User's guide for the national hydrography dataset plus (NHDPlus) high resolution

Moore, Richard B.; McKay, Lucinda D.; Rea, Alan; Bondelid, Timothy R.; Price, Curtis; Dewald, Thomas G.; Johnston, Craig

doi:10.3133/ofr20191096

Cited by 69 publications

(73 citation statements)

References 5 publications

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“…Models tended to perform poorest during these low flow months, indicating that more information about subsurface conditions is likely needed beyond the simplified bedrock categories we used. Hydrologic regions also contributed to the models in all months but May F I G U R E 5 Model performance comparisons for mean annual streamflow (mm) calculated for decadal periods of record using the model developed in this study, USGS StreamStats regressions (Capesius & Stephens, 2009), NHD water balance model estimates (NHDa, QAMA in Moore et al, 2019) indicating that the region variable accounts for some differences in streamflow generation across the state that are not well captured by the other predictor variables. For example, the southwestern region has higher flow during the summer, potentially because of the influence of the North American Monsoon, but this monsoonal effect is not captured by the mean annual climate variables we used.…”

Section: Discussionmentioning

confidence: 90%

“…To evaluate how the models performed relative to other available streamflow estimates, we compared their performance to that of the USGS StreamStats regression equations for Colorado (Capesius & Stephens, 2009) and the mean annual flow estimates included in NHDPlus (Moore et al, 2019…”

Section: Model Development and Evaluation Methodsmentioning

confidence: 99%

“…Predicted versus observed mean annual streamflow for this study (a,b), USGS StreamStats(Capesius & Stephens, 2009;, and NHD HR using the original water balance model estimates (QAMA, e,f) and the gage-adjusted estimates (QEMA, g,h)(Moore et al, 2019), with colors/shapes indicating whether the watershed has flow modifications. Plots shown as both areanormalized total annual discharge (mm) and annual average volumetric discharge (cfs) [Color figure can be viewed at wileyonlinelibrary.com] F I G U R E 3 Predicted versus observed mean monthly streamflow for this study with colors/shapes indicating whether the watershed had flow modifications [Color figure can be viewed at wileyonlinelibrary.com]…”

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confidence: 99%

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Predicting mean annual and mean monthly streamflow in Colorado ungauged basins

Eurich

Kampf

Hammond

et al. 2021

River Research & Apps

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River managers often need estimates of streamflow for ungauged streams. These estimates can be used in water rights acquisitions, in-stream flow management, habitat assessment, water quality planning, and stream hazard identification. This publication describes new regression models for predicting mean annual and mean monthly streamflow in Colorado. Unlike previous regional regression studies, the new models incorporate snow persistence (SP), the fraction of time a watershed remains snow covered. Models were developed using streamflow data from 131 watersheds with drainage areas <1,500 km 2 , no transbasin diversions, and <10% urban area. In addition to SP, topographic, climate, geologic, and hydrologic region variables were used in model predictions. All new models had very good performance, with <6% absolute bias and stronger performance compared to current regional regression models in StreamStats. The mean annual model had the strongest performance, with Nash-Sutcliffe efficiency coefficient (NSE) of 0.93 and <2% absolute bias. Mean monthly models had best performance during snowmelt runoff months (May-Jul; NSE ≥0.79; absolute % bias ≤ 4) and weaker performance during low flow months (Aug-Apr; NSE ≥ 0.59; % bias ≤ 5). Tests of the mean annual model using decadal average streamflow from 1910s to 2000s show very good performance (NSE > 0.75), but predictions were biased low by 14-28% in wetter decades. All equations and coefficients needed to run the models are presented in the publication appendix, and the associated data release includes the spatial data and model code, which can be applied using R or within an R-based Shiny web app.

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Section: Discussionmentioning

confidence: 90%

Section: Model Development and Evaluation Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting mean annual and mean monthly streamflow in Colorado ungauged basins

Eurich

Kampf

Hammond

et al. 2021

River Research & Apps

View full text Add to dashboard Cite

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“…Both medium-and high-resolution NHD products provide volumetric discharges that were estimated using an enhanced unit runoff method involving estimation of unit runoff for each catchment and then accumulation downstream and comparison with measured average flows at gages. Differences at each gage were applied to adjust the upstream unit runoff and provide long-term estimates of streamflow for all reaches (Moore et al, 2019). The wetted surface areas of streams and rivers were approximated from an estimate of the velocity and width using the equations of Jobson (1996) and Leopold and Maddock (1953).…”

Section: River Corridor Metricsmentioning

confidence: 99%

The River Corridor's Evolving Connectivity of Lotic and Lentic Waters

Harvey

Schmadel

2021

Front. Water

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River corridors supply a substantial proportion of the fresh water for societal and ecological needs. Individual functions of flowing (lotic) streams and rivers and ponded (lentic) waterbodies such as lakes and reservoirs are well-studied, but their collective functions are not as well understood. Here we bring together nationally consistent river corridor datasets to characterize the contributions of lotic and lentic features and to estimate changes over the past centuries. High-resolution datasets describing waterbodies across 10 million kilometers of the conterminous U.S. (CONUS) river network were classified by waterbody type and origin (historic vs. human-made or intensively managed), surface areal coverage, and degree of connectivity as estimated by a change in water residence timescale in river corridors. Four centuries of human disturbance drove large swings in river corridor makeup, with a transition toward more lotic systems caused by beaver extirpation and abandonment of waterwheel mill ponds by end of the nineteenth century. The twentieth century saw a vast expansion (49%) in river corridor areal coverage resulting from construction and management of small ponds and reservoirs for drinking water, hydropower, irrigation and livestock watering, and stormwater control. Water residence timescale in river corridors doubled or quadrupled over large areas, and more in specific locations, during the twentieth century as a result of the increased coverage of reservoirs and managed small ponds. Although reservoirs and lakes now dominate river corridor surface areas, we found that the growing number of small ponds impacts a greater proportion of network length through their influence on headwater streams where most water and chemical runoff enters the river corridor. We close with an agenda for integrated modeling of the physical, biogeochemical, and ecological drivers of river corridor functions, trajectories of change, and management opportunities.

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“…The NHDplus dataset provides the most complete hydrologic mapping of the US; however spatial inconsistences and noise in these datasets makes it difficult to this data directly for gridded hydrologic modeling. The NHDplus stream network is derived from various topographic map sources, leading to spatial inconsistencies and inaccuracies in river network (Moore et al, 2019). Spatial discrepancies have also been found between NHDplus and local higher-resolution light detection and ranging data (LiDAR) derived stream network (Samu, 2012).…”

mentioning

confidence: 99%

A national topographic dataset for hydrological modeling over contiguous United States

Zhang¹,

Condon²,

Tran³

et al. 2020

Preprint

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Abstract. Topography is a fundamental input to hydrologic models critical for generating realistic streamflow networks as well as infiltration and groundwater flow. Although there exist several national topographic datasets for the United States, they may not be compatible with gridded models that require hydrologically consistent Digital Elevation Models (DEMs). Here, we present a national topographic dataset developed support physically based hydrologic simulations at 1 km and 250 m spatial resolution over contiguous United States. The workflow is described step-by-step in two parts (a) DEM processing using a Priority Flood algorithm to ensure hydrologically consistent drainage networks and (b) slope calculation and smoothing to improve drainage performance. The accuracy of derived stream network is evaluated by comparing the derived drainage area to drainage areas reported by the national stream gage network. The slope smoothing steps are evaluated using the runoff simulations with an integrated hydrologic model. The processed DEM is designed to capture the topographic features and improve the runoff simulations for the models solving partial differential equations. The workflow uses an open-source R package and all output datasets and processing scripts are available and fully documented here. All of the output datasets and scripts for processing are published through Cyverse at 250 m and 1 km resolution. The DOI link for the dataset is https://doi.org/10.25739/e1ps-qy48 (Zhang and Condon, 2020).

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User's guide for the national hydrography dataset plus (NHDPlus) high resolution

Cited by 69 publications

References 5 publications

Predicting mean annual and mean monthly streamflow in Colorado ungauged basins

Predicting mean annual and mean monthly streamflow in Colorado ungauged basins

The River Corridor's Evolving Connectivity of Lotic and Lentic Waters

A national topographic dataset for hydrological modeling over contiguous United States

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