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At least 37 records · Page 2Linked to original sources
USGS2022

A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty

An open-source tool has been developed to facilitate constrained single- and multi-objective optimization under uncertainty (CMOU) analyses. The tool uses the well-known PEST interface protocols to communicate with the underlying forward simulation, making it non-intrusive. The tool contains a built-in parallel run manager to make use of heterogeneous and distributed computing resources. Several popular and well-known evolutionary algorithms are implemented and can be combined with a range of approaches to represent uncertainty in model-derived constraint/objective values. These attributes serve to address the current barrier to adopt advanced CMOU analyses for a wide range of decision-support problems across the environmental modeling spectrum. We demonstrate the capabilities of the CMOU tool on a well-known analytical benchmark problem that we augmented to include uncertainty, as well as on a synthetic density-dependent coastal groundwater management benchmark problem. Both demonstrations highlight the importance of explicitly accounting for uncertainty to convey risk and reliability in pareto-optimal design.

Environmental Modelling & Software↗
USGS2022

Increasing the uptake of ecological model results in policy decisions to improve biodiversity outcomes

Models help decision-makers anticipate the consequences of policies for ecosystems and people; for instance, improving our ability to represent interactions between human activities and ecological systems is essential to identify pathways to meet the 2030 Sustainable Development Goals. However, use of modeling outputs in decision-making remains uncommon. We share insights from a multidisciplinary National Socio-Environmental Synthesis Center working group on technical, communication, and process-related factors that facilitate or hamper uptake of model results. We emphasize that it is not simply technical model improvements, but active and iterative stakeholder involvement that can lead to more impactful outcomes. In particular, trust- and relationship-building with decision-makers are key for knowledge-based decision making. In this respect, nurturing knowledge exchange on the interpersonal (e.g., through participatory processes), and institutional level (e.g., through science-policy interfaces across scales), represent promising approaches. To this end, we offer a generalized approach for linking modeling and decision-making.

Environmental Modelling & Software↗
USGS2022

Combining process-based and data-driven approaches to forecast beach and dune change

Producing accurate hindcasts and forecasts with coupled models is challenging due to complex parameterizations that are difficult to ground in observational data. We present a calibration workflow that utilizes a series of machine learning algorithms paired with Windsurf, a coupled beach-dune model (Aeolis, the Coastal Dune Model, and XBeach), to produce hindcasts and forecasts of morphologic change along Bogue Banks, North Carolina. Neural networks paired with genetic algorithms allow us to fine tune calibration parameters for the hindcast, and then a long short-term memory neural network, trained on the hindcast, produces a 4-year forecast. We compare our hindcasts to observations from 2016 to 2017 and find they successfully reproduce observed modes of dune and beach change except for seaward growth of the dune face. We compare our forecasts to observations from 2016 to 2020 and find that they produce reasonably accurate predictions of dune change except when there are significant instances of erosion during the forecast period.

North Carolina↗
USGS2022

Climate matching with the climatchR R package

Climate matching allows comparisons of climatic conditions between different locations to understand location and species range climatic suitability. The approach may be used as part of horizon scanning exercises such as those conducted for invasive species. We implemented the CLIMATCH algorithm into an R package, climatchR . The package allows automated and scripted climate matching exercises across all steps from downloading data to summarizing species climate matches. We also show how climatchR may be used with high-throughput computing to process many species. For example, we were able to calculate climate scores for over 8,000 species in less than 3 days using this package. This automation allows high-throughput processing of species data, a new development for improving the efficiency and speed of climate matching and horizon scanning.

Environmental Software & Modeling↗
USGS2024

Comparing single and multiple objective constrained optimization algorithms for tuning a groundwater remediation system

Groundwater flow and particle tracking models are critical tools to simulate the natural system, contaminant fate and transport, and effects of remediation. Constrained optimization uses models to systematically explore the interplay between remedial design and contaminant fate, considering uncertainty. Sequential Linear Programming (SLP) provides a design alternative addressing a single goal (e.g. maximum hydraulic containment, maximum mass removal). Multi-objective algorithms like Nondominated Sorting Genetic Algorithm (NSGA-II) explore the tradeoffs among such objectives and more (e.g. cost, public-supply well contamination). We explore both approaches at a contaminated site in Long Island, New York USA. We compare the algorithms and ramifications on results. NSGA-II explores, at additional computational cost, explicit tradeoffs among multiple objectives, providing additional insights relative to SLP. The NGSA-II algorithm allows for graphical consideration of three objectives. SLP decision variables often settle at predetermined bounds. Bounds assignment thus differs from parameter estimation; bounds must be acceptable rather than safeguards.

New York↗
USGS2024

A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA

Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R 2 ) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2 km. Hence, we interpret our point-scale R 2 of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison.

Delaware, New Jersey, New York, Pennsylvania↗
USGS2024

A probabilistic approach to training machine learning models using noisy data

Machine learning (ML) models are increasingly popular in environmental and hydrologic modeling, but they typically contain uncertainties resulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approach that combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisy data, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distinguish between outliers and erroneous data. The new algorithm recognizes that there is no unique way to split the training data into noisy and clean data, and thus produces an ensemble of plausible splits. The algorithm successfully detected noisy data in synthetic benchmark problems with varying complexity and a real-world public supply water withdrawal dataset. The algorithm is generic and flexible, making it suitable for application across a broad range of hydrologic and environmental disciplines.

Environmental Modelling & Software↗
USGS2025

METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed

Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis of monitoring and modeling data, named “Monitored and Expected Total Reduction Indicator for the Chesapeake (METRIC),” was developed to provide spatially explicit trends for the subwatersheds of the Chesapeake Bay. METRIC contains up-to-date information on nitrogen, phosphorus, and sediment at 83, 66, and 66 stations, respectively, which can help watershed managers gauge expectations on the trajectory and pace of progress at localized scales. These results were further synthesized to better understand the spatial patterns of the response classes ( i.e. , agreement between the expected and monitored trends) across the Chesapeake Bay watershed.

Chesapeake Bay watershed↗
USGS2025

Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm

Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R 2 for annual 7-day maximum = 0.76; R 2 for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.

contiguous United States↗
USGS2026

Multivariate SWAT streamflow and surface water storage calibration enables upper Mississippi River Basin wetland change scenarios

Surface water storage (e.g., wetlands, lakes) is not typically considered in hydrological model calibrations. We tested a multivariate calibration process, incorporating Sentinel-1 and -2 surface water storage, for a Soil and Water Assessment Tool model across the 0.5 million km 2 Upper Mississippi River Basin. While 19% of the 2000 parameter sets adequately simulated discharge (Kling-Gupta efficiency >0.5), only 5% also adequately simulated surface water storage (mean absolute error <2 m), reducing model output uncertainty. Using the best calibrated model, we found that changes in surface water storage capacity most strongly affected discharge during the first annual peak flow (i.e., floods), when storage was filling. Increases in upstream surface water storage capacity resulted in projected decreases in peak flow and flashiness, with changes persisting downstream to the watershed outlet. Our findings demonstrate the importance of surface water storage in multivariate model calibration processes to inform river discharge and flood impact predictions.

Illinois, Indiana, Iowa, Minnesota, Missouri, Wisc↗
USGS2013

International summit on integrated environmental modeling

This report describes the International Summit on Integrated Environmental Modeling (IEM), held in Reston, VA, on 7th-9th December 2010. The meeting brought together 57 scientists and managers from leading US and European government and non-governmental organizations, universities and companies together with international organizations convened over a number of years, including: the US Environmental Protection Agency (USEPA) workshop on Collaborative Approaches to Integrated Modeling: Better Integration for Better Decisionmaking (December, 2008); the AGU Fall Meeting, San Francisco (December 2009); and the International Congress on Environmental Modeling and Software (July 2010). From these meetings there is now recognition that many separate communities are involved in developing IEM. The aim of the Summit was to bring together two key groupings, the US and Europe, with the intention of creating a community open to all.

Conference Paper↗
USGS2020

Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis

PEST++ Version 5 extends and enhances the functionality of the PEST++ Version 3 software suite, providing environmental modeling practitioners access to updated Version 3 tools as well as new tools to support decision making with environmental models. Version 5 of PEST++ includes tools for global sensitivity analysis (PESTPP-SEN); least-squares parameter estimation with integrated first-order, second-moment parameter and forecast uncertainty estimation (PESTPP-GLM); an iterative, localized ensemble smoother (PESTPP-IES); and a tool for management optimization under uncertainty (PESTPP-OPT). Additionally, all PEST++ Version 5 tools have a built-in fault-tolerant, multithreaded parallel run manager and are model independent, using the same protocol as the widely used PEST software suite. PEST++ Version 5 is consistent with PEST++ Version 3 conventions and design philosophy. The software’s emphasis continues to target efficient and optimized algorithms that have proven beneficial in decision-support settings and can accommodate large, highly parameterized problems. Expanded and new capabilities are now available to express uncertainty using Monte Carlo and analytical uncertainty approaches and allow evaluation of thousands to millions of parameters. New management optimization capabilities in Version 5 also allow environmental models to be used to answer management questions using multiple societal constraints in a risk-based framework. The PEST++ Version 5 software suite can be compiled for Microsoft Windows® and Unix-based operating systems such as Apple and Linux®; the source code is available with a Microsoft Visual Studio® 2019 solution; and CMake support for all three operating system is also provided. PEST++ Version 5 continues to build a foundation for an open-source framework capable of producing model-independent, robust, and efficient decision-support tools for large environmental models. The functionality of each of the PEST++ tools are demonstrated on a simple example problem. Implications of decisions used when using the PEST++ suite tools are also discussed.

Techniques and Methods↗
USGS2020

A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support

Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the “stochastic impulse-response emulator” (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context.

Environmental Modelling and Software↗
USGS2022

NWTOPT — A hyperparameter optimization approach for selection of environmental model solver settings

Hyperparameter optimization approaches were applied to improve performance and accuracy of groundwater flow models. Freely available new software, NWTOPT, is described that uses Tree of Parzen Estimators (TPE) and Random Search algorithms to optimize MODFLOW-NWTs solver settings. We ran 3500 trials on a steady-state and transient model. To quantify the performance of candidate solver settings, we defined a loss function based on time elapsed and mass balance error of the MODFLOW-NWT forward run. Before optimization the steady- state model ran in ~12 min and the transient model ran in ~5 h with acceptable mass balance error (<1%). After optimization runtimes were reduced to ~2.7 min (steady state) and ~48 min (transient) with errors below 0.1%. In both cases TPE found hyperparameters that resulted in faster running and lower error models than those found by Random Search. The time to complete the optimization trials was also shorter with the TPE algorithm.

Environmental Modelling and Software↗
USGS2007

Section 4. The GIS Weasel User's Manual

INTRODUCTION The GIS Weasel was designed to aid in the preparation of spatial information for input to lumped and distributed parameter hydrologic or other environmental models. The GIS Weasel provides geographic information system (GIS) tools to help create maps of geographic features relevant to a user's model and to generate parameters from those maps. The operation of the GIS Weasel does not require the user to be a GIS expert, only that the user have an understanding of the spatial information requirements of the environmental simulation model being used. The GIS Weasel software system uses a GIS-based graphical user interface (GUI), the C programming language, and external scripting languages. The software will run on any computing platform where ArcInfo Workstation (version 8.0.2 or later) and the GRID extension are accessible. The user controls the processing of the GIS Weasel by interacting with menus, maps, and tables. The purpose of this document is to describe the operation of the software. This document is not intended to describe the usage of this software in support of any particular environmental simulation model. Such guides are published separately.

Techniques and Methods↗
USGS2023

Heavy: Software for forward-modeling gravity change from MODFLOW output

Fortran software, named Heavy, was developed to simulate gravity change due to water-storage change in MODFLOW groundwater models. Heavy is compatible with MODFLOW-2005 and MODFLOW-NWT models using the layer-property flow or upstream weighting packages. All of the necessary information for the gravity calculation—the geometry of the model cells, the storage coefficient, and head change—is present within the existing MODFLOW model files and no additional information is necessary. Gravity change is calculated at each time step, for each layer, at user specified locations or at a grid of hypothetical positions across the model. The software has been validated using analytical gravity solutions and three example MODFLOW models are included for demonstration. Heavy leverages the input/output routines from MODFLOW and is orders of magnitude faster than previous efforts using interpreted languages such as Python or MATLAB. The objective of the software is to facilitate repeat microgravity field measurements for groundwater-flow model calibration.

Environmental Modelling and Software↗
USGS2015

A bootstrap method for estimating uncertainty of water quality trends

Estimation of the direction and magnitude of trends in surface water quality remains a problem of great scientific and practical interest. The Weighted Regressions on Time, Discharge, and Season (WRTDS) method was recently introduced as an exploratory data analysis tool to provide flexible and robust estimates of water quality trends. This paper enhances the WRTDS method through the introduction of the WRTDS Bootstrap Test (WBT), an extension of WRTDS that quantifies the uncertainty in WRTDS-estimates of water quality trends and offers various ways to visualize and communicate these uncertainties. Monte Carlo experiments are applied to estimate the Type I error probabilities for this method. WBT is compared to other water-quality trend-testing methods appropriate for data sets of one to three decades in length with sampling frequencies of 6–24 observations per year. The software to conduct the test is in the EGRETci R-package.

Environmental Modelling and Software↗
USGS2017

Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar

In recent years, lightweight, inexpensive, vessel-mounted ‘recreational grade’ sonar systems have rapidly grown in popularity among aquatic scientists, for swath imaging of benthic substrates. To promote an ongoing ‘democratization’ of acoustical imaging of shallow water environments, methods to carry out geometric and radiometric correction and georectification of sonar echograms are presented, based on simplified models for sonar-target geometry and acoustic backscattering and attenuation in shallow water. Procedures are described for automated removal of the acoustic shadows, identification of bed-water interface for situations when the water is too turbid or turbulent for reliable depth echosounding, and for automated bed substrate classification based on singlebeam full-waveform analysis. These methods are encoded in an open-source and freely-available software package, which should further facilitate use of recreational-grade sidescan sonar, in a fully automated and objective manner. The sequential correction, mapping, and analysis steps are demonstrated using a data set from a shallow freshwater environment.

Environmental Modelling and Software↗