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.
The complexity of water resource issues, its interconnectedness to other systems, and the involvement of competing stakeholders often overwhelm decision-makers and inhibit the creation of clear management strategies. While a range of modeling tools and procedures exist to address these problems, they tend to be case specific and generally emphasize either a quantitative and overly analytic approach or present a qualitative dialogue-based approach lacking the ability to fully explore consequences of different policy decisions. The integration of these two approaches is needed to drive toward final decisions and engender effective outcomes. Given these limitations, the Computer Assisted Dispute Resolution system (CADRe) was developed to aid in stakeholder inclusive resource planning. This modeling and negotiation system uniquely addresses resource concerns by developing a spatially varying system dynamics model as well as innovative global optimization search techniques to maximize outcomes from participatory dialogues. Ultimately, the core system architecture of CADRe also serves as the cornerstone upon which key scientific innovation and challenges can be addressed.
Public mediated resource planning is quickly becoming the norm rather than the exception. Unfortunately, supporting tools are lacking that interactively engage the public in the decision-making process and integrate over the myriad values that influence water policy. In the pages of this report we document the first steps toward developing a specialized decision framework to meet this need; specifically, a modular and generic resource-planning ''toolbox''. The technical challenge lies in the integration of the disparate systems of hydrology, ecology, climate, demographics, economics, policy and law, each of which influence the supply and demand for water. Specifically, these systems, their associated processes, and most importantly the constitutive relations that link them must be identified, abstracted, and quantified. For this reason, the toolbox forms a collection of process modules and constitutive relations that the analyst can ''swap'' in and out to model the physical and social systems unique to their problem. This toolbox with all of its modules is developed within the common computational platform of system dynamics linked to a Geographical Information System (GIS). Development of this resource-planning toolbox represents an important foundational element of the proposed interagency center for Computer Aided Dispute Resolution (CADRe). The Center's mission is to manage water conflict through the application of computer-aided collaborative decision-making methods. The Center will promote the use of decision-support technologies within collaborative stakeholder processes to help stakeholders find common ground and create mutually beneficial water management solutions. The Center will also serve to develop new methods and technologies to help federal, state and local water managers find innovative and balanced solutions to the nation's most vexing water problems. The toolbox is an important step toward achieving the technology development goals of this center.
Time domain reflectometry (TDR) operates by propagating a radar frequency electromagnetic pulse down a transmission line while monitoring the reflected signal. As the electromagnetic pulse propagates along the transmission line, it is subject to impedance by the dielectric properties of the media along the transmission line (e.g., air, water, and sediment), reflection at dielectric discontinuities (e.g., air-water or water-sediment interface), and attenuation by electrically conductive materials (e.g., salts and clays). Taken together, these characteristics provide a basis for integrated stream monitoring, specifically, concurrent measurement of stream stage, channel profile, and aqueous conductivity. Requisite for such application is a means of extracting the desired stream parameters from measured TDR traces. Analysis is complicated by the fact that interface location and aqueous conductivity vary concurrently and multiple interfaces may be present at any time. For this reason a physically based multisection model employing the S11 scatter function and Debeye parameters for dielectric dispersion and loss is used to analyze acquired TDR traces. Here we explore the capability of this multisection modeling approach for interpreting TDR data acquired from complex environments, such as encountered in stream monitoring. A series of laboratory tank experiments was performed in which the depth of water, depth of sediment, and conductivity were varied systematically. Comparisons between modeled and independently measured data indicate that TDR measurements can be made with an accuracy of {+-} 3.4 x 10{sup -3} m for sensing the location of an air-water or water-sediment interface and {+-} 7.4% of actual for the aqueous conductivity.
How might the quality of a city's delivered water be compromised through natural or malevolent causes? What are the consequences of a contamination event? What water utility assets are at greatest risk to compromise? Utility managers have been scrambling to find answers to these questions since the events of 9/11. However, even before this date utility mangers were concerned with the potential for system contamination through natural or accidental causes. Unfortunately, an integrated tool for assessing both the threat of attack/failure and the subsequent consequence is lacking. To help with this problem we combine Markov Latent Effects modeling for performing threat assessment calculations with the widely used pipe hydraulics/transport code, EPANET, for consequences analysis. Together information from these models defines the risk posed to the public due to natural or malevolent contamination of a water utility system. Here, this risk assessment framework is introduced and demonstrated within the context of vulnerability assessment for water distribution systems.
Time domain reflectometry (TDR) operates by propagating a radar frequency electromagnetic pulse down a transmission line while monitoring the reflected signal. As the electromagnetic pulse propagates along the transmission line, it is subject to impedance by the dielectric properties of the media along the transmission line (e.g., air, water, sediment), reflection at dielectric discontinuities (e.g., air-water or water-sediment interface), and attenuation by electrically conductive materials (e.g., salts, clays). Taken together, these characteristics provide a basis for integrated stream monitoring; specifically, concurrent measurement of stream stage, channel profile and aqueous conductivity. Here, we make novel application of TDR within the context of stream monitoring. Efforts toward this goal followed three critical phases. First, a means of extracting the desired stream parameters from measured TDR traces was required. Analysis was complicated by the fact that interface location and aqueous conductivity vary concurrently and multiple interfaces may be present at any time. For this reason a physically based multisection model employing the S11 scatter function and Cole-Cole parameters for dielectric dispersion and loss was developed to analyze acquired TDR traces. Second, we explored the capability of this multisection modeling approach for interpreting TDR data acquired from complex environments, such as encountered in stream monitoring. A series of laboratory tank experiments were performed in which the depth of water, depth of sediment, and conductivity were varied systematically. Comparisons between modeled and independently measured data indicate that TDR measurements can be made with an accuracy of {+-}3.4x10{sup -3} m for sensing the location of an air/water or water/sediment interface and {+-}7.4% of actual for the aqueous conductivity. Third, monitoring stations were sited on the Rio Grande and Paria rivers to evaluate performance of the TDR system under normal field conditions. At the Rio Grande site (near Central Bridge in Albuquerque, New Mexico) continuous monitoring of stream stage and aqueous conductivity was performed for 6 months. Additionally, channel profile measurements were acquired at 7 locations across the river. At the Paria site (near Lee's Ferry, Arizona) stream stage and aqueous conductivity data were collected over a 4-month period. Comparisons drawn between our TDR measurements and USGS gage data indicate that the stream stage is accurate within {+-}0.88 cm, conductivity is accurate within {+-}11% of actual, and channel profile measurements agree within {+-}1.2 cm.
Social and ecological scientists emphasize that effective natural resource management depends in part on understanding the dynamic relationship between the physical and non-physical process associated with resource consumption. In this case, the physical processes include hydrological, climatological and ecological dynamics, and the non-physical process include social, economic and cultural dynamics among humans who do the resource consumption. This project represents a case study aimed at modeling coupled social and physical processes in a single decision support system. In central New Mexico, individual land use decisions over the past five decades have resulted in the gradual transformation of the Middle Rio Grande Valley from a primarily rural agricultural landscape to a largely urban one. In the arid southwestern U.S., the aggregate impact of individual decisions about land use is uniquely important to understand, because scarce hydrological resources will likely limit the viability of resulting growth and development trajectories. This decision support tool is intended to help planners in the area look forward in their efforts to create a collectively defined 'desired' social landscape in the Middle Rio Grande. Our research question explored the ways in which socio-cultural values impact decisions regarding that landscape and associated land use. Because of the constraints hydrological resources place on land use, we first assumed that water use, as embodied in water rights, was a reasonable surrogate for land use. We thought that modeling the movement of water rights over time and across water source types (surface and ground) would provide planners with insight into the possibilities for certain types of decisions regarding social landscapes, and the impact those same decisions would have on those landscapes. We found that water rights transfer data in New Mexico is too incomplete and inaccurate to use as the basis for the model. Furthermore, because of its lack of accuracy and completeness, water rights ownership was a poor indicator of water and land usage habits and patterns. We also found that commitment among users in the Middle Rio Grande Valley is to an agricultural lifestyle, not to a community or place. This commitment is conditioned primarily by generational cohort and past experience. If conditions warrant, many would be willing to practice the lifestyle elsewhere. A related finding was that sometimes the pressure to sell was not the putative price of the land, but the taxes on the land. These taxes were, in turn, a function of the level of urbanization of the neighborhood. This urbanization impacted the quality of the agricultural lifestyle. The project also yielded some valuable lessons regarding the model development process. A facilitative and collaborative style (rather than a top-down, directive style) was most productive with the inter-disciplinary , inter-institutional team that worked on the project. This allowed for the emergence of a process model which combined small, discipline- and/or task-specific subgroups with larger, integrating team meetings. The project objective was to develop a model that could be used to run test scenarios in which we explored the potential impact of different policy options. We achieved that objective, although not with the level of success or modeling fidelity which we had hoped for. This report only describes very superficially the results of test scenarios, since more complete analysis of scenarios would require more time and effort. Our greatest obstacle in the successful completion of the project was that required data were sparse, of poor quality, or completely nonexistent. Moreover, we found no similar modeling or research efforts taking place at either the state or local level. This leads to a key finding of this project: that state and local policy decisions regarding land use, development, urbanization, and water resource allocation are being made with minimal data and without the benefit of economic or social policy analysis.
Water resource scarcity around the world is driving the need for the development of simulation models that can assist in water resources management. Transboundary water resources are receiving special attention because of the potential for conflict over scarce shared water resources. The Rio Grande/Rio Bravo along the U.S./Mexican border is an example of a scarce, transboundary water resource over which conflict has already begun. The data collection and modeling effort described in this report aims at developing methods for international collaboration, data collection, data integration and modeling for simulating geographically large and diverse international watersheds, with a special focus on the Rio Grande/Rio Bravo. This report describes the basin, and the data collected. This data collection effort was spatially aggregated across five reaches consisting of Fort Quitman to Presidio, the Rio Conchos, Presidio to Amistad Dam, Amistad Dam to Falcon Dam, and Falcon Dam to the Gulf of Mexico. This report represents a nine-month effort made in FY04, during which time the model was not completed.