Geology Reports⌕ Search

USGS · 1015051

Stream chemistry modeling of two watersheds in the Front Range, Colorado

Abstract

We investigated the hydrologic, geochemical, and biogeochemical controls on stream chemical composition on the Green Lakes Valley and Andrews Creek watersheds using the alpine hydrochemical model (AHM). Both sites had comparable data sets from 1994 and 1996, including high‐resolution spatial data and high‐frequency time series of hydrology, geochemistry, and meteorology. The model of each watershed consisted of three terrestrial subunits (soil, talus, and rock), with the routing between the subunits determined by spatial land cover data. Using 1994 data for model calibration and 1996 data for evaluation, AHM captured the dominant processes and successfully simulated daily stream chemical composition on both watersheds. These results confirm our procedure of using spatial and site‐specific field and laboratory data to generate an initial catchment model and then calibrating the model to calculate effective parameters for unmeasured processes. A net source of nitrogen was identified in the Andrews Creek watershed during the spring snowmelt period, whereas nitrogen was immobilized in the Green Lakes Valley. This difference was most likely due to the larger and more dominant area of talus in the Andrews Creek watershed. Our results also indicate that routing of snowmelt through either soil or talus material is sufficient for retention of H + and release of base cations but that N retention is more important on areas mapped as soil. Owing to the larger ionic pulse and larger fraction of surface runoff the Green Lakes Valley was more sensitive to a doubling of wet deposition chemistry than the Andrews Creek watershed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thomas Meixner, Roger C. Bales, Mark W. Williams, Donald H. Campbell, Jill S. Baron. 2000. Stream chemistry modeling of two watersheds in the Front Range, Colorado. https://doi.org/10.1029/1999wr900248

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

KEEP EXPLORING

Related USGS reports

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↗

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↗

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↗