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Results for “Geoscientific Model Development”

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At least 19 recordsLinked to original sources
USGS2011

Pliocene Model Intercomparison Project (PlioMIP): experimental design and boundary conditions (Experiment 2)

The Palaeoclimate Modelling Intercomparison Project has expanded to include a model intercomparison for the mid-Pliocene warm period (3.29 to 2.97 million yr ago). This project is referred to as PlioMIP (the Pliocene Model Intercomparison Project). Two experiments have been agreed upon and together compose the initial phase of PlioMIP. The first (Experiment 1) is being performed with atmosphere-only climate models. The second (Experiment 2) utilises fully coupled ocean-atmosphere climate models. Following on from the publication of the experimental design and boundary conditions for Experiment 1 in Geoscientific Model Development, this paper provides the necessary description of differences and/or additions to the experimental design for Experiment 2.

Geoscientific Model Development↗
USGS2026

The path to FAIR research models: Lessons learned

Numerical modeling of Earth surface processes emerged as an important scientific tool in the late 1960s to mid-1970s, driven by the development of finite element methods in computer science. These advancements, initially applied in civil engineering, enabled scientists to simulate complex geological phenomena. At that time, models were often only described in publications, access was limited to researchers with direct connections to the developers, and the code was rarely documented for reuse, limiting their application beyond the original research context. The FAIR principles (Findability, Accessibility, Interoperability, and Reusability) as applied to data began to take shape in the 21st century with the rise of open science, digital repositories, and standardized data sharing frameworks. In the late 2010s, grassroots movements began to apply some of the FAIRness goals to numerical models. Subsequently, more formalized FAIR model principles were developed that addressed the specific needs of the scientific modeling community, resulting in the formulation of the FAIR principles for research software (FAIR4RS). In this study, we examine the development and implementation of strategies by two geoscience research infrastructures – the CSDMS (Community Surface Dynamics Modeling System) Model Repository and the U.S. Geological Survey Model Catalog – to enhance the FAIRness of models guided by FAIR4RS. Some of the development and implementation efforts described predate the formalization of FAIR and FAIR4RS principles, making this an ongoing and adaptive process. We evaluate the temporal progression towards increased FAIR4RS alignment across three phases of research infrastructure development: prototype, refinement, and growth and iteration. Although certain principles were more straightforward to implement early in prototypes of the catalog infrastructures, others required broader community collaboration during refinement, and some continue to pose practical challenges in the growth and iteration phase. By tracing these dynamics, our aim is to provide insights that can guide other modeling initiatives in effectively adopting FAIR4RS principles within their communities.

Geoscientific Model Development↗
USGS2018

GSFLOW-GRASS v1.0.0: GIS-enabled hydrologic modeling of coupled groundwater–surface-water systems

The importance of water moving between the atmosphere and aquifers has led to efforts to develop and maintain coupled models of surface water and groundwater. However, developing inputs to these models is usually time-consuming and requires extensive knowledge of software engineering, often prohibiting their use by many researchers and water managers, thus reducing these models' potential to promote science-driven decision-making in an era of global change and increasing water resource stress. In response to this need, we have developed GSFLOW–GRASS, a bundled set of open-source tools that develops inputs for, executes, and graphically displays the results of GSFLOW, the U.S. Geological Survey's coupled groundwater and surface-water flow model. In order to create a robust tool that can be widely implemented over diverse hydro(geo)logic settings, we built a series of GRASS GIS extensions that automatically discretizes a topological surface-water flow network that is linked with an underlying gridded groundwater domain. As inputs, GSFLOW–GRASS requires at a minimum a digital elevation model, a precipitation and temperature record, and estimates of channel parameters and hydraulic conductivity. We demonstrate the broad applicability of the toolbox by successfully testing it in environments with varying degrees of drainage integration, landscape relief, and grid resolution, as well as the presence of irregular coastal boundaries. These examples also show how GSFLOW–GRASS can be implemented to examine the role of groundwater–surface-water interactions in a diverse range of water resource and land management applications.

Geoscientific Model Development↗
USGS2020

Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model

The coupled biophysical interactions between submerged aquatic vegetation (SAV), hydrodynamics (currents and waves), sediment dynamics, and nutrient cycling have long been of interest in estuarine environments. Recent observational studies have addressed feedbacks between SAV meadows and their role in modifying current velocity, sedimentation, and nutrient cycling. To represent these dynamic processes in a numerical model, the presence of SAV and its effect on hydrodynamics (currents and waves) and sediment dynamics was incorporated into the open-source Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. In this study, we extend the COAWST modeling framework to account for dynamic changes of SAV and associated epiphyte biomass. Modeled SAV biomass is represented as a function of temperature, light, and nutrient availability. The modeled SAV community exchanges nutrients, detritus, dissolved inorganic carbon, and dissolved oxygen with the water-column biogeochemistry model. The dynamic simulation of SAV biomass allows the plants to both respond to and cause changes in the water column and sediment bed properties, hydrodynamics, and sediment transport (i.e., a two-way feedback). We demonstrate the behavior of these modeled processes through application to an idealized domain and then apply the model to a eutrophic harbor where SAV dieback is a result of anthropogenic nitrate loading and eutrophication. These cases demonstrate an advance in the deterministic modeling of coupled biophysical processes and will further our understanding of future ecosystem change.

Geoscientific Model Development↗
USGS2019

Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model

Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modelling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different Plant Function Types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrush-steppe. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP), using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach: 1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. 2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. 3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two Eddy Covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent.

Great Basin↗
USGS2019

A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants.

Geoscientific Model Development↗
USGS2014

Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model

A new process-based model TRIPLEX-GHG was developed based on the Integrated Biosphere Simulator (IBIS), coupled with a new methane (CH 4 ) biogeochemistry module (incorporating CH 4 production, oxidation, and transportation processes) and a water table module to investigate CH 4 emission processes and dynamics that occur in natural wetlands. Sensitivity analysis indicates that the most sensitive parameters to evaluate CH 4 emission processes from wetlands are r (defined as the CH 4 to CO 2 release ratio) and Q 10 in the CH 4 production process. These two parameters were subsequently calibrated to data obtained from 19 sites collected from approximately 35 studies across different wetlands globally. Being heterogeneously spatially distributed, r ranged from 0.1 to 0.7 with a mean value of 0.23, and the Q 10 for CH 4 production ranged from 1.6 to 4.5 with a mean value of 2.48. The model performed well when simulating magnitude and capturing temporal patterns in CH 4 emissions from natural wetlands. Results suggest that the model is able to be applied to different wetlands under varying conditions and is also applicable for global-scale simulations.

Geoscientific Model Development↗
USGS2022

A framework for ensemble modelling of climate change impacts on lakes worldwide: The ISIMIP lake sector.

Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios for ISIMIP phases 2 and 3. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various representative greenhouse gas concentration pathways (RCPs), all consistently bias-corrected on a 0.5 ∘ × 0.5 ∘ global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and using uncalibrated models for 17 500 lakes defined for all global grid cells containing lakes. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.

Geoscientific Model Development↗
USGS2019

Paleo calendar-effect adjustments in time-slice and transient climate-model simulations (PaleoCalAdjust v1.0): Impact and strategies for data analysis

The “paleo calendar effect” is a common expression for the impact that changes in the length of months or seasons over time, related to changes in the eccentricity of Earth's orbit and precession, have on the analysis or summarization of climate-model output. This effect can have significant implications for paleoclimate analyses. In particular, using a “fixed-length” definition of months (i.e., defined by a fixed number of days), as opposed to a “fixed-angular” definition (i.e., defined by a fixed number of degrees of the Earth's orbit), leads to comparisons of data from different positions along the Earth's orbit when comparing paleo with modern simulations. This effect can impart characteristic spatial patterns or signals in comparisons of time-slice simulations that otherwise might be interpreted in terms of specific paleoclimatic mechanisms, and we provide examples for 6, 97, 116, and 127 ka. The calendar effect is exacerbated in transient climate simulations in which, in addition to spatial or map-pattern effects, it can influence the apparent timing of extrema in individual time series and the characterization of phase relationships among series. We outline an approach for adjusting paleo simulations that have been summarized using a modern fixed-length definition of months and that can also be used for summarizing and comparing data archived as daily data. We describe the implementation of this approach in a set of Fortran 90 programs and modules (PaleoCalAdjust v1.0).

Geoscientific Model Development↗
USGS2022

Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not

The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.

Geoscientific Model Development↗
USGS2024

NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps

Models of rocky-coast erosion help us understand the physical phenomena that control coastal morphology and evolution, infer the processes shaping coasts in remote environments, and evaluate risk from natural hazards and future climate change. Existing models, however, are highly complex, are computationally expensive, and depend on many input parameters; this limits our ability to explore planform erosion of rocky coasts over long timescales (thousands to millions of years) and over a range of conditions. In this paper, we present a simplified cellular model of coastline evolution in closed basins through uniform erosion and wave-driven erosion. Uniform erosion is modeled as a constant rate of retreat. Wave erosion is modeled as a function of fetch, the distance over which the wind blows to generate waves, and the angle between the incident wave and the shoreline. This reduced-complexity model can be used to evaluate how a detachment-limited coastal landscape reflects climate, sea-level history, material properties, and the relative influence of different erosional processes.

Geoscientific Model Development↗
USGS2025

Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0

Models of coastal barrier geomorphic and ecologic change are valuable tools for understanding and predicting when, where, and how barriers evolve and transition between ecogeomorphic states. Few existing models of barrier systems are designed to operate over spatiotemporal scales congruous with effective management practices (i.e., decades/kilometers, referred to herein as “mesoscales”), incorporate important ecogeomorphic feedbacks, and provide probabilistic projections of future change. Here, we present a new numerical model designed to address these gaps by explicitly yet efficiently simulating coupled aeolian, marine, vegetation, and shoreline components of barrier evolution over spatiotemporal scales relevant to management. The Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) simulates subaerial ecomorphologic change of undeveloped barrier systems over kilometers and decades using meter-scale spatial resolution and weekly time steps. MEEB applies simplified parameterizations to represent and couple key ecogeomorphic processes: dune growth, vegetation expansion and mortality, beach and foredune erosion, barrier overwash, and shoreline and shoreface change. The model is parameterized and calibrated with observed elevation, vegetation, and water level data for a case study site of North Core Banks, NC, USA. Simulated ecogeomorphic change in model hindcasts agrees well with observations, demonstrating both favorable skill scores and qualitatively correct behavior. We also describe an additional model framework for producing probabilistic projections that account for uncertainties related to future forcing conditions and intrinsic stochastic dynamics and demonstrate the probabilistic framework's utility with example forecast simulations. As a mesoscale model, MEEB is designed to investigate questions about future barrier ecogeomorphic change of moderate complexity, offering semi-qualitative predictions and semi-quantitative explanations. For example, MEEB can be used to investigate how climate-induced shifts in ecological composition may alter the likelihood of morphologic impacts or to generate probabilistic projections of ecogeomorphic state change.

Geoscientific Model Development↗
USGS2017

Sensitivity analysis of a coupled hydrodynamic-vegetation model using the effectively subsampled quadratures method

Coastal hydrodynamics can be greatly affected by the presence of submerged aquatic vegetation. The effect of vegetation has been incorporated into the Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modeling System. The vegetation implementation includes the plant-induced three-dimensional drag, in-canopy wave-induced streaming, and the production of turbulent kinetic energy by the presence of vegetation. In this study, we evaluate the sensitivity of the flow and wave dynamics to vegetation parameters using Sobol' indices and a least squares polynomial approach referred to as Effective Quadratures method. This method reduces the number of simulations needed for evaluating Sobol' indices and provides a robust, practical, and efficient approach for the parameter sensitivity analysis. The evaluation of Sobol' indices shows that kinetic energy, turbulent kinetic energy, and water level changes are affected by plant density, height, and to a certain degree, diameter. Wave dissipation is mostly dependent on the variation in plant density. Performing sensitivity analyses for the vegetation module in COAWST provides guidance for future observational and modeling work to optimize efforts and reduce exploration of parameter space.

Geoscientific Model Development↗
USGS2011

Evaluation of a present-day climate simulation with a new coupled atmosphere-ocean model GENMOM

We present a new, non-flux corrected AOGCM, GENMOM, that combines the GENESIS version 3 atmospheric GCM (Global Environmental and Ecological Simulation of Interactive Systems) and MOM2 (Modular Ocean Model version 2) nominally at T31 resolution. We evaluate GENMOM by comparison with reanalysis products (e.g., NCEP2) and three models used in the IPCC AR4 assessment. GENMOM produces a global temperature bias of 0.6 ◦C. Atmospheric features such as the jet stream structure and major semi-permanent sea level pressure centers are well simulated as is the mean planetary-scale wind structure that is needed to produce the correct position of stormtracks. Most ocean surface currents are reproduced except where they are not resolvable at T31 resolution. Overall, GENMOM captures reasonably well the observed gradients and spatial distributions of annual surface temperature and precipitation and the simulations are on par with other AOGCMs. Deficiencies in the GENMOM simulations include a warm bias in the surface temperature over the southern oceans, a split in the ITCZ and weaker-than-observed overturning circulation.

Geoscientific Model Development↗
USGS2013

Towards a publicly available, map-based regional software tool to estimate unregulated daily streamflow at ungauged rivers

Streamflow information is critical for addressing any number of hydrologic problems. Often, streamflow information is needed at locations that are ungauged and, therefore, have no observations on which to base water management decisions. Furthermore, there has been increasing need for daily streamflow time series to manage rivers for both human and ecological functions. To facilitate negotiation between human and ecological demands for water, this paper presents the first publicly available, map-based, regional software tool to estimate historical, unregulated, daily streamflow time series (streamflow not affected by human alteration such as dams or water withdrawals) at any user-selected ungauged river location. The map interface allows users to locate and click on a river location, which then links to a spreadsheet-based program that computes estimates of daily streamflow for the river location selected. For a demonstration region in the northeast United States, daily streamflow was, in general, shown to be reliably estimated by the software tool. Estimating the highest and lowest streamflows that occurred in the demonstration region over the period from 1960 through 2004 also was accomplished but with more difficulty and limitations. The software tool provides a general framework that can be applied to other regions for which daily streamflow estimates are needed.

Geoscientific Model Development↗
USGS2015

RRAWFLOW: Rainfall-Response Aquifer and Watershed Flow Model (v1.15)

The Rainfall-Response Aquifer and Watershed Flow Model (RRAWFLOW) is a lumped-parameter model that simulates streamflow, spring flow, groundwater level, or solute transport for a measurement point in response to a system input of precipitation, recharge, or solute injection. I introduce the first version of RRAWFLOW available for download and public use and describe additional options. The open-source code is written in the R language and is available at http://sd.water.usgs.gov/projects/RRAWFLOW/RRAWFLOW.html along with an example model of streamflow. RRAWFLOW includes a time-series process to estimate recharge from precipitation and simulates the response to recharge by convolution, i.e., the unit-hydrograph approach. Gamma functions are used for estimation of parametric impulse-response functions (IRFs); a combination of two gamma functions results in a double-peaked IRF. A spline fit to a set of control points is introduced as a new method for estimation of nonparametric IRFs. Several options are included to simulate time-variant systems. For many applications, lumped models simulate the system response with equal accuracy to that of distributed models, but moreover, the ease of model construction and calibration of lumped models makes them a good choice for many applications (e.g., estimating missing periods in a hydrologic record). RRAWFLOW provides professional hydrologists and students with an accessible and versatile tool for lumped-parameter modeling.

Geoscientific Model Development↗
USGS2013

Improving predictive power of physically based rainfall-induced shallow landslide models: a probablistic approach

Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters are analyzed statistically, and compared to the original (deterministic) model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, i.e. the Frontignano (Italy) and the Mukilteo (USA) areas, respectively. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the Message Passing Interface (MPI) on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

Geoscientific Model Development and Discussions↗