Assimilation of SWOT WSE to Improve NOAA National Water Model Initialization and Streamflow Prediction
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WRF-Hydro↗
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Evaluating the WRF-Hydro Modeling System in Alaska
National Water Model (NWM) implemented operationally in August 2016 to improve hydrological prediction (OWP, 2017). (1) Four operational configurations (2) Only covers contiguous United States (US). NWM is instantiation of Weather Research and Forecasting model hydrological extension package (WRF-Hydro)(Gochis et al., 2013) coupled with Noah Land Surface Model with Multi-Parameterization options (Noah-MP)(Niu et al., 2011). WRF-Hydro is extensible, high-resolution hydrologic routing and streamflow modeling framework, coupling column land surface, terrain routing, and channel routing modules (NCAR, 2017). This project uses experimental version of WRF-Hydro in Alaska mimicking the NWM to: (1) Identify modeling challenges for NWM development in Alaska (2) Assess WRF-Hydro and NWM ability to represent unique hydrological processes of arctic regions and accurately predict high and low flow events (3) Examine impacts of assimilating Surface Water Ocean Topography (SWOT) (Biancamaria et al., 2016) observations to improve model initialization.
Hydrology↗
SWOT Applications for WRF-Hydro Modeling in Alaska
The Surface Water Ocean Topography (SWOT) mission, launching next year, will provide high-spatial resolution measurements of terrestrial surface water, including global rivers with widths greater than 50-100 m. SWOT measurements are naturally suited for stream hydrology, and many previous studies have worked to quantify the impact of SWOT observations on the modeling of channel flow. This work highlights the application of SWOT for WRF-Hydro modeling in Alaska for data assimilation and model calibration to support ongoing National Oceanic and Atmospheric Administration (NOAA) National Water Model development. Results demonstrate the effectiveness of using SWOT discharge estimates to calibrate WRF-Hydro in ungauged basins, and quantifies the impact of SWOT data assimilation on WRF-Hydro performance.
Nicholas Elmer↗
Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network
River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.
Andrew T. White↗
A Large Dataset of Fluvial Hydraulic and Geometry Attributes Derived From USGS Field Measurement Records
Accurate representation of river channel geometry is important for hydrologic and hydraulic modeling of fluvial systems. Often, channel geometry is estimated using simple rating curves that can be applied across various spatial scales. However, such methods are limited to power law relations that do not employ many potentially relevant catchment and river attributes. This paper introduce a new dataset, IFMHA (Inventory of Field Measurement of Hydraulic Attributes), to enable research studies on channel geometry and streamflow characteristics. IFMHA is derived from the National Water Information System (NWIS) site inventory for surface water field measurements and stream attributes from the National Hydrography Dataset (NHD). IFMHA includes 2,802,532 records from 10,050 sites (NWIS streamgaging stations). The dataset utility is demonstrated here by presenting a series of conceptual models for estimating channel geometry parameters (i.e., channel mean depth, channel maximum depth, wetted perimeter, and roughness) based on the available field attributes within IFMHA. Such a dataset and attributed channel geometry parameters can enhance the performance of operational flood forecasting frameworks (e.g. National Water Model) by providing more accurate initial conditions used in hydrologic and hydraulic routing models.
Hydrology↗
SWOT Applications in Alaska
Use of SWOT for model calibration and assimilation are encouraging, but further work is needed: account for uncertainty in meteorological forcing (calibration) and apply ensemble Kalman Smoother (data assimilation). Lessons learned can inform development of operational NOAA National Water Model Alaskan domain and other SWOT applications.
SWOT↗
Remote sensing inputs to National Model Implementation Program for water resources quality improvement
The Lake Herman watershed in southeastern South Dakota has been selected as one of seven water resources systems in the United States for involvement in the National Model Implementation Program (MIP). MIP is a pilot program initiated to illustrate the effectiveness of existing water resources quality improvement programs. The Remote Sensing Institute (RSI) at South Dakota State University has produced a computerized geographic information system for the Lake Herman watershed. All components necessary for the monitoring and evaluation process were included in the data base. The computerized data were used to produce thematic maps and tabular data for the land cover and soil classes within the watershed. These data are being utilized operationally by SCS resource personnel for planning and management purposes.
Eidenshink, J. C.↗
Gila National Forest Water Resources III - Modeling Mid-term Impacts of Wildfire Restoration Methods on the Vegetation Recovery and Hydrologic Processes in Gila National Forest
Explore the source record for details and available documents.
DEVELOP Presentation↗
National economic models of industrial water use and waste treatment
The effects of air emission and solid waste restrictions on production costs and resource use by industry is investigated. A linear program is developed to analyze how resource use, production cost, and waste discharges in different types of production may be affected by resource limiting policies of the government. The method is applied to modeling ethylene and ammonia plants at the design stage. Results show that the effects of increasingly restrictive wastewater effluent standards on increased energy use were small in both plants. Plant models were developed for other industries and the program estimated effects of wastewater discharge policies on production costs of industry.
Thompson, R. G.↗
Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?
The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.
Shukla, Shraddhanand↗
Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?
The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.
Shukla, Shraddhanand↗
The annual cycle of stratospheric water vapor in a general circulation model
The application of general circulation models (GCM's) to stratospheric chemistry and transport both permits and requires a thorough investigation of stratospheric water vapor. The National Center for Atmospheric Research has redesigned its GCM, the Community Climate Model (CCM2), to enable studies of the chemistry and transport of tracers including water vapor; the importance of water vapor to the climate and chemistry of the stratosphere requires that it be better understood in the atmosphere and well represented in the model. In this study, methane is carried as a tracer and converted to water; this simple chemistry provides an adequate representation of the upper stratospheric water vapor source. The cold temperature bias in the winter polar stratosphere, which the CCM2 shares with other GCM's, produces excessive dehydration in the southern hemisphere, but this dry bias can be ameliorated by setting a minimum vapor pressure. The CCM2's water vapor distribution and seasonality compare favorably with observations in many respects, though seasonal variations including the upper stratospheric semiannual oscillation are generally too small. Southern polar dehydration affects midlatitude water vapor mixing ratios by a few tenths of a part per million, mostly after the demise of the vortex. The annual cycle of water vapor in the tropical and northern midlatitude lower stratosphere is dominated by drying at the tropical tropopause. Water vapor has a longer adjustment time than methane and had not reached equilibrium at the end of the 9 years simulated here.
Mote, Philip W.↗
Remote sensing of atmospheric temperature profiles with the Nimbus 5 microwave spectrometer
The paper discusses the accuracy of atmospheric vertical temperature profile measurements performed by the Nimbus 5 microwave spectrometer and compares results obtained during the first 6 months of its operation with independent ground-truth measurements (coincident radiosonde measurements). The microwave spectrometer can measure temperature profiles averaged over the instrument weighting function (about 10 km) layers with a root-mean-square accuracy of a few tenths of a degree K for measurement integration times of 16 s. Lower tropospheric temperature data from the spectrometer measurements is used in the numerical weather prediction model of the National Meteorological Center.
Waters, J. W.↗
National Water Level Forecast (NWLF): An open-source customizable web application for the GEOGLOWS ECMWF global hydrological model
Explore the source record for details and available documents.
Darlly Rojas-Lesmes↗
Gila Water Resources III - Modeling the Impacts of Post-fire Restoration Methods on Vegetation Recovery in the Gila National Forest
In recent years, wildfires in New Mexico’s Gila National Forest have become increasingly common and more severe. Wildfires can have powerful impacts on hydrology and soil stability, including erosion, flooding, and debris-flows that threaten lives and infrastructure downstream. Vegetation restoration treatments like seeding and mulching can mitigate these effects and facilitate ecosystem recovery. Understanding the effectiveness of various restoration methods is vital to planning a cost-effective and successful post-fire recovery strategy. The immediate response to a fire on US Forest Service land is coordinated by a Burned Area Emergency Response (BAER) team, a group responsible for mitigating immediate post-fire risks to human life, property, and critical natural and cultural resources. This study created a proof-of-concept methodology for a decision-support tool designed to help BAER teams identify the restoration treatments most likely to succeed in a given burned area. Leveraging random forest regression, Google Earth Engine, and Landsat 7 and 8 Earth observations, this study modeled vegetation recovery after the 2013 Silver Fire for seeded areas, seeded/mulched areas, and untreated areas. Treatment type and initial burn severity were the largest drivers of vegetation recovery across the landscape. Seeded/mulched areas showed higher recovery levels than untreated areas three months post-fire, but by four years post-fire, treated and untreated areas displayed similar recovery levels. To produce a robust predictive tool for the Gila National Forest, the model should be trained on many more fires and incorporate post-fire weather conditions into the process. Such a model will help partners ensure efficient resource use and plan effective post-fire restoration strategies.
DEVELOP Project Summary↗
Ditching Investigation of a 1/15-Scale Model of the Convair-Liner Airplane
A ditching investigation of a model of the Convair-Liner airplane was made to observe the behavior and determine the safest procedure for making an emergency water landing. The ditching model was designed and constructed by the National Advisory Committee for Aeronautics. Design information on the airplane was furnished by the Consolidated Vultee Aircraft Corporation. A three-view drawing of the airplane is shown. The investigation was made in calm water at the Langley tank no. 2 monorail.
Fisher, Lloyd J.↗
Brazil is in water crisis–it needs a drought plan
To avoid crop failures and soaring power costs, Brazil needs to diversify sources, monitor soil moisture, model local hydroclimate dynamics and treat water as a national security priority.
Augusto Getirana↗
Water Flow Performance of a Superscale Model of the Fastrac Liquid Oxygen Pump
As part of the National Aeronautics and Space Administration's ongoing effort to lower the cost of access to space, the Marshall Space Flight Center has developed a rocket engine with 60,000 pounds of thrust for use on the Reusable Launch Vehicle technology demonstrator slated for launch in 2000. This gas generator cycle engine, known as the Fastrac engine, uses liquid oxygen and RP-1 for propellants and includes single stage liquid oxygen and RP-1 pumps and a single stage supersonic turbine on a common shaft. The turbopump design effort included the first use and application of new suction capability prediction codes and three-dimensional blade generation codes in an attempt to reduce the turbomachinery design and certification costs typically associated with rocket engine development. To verify the pump's predicted cavitation performance, a water flow test of a superscale model of the Fastrac liquid oxygen pump was conducted to experimentally evaluate the liquid oxygen pump's performance at and around the design point. The water flow test article replicated the flow path of the Fastrac liquid oxygen pump in a 1.582x scale model, including scaled seal clearances for correct leakage flow at a model operating speed of 5000 revolutions per minute. Flow entered the 3-blade axial-flow inducer, transitioned to a shrouded, 6-blade radial impeller, and discharged into a vaneless radial diffuser and collection volute. The test article included approximately 50 total and static pressure measurement locations as well as flush-mounted, high frequency pressure transducers for complete mapping of the pressure environment. The primary objectives of the water flow test were to measure the steady-state and dynamic pressure environment of the liquid oxygen pump versus flow coefficient, suction specific speed, and back face leakage flow rate. Results showed excellent correlation between the predicted and experimentally measured pump head rise at low suction specific speeds. Likewise, only small circumferential variations in steady-state impeller exit and radial diffuser pressure distributions were observed from 80% to 120% of the design flow coefficient, matching the computational predictions and confirming that the integrated design approach has minimized any exit volute-induced distortions. The test article exhibited suction performance trends typically observed in inducer designs with virtually constant head rise with decreasing inlet pressure until complete pump head breakdown. Unfortunately, the net positive suction head at 3% head fall-off occurred far below that predicted at all tested flow coefficients, resulting in a negative net positive suction head margin at the design point in water. Additional testing to map the unsteady pressure environment was conducted and interesting rotating phenomena at the inducer inlet were observed. These rotating phenomena's cell numbers, direction, and speed were correlated with pump operating parameters. The impact of the unsteady phenomena and their corresponding energy losses on the unexpectedly poor pump performance is also discussed.
Skelley, Stephen↗