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DOE OSTI2022

Evaluation of CMIP6 GCMs Over the CONUS for Downscaling Studies

Despite the necessity of Global Climate Models (GCMs) sub-selection in downscaling studies, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we develop a weighted averaging technique to remove the redundancy in the evaluation criteria and rank 37 GCMs from the sixth phase of the Coupled Models Intercomparison Project over the contiguous United States. GCMs are rated based on their average performance across 66 evaluation measures in the historical period (1981–2014) after each metric is weighted between zero and one, depending on its uniqueness. The robustness of the outcome is tested by repeating the process with the empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. A disparity in GCMs' behavior related to their sensitivity to the size of the evaluation suite is observed, highlighting the need for comprehensive multi-variable GCMs evaluation at varying timescales for determining their skillfulness over a region. The sub-selection goal is to use a representative set of skillful models over the region of interest without substantial overlap in their future climate responses and modeling errors in representing historical climate. Additional analyses of GCMs' independence and spread in their future projections provide the necessary information to objectively select GCMs while keeping all aspects of necessity in view.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Progress in Simulating the Quasi–Biennial Oscillation in CMIP Models

The quasi–biennial oscillation (QBO) of the zonal mean zonal wind is the primary mode of variability in the tropical lower stratosphere. The QBO is characterized by alternating easterly westerly shear layers that descend down from ~10 to 100 hPa. The QBO is also seen in lower stratospheric tropical temperature, water vapor, and ozone and affects tropospheric variability through various teleconnections. We examine here the progress in simulating the QBO in the Coupled Model Intercomparison Project (CMIP) models, more specifically in CMIP3, CMIP5, and CMIP6 models. We show that the number of models that are able to simulate the QBO has increased from 0 in CMIP3, to 5 in CMIP5, to 15 in CMIP6. While the number of models with an internally generated QBO has tripled from CMIP5 to CMIP6, the fidelity of the simulation averaged over the CMIP models has not improved. We show that CMIP5 and CMIP6 models represent the QBO period and latitudinal extent quite well; however the QBO amplitude is shifted upwards relative to observations resulting in large underestimation of QBO amplitude at all levels below 20 hPa. As a result, the underestimation of QBO amplitude in the lowermost stratosphere and lack of variations downward to the tropopause and below will likely impact the quality of teleconnections seen in the current generation Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Quantifying CanESM5 and EAMv1 sensitivities to Mt. Pinatubo volcanic forcing for the CMIP6 historical experiment

Large volcanic eruptions reaching the stratosphere have caused marked perturbations to the global climate including cooling at the Earth's surface, changes in large-scale circulation and precipitation patterns and marked temporary reductions in global ocean heat content. Many studies have investigated these effects using climate models; however, uncertainties remain in the modelled response to these eruptions. This is due in part to the diversity of forcing datasets that are used to prescribe the distribution of stratospheric aerosols resulting from these volcanic eruptions, as well as uncertainties in optical property derivations from these datasets. To improve this situation for the sixth phase of the Coupled Model Intercomparison Project (CMIP6), a two-step process was undertaken. First, a combined stratospheric aerosol dataset, the Global Space-based Stratospheric Aerosol Climatology (GloSSAC; 1979–2016), was constructed. Next, GloSSAC, along with information from ice cores and Sun photometers, was used to generate aerosol distributions, characteristics and optical properties to construct a more consistent stratospheric aerosol forcing dataset for models participating in CMIP6. This “version 3” of the stratospheric aerosol forcing has been endorsed for use in all contributing CMIP6 simulations. Recent updates to the underlying GloSSAC from version 1 to version 1.1 affected the 1991–1994 period and necessitated an update to the stratospheric aerosol forcing from version 3 to version 4. As version 3 remains the official CMIP6 input, quantification of the impact on radiative forcing and climate is both relevant and timely for interpreting results from experiments such as the CMIP6 historical simulations. This study uses two models, the Canadian Earth System Model version 5 (CanESM5) and the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1), to estimate the difference in instantaneous radiative forcing in simulated post-Pinatubo climate response when using version 4 instead of version 3. Differences in temperature, precipitation and radiative forcings are generally found to be small compared to internal variability. An exception to this is differences in monthly temperature anomalies near 24 km altitude in the tropics, which can be as large as 3 °C following the eruption of Mt. Pinatubo.

58 GEOSCIENCES↗
DOE OSTI2022

Understanding Uncertainties in Tropical Cyclone Rainfall Hazard Modeling Using Synthetic Storms

Tropical cyclone (TC) rainfall hazard assessment is subject to the bias in TC climatology estimation from climate simulations or synthetic downscaling. In this study, we investigate the uncertainty in TC rainfall hazard assessment induced by this bias using both rain gauge and radar observations and synthetic-storm-model-coupled TC rainfall simulations. We identify the storm’s maximum intensity, impact duration, and minimal distance to the site to be the three most important storm parameters for TC rainfall hazard, and the relationship between the important storm parameters and TC rainfall can be well captured by a physics-based TC rainfall model. The uncertainty in the synthetic rainfall hazard induced by the bias in TC climatology can be largely explained by the bias in the important storm parameters simulated by the synthetic storm model. Correcting the distribution of the most biased parameter may significantly improve rainfall hazard estimation. Bias correction based on the joint distribution of the important parameters may render more accurate rainfall hazard estimations; however, the general technical difficulties in resampling from high-dimensional joint probability distributions prevent more accurate estimations in some cases. The results of the study also support future investigation of the impact of climate change on TC rainfall hazards through the lens of future changes in the identified important storm parameters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Partial synchronization in networks of non-linearly coupled oscillators: The Deserter Hubs Model

We study the Deserter Hubs Model: a Kuramoto-like model of coupled identical phase oscillators on a network, where attractive and repulsive couplings are balanced dynamically due to nonlinearity of interactions. Under weak force, an oscillator tends to follow the phase of its neighbors, but if an oscillator is compelled to follow its peers by a sufficient large number of cohesive neighbors, then it actually starts to act in the opposite manner, i.e., in anti-phase with the majority. Analytic results yield that if the repulsion parameter is small enough in comparison with the degree of the maximum hub, then the full synchronization state is locally stable. Numerical experiments are performed to explore the model beyond this threshold, where the overall cohesion is lost. We report in detail partially synchronous dynamical regimes, like stationary phase-locking, multistability, periodic and chaotic states. Via statistical analysis of different network organizations like tree, scale-free, and random ones, we found a measure allowing one to predict relative abundance of partially synchronous stationary states in comparison to time-dependent ones.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations

In the United States transportation sector, Light-Duty Vehicles (LDVs) are the largest energy consumers and CO 2 emitters. Electrification of LDVs is posed as a potential solution, but SI engines can still contribute to decarbonization. Car manufacturers have turned to unconventional engine operation to increase the efficiency of Spark-Ignition (SI) engines and reduce the carbon emissions of their fleets. Dilute, lean, and stratified-charge engine operation has the potential for engine efficiency improvements at the expense of increased cyclic variability and combustion instability. At such demanding engine conditions, the spark ignition event is key for flame initiation and propagation and for enhanced combustion stability. Reliable and accurate spark ignition models can help design ignition systems that reduce cyclic variability. Multiple computational spark-ignition models exist that perform well under conventional conditions, but the underlying physics needs to be expanded, for unconventional engine operation. In this paper, a hybrid Lagrangian–Eulerian Spark-Ignition (LESI) model is coupled with different turbulent flame propagation models for engine simulations. LESI relies on Lagrangian arc tracking and Eulerian energy deposition. The LESI model is coupled with the Well-Stirred Reactor (WSR), Thickened Flame Model (TFM), and g-equation model and used to simulate several cycles of a Direct-Injection Spark-Ignition (DISI) engine using a commercial Computational Fluid Dynamics (CFD) engine solver. The results showcase the successful coupling of LESI with the combustion models. Global engine metrics, such as pressure and Apparent Heat Release Rate (AHRR), for each simulation setup are compared to experimental engine results, for validation. In addition, results highlight the successful prediction of spark channel movement by comparing simulation images to experimental optical engine images. Finally, the successful coupling of LESI to combustion models, making it a usable model in the engine modeling community, is emphasized and future development details are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6

The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). The representation of marine biogeochemistry has progressed within the current generation of Earth system models. However, it remains difficult to identify which model updates are responsible for a given improvement. In addition, the full potential of marine biogeochemistry in terms of Earth system interactions and climate feedback remains poorly examined in the current generation of Earth system models. Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Argo Observations of the Deep Mixing Band in the Southern Ocean: A Salinity Modeling Challenge

The Southern Ocean plays an important role in mediating oceanic uptake of CO 2 and heat due to a strong meridional overturning circulation. Gridded Argo float data for 2004–2017 were used to evaluate subsurface processes at the mixed layer depth (MLD) that occur in a narrow deep mixing band. Shifts in the value of the Turner Angle at the MLD indicate that early in the season the MLD deepens slowly as it encounters and is stabilized by a subsurface salt maximum. By September mixing has penetrated this salinity feature and the rate of deepening is faster once the MLD is deeper than the depth where the maximum salinity occurs (~150–200 m). This distinctive salinity layer is the result of surface Ekman transport of fresh water from the south and subsurface advection of high-salinity water from the north. Two configurations of the Community Earth System Model (CESM) ocean-ice forced hindcast experiments—one with 1° and the other with 0.1° horizontal resolution (Parallel Ocean Program low and high resolutions [POP-LR and POP-HR], respectively)—are compared with the Argo data for 2005–2009. POP-LR has a shallow MLD bias common to many Fifth Coupled Models Intercomparison Project (CMIP5) models, while POP-HR has a mix of deep and shallow MLD biases. While both models were able to replicate the large-scale processes leading to formation of a high-salinity layer, the salinity feature in POP-HR is too strong and deep. Neither model was able to replicate the vertical mixing processes leading to penetration of the subsurface salt maximum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mechanical and Thermal Properties for Uranium and U–6Nb Alloy from First-Principles Theory

Elasticity, lattice dynamics, and thermal expansion for uranium and U–6Nb alloy (elastic moduli) are calculated from density functional theory that is extended to include orbital polarization (DFT+OP). Introducing 12.5 at.% of niobium, substitutionally, in uranium softens all the cii elastic moduli, resulting in a significantly softer shear modulus (G). Combined with a nearly invariant bulk modulus (B), the quotient B/G increases dramatically for U–6Nb, suggesting a more ductile material. Lattice dynamics from a harmonic model coupled with a DFT+OP electronic structure is applied for α uranium, and the obtained phonon density of states compares well with inelastic neutron-scattering measurements. The Debye temperature associated with the lattice dynamics falls within the range of experimentally observed Debye temperatures and it also validates our quasi-harmonic (QH) phonon model. The QH Debye–Grüneisen phonon method is combined with a DFT+OP electronic structure and used to explore the anisotropic thermal expansion in α uranium. The anomalous negative thermal expansion (contraction) of the b lattice parameter of the α-phase orthorhombic cell is relatively well reproduced from a free-energy model consisting of QH-phonon and DFT+OP electronic structure contributions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Chemistry in an evolving protoplanetary disk: Effects on terrestrial planet composition

The composition of planets is largely determined by the chemical and dynamical evolution of the disk during planetesimal formation and growth. To predict the diversity of exoplanet compositions, previous works modeled planetesimal composition as the equilibrium chemical composition of a protoplanetary disk at a single time. However, planetesimals form over an extended period of time, during which elements sequentially condense out of the gas as the disk cools and are accreted onto planetesimals. To account for the evolution of the disk during planetesimal formation, we couple models of disk chemistry and dynamics with a prescription for planetesimal formation. We then follow the growth of these planetesimals into terrestrial planets with N-body simulations of late-stage planet formation to evaluate the effect of sequential condensation on the bulk composition of planets. We find that our model produces results similar to those of earlier models for disks with C/O ratios close to the solar value (0.54). However, in disks with C/O ratios greater than 0.8, carbon-rich planetesimals form throughout a much larger radial range of the disk. Furthermore, our model produces carbon-rich planetesimals in disks with C/O ratios as low as ∼0.65, which is not possible in the static equilibrium chemistry case. These results suggest that (1) there may be a large population of short-period carbon-rich planets around moderately carbon-enhanced stars (0.65 < C/O < 0.8) and (2) carbon-rich planets can form throughout the terrestrial planet region around carbon-rich stars (C/O > 0.8).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model

A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50°N–50°S at relatively high spatial (~12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is ~0.9 and the false alarm ratio is ~0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30°S–30°N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. Finally, there were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

flood modelling, flood detection, VIC, DRT, DRIVE,↗
DOE OSTI2025

Climate models project increasing precipitation in the US Southwest in 2024–2100

We analyzed the measured precipitation in the southwestern US and found that, from 1900 to 2024, precipitation decreased at an average rate of − 1.2 cm per year per 100 years. The most significant precipitation decreases occurred in the three-month period from February to April. At the same time, precipitation during the southwestern monsoon season (July, August, and September) remained relatively stable. The ensemble mean of all CMIP6 (Coupled Model Intercomparison Project phase 6) climate models, along with the regression model incorporating anthropogenic aerosol (AER) and Pacific Decadal Oscillation (PDO) as predictors, projects precipitation to increase from 2024 to 2100. This projected precipitation increase relies on the anticipated decrease in emissions of anthropogenic aerosols, which is associated with the transition from fossil fuel burning to renewable and nuclear energy sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Hybrid Inverse Parameter Identification of Fully Coupled Ductile Damage Model for Steel Sheet DP600 with Two Different Algorithms: Trust Region and Genetic Algorithms

Ductile damage model has been widely regarded as a valuable method to predict the failure of sheet metal. Based on the thermodynamic theory and continuum damage mechanics, the fully coupled ductile damage model can be developed, which also can better predict the initiation and growth of the fracture. But the identifications of model parameters with theoretical methodology are difficult due to the complex coupling relationships existing among all state variables. The inverse methodology is regarded as a good method to resolve the problem. In this paper, the recently proposed fully coupled ductile damage model is chosen to investigate the deformation behavior of DP600 steel, in which the mixed saturation isotropic and kinematic hardenings are taken into account and fully coupled with the ductile damage. During the identification process, the least square formula of the error between experimental and numerical results is selected as the target function. The trust region algorithm and genetic algorithm are used with the help of MATLAB for the identification of three damage parameters. Finally, by comparing the experimental and numerical results, the validations of two algorithms are proved. The efficiency of the optimization process with trust region algorithm is higher, but with lower accuracy. Meanwhile, the optimization process is greatly affected by the chosen initial values of the ductile damage parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Regional hydrologic response to climate change in the conterminous United States using high-resolution hydroclimate simulations

Despite the fact that Global Climate Model (GCM) outputs have been used to project hydrologic impacts of climate change using off-line hydrologic models for two decades, many of these efforts have been disjointed applications or at least calibrations have been focused on individual river basins and using a few of the available GCMs. This study improves upon earlier attempts by systematically projecting hydrologic impacts for the entire conterminous United States (US), using outputs from ten GCMs from the latest Coupled Model Intercomparison Project phase 5 (CMIP5) archive, with seamless hydrologic model calibration and validation techniques to produce a spatially and temporally consistent set of current hydrologic projections. The Variable Infiltration Capacity (VIC) model was forced with ten-member ensemble projections of precipitation and air temperature that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24 (~4 km) grid resolution for the baseline (1966 2005) and future (2011 2050) periods under the Representative Concentration Pathway 8.5. Based on regional analysis, the VIC model projections indicate an increase in winter and spring total runoff due to increases in winter precipitation of up to 20% in most regions of the US. However, decreases in snow water equivalent (SWE) and snow-covered days will lead to significant decreases in summer runoff with more pronounced shifts in the time of occurrence of annual peak runoff projected over the eastern and western US. In contrast, the central US will experience year-round increases in total runoff, mostly associated with increases in both extreme high and low runoff. Furthermore, the projected hydrological changes described in this study have implications for various aspects of future water resource management, including water supply, flood and drought preparation, and reservoir operation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Elastohydrodynamics of roll-to-roll UV-cure imprint lithography

Inextensible cylindrical shell theory and lubrication theory combine into a model for the elastohydrodynamics of a rolling-imprint modality of nanoimprint lithography (NIL). Foil-bearing theory describes the formation of the lubrication gap due to relative motion between a tensioned substrate and a rigid, cylindrical surface. Reproduction of the results of foil-bearing theory for both stiff and perfectly-flexible substrates validates this coupled model and reveals a highly predictable region of uniformity that provides low shear stress conditions ideal for UV-cure. Here, these results show theoretical limitations that are used to construct an operating window for predicting rolling-mode NIL process feasibility.

42 ENGINEERING↗
DOE OSTI2013

Modeling aerosols and their interactions with shallow cumuli during the 2007 CHAPS field study

The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is used to simulate relationships between aerosols and clouds in the vicinity of Oklahoma City during the June 2007 Cumulus Humilis Aerosol Processing Study (CHAPS). The regional scale simulation completed using 2 km horizontal grid spacing evaluates four important relationships between aerosols and shallow cumulus clouds observed during CHAPS. First, the model reproduces the trends of higher nitrate volume fractions in cloud droplet residuals compared to interstitial non-activated aerosols, as measured using the Aerosol Mass Spectrometer. Comparing simulations with cloud chemistry turned on and off, we show that nitric acid vapor uptake by cloud droplets explains the higher nitrate content of cloud droplet residuals. Second, as documented using an offline code, both aerosol water and other inorganics (OIN), which are related to dust and crustal emissions, significantly affect predicted aerosol optical properties. Reducing the OIN content of wet aerosols by 50% significantly improves agreement of model predictions with measurements of aerosol optical properties. Third, the simulated hygroscopicity of aerosols is too high as compared to their hygroscopicity derived from cloud condensation nuclei and particle size distribution measurements, indicating uncertainties associated with simulating size-dependent chemical composition and treatment of aerosol mixing state within the model. Fourth, the model reasonably represents the observations of the first aerosol indirect effect where pollutants in the vicinity of Oklahoma City increase cloud droplet number concentrations and decrease the droplet effective radius. While previous studies have often focused on cloud-aerosol interactions in stratiform and deep convective clouds, this study highlights the ability of regional-scale models to represent some of the important aspects of cloud-aerosol interactions associated with fields of short-lived shallow cumuli.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Phase II Nevada Water Resources Data, Modeling, and Visualization (DMV) Center (Final Report)

Water is unquestionably a critical resource throughout the United States. In the semi-arid west -- an area stressed by increase in human population and sprawl of the built environment -- water is the most important limiting resource. Crucially, science must understand factors that affect availability and distribution of water. To sustain growing consumptive demand, science needs to translate understanding into reliable and robust predictions of availability under weather conditions that could be average but might be extreme. These predictions are needed to support current and long-term planning. Similar to the role of weather forecast and climate prediction, water prediction over short and long temporal scales can contribute to resource strategy, governmental policy and municipal infrastructure decisions, which are arguably tied to the natural variability and unnatural change to climate. Change in seasonal and annual temperature, precipitation, snowmelt, and runoff affect the distribution of water over large temporal and spatial scales, which impact the risk of flooding and the groundwater recharge. Anthropogenic influences and impacts increase the complexity and urgency of the challenge. The goal of this project has been to develop a decision support framework of data acquisition, digital modeling, and 3D visualization. This integrated framework consists of tools for compiling, discovering and projecting our understanding of processes that control the availability and distribution of water. The framework is intended to support the analysis of the complex interactions between processes that affect water supply, from controlled availability to either scarcity or deluge. The developed framework enables DRI to promote excellence in water resource management, particularly within the Lake Tahoe basin. In principle, this framework could be replicated for other watersheds throughout the United States. Phase II of this project builds upon the research conducted during Phase I, in which the hydrologic framework was investigated and the development initiated. Phase II concentrates on practical implementation of the earlier work but emphasizes applications to the hydrology of the Lake Tahoe basin. Phase 1 efforts have been refined and extended by creating a toolset for geographic information systems (GIS) that is usable for disparate types of geospatial and geo-referenced data. The toolset is intended to serve multiple users for a variety of applications. The web portal for internet access to hydrologic and remotely sensed product data, prototyped in Phase I, has been significantly enhanced. The portal provides high performance access to LANDSAT-derived data using techniques developed during the course of the project. The portal is interactive, and supports the geo-referenced display of hydrologic information derived from remotely sensed data, such as various vegetative indices used to calculate water consumption. The platform can serve both internal and external constituencies using inter-operating infrastructure that spans both sides of the DRI firewall. The platform is intended grow its supported data assets and to serve as a template for replication to other geographic areas. An unanticipated development during the project was the use of ArcGIS software on a new computer system, called the IBM PureSytems, and the parallel use of the systems for faster, more efficient image processing. Additional data, independent of the portal, was collected within the Sagehen basin and provides detailed information regarding the processes that control hydrologic responses within mountain watersheds. The newly collected data include elevation, evapotranspiration, energy balance and remotely sensed snow-pack data. A Lake Tahoe basin hydrologic model has been developed, in part to help predict the hydrologic impacts of climate change. The model couples both the surface and subsurface hydrology, with the two components having been independently calibrated. Results from the coupled simulations involving both surface water and groundwater processes show that it is possible to fairly accurately simulate lake effects and water budget variables over a wide range of dry and wet cycles in the historical record. The Lake Tahoe basin is representative of the hydrology, topography and climate throughout the Sierra Nevada Range, and the entire model development is prototypical of the efforts required to replicate the decision support framework to other locales. The Lake Tahoe model in particular, could allow water managers to evaluate more accurately components of the water budget (ET, runoff, groundwater, etc) and to answer important questions regarding water resources in northern Nevada. This report discusses the geographic scale and the hydrologic complexity of the calibrated model developed as part of this project, as well as simulation results for historical and future climate projects To enable human-driven data exploration and discovery, de novo software for a globalized rendering module that extends the capability of our evolving custom visualization engine from Phase I (called SMEngine) has been developed. The new rendering component, called Horizon, supports terrain rendering capable of displaying and interrogating both remotely sensed and modeled data. The development of Horizon necessitated adaptation of the visualization engine to allow extensible integration of components such as the global rendering module and support for associated features. The resulting software is general in its GIS capability, but a specific Lake Tahoe visualization application suitable for immersive decision support in the DRIVE6 virtual reality facility has been developed. During the development, various features to enhance the value of the visualization experience were explored, including the use of hyperspectral image overlays. An over-arching goal of the visualization aspect of the project has been to develop and demonstrate the CAVE (CAVE Automatic Virtual Environment) as a practical tool for hydrologic research.

54 ENVIRONMENTAL SCIENCES↗