Search USGS⌕ Search
Home/Archive/USGS

USGS · 70021079

High-resolution Monte Carlo simulation of flow and conservative transport in heterogeneous porous media: 1. Methodology and flow results

R.L. Naff·D.F. Haley·E.A. Sudicky
View original source ↗Export metadata ↓

Abstract

In this, the first of two papers concerned with the use of numerical simulation to examine flow and transport parameters in heterogeneous porous media via Monte Carlo methods, various aspects of the modelling effort are examined. In particular, the need to save on core memory causes one to use only specific realizations that have certain initial characteristics; in effect, these transport simulations are conditioned by these characteristics. Also, the need to independently estimate length scales for the generated fields is discussed. The statistical uniformity of the flow field is investigated by plotting the variance of the seepage velocity for vector components in the x , y , and z directions. Finally, specific features of the velocity field itself are illuminated in this first paper. In particular, these data give one the opportunity to investigate the effective hydraulic conductivity in a flow field which is approximately statistically uniform; comparisons are made with first- and second-order perturbation analyses. The mean cloud velocity is examined to ascertain whether it is identical to the mean seepage velocity of the model. Finally, the variance in the cloud centroid velocity is examined for the effect of source size and differing strengths of local transverse dispersion.

Explore related subjects

Water Resources ResearchArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R.L. Naff, D.F. Haley, E.A. Sudicky. 1998. High-resolution Monte Carlo simulation of flow and conservative transport in heterogeneous porous media: 1. Methodology and flow results. https://doi.org/10.1029/97wr02712

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

A demonstrated workflow to efficiently identify better performing process-based model configurations

There are often competing options for process representation when designing process-based hydrologic models. Comparing candidate model configurations during model development can be computationally expensive and existing methods for evaluating prior-data conflict often cannot distinguish between multiple models with low prior-data conflict. Here, we demonstrate a workflow to efficiently identify better performing models by comparing prior model output distributions to observed data distributions using a metric referred to as Mahalanobis distance deviation (𝑀 D ). The 𝑀 D metric was developed to incorporate the notion of balancing model fit with model complexity into prior-data conflict approaches. Ninety-six different configurations of the Precipitation-Runoff Modeling System were designed for the East River watershed, Colorado by considering uncertainty in representing spatial discretization, forcing data, potential evapotranspiration, soil zone flow, snow depletion curve, and transpiration period processes. The prior-to-calibration adequacy of each model was estimated by calculating 𝑀 D for observed streamflow, areal runoff, actual evapotranspiration, groundwater recharge, soil moisture, and snow-covered area. All 96 models were then calibrated using the PEST++ iterative ensemble smoother to quantify their performance and validate the 𝑀 D results. The 𝑀 D metrics calculated from prior parameter ensembles as small as 25 exhibited strong correlation with calibrated objective function values highlighting the substantial information on model performance that can be gained prior to calibration through the workflow. Spatial discretization and forcing data were most influential on model performance and their better performing representations were most easily identified. This work demonstrates an efficient approach to identify better performing model configurations and the processes driving those improvements.

Water Resources Research↗
USGS2026

Evaluating SWOT satellite data with gage information in the United States and Canada

The Surface Water and Ocean Topography (SWOT) satellite mission represents a transformative approach to water resources management, offering high-resolution data on water-surface elevations (WSE) and extents. This study evaluates SWOT WSE accuracy in the United States and Canada with in situ streamgage and lake and reservoir gage data. We compare SWOT Level 2 Hydrology data products (Version D) to in situ timeseries of WSE on 335,531 paired observations across 2,629 gages. SWOT data accuracy and temporal coverage are explored as functions of various SWOT quality filters. After applying a standard set of defined filters and thresholds to compare paired measurements of SWOT and in situ observations (both referenced to the Earth Gravitational Model 2008), we find 1-σ (68th percentile) absolute differences of 20 and 11 cm, and median biases of 2.8 and 0.1 cm, for filtered river node and lake data, respectively. River reach and lake comparisons may be biased due to scale mismatches between in situ point-based data and spatially averaged SWOT data. Applying SWOT data quality filters improved median paired elevation R2 between unfiltered and filtered data from 0.12 to 0.92 across all reach, node, and lake observations. Overall, we find that 60.0% and 73.8% of all paired SWOT node and lake WSE observations that satisfy SWOT-observable criteria (ice-free observations from sufficiently large waterbodies within the SWOT cross track range) have elevations within 25 cm of reported gage observations. These comparisons also provide a baseline benchmark that can help inform continued SWOT algorithm refinement.

Water Resources Research↗
USGS2026

Big data for small streams: Leveraging a coarse national model to enhance fine-resolution stream temperature predictions

Predictive stream temperature models are commonly used to address spatial and temporal gaps in observational data because stream temperature information is essential for environmental management decisions. National models capture broad patterns driven by climate, topography, and seasonality, but often lack the spatial resolution to simulate fine-scale variation in groundwater discharge, riparian shading, or tributary inflows. In contrast, watershed models may simulate finer-resolution patterns but miss regional drivers or be limited by scarcity of observations. To generate watershed-extent, fine-resolution stream temperature predictions, we developed 18 multiscale machine-learning models that leverage (a) a broad-extent, coarse-resolution stream temperature model and (b) finer-resolution observations and predictor data. The coarse-resolution model simulates daily stream temperature across the conterminous United States on the National Hydrologic Geospatial Fabric (13.7 km median reach length; 865,364 km total). The fine-resolution models simulate daily stream temperature on the National Hydrography Dataset Plus version 2 (1.5 km median; 5,192,808 km total). We compared multiscale models to seven purely broad- (national) or watershed-extent models, testing them in eight watersheds (436–985 km 2 ) with differing hydrologic settings and under realistic data availability scenarios. Especially when observations within a watershed were scarce, multiscale models generated more accurate predictions than national or watershed-specific models. A recurrent graph convolution neural network pretrained on coarse predictions and fine-tuned on watershed-specific observations was most accurate across watersheds and data scenarios. These results highlight that leveraging both coarse, national results and watershed-specific data can increase model accuracies, particularly when observations within a watershed are spatially or temporally limited.

Water Resources Research↗