The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States

Regan, R. Steven; Juracek, Kyle E.; Hay, Lauren; Markstrom, Steven L.; Viger, Roland J.; Driscoll, Jessica M.; LaFontaine, Jacob H.; Norton, Parker A.

doi:10.1016/j.envsoft.2018.09.023

Cited by 44 publications

(46 citation statements)

References 55 publications

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“…The RGHW was divided into 28 HRUs (Figure 1b), which are a subset of the CONUS‐scale geospatial fabric (GSF) HRUs (Viger 2014; Viger and Bock 2014) and the USGS National Hydrologic Model infrastructure for use with PRMS (Regan et al 2018; Regan et al 2019). The Rio Grande near Del Norte streamflow gauge was defined as the model outlet and GSF features for all upstream HRUs were used in the model, herein referred to as RGHW‐PRMS (Penn 2019).…”

Section: Methodsmentioning

confidence: 99%

Changes in Climate and Land Cover Affect Seasonal Streamflow Forecasts in the Rio Grande Headwaters

Penn

Clow

Sexstone

et al. 2020

J American Water Resour Assoc

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Section: Methodsmentioning

confidence: 99%

Changes in Climate and Land Cover Affect Seasonal Streamflow Forecasts in the Rio Grande Headwaters

Penn

Clow

Sexstone

et al. 2020

J American Water Resour Assoc

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“…For example, enabling linked models to run in a repetitive sequence or automating the adjustment of boundary conditions is not always easily completed. This is especially true in webbased application, such as USGS's National Hydrologic Model (Regan et al, 2019). The existing frameworks generally do not allow for the addition of scripts that would guide the modeling process in addition to the existing architecture.…”

Section: Model Couplingmentioning

confidence: 99%

Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management

Gregory

Chen

et al. 2020

Front. Water

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Due to the complexity and heterogeneity inherent to the hydrologic cycle, the modeling of physical water processes has historically and inevitably been characterized by a broad spectrum of disciplines including data management, visualization, and statistical analyses. This is further complicated by the sub-disciplines within the water science community, where specific aspects of water processes are modeled independently with simplification and model boundary integration receiving little attention. This can hinder current and future research efforts to understand, explore, and advance water science. We developed the Virtual Watershed Platform to improve understanding of hydrologic processes and more generally streamline model-data integration and data integration with tools for data visualization, analysis, and management. Currently, four models have been developed as components and integrated into the overall platform, demonstrating data prepossessing (e.g., sub gridding), data interaction, model execution, and visualization capabilities. The developed data management technologies provide a suite of capabilities, enabling diverse computation capabilities, data storage capacity, connectivity, and accessibility. The developed Virtual Watershed Platform explored the use of virtual reality and 3D visualization for scientific experimentation and learning, provided web services for the transfer of data between models and centralized data storage, enabled the statistical distribution of hydrometeorological model input, and coupled models using multiple methods, both to each other and to a distributed data management and visualization system.

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“…The National Hydrologic Model infrastructure used with the Precipitation-Runoff Modeling System (NHM-PRMS) is a physically based daily timestep hydrologic model that simulates components of the hydrologic cycle for 109,951 HRUs across the CONUS [13,14,33,44,45]. The NHM infrastructure consists of three components: (1) a physical model code, (2) climate input datasets, and (3) the spatially distributed modeling units and parameters (Geospatial Fabric).…”

Section: Nhm-prmsmentioning

confidence: 99%

“…This critical component of streamflow is important for sustaining surface water quantity and quality during low flows, drier months of the year, or during periods of drought [3][4][5][6][7][8][9]. Quantification of the base flow across the nation is important for assessing the long-term effects of climatic, landscape, and anthropogenic influences on water quantity and stream ecosystem health [10][11][12], in addition to providing critical validation and calibration information for national-extent models [13,14]. The hydrograph separation techniques described in this study were applied to estimate base flow across the conterminous United States (CONUS) and are valuable in providing validation or calibration data for national-extent hydrologic models, further improving our capacity for understanding and predicting hydrologic conditions for the nation.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Base Flow by Optimal Hydrograph Separation for the Conterminous United States and Implications for National-Extent Hydrologic Models

Foks

Raffensperger

Penn

et al. 2019

Water

Self Cite

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Optimal hydrograph separation (OHS) uses a two-parameter recursive digital filter that applies specific conductance mass-balance constraints to estimate the base flow contribution to total streamflow at stream gages where discharge and specific conductance are measured. OHS was applied to U.S. Geological Survey (USGS) stream gages across the conterminous United States to examine the range/distribution of base flow inputs and the utility of this method to build a hydrologic model calibration dataset. OHS models with acceptable goodness-of-fit criteria were insensitive to drainage area, stream density, watershed slope, elevation, agricultural or perennial snow/ice land cover, average annual precipitation, runoff, or evapotranspiration, implying that OHS results are a viable calibration dataset applicable in diverse watersheds. OHS-estimated base flow contribution was compared to base flow-like model components from the USGS National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS). The NHM-PRMS variable gwres_flow is most conceptually like a base flow component of streamflow but the gwres_flow contribution to total streamflow is generally smaller than the OHS-estimated base flow contribution. The NHM-PRMS variable slow_flow, added to gwres_flow, produced similar or greater estimates of base flow contributions to total streamflow than the OHS-estimated base flow contribution but was dependent on the total flow magnitude.

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The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States

Cited by 44 publications

References 55 publications

Changes in Climate and Land Cover Affect Seasonal Streamflow Forecasts in the Rio Grande Headwaters

Changes in Climate and Land Cover Affect Seasonal Streamflow Forecasts in the Rio Grande Headwaters

Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management

Estimation of Base Flow by Optimal Hydrograph Separation for the Conterminous United States and Implications for National-Extent Hydrologic Models

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