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Konrad Hafen (Former Employee)

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Estimating streamflow permanence with the watershed erosion prediction project model: Implications for surface water presence modeling and data collection

Many data collection efforts and modeling studies have focused on providing accurate estimates of streamflow while fewer efforts have sought to identify when and where surface water is present and the duration of surface water presence in stream channels, hereafter referred to as streamflow permanence. While physically-based hydrological models are frequently used to explore how water quantity may
Authors
Konrad Hafen, Kyle Blasch, Paul E. Gessler, Jason Dunham, Erin Brooks
By
Water Resources Mission Area, Idaho Water Science Center

Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models

Process-based, large-scale (e.g., conterminous United States [CONUS]) hydrologic models have struggled to achieve reliable streamflow drought performance in arid regions and for low-flow periods. Deep learning has recently seen broad implementation in streamflow prediction and forecasting research projects throughout the world with performance often equaling or exceeding that of process-based mode
Authors
Scott Douglas Hamshaw, Phillip J. Goodling, Konrad Hafen, John C. Hammond, Ryan R. McShane, Roy Sando, Apoorva Ramesh Shastry, Caelan E. Simeone, David Watkins, Elaheh (Ellie) White, Michael Wieczorek
By
Ecosystems Mission Area, Water Resources Mission Area, Climate Research and Development Program, Idaho Water Science Center, Oregon Water Science Center, Wyoming-Montana Water Science Center

Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data

Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end user
Authors
Tyler Victor King, Stephen Hundt, Konrad Hafen, Victoria G. Stengel, Scott D Ducar
By
Water Resources Mission Area, Idaho Water Science Center, Oklahoma-Texas Water Science Center

Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989-2018

The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year
Authors
Roy Sando, Kristin Jaeger, William H. Farmer, Theodore B. Barnhart, Ryan R. McShane, Toby L. Welborn, Kendra E. Kaiser, Konrad Hafen, Kyle W. Blasch, Benjamin C. York, Alden Shallcross
By
Water Resources Mission Area, Oklahoma-Texas Water Science Center, Washington Water Science Center, Wyoming-Montana Water Science Center, Dakota Water Science Center

Precision of headwater stream permanence estimates from a monthly water balance model in the Pacific Northwest, USA

Stream permanence classifications (i.e., perennial, intermittent, ephemeral) are a primary consideration to determine stream regulatory status in the United States (U.S.) and are an important indicator of environmental conditions and biodiversity. However, at present, no models or products adequately describe surface water presence for regulatory determinations. We modified the Thornthwaite monthl
Authors
Konrad Hafen, Kyle Blasch, Paul E. Gessler, Roy Sando, Alan H. Rea
By
Water Resources Mission Area, National Geospatial Program, Idaho Water Science Center

Beyond streamflow: Call for a national data repository of streamflow presence for streams and rivers in the United States

Observations of the presence or absence of surface water in streams are useful for characterizing streamflow permanence, which includes the frequency, duration, and spatial extent of surface flow in streams and rivers. Such data are particularly valuable for headwater streams, which comprise the vast majority of channel length in stream networks, are often non-perennial, and are frequently the mos
Authors
Kristin Jaeger, Konrad Hafen, Jason B. Dunham, Ken M. Fritz, Stephanie K. Kampf, Theodore B. Barnhart, Kendra E. Kaiser, Roy Sando, Sherri L Johnson, Ryan R. McShane, Sarah Beth Dunn
By
Water Resources Mission Area, Washington Water Science Center

The influence of climate variability on the accuracy of NHD perennial and non-perennial stream classifications

National Hydrography Dataset (NHD) stream permanence classifications (SPC; perennial, intermittent, and ephemeral) are widely used for data visualization and applied science, and have implications for resource policy and management. NHD SPC were assigned using a combination of topographic field surveys and interviews with local residents. However, previous studies indicate that non‐NHD, in situ st
Authors
Konrad Hafen, Kyle Blasch, Alan H. Rea, Roy Sando, Paul Gessler
By
Water Resources Mission Area, Idaho Water Science Center

Probability of streamflow permanence model (PROSPER): A spatially continuous model of annual streamflow permanence throughout the Pacific Northwest

The U.S. Geological Survey (USGS) has developed the PRObability of Streamflow PERmanence (PROSPER) model, a GIS raster-based empirical model that provides streamflow permanence probabilities (probabilistic predictions) of a stream channel having year-round flow for any unregulated and minimally-impaired stream channel in the Pacific Northwest region, U.S. The model provides annual predictions for
Authors
Kristin Jaeger, Roy Sando, Ryan R. McShane, Jason B. Dunham, David Hockman-Wert, Kendra E. Kaiser, Konrad Hafen, John Risley, Kyle Blasch
By
Water Resources Mission Area, Ecosystems Mission Area, Wyoming-Montana Water Science Center, Forest and Rangeland Ecosystem Science Center, Idaho Water Science Center, Oregon Water Science Center, Washington Water Science Center
link
USGS

Integrating stream gage records, water presence observations, and models to improve hydrologic prediction in stream networks

Develop a process-guided deep learning modeling framework to integrate high-frequency streamflow data from gages, discrete streamflow measurements, surface water presence/absence observations, and streamflow model outputs to improve hydrological predictions on small streams.
By
Community for Data Integration (CDI), Community for Data Integration (CDI)
link

Integrating stream gage records, water presence observations, and models to improve hydrologic prediction in stream networks

Develop a process-guided deep learning modeling framework to integrate high-frequency streamflow data from gages, discrete streamflow measurements, surface water presence/absence observations, and streamflow model outputs to improve hydrological predictions on small streams.
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Measuring Discharge with an ADCP, Clear Lakes Spring, Idaho

Estimating Spring Discharge to the Snake River, Milner Dam to King Hill, Southern Idaho

Groundwater discharges to the Snake River from numerous volcanic rock springs along the northern canyon wall between Milner Dam and King Hill. Water-resource managers need to be able to quantify the amount of this water to understand the eastern Snake River Plain aquifer's recharge, storage, and discharge. When completed, this study will provide the Idaho Department of Water Resources with an...
By
Idaho Water Science Center
link

Estimating Spring Discharge to the Snake River, Milner Dam to King Hill, Southern Idaho

Groundwater discharges to the Snake River from numerous volcanic rock springs along the northern canyon wall between Milner Dam and King Hill. Water-resource managers need to be able to quantify the amount of this water to understand the eastern Snake River Plain aquifer's recharge, storage, and discharge. When completed, this study will provide the Idaho Department of Water Resources with an...
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A concrete weir during very low streamflows.

State of the Science in Streamflow Modeling in the North Central Region to Address Partner Needs for Water Availability Under Drought Conditions

Land and water managers often rely on hydrological models to make informed management decisions. Understanding water availability in streams, rivers, and reservoirs during high demand periods that coincide with seasonal low flows can affect how water managers plan for its distribution for human consumption while sustaining aquatic ecosystems. Substantial advancement in hydrological modeling has oc
By
Climate Adaptation Science Centers
link

State of the Science in Streamflow Modeling in the North Central Region to Address Partner Needs for Water Availability Under Drought Conditions

Land and water managers often rely on hydrological models to make informed management decisions. Understanding water availability in streams, rivers, and reservoirs during high demand periods that coincide with seasonal low flows can affect how water managers plan for its distribution for human consumption while sustaining aquatic ecosystems. Substantial advancement in hydrological modeling has oc
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Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins

This metadata record describes a series of data sets of natural, climatic, and anthropogenic landscape features processed as model inputs for the Data-Driven Drought Prediction Project of the Water Resources Mission Area Drought Program. These data are linked to two different spatial units: the National Hydrologic Geospatial Fabric version 1.1 (nhgfv1.1) including their associated individual catch
By
Maryland-Delaware-D.C. Water Science Center

Chlorophyll-a concentrations and algal bloom condition paired with Sentinel-2 aquatic reflectance values collected for Brownlee Reservoir, ID from 2015 through 2020

This data release presents two calibration datasets that relate aquatic reflectance derived from Sentinel-2 satellite imagery with algal bloom conditions in the Brownlee Reservoir on the Idaho Oregon border. These datasets were developed to evaluate remote sensing methods for identifying algal blooms in Brownlee Reservoir like those from July 2022 that are illustrated in field photo (left) and sat
By
Idaho Water Science Center

Potentially Resolvable National Hydrography Dataset Waterbodies and Flowlines from Landsat Images in the United States (excluding Alaska)

This data release presents two datasets including waterbodies (reservoirs, lakes, ponds, wetlands, etc.) and flowlines (stream reaches) from the high-resolution National Hydrography Dataset Plus (NHDPlus HR) that are potentially observable from Landsat images for the United States (excluding Alaska). To determine where National Hydrography Dataset Plus high resolution (NHDPlus HR; USGS 2019) featu
By
Idaho Water Science Center

Sensitivity and precision of stream permanence estimates (1977-2019) from the USGS Thornthwaite Monthly Water Balance Model in the Pacific Northwest, USA

This dataset includes inputs and results for parameterizing the USGS Thornthwaite Monthly Water Balance Model (MWBM) to simulate annual stream permanence on National Hydrography Dataset (NHD) stream reaches. Also included are results from sensitivity analysis of MWBM parameters to final stream permanence classification (permanent or nonpermanent). The dataset includes files that link PRISM climate
By
Idaho Water Science Center
link

NHD Waterbodies Potentially Resolvable from LANDSAT

A new data release, "Potentially Resolvable National Hydrography Dataset Waterbodies and Flowlines from Landsat Images in the United States (excluding...

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