sandia National Laboratories and General Motors cooperated on the development of the Biofuels Deployment Model (BDM) to assess the feasibility, implications, limitations, and enablers of producing 90 billion gallons of ethanol per year by 2030. Leveraging the past investment, a decision support model based on the BDM is being developed to assist investors, entrepreneurs, and decision makers in evaluating the costs and benefits associated with biofuels development in the U.S.-Mexico border region. Specifically, the model is designed to assist investors and entrepreneurs in assessing the risks and opportunities associated with alternative biofuels development strategies along the U.S.-Mexico border, as well as, assist local and regional decision makers in understanding the tradeoffs such development poses to their communities. The decision support model is developed in a system dynamics framework utilizing a modular architecture that integrates the key systems of feedstock production, transportation, and conversion. The model adopts a 30-year planning horizon, operating on an annual time step. Spatially the model is disaggregated at the county level on the U.S. side of the border and at the municipos level on the Mexican side. The model extent includes Luna, Hildalgo, Dona Anna, and Otero counties in New Mexico, El Paso and Hudspeth counties in Texas, and the four munipos along the U.S. border in Chihuahua. The model considers a variety of feedstocks; specifically, algae, gitropha, castor oil, and agricultural waste products from chili and pecans - identifying suitable lands for these feedstocks, possible yields, and required water use. The model also evaluates the carbon balance for each crop and provides insight into production costs including labor demands. Finally, the model is fitted with an interactive user interface comprised of a variety of controls (e.g., slider bars, radio buttons), descriptive text, and output graphics allowing stakeholders to directly explore the tradeoffs between alternative biofuels development scenarios.
This document outlines ways to more effectively communicate with U.S. Federal decision makers by outlining the structure, authority, and motivations of various Federal groups, how to find the trusted advisors, and how to structure communication. All three branches of Federal governments have decision makers engaged in resolving major policy issues. The Legislative Branch (Congress) negotiates the authority and the resources that can be used by the Executive Branch. The Executive Branch has some latitude in implementation and prioritizing resources. The Judicial Branch resolves disputes. The goal of all decision makers is to choose and implement the option that best fits the needs and wants of the community. However, understanding the risk of technical, political and/or financial infeasibility and possible unintended consequences is extremely difficult. Primarily, decision makers are supported in their deliberations by trusted advisors who engage in the analysis of options as well as the day-to-day tasks associated with multi-party negotiations. In the best case, the trusted advisors use many sources of information to inform the process including the opinion of experts and if possible predictive analysis from which they can evaluate the projected consequences of their decisions. The paper covers the following: (1) Understanding Executive and Legislative decision makers - What can these decision makers do? (2) Finding the target audience - Who are the internal and external trusted advisors? (3) Packaging the message - How do we parse and integrate information, and how do we use computer simulation or models in policy communication?
Currently, electrical power generation uses about 140 billion gallons of water per day accounting for over 39% of all freshwater withdrawals thus competing with irrigated agriculture as the leading user of water. Coupled to this water use is the required pumping, conveyance, treatment, storage and distribution of the water which requires on average 3% of all electric power generated. While water and energy use are tightly coupled, planning and management of these fundamental resources are rarely treated in an integrated fashion. Toward this need, a decision support framework has been developed that targets the shared needs of energy and water producers, resource managers, regulators, and decision makers at the federal, state and local levels. The framework integrates analysis and optimization capabilities to identify trade-offs, and 'best' alternatives among a broad list of energy/water options and objectives. The decision support framework is formulated in a modular architecture, facilitating tailored analyses over different geographical regions and scales (e.g., national, state, county, watershed, NERC region). An interactive interface allows direct control of the model and access to real-time results displayed as charts, graphs and maps. Ultimately, this open and interactive modeling framework provides a tool for evaluating competing policy and technical options relevant to the energy-water nexus.
IAMG 2009 - Computational Methods for the Earth, Energy and Environmental Sciences
Klise, Katherine A.; Mckenna, Sean A.; Tidwell, Vincent C.; Lane, Jonathan W.; Weissmann, Gary S.; Wawrzyniec, Tim F.; Nichols, Elizabeth M.
While connectivity is an important aspect of heterogeneous media, methods to measure and simulate connectivity are limited. For this study, we use natural aquifer analogs developed through lidar imagery to track the importance of connectivity on dispersion characteristics. A 221.8 cm by 50 cm section of a braided sand and gravel deposit of the Ceja Formation in Bernalillo County, New Mexico is selected for the study. The use of two-point (SISIM) and multipoint (Snesim and Filtersim) stochastic simulation methods are then compared based on their ability to replicate dispersion characteristics using the aquifer analog. Detailed particle tracking simulations are used to explore the streamline-based connectivity that is preserved using each method. Connectivity analysis suggests a strong relationship between the length distribution of sand and gravel facies along streamlines and dispersion characteristics.
Water quality often limits the potential uses of scarce water resources in semiarid and arid regions. To best manage water quality one must understand the sources and sinks of both solutes and water to the river system. Nutrient concentration patterns can identify source and sink locations, but cannot always determine biotic processes that affect nutrient concentrations. Modeling tools can provide insight into these large-scale processes. To address questions about large-scale nitrogen removal in the Middle Rio Grande, NM, we created a system dynamics nitrate model using an existing integrated surface water--groundwater model of the region to evaluate our conceptual models of uptake and denitrification as potential nitrate removal mechanisms. We modeled denitrification in groundwater as a first-order process dependent only on concentration and used a 5% denitrification rate. Uptake was assumed to be proportional to transpiration and was modeled as a percentage of the evapotranspiration calculated within the model multiplied by the nitrate concentration in the water being transpired. We modeled riparian uptake as 90% and agricultural uptake as 50% of the respective evapotranspiration rates. Using these removal rates, our model results suggest that riparian uptake, agricultural uptake and denitrification in groundwater are all needed to produce the observed nitrate concentrations in the groundwater, conveyance channels, and river as well as the seasonal concentration patterns. The model results indicate that a total of 497 metric tons of nitrate-N are removed from the Middle Rio Grande annually. Where river nitrate concentrations are low and there are no large nitrate sources, nitrate behaves nearly conservatively and riparian and agricultural uptake are the most important removal mechanisms. Downstream of a large wastewater nitrate source, denitrification and agricultural uptake were responsible for approximately 90% of the nitrogen removal.
Solute plumes are believed to disperse in a non-Fickian manner due to small-scale heterogeneity and variable velocities that create preferential pathways. In order to accurately predict dispersion in naturally complex geologic media, the connection between heterogeneity and dispersion must be better understood. Since aquifer properties can not be measured at every location, it is common to simulate small-scale heterogeneity with random field generators based on a two-point covariance (e.g., through use of sequential simulation algorithms). While these random fields can produce preferential flow pathways, it is unknown how well the results simulate solute dispersion through natural heterogeneous media. To evaluate the influence that complex heterogeneity has on dispersion, we utilize high-resolution terrestrial lidar to identify and model lithofacies from outcrop for application in particle tracking solute transport simulations using RWHet. The lidar scan data are used to produce a lab (meter) scale two-dimensional model that captures 2-8 mm scale natural heterogeneity. Numerical simulations utilize various methods to populate the outcrop structure captured by the lidar-based image with reasonable hydraulic conductivity values. The particle tracking simulations result in residence time distributions used to evaluate the nature of dispersion through complex media. Particle tracking simulations through conductivity fields produced from the lidar images are then compared to particle tracking simulations through hydraulic conductivity fields produced from sequential simulation algorithms. Based on this comparison, the study aims to quantify the difference in dispersion when using realistic and simplified representations of aquifer heterogeneity.