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Creating better models to predict subsurface water flow and transport

river soil

New framework improves the predictions of subsurface sediment permeability

February 19, 2020
February 17, 2020
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The Science
Co-authors of a paper in Water Resources Research led by PNNL researchers developed a new iterative data assimilation framework to more accurately describe the permeability of subsurface sediments in numerical models when using facies, a system that classifies dissimilar sediments into distinct geological units that share important features of interest to modelers. The iterative framework applies data from field observations and experiments to inform the delineation of facies at the start of each model run. Further refinements are achieved at each iteration through the application of statistical constraints that maintain geologic continuity among adjacent locations.

Distribution of Facies
Spatial distribution of three facies (red, yellow and blue colors) in a 2D vertical cross section of a 3D case. Figures show the new method provides a more accurate and continuous estimation of facies distribution compared to the conventional method. White colors in the figures are bore samples and black dots are the conditioning points selected by the new method.

The Impact
Spatial distribution of three facies (red, yellow and blue colors) in a 2D vertical cross section of a 3D case. Figures show the new method provides a more accurate and continuous estimation of facies distribution compared to the conventional method. White colors in the figures are bore samples and black dots are the conditioning points selected by the new method.

More realistic numerical representations of the permeability of subsurface sediments lead to improved predictions of groundwater flow and the concentration of constituents that are transported with the flow. The data assimilation framework can also be applied to estimate other subsurface properties from field measurements, or from data from other systems such as watersheds, as long as they can be categorized into a few discrete representative units.

Summary
Observational data on subsurface permeability is limited for most watersheds because of the impracticality of digging enough boreholes or wells to capture the heterogeneous nature of the subsurface environment. To solve for this limitation, researchers have widely adopted approaches that estimate permeability from field experiments such as a) measuring how water levels at a cluster of wells change when water is pumped at a nearby well, or b) monitoring how quickly a tracer released at one well reaches other wells in the aquifer. The U.S. Department of Energy’s Hanford 300 Area Integrated Field Research Challenge site, for example, is well characterized from data assimilation methods that were used to understand the long-term persistence of nuclear fuel fabrication wastes disposal from 1943 to 1975.

The use of a facies approach to segment the subsurface reduces complexity in numerical models by grouping heterogeneous sediments into distinct homogenous units defined by hydraulic, physical and or chemical properties. A major difficulty with existing facies-based approaches in numerical models is that each facies is treated as its own, independent unit. Therefore, these models fail to capture the spatial continuity of subsurface sediments. The authors of this paper developed a framework that maintains continuity between neighboring facies in numerical models and thus better reflects true subsurface geology, and thereby groundwater movement. The improvements come from an iterative data assimilation approach that incorporates direct and indirect data about subsurface permeability gathered from field observations and experiments at the start of each model run as well as the application of statistical constraints about subsurface geology. The data assimilation and statistical constraint steps are re-imposed for each iteration, leading to refined facies delineation. This framework reduces uncertainty about the spatial distribution of sediment types in the subsurface, which results in more accurate predictions of groundwater flow and constituent transport.

The authors evaluated the performance of the new framework on a two-dimensional, two-facies model and a three-dimensional, three-facies model of DOE’s well-characterized Hanford 300 Area that were conceptualized from borehole and field tracer experiments. The results of the research shows that the framework can identify facies spatial patterns and reproduce tracer breakthrough curves with much improved accuracy over facies-based approaches that lack spatial continuity constraints. With additional data, the authors say that the framework can also be used to categorize biogeochemical reactive units in an aquifer.

Contact
Xingyuan Chen, Earth Scientist, Xingyuan.Chen@pnnl.gov

Funding
Funding for this research came from DOE Office of Science BER, PNNL Subsurface Biogeochemical Research SFA.

Song, X., Chen, X., Ye, M., Dai, Z., Hammond, G., And Zachara, J.M. (2019). Delineating facies spatial distribution by integrating ensemble data assimilation and Indicator Geostatistics with level-set transformation. Water Resources Research, 55. https://doi.org/10.1029/2018WR023262

March 9, 2019
JANUARY 21, 2020
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Forensic Proteomics: Beyond DNA Profiling

A new book by PNNL biochemist Erick Merkley details forensic proteomics, a technique that directly analyzes proteins in unknown samples, in pursuit of making proteomics a widespread forensic method when DNA is missing or ambiguous.
DECEMBER 11, 2019
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PNNL to Lead New Grid Modernization Projects

PNNL will lead three new grid modernization projects funded by the Department of Energy. The projects focus on scalability and usability, networked microgrids, and machine learning for a more resilient, flexible and secure power grid.
DECEMBER 6, 2019
Web Feature

Converging on Coastal Science

Advancing a more collective understanding of coastal systems dynamics and evolution is a formidable scientific challenge. PNNL is meeting the challenge head on to inform decisions for the future.
NOVEMBER 26, 2019
Web Feature

Conquering Peak Power

PNNL’s Intelligent Load Control technology manages and adjusts electricity use in buildings when there’s peak demand on the power grid.

Moments Matter When It Comes to Modeling Rain

Rain

Getting rain properties correct in atmospheric models is critical for accurately representing the structure and evolution of cloud systems.

Improved representation of rain microphysics led to more accurate simulations of surface precipitation

March 12, 2019
October 22, 2019
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The Science
In atmospheric models, raindrop properties such as rainfall rate are usually described based on the raindrop size distribution. For example, heavy rain rates may have a wider raindrop size distribution than light rain. To improve predictions of rain that reaches the surface, the primary question has been, how can models better represent the evolution of raindrop size distribution in space and time in the atmosphere? A team led by researchers at the U.S. Department of Energy’s (DOE) Pacific Northwest National Laboratory improved the representation of rain microphysics by predicting the shape parameter of raindrop size distributions in a recently developed cloud microphysics scheme. They found that under a wide range of atmospheric conditions, their advanced representation delivered surface rain properties similar to those produced by a benchmark scheme, but with less computational resources.

The Impact
Because raindrops play a major role in the vertical redistribution of heat and moisture in the atmosphere, they are a critical component for modeling the structure and evolution of cloud systems such as mesoscale convective systems. These systems are major sources of heavy rain in the central United States. A proper representation of rain in numerical models is not only vital to predict surface precipitation, but also to accurately simulate environmental conditions and circulation patterns. The advanced rain microphysics representation from this study improves simulations of rain properties under various atmospheric conditions, and it can be used to increase accuracy of weather and climate models. The scheme will be implemented in DOE’s Energy Exascale Earth System Model (E3SM).

Summary
Cloud microphysics schemes in weather and climate models usually predict two moments—the total number and mass—of the raindrop size distribution. Researchers upgraded the Predicted Particle Properties (P3) cloud scheme by adding another predicted variable—shape parameter—for raindrop size distribution, turning the two-moment scheme into a three-moment scheme for raindrop representation. They also developed and incorporated a new parameterization for drop-drop collisions—when two drops collide—and the breakup of large drops into smaller ones. 
To evaluate those new developments, the research team tested them with an idealized rain model. The model simulated 450 rain scenarios that were initialized by different raindrop size distributions and environmental conditions. Researchers compared the simulated surface rain properties against those from a detailed and computationally costly reference scheme. The team found that, depending on initial rain intensity, up to 95 percent of simulations with the new developments produced raindrop sizes and surface rain rates within ±20 percent biases from the reference results. This was a considerable improvement from the original two-moment scheme, which only reached 4 percent using the same criteria for comparisons with the reference results. Sensitivity tests showed that both the added degree of freedom—the additional variable for raindrop size distribution—and the new process parameterization contributed to the improvements.

PI Contact
Jiwen Fan, Pacific Northwest National Laboratory, jiwen.fan@pnnl.gov  

Funding
This research was supported by the Climate Model Development and Validation program funded by the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science. Model simulations were performed using PNNL Institutional Computing.

Paukert M, J Fan, PJ Rasch, H Morrison, JA Milbrandt, J Shpund, and A. Khain. 2019. “Three-Moment Representation of Rain in a Bulk Microphysics Model.” Journal of Advances in Modeling Earth Systems 11(1):257−277, https://doi.org/10.1029/2018MS001512.

Influence of Groundwater Extraction Costs and Resource Depletion Limits on Simulated Global Nonrenewable Water Withdrawals over the 21st Century

Groundwater

Recent PNNL research suggests that rising groundwater extraction costs will force users to turn to other sources of water.

Global groundwater depletion is projected to peak and then decline during this century as costs of using it change

April 16, 2019
October 22, 2019
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The Science
Because water is a fundamental human need, estimating future supplies is important. Researchers at the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) coupled regionally varying groundwater availability and extraction cost estimates with continually adjusted demands for water in a simulation that covered multiple sectors around the world. As groundwater levels dropped, imposing greater capital and energy costs to bring water to the surface, modeled water use sectors responded by drawing from other water resources. These behaviors resulted in a marked peak and decline in the rate of global groundwater depletion.

The Impact
Previously it was assumed that the rate of global groundwater depletion would increase steadily over the 21st century as humans demanded more water—particularly for crop production. This work suggests that groundwater depletion may actually decline, because the increasing costs of pumping will force water users to adapt by turning to less expensive sources, which are often in regions where renewable water remains plentiful.

Summary
In many regions of the world, groundwater reserves are being depleted rapidly. This raises concerns for the sustainability of irrigated agriculture and global food supplies. It is therefore important to study groundwater depletion and possible exhaustion of water resources at a global scale. A problem for such analysis is the lack of detailed understanding of when a depleting resource becomes unviable for further exploitation. The question is not simply how much water is physically available; we need to know when the financial costs and environmental effects of extracting more groundwater render the resource unviable for human applications. To study these effects, PNNL researchers employed a global, gridded data set that specifies the cost of groundwater extraction as a function of depletion. Then, using the Global Change Assessment Model (GCAM), they simulated water users as economic decision makers to understand how they would adapt as extraction costs increased. Results indicated that future rates of global groundwater depletion would be heavily moderated by increasing extraction costs. Regions that depleted water to costly levels lost competitive advantage for crop production, which shifted to regions where water resources were less costly and more plentiful. The team concluded that extraction costs must be included in simulations for projections of global groundwater depletion to be reliable.

PI Contact
Leon Clarke, Pacific Northwest National Laboratory, leon.clarke@pnnl.gov

PNNL Contact
Mohamad Hejazi, Pacific Northwest National Laboratory, Mohamad.Hejazi@pnnl.gov  

Funding
This research was supported by the U.S. Department of Energy Office of Science, Biological and Environmental Research through the Multisector Dynamics, Earth and Environmental System Modeling Program. 

Turner SWD, M Hejazi, C Yonkofski, SH Kim, and P Kyle. 2019. “Influence of Groundwater Extraction Costs and Resource Depletion Limits on Simulated Global Nonrenewable Water Withdrawals Over the Twenty-First Century.” Earth’s Future 7(2):123−135. https://doi.org/10.1029/2018ef001105.

Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning

Bend

The BEND model can combine information on building technologies, climate, and population to forecast hourly building energy demand for regions the size of electric power balancing authorities. This calibration aligns the results with historical data to make them more accurate.

A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in building energy demand

June 6, 2019
October 22, 2019
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The Science
Aggregated building energy demand models, which are based on combining the outcomes of many individual building simulations, are an emerging tool for long-term energy planning at multiple spatial scales. They can be used to understand and project changes in building energy demand due to changes in population, climate, and building technologies. However, these models can be hard to calibrate because real-world data availability at the appropriate temporal, spatial, and sectoral scales is often limited. A new approach developed at the U.S. Department of Energy’s (DOE’s) Pacific Northwest National Laboratory (PNNL) allows these aggregate models to be calibrated at multiple scales. Researchers used this new method to calibrate PNNL’s Building ENergy Demand (BEND) aggregate model. Once calibrated, BEND successfully captured year-to-year changes in building energy demand due to changes in weather.

The Impact
Unlike more traditional statistical methods, physically based aggregate models such as BEND can fully capture the dynamic relationships between hourly building energy demand and population, climate, and building technologies. As a result, these models are valuable tools for understanding multisectoral dynamics. The new approach allows BEND and other aggregate models to be calibrated at the scale at which they will be applied, overcoming a key limitation of this class of models. Models such as BEND will improve model projections of future building energy demand at different scales and refine long-term energy planning through integration with grid operations and resource planning models.

Summary
PNNL’s BEND model is one of an emerging class of models designed to capture total and hourly building energy demand resulting from the aggregation of tens to hundreds of thousands of individual building simulations. Historically, these aggregate models have proven difficult to calibrate because there is a limited amount of target data available at relevant space, time, and sectoral scales. Researchers developed and demonstrated a novel approach to calibrate BEND, using approximately 100,000 individual simulations of DOE’s EnergyPlus model, against the best available data at the geographic scale of balancing authorities (electricity management subregions). Once calibrated, BEND captured year-to-year changes in total and peak building energy demand due to variations in weather within these areas. The study applied PNNL’s new calibration approach to the western United States, but the method can be applied to regions across the world with similar data and scale challenges. Researchers also suggested areas in which improved data collection and sharing would help to further refine these emerging models.

PI Contact
Jennie Rice, Pacific Northwest National Laboratory, jennie.rice@pnnl.gov
Ian Kraucunas, Pacific Northwest National Laboratory, ian.kraucunas@pnnl.gov 

Funding
This research was supported by the DOE Office of Science as part of research in the MultiSector Dynamics, Earth and Environmental System Modeling Program. A portion of the research was performed using PNNL’s Institutional Computing resources. 

Taylor ZT, Y Xie, CD Burleyson, N Voisin, and I Kraucunas. 2019. “A multi-scale calibration approach for process-oriented aggregated building energy demand models.” Energy and Buildings 191:82‒94. https://doi.org/10.1016/j.enbuild.2019.02.018.

A Global Hydrologic Framework to Accelerate Scientific Discovery

Hydro

A new version of the Xanthos global hydrology model enables researchers to rapidly address complex multisector dynamics science challenges, such as determining water scarcity impacts on land use, and could help accelerate energy-water-land research even more.

A new version of the Xanthos model enables researchers to rapidly address evolving energy-water-land science challenges

June 11, 2019
October 22, 2019
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The Science
Research in energy-water-land systems—and understanding how water scarcity can affect these systems—is rapidly progressing. To quantify changes in—and the effects of—future freshwater availability, a team from the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) developed the Xanthos global hydrology model, which can be used independently or as a part of a coupled modeling system. Researchers can use Xanthos to explore how different climate, socioeconomic, or energy scenarios affect regional and global water availability over the 21st century. In version 2 of the model, the PNNL team built a component-based framework to rapidly incorporate and test new hydrologic process modules—or code—within a robust environment, complete with diagnostics and calibration capabilities.

The Impact
Within Xanthos version 2, scientists can easily incorporate and test evolving hydrologic process models as components, or selectable, compatible parts of the Xanthos framework. For example, researchers can customize a specific configuration of components to address the role of snowmelt in characterizing drought or to explore the role of model assumptions in characterizing the uncertainty of complex coupled energy-water-land systems. The process of incorporating and testing models within the component-based framework is faster than what it would take to reproduce an entire configuration of models if users added their own from scratch. The usability of Xanthos version 2 enables researchers to rapidly address complex multisector dynamics science challenges, such as determining water scarcity impacts on land use, and could help accelerate energy-water-land research even more.

Summary
Xanthos, an open-source model written in Python, provides a solid foundation for continued advances in global water dynamics science. The model simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. Xanthos also can serve as the freshwater supply component of the Global Change Assessment Model (GCAM), also developed at PNNL. 
For Xanthos version 2, the PNNL team built upon the original version by creating a component-based framework in which users can swap out or extend previously existing core components of the model—potential evapotranspiration (PET), runoff generation, and river routing—without starting from scratch. For example, users can now swap out the potential evapotranspiration core component with one they think captures the dynamics of the systems and processes they are trying to model. The component-style architecture of Xanthos version 2 enables researchers to quickly incorporate and test the latest energy-water-land research in a stable modeling environment with prebuilt diagnostics. The PNNL team also created a robust default configuration that includes a calibration module, hydropower modules, and new PET modules. These enhancements are now available to the scientific community in Xanthos version 2, which can be downloaded from GitHub.

PI Contact
Mohamad Hejazi, Pacific Northwest National Laboratory, Mohamad.Hejazi@pnnl.gov

Funding
This research was supported by the U.S. Department of Energy Office of Science as part of research in the MultiSector Dynamics, Earth and Environmental System Modeling program.

Vernon CR, MI Hejazi, SWD Turner, Y Liu, CJ Braun, X Li, and RP Link. 2019. “A Global Hydrologic Framework to Accelerate Scientific Discovery.” Journal of Open Research Software 7(1):1, http://doi.org/10.5334/jors.245.

Making 1+1 Bigger than 2

modules

A study highlighted progress and pitfalls in coupling different modules within weather, climate, and Earth system models, demonstrating the pervasiveness of coupling problems in current models as well as highlighting recent progress.

A state-of-the-science review highlighted progress and pitfalls in coupling different modules within weather, climate, and Earth system models

February 18, 2019
October 22, 2019
Highlight

The Science
Modern computer models for weather, climate, and Earth systems contain numerous modules that simulate the complex workings of distinct physical and dynamical phenomena, such as ocean currents, atmospheric winds, clouds, and river flows. These individual modules are typically developed by separate groups of researchers and then connected to form a comprehensive model system. The two-step approach can lead to inaccurate results in current model simulations. An international team of researchers, including scientists from the U.S. Department of Energy’s Pacific Northwest National Laboratory, reviewed the state of the science in module coupling and identified possible ways to address some key challenges.

The Impact
Most model development work typically focuses on individual modules, resulting in a research gap in the simulation of weather and climate, which are the sum of many processes. This review paper demonstrates the pervasiveness of coupling problems in current models and highlights recent progress in module coupling. The paper also provides illustrative examples of coupling issues, such as insufficient frequency of information exchange between modules and double counting of processes by multiple modules. Such issues could, for example, strongly affect how much rain is predicted in a simulation of storms. The coupling issues must be overcome for each module to realize its full potential and improve the overall predictive skill of the entire model system. This is needed for the improvement of existing modules as well as in the design of new modules or model systems. 

Summary
The compartmentalization of model codes and development teams is natural and also necessary to manage the endeavor of modeling complex weather, climate, and Earth systems, which are influenced by many processes. As a result, three types of issues can occur: First, the artificial distinction between physical phenomena based on whether they can be spatially resolved or not can lead to double counting or undercounting of processes. Second, infrequent information exchange between interacting modules can result in biases, or offsets from observations, in the balance between them. Third, inconsistent approximations employed by different modules can lead to unintended violation of basic physical principles. The topic of coupling typically receives little attention during model development, and more evidence suggests that this gap in research is becoming a bottleneck in further improving the corresponding models.
Aimed at increasing awareness of such issues, this review describes the symptoms of coupling problems using examples from existing literature and discusses their root causes and possible methods for analyzing the problems. The paper reflects the findings of many research highlights from an international biennial workshop series on physics-dynamics coupling. Since the first workshop in 2014, this small but evolving event series has gained attention from international weather, climate and Earth system model development groups and research sponsors to join forward-thinking discussions and address the ubiquitous coupling problems. Topics discussed in the workshop series and the paper include: conceptual inconsistencies in model equations; numerical methods for solving the equations with computers; sensitivities of model results to choices made for the numerical calculations and strategies for analyzing such sensitivities; and methods for making the computations more efficient on supercomputers.

PNNL Contacts
Hui Wan, Pacific Northwest National Laboratory, Hui.Wan@pnnl.gov
L. Ruby Leung, Pacific Northwest National Laboratory, Ruby.Leung@pnnl.gov

Funding
The U.S. Department of Energy (DOE) Office of Science funded Hui Wan’s, Peter M. Caldwell’s, and Philip J. Rasch’s contributions to the paper as part of the Scientific Discovery through Advanced Computing (SciDAC) program. PNNL’s Linus Pauling Distinguished Postdoctoral Fellowship partially supported earlier contributions by Hui Wan. DOE Office of Science Grants DE-SC0006684 and DE-SC0003990 supported Christiane Jablonowski and Diana R. Thatcher. The DOE Office of Science, Biological and Environmental Research funded Koichi Sakaguchi’s and L. Ruby Leung’s contribution to this work as part of the Regional and Global Model Analysis, Earth and Environmental System Modeling program. Part of the numerical simulations presented in the paper used computational resources from the National Energy Research Scientific Computing Center (NERSC), a DOE user facility supported by the Office of Science under Contract DE-AC02-05CH11231, and PNNL Institutional Computing.

Gross M, H Wan, PJ Rasch, PM Caldwell, DL Williamson, D Klocke, C Jablonowski, DR Thatcher, N Wood, M Cullen, B Beare, M Willett, F Lemarié, E Blayo, S Malardel, P Termonia, A Gassmann, PH Lauritzen, H Johansen, CM Zarzycki, K Sakaguchi, and R Leung. 2018. “Physics–Dynamics Coupling in Weather, Climate, and Earth System Models: Challenges and Recent Progress.” Monthly Weather Review 146:3505–3544, https://doi.org/10.1175/MWR-D-17-0345.1.