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Hydrology Laboratory Research Modeling System (HL-RMS)
Introduction:
Office of Hydrologic Development
National Weather Service
National Oceanic and Atmospheric Administration
Fekadu Moreda
Presented to Mid-Atlantic River Forecasting Center
February, 15, 2005
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Over View
1) Historical Perspective
2) Motivation
3) Definition of a Distributed Hydrologic Model
4) Structure of HL-RMS and Components
5) Parameterization
6) Forcings (Precipitation, Temperature, Evaporation)
7) Case Study
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(1) Historical Perspective• Rational formula
• Unit Hydrograph
• Event based model
• Continuous simulation models
• Semi-distributed models
• Fully Distributed models
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(2) Motivation for distributed Models• Availability of high resolution data: basin properties and /forcings • Better stream flow forecasting• River and flash flood forecasting, • Soil moisture products• Snow cover• Potential extension to environmental models
– Non-point source pollution– Land-use change (can account for burn areas)– Erosion studies– Landslide/mudslide/soil strength applications
• Land-atmosphere interactions for meteorological and climate applications• Groundwater recharge and contamination studies• Others
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(3) Definition of a Distributed Hydrologic model
-(informal definition)
a model which accounts for the spatial variability of factors affecting runoff generation:- precipitation - temperature- terrain - soils - vegetation- land use- channel shape
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Discharge hydrograph at the outlet
Generic Modeling StepsLumped Model Distributed Model
Discharge hydrograph at any model element
Lumped runoff and soil moisture states
Distributed runoff andSoil moisture states
Apply distributed routing model
Apply unit hydrograph
Derive mean areal precipitation (MAP)
Compute basin runoff
Derive model element precipitation
Compute model element runoff
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1. Rainfall, properties averaged over basin
2. One rainfall/runoff model
3. Prediction at only one point
1. Rainfall, properties in each grid
2. Rainfall/runoff model in each grid
3. Prediction at many points
Lumped Distributed Hydrologic Modeling Approaches
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Hydrology LabDistributed Model
(HL-Research Modeling System HL-RMS)
• Modular, flexible modeling system• Gridded (or small basin) structure• Independent rain+melt calculations for each grid cell
(SNOW-17)• Independent rainfall-runoff calculations for each grid cell
– Sacramento Soil Moisture Accounting (SAC-SMA) – Continuous Antecedent Precipitation Index (CONT-API)
• Grid to grid routing of runoff (kinematic)• Channel routing (kinematic & Muskingum-Cunge)
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INTERFLOWSURFACERUNOFF
INFILTRATIONTENSION
TENSION TENSION
PERCOLATION
LOWERZONE
UPPERZONE
PRIMARYFREE
SUPPLE-MENTAL
FREE
RESERVED RESERVED
FREE
EVAPOTRANSPIRATION
BASEFLOW
SUBSURFACEOUTFLOW
DIRECTRUNOFF
Precipitation
The surface and base flow components for each grid is obtained from a SAC-SMA rainfall –runoff model
HL-RMS Elements
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1st Quadrant
4th Quadrant
3rd Quadrant
2nd
Quadrant
SM
I/SM
IX=1
.0=0
.9
Fs=FRSX.CRAIF
FRSXFs
0.0
0.5
1.0
Fg=CG(AIf-AICR)
Fg
AICR
AI f
AI
AP
I
AIXW.CWAPI
AIXD.CDAPI
AIXD
AIXW
The API MODEL
The surface and base flow components for each grid is obtained from a CONT-API rainfall –runoff model
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SNOW-17
SNOW 17 model is used in each
element
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Distributed routing• Translates distributed runoff into distributed stream flow
• With distributed routing, flow velocity in each element is dependent on flow level
• Different flows (states) are computed for each element in a stream network. Unit graph only produces flows at basin outlets.
• Commonly used approach: numerical solution to the 1-D equations for momentum and mass conservation
2. Lumped and distributed modeling
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Surface Runoff SAC-SMA /CONT-API
Base flowHillslope routing
Channel routing
Components of HL-RMS
SNOW Model SNOW-17
Stream Flow
(P, T)
rain+melt
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(4) Parameterization
a) Basic watershed properties
b) SNOW-17 model parameters
c) Cont-API parameters
d) Routing parameters
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(a) Basic watershed properties
• Digital Elevation Model (DEM)
• Available for each of RFC with 400m resolution. 4km resolution (HRAP) is used in HL-RMS
• Directly used in the SNOW-17 model
• Flow Direction and Accumulations are derived from DEM
• Location of outlets (lat long HRAP)
• Connectivity file – ASCII file
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Connectivity of Pixels
Basins in MARFC
Saxton
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Connectivity file
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(b) SNOW-17 Parameter Grids
• Ongoing work to develop distributed snow parameters• Use of Elevation (DEM) at HRAP grid cell• The traditional snow depletion curve may be replaced by
two methods.
– i) Assuming SI=0 => for a given time step in a pixel this snow or no snow
– ii) Assuming a 45 degree depletion line for each grid. Since the 4km grid is much smaller than a a basin scale, this method will assume uniform coverage and depletion in a pixel
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(c) CONT –API Parameter Grids
• A priori parameters for 11 parameters derived from lumped model
• Use lumped model parameters for others
• Use the Evaporation index only
• No frozen ground option
• Parameters can be replaced by a lumped value or adjusted by a factor
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(d) Routing Parameter Grids
Hillslope routing parameter grids:
Hillslope slope (Sh)
Hillslope roughness (nh)
Channel density (D)
Channel routing:
Channel slope (Sc)
Channel roughness (nc)
Channel width and shape parameters (a, b)
-OR-
Specific discharge (a) and shape parameter (b) from a discharge cross-sectional area relationship
baAQ
(a, b)
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METHOD TO ESTIMATE CHANNEL ROUTING PARAMETERS
• Momentum equation describing steady, uniform flow:
– Q is flow [L3/T]
– A is cross-section area [L2]Parameters a and b must be estimated for each model grid cell. Basic Idea: (1) Estimate channel parameters at basin outlet using USGS
flow measurement data. (2) Estimate parameters in upstream cells using relationships from geomorphology and hydraulics. Two methods are being tested:
baAQ
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Channel Shape Method:
•Assume simple channel shape. (B = width, H = depth)
•From USGS data, estimate α, β, and channel roughness (n) at the outlet
•Using an empirical equation, estimate local parameter nc using channel slope (So) and drainage area (Fo) at the outlet. Estimate ni at upstream cells.
•For a selected flow level at the outlet, estimate spatially variable ai values (for each cell i) using Qi and Ai estimates derived from geomorphological relationships (see below)
• Assume β is spatially constant within a basin and compute ai and bi at each cell using ai b, and ni,
HB HH )1(
00011.0272.0 FSnn ci(Tokar and Johnson 1995)
1)()1(
i
ii H
A2
3
21
i
ii
i
i
S
nAQ
H
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Rating Curve Method:
• Determine ao and bo at the outlet directly from regression on the flow measurement data.
• Using the same geomorphological relationships as in the channel shape method, equations for estimating ai and bi can be derived:
– Geomorphological Assumptions:
• On average, flow is a simple function of drainage area and downstream flow. Leopold (1994), Figure 5.7 suggests g may vary from 0.65 to 1 in different parts of the U.S. Results shown here use g = 1 and g = 0.8.
• On average, cross-sectional area of flow can be related to stream order. Rl is Horton’s length ratio, k is stream order
ob
io
o
ii r
aF
Fa
1 oi bb
o
i
o
i
F
F
Q
Q
io
okik
kklo
ii RA
Ar
)83.083.0(013.0
(Gorbunov 1971)
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6) Forcings
a) Gridded Precipitation
b) Temperature
c) Evaporation
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(a) Gridded precipitation
• Gridded products archived: http://dipper.nws.noaa.gov/hdsb/data/nexrad/nexrad.html
• -available products:
– GAGEONLY– RMOSAIC– MPE (XMRG)– One file for one hour for the entire RFC
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(b) Gridded Temperature
• Gridded products archived are available:
• Hydrometeorology group: David Kitzmiller
• Use of the MAT for the basins to generate grid products
• Requires
– A program to generate grids
– Basin definitions (connectivity file)
– MAT for each basin
– Elevation map
– Regional lapse rate
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(c) Gridded Evaporation
• Evaporation is essential for CONT-API
• Only the evaporation option is tested
• For now we will use seasonal evaporations
• Monthly adjustments are used
• Maps are available in CAP (Calibration Assistant Program)
0
1
2
3
4
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecEv
apor
atio
n (m
m/d
ay)
PE
IPEA
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(7) Case study
• Juniata River Basin (11 subbasins)
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First HL-RMS Run for Juniata
Outlet, Juniata at Newport
Saxton, Interior point
Williamsburg, Interior point
- Model resolution 4km x 4km
- Total number of pixels =497
- Watershed area = 8687 km2
- Model parameters = a priori
- Channel parameters are derived from USGS measurements at New port.
WLBWLB
SPKSPK
SXTSXT
HUNHUN
PORPOR
REEREE
RTBRTB
LWSLWS NPTNPT
SLYSLY
MPLMPL
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Comparison of simulation
0
4
8
12
010503 110503 210503 310503 100603 200603 300603 100703 200703 300703
(a) Mean Areal Precipitation
Pre
cipi
tati
on (
mm
)
(b) Comparsion of simulated and observed hydrographs
0
100
200
300
400
500
600
010503 110503 210503 310503 100603 200603 300603 100703 200703 300703
Time (ddmmyy)
flow
m3 /s
Distributed
Lumped
Observed
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Performance Statistics
Table 1 Event based performances of distributed and lumped models for SXTP1 basin.
Events volume(mm) Peak flow (m3/s) rmod Nash R2 Start End date Obs lump dist Obs lump dist lump dist lump dist 5/01- 5/15/2003 40 41 46 241 170 209 .65 .86 .79 .76 5/16- 5/24/2003 40 36 30 244 200 192 .77 .71 .86 .26 5/31- 6/12/2003 81 86 100 520 365 406 .56 .42 .76 -0.19 6/20- 7/1/2003 25 29 25 158 129 166 .84 .62 .86 .45
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Summary
• Introduced distributed hydrologic modeling
• Develop skill in handling distributed data, parameter, and output
• Distributed model complements the existing operation
• Opportunities in future to apply to small basins, interior points for flash flood