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Overview
The Coastal and Inland FLooding Observation and Warning (CI-FLOW) Project consortium is working with NCSU to couple their existing estuary model, watershed water quality model, and an estuary water quality model to the NSSL multi-sensor precipitation estimation system and NWS distribution hydrologic model. The proposed activity is to create the generic mathematical couplings necessary for the investigation of the responses of primary ecosystem parameters to flooding events. The resulting CI-FLOW demonstration program will facilitate the evaluation and testing of new technologies and techniques to produce accurate and timely identification of coastal, estuary and inland floods, flash floods and their impacts on the coastal ecosystem. Project CI-FLOW is focused on the Tar-Pamlico River Basin in North Carolina.
NEW! CI-FLOW 5 Year Science Plan (.doc, 2.4 MB)
Download PowerPoint Presentation: CI-FLOW Project Overview (Dec. 2004) (.ppt, 3.0 MB)
CI-FLOW One Page Project Description (.pdf, 369 kB)
Background
In February 2000, the National Severe Storms Laboratory (NSSL), National Sea Grant (NSG) College Program, University of Oklahoma, North Carolina State University (NCSU), and the North and South Carolina Sea Grant programs established a joint project, centered in North Carolina areas affected by Hurricane Floyd. The original collaborators were later joined by the National Weather Service Office of Hydrologic Development and the National Environmental Satellite, Data and Information Service (NESDIS). The primary demonstration area was the Tar-Pamlico River basin. This project, called CI-FLOW, has established a research and demonstration program for the evaluation and testing of new technologies and techniques to produce accurate and timely identification of inland and coastal floods and flash floods.
The previous activities of Project CI-FLOW include 1) implementation in
the Tar River Basin of QPE-SUMS (Quantitative Precipitation Estimation
and Segregation Using Multiple Sensors; Gourley et al. 2001), a cutting
edge multi-sensor precipitation estimation technique, 2) implementation
of Vflo™, a physics-based distributed hydrologic model (Vieux 2001; Vieux
and Vieux 2002), and coupling of Vflo™ with QPE-SUMS, 3) coupling of the
NCSU Estuary-Lower River Flood model (Xie and Pietrafesa 1999) with output
from both QPE-SUMS and Vflo™, and 4) replacement of the Vflo™ model with
the NWS Hydrology Laboratory's distributed model and linking the
model to QPE-SUMS.
In 2003, Vflo™ and QPE-SUMS were producing real-time estimates of precipitation and river stage for key points along the Tar River Basin. The initial products are available to researchers, forecasters, and other potential users through the Internet in real-time. During that time, our team members at the SC Sea Grant Extension office were able to collect information on the critical hot spots for flooding along the Tar-Pamlico River Basin. This information was helpful in determining locations where river stage and flow forecasts will be needed.
In
2004, the NWS/OH Hydrology
Lab Research Modeling System (HL-RMS) was introduced and coupled
with the FLDWAV channel model to eliminate proprietary software
source code and licensing issues (Koren et al., 2004). HL-RMS performed
very well in NOAA's NWS-sponsored Distributed Model Intercomparison Project
(DMIP) (Smith et al., 2004; Reed et al., 2004). DMIP garnered participation
from 12 leading distributed modeling researchers in Canada, Denmark,
New Zealand, China, and the US. DMIP was the first extensive comparison
of distributed hydrologic models amongst themselves and to traditional
lumped models. Results from DMIP proved that HL-RMS performed very well and
is a scientifically sound tool for I modeling river basin hydrology (Reed et
a1., 2004)
Like HL-RMS, FLDWAV represents the 'state-of-the-science' in one-dimensional hydraulic channel routing. FLDWAV is the product of a long and intense development process (Fread, 1992; Fread et a1., 1988), and has been proven to accurately reproduce observed river stages and discharges during flood events. Algorithms in FLDWAV solve the complete one-dimensional St. Venant equations for unsteady flow. In addition, FLDWAV has the capability of modeling the effects of a growing list of non-standard hydraulic features including the ability to model dendritic rivers systems and channel networks, to account for the effects of off-channel storage areas connected to the waterway or separated by levees, the ability to account for the effects of hydraulic structures (dams, bridges, levees), and the ability to handle flows in the subcritical and/or supercritical flow regime. FLDWAV is being used operationally to model several coastal rivers including the Columbia, lower Mississippi, and St. Johns rivers. It also serves as the basis for the generation of several types of forecast flood inundation maps (Cajina et aI., 2002).
Under last year's CI-FLOW work plan, HL-RMS and FLDWAV were modified to create a direct linkage between the two. Now, output from HL-RMS can be used as direct input into FLDWAV, allowing the two models to function as 'stand-alone' components that do not need to be tied to any current river forecasting system such as that used by NOAA's NWS. We believe that this combination is one of the first (if not the first) linkage of operational distributed models and advanced channel routing models for river forecasting.An initial coupling of QPE SUMS with the OH Distributed Modeling System (HL-RMS) occurred by converting QPE SUMS onto the HRAP grid at 1 km resolution. In addition, the HL-RMS was coupled with the OH channel routing model. Furthermore, an effort to connect the channel model and the Estuary model was begun.
New Activity
Water quality modeling
The newest activity that has just begun includes implementation of a precipitation-driven watershed water quality model on the Tar-Pamlico River. The output from the watershed model will be directly coupled with an estuary water quality model, which will be further linked with the existing NCSU estuary- lower river flood model. The integrated modeling system will then be capable to explore responses of primary ecosystem parameters, such as nutrients, dissolved oxygen, and primary production, to upland flooding events.
The watershed water quality model is based on a public domain software program HSPF (Hydrologic Software Program – Fortran) distributed by the U.S. EPA's Center for Exposure Assessment Modeling. HSPF is a comprehensive watershed simulation model designed to simulate a wide range of hydrologic and water quality processes (Bicknell et al., 2001). The model simulates various forms of nutrients (e.g. ammonium, nitrate, organic nitrogen and phosphate) based on time series of precipitation data and land use characteristics at the river basins.
The estuary water quality model is based on CE-QUAL-ICM from US Army Corps of Engineers, Waterways Experiment Station (Cerco and Cole, 1994) and HEM3D (Hydrodynamic Eutrophication Model in Three Dimension) from the Virginia Institute of Marine Science (Part et al., 1995). Both models have been heavily used in water quality modeling in Chesapeake Bay and its tributaries (Cerco, 1995; Lin and Kuo, 2003). Upon receiving the nutrient loadings from the watershed model, the estuary water quality model simulates spatial and temporal distributions of multiple components of nutrients, dissolved oxygen, carbon and phytoplankton.
Model Calibration
Once a model is selected, it must be calibrated for a watershed (James and Burges, 1982). Calibration is widely recognized as a critical step in the application of any hydrologic or channel routing model. This year we will focus on the calibration of HL-RMS and FLDWAV. As seen in the purple boxes in Figure 1, the models are major components of the CI-FLOW system. Implicit in the process of calibration is the collection, quality control, and formatting of all data needed for the calibration process.
HL-RMS takes the gridded rainfall observations from QPE-SUMS and converts the rainfall to runoff and streamflow in each grid. Runoff and streamflow from each grid move down hill to the next grid, eventually making their way to the main Tar River channel below Tarboro. For HL-RMS, calibration involves deriving the best values of the model parameters in each of the grid cells in the Tar River basin. Here, initial parameters describing the type of soils, the slope of the land, and the type of vegetation must be specified. HL-RMS assumes each grid cell contains a river, so the size, shape, and flow resistance of the river must be numerically described. In calibration, these initial parameters are adjusted until the computed flows agree with observed flows measured by the US Geological Survey. Calibration of distributed models is an active area of research. In NOAA’s NWS method, traditional methods of hydrologic model calibration (Smith et al., 2003) are combined with emerging techniques to adjust the parameters in each grid cell.
Determine Statistical Biases for HL-RMS
The Hydrometeorology Group of HL will analyze the precipitation input to HL-RMS as necessary during the hydrologic model calibration process. Such an analysis might be necessary to determine if statistical biases in the precipitation fields emerge or change during the calibration period. The analysis will be a thorough comparison with an independent set of reference rain gauge reports.
FLDWAV Connectivity and Calibration
FLDWAV takes all of the runoff and streamflow generated by HL-RMS and routes the combined inflows through the Tar River system which includes the Tar River from Rocky Mount, NC to the head of the Pamlico Sound and all of its major tributaries within the routing reach. Water levels and discharges are generated at all locations in the river system for all times. In addition, the computed river levels can be used to generate flood inundation maps.
In order to calibrate the river system, FLDWAV requires the following information (Sylvestre et al., 2002): the river system defined based on hydraulic conditions; inflows; observed river levels and discharges (if available) at the gage locations; channel roughness represented by Manning; cross section data representing the topography; and a description of any critical hydraulic structures (.e.g., dams, bridges, levees) in the river system. Rivers with significant backwater and/or significant attenuation due to storage will be modeled dynamically, while rivers that simply add to the flow will be modeled as lateral inflow. Topographic information will be obtained from LIDAR-based Digital Elevation Model data. The roughness coefficients will be adjusted until the difference between the computed and observed water level time series has been minimized at each gage location. To ensure the correct volume is maintained in the river system, the relationship between the effective and inactive storage will be adjusted until the difference between the computed and observed discharges is minimized. The FLDWAV parameters will be calibrated for both record high flows and low flows to ensure model stability when running operationally. They will be verified with an independent flood.
Since the hydrologic and hydraulic model calibrations will be done concurrently, discharge and water level data will be obtained from the USGS and NWS archives. After the calibrations have been completed, inflows from HL-RMS will be used. An assessment will be done to determine the best way to represent the gridded inflows in FLDWAV.
Improved QPE
A primary goal of CI-FLOW is to develop and identify an optimum set of techniques and algorithms to serve as a state-of-the-science NOAA multi-sensor QPE. Currently within NOAA, there are three primary research activities focused on the production of near-real time high-resolution multi-sensor QPE.
NSSL: National Mosaic for Quantitative Precipitation (NMQ/Q2). The NMQ/Q2 consists of a prototype real-time computing system that generates three-dimensional mosaics of radar reflectivity and a suite of derived products including multiple rainfall products. The system has also been designed to ingest all relevant grids for rainfall estimation purposes such as multiple radar, rain gauges, satellite imagery, model output, and lightning flashes. The reflectivity mosaics and derived products are interpolated to a 1-km CONUS grid mesh on 31 vertical layers and are updated every 5 min. The system can process radar reflectivity into rainrates outside the geographic boundaries covered by operational WSR-88D precipitation products. Reflectivity data are quality controlled using several reflectivity and velocity-based fields in a neural network. A technique for correcting rainfall estimates for reflectivity profile effects has been implemented. A capability for ingesting data from radar networks other than WSR-88D is under development. Lastly, different techniques for adjusting rainfall accumulations using collocated rain gauge reports have been implemented.
OHD: Multisensor Precipitation Estimator (MPE). The MPE function within the Advanced Weather Interactive Processing System (AWIPS), integrates rain gauge, radar, and satellite estimates into fields covering the area of responsibility for individual WFO's and RFC's. MPE includes a large suite of interactive tools for quality control (QC) of all inputs, particularly interactive and automated rain gauge QC. The rainfall estimates are interpolated to a 4-km grid and updated hourly. An enhanced MPE (EMPE) is under final development; EMPE will create 1-km grids on a subhourly update cycle.
NESDIS: Hydro-Estimator (HE). The HE hourly rainfall product is based on satellite infrared (IR) signatures for rainfall rate modulated by precipitable water and lower tropospheric mean relative humidity. An improved calibration of this algorithm is under development. Since its inputs come from geostationary satellites and numerical weather prediction models, coverage by the HE is nearly continuous in time and space across the conterminous United States and adjacent areas. Presently, HE output is ingested by MPE.
The CI-FLOW project will leverage these existing activities to create a research collaboration between NSSL, OHD, and NESDIS focused on the production of the most accurate multi-sensor QPE updated every 5 to 6 minutes on a 1-km spatial scale. This research collaboration will:
- Document existing QPE techniques and algorithms
- Compare the performance of these techniques and algorithms in past precipitation events to actual ground-truth rain gauge reports
- Identify the most accurate and efficient techniques and algorithms to incorporate into a NOAA QPE product
- Evaluate the impact of this NOAA QPE on Tar River basin streamflow simulations produced by advanced distributed hydrologic models
Additional Radars
By leveraging the previous successes of Project CI-FLOW, two complimentary proposals (e.g., NOAA HPCC and NSSL DRDF) have been funded to include both FAA TDWR radar data and Canadian radar data in the algorithms that estimate the precipitation (Figure 2). The data from these radars are not only taken closer to the ground (less chance for errors due to evaporation, etc.), but will also help fill in gaps between the NWS radars, thus improving overall QPE accuracy and coverage. Canadian radar data will provide additional sampling over the Great Lakes and Great Lake watershed headwaters in Canada. Several TDWRs in the Tar-Pamlico basin should lead to improvements in the precipitation estimation in the basin.
Enhanced Coverage
The QPE-SUMS coverage along coastal regions will be expanded from 230 km to 460 km, increasing the coverage over the ocean to provide earlier detection and better short-term prediction of precipitation. Collectively these efforts should improve the QPE used as input to the NWS and NCSU coupled model system and, therefore, improve the likelihood for improved flash flood forecasts.
