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

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

(1) Historical Perspective• Rational formula

• Unit Hydrograph

• Event based model

• Continuous simulation models

• Semi-distributed models

• Fully Distributed models

(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

(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

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

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

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)

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

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

SNOW-17

SNOW 17 model is used in each

element

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

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

(4) Parameterization

a) Basic watershed properties

b) SNOW-17 model parameters

c) Cont-API parameters

d) Routing parameters

(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

Connectivity of Pixels

Basins in MARFC

Saxton

Connectivity file

(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

(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

(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)

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

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

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)

6) Forcings

a) Gridded Precipitation

b) Temperature

c) Evaporation

(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

(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

(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

(7) Case study

• Juniata River Basin (11 subbasins)

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

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

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

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