Use of GIS for Hydrologic Model Parameter Estimation

OHD/HSMB/Hydrologic Modeling GroupSeann Reed (presenter), Ziya Zhang, Yu Zhang, Victor Koren, Fekadu Moreda, Michael Smith, Zhengtao Cui

Presented at the RFC GIS Workshop, OHRFCJuly 17, 2007

Outline

• Gridded a-priori parameter estimation procedures– SAC-SMA– PE, PE Adjustment Factors– Snow-17– Distributed model routing

• Calibration Assistance Program (CAP)

• Polar stereographic/HRAP• Xmrgtoasc, asctoxmrg

Pre-processing

Delivery

A priori SAC-SMA Parameter Grids

Victor Koren methodology inputs:– SCS curve number; assumed dry antecedent conditions– total soil column depth– texture by layer

Three versions now being tested:• STATSGO only (original)

– Miller and White (1998) 1-km gridded STATSGO– Curve numbers vary spatially as a function of hydrologic soil group but not land use;

assumed “pasture or range land use” – CONUS coverage

• STATSGO-GLCC– GLCC: Global Land Cover Characterization (1-km resolution)– Explicitly account for Land Use/Land Cover variations– CONUS coverage

• SSURGO-NLCD– SSURGO: State Soil Geographic Database– NLCD: National Land Cover Database– Higher resolution inputs– Parameters derived for 25 states in southern US so far

• State Soil Geographic Database (STATSGO)– A Mapunit groups similar soils

and may contain several non-contiguous polygons; each polygon may contain multiple soil types

– Mapunit sizes ~ 102 – 103 km2

– Attribute tables contain soil property information by layer

• Soil Survey Geographic Database (SSURGO)– ~ 4 to 20 times more detail– Polygon data for all counties

expected to be available in standard digital format by 2008

Surface Soil Textures in a 600 km2 Basin

SLSSLL

SILCLSICLOther(water, rock, etc.)

STATSGO vs. SSURGO

STATSGO and SSURGO contain both spatial and tabular information.

SSURGO data schematic from Zhang et al. (2007), in review

Complex Soil Survey Databases Must Be Simplified

From Zhang et al. (2007)

• This slide describes our assumptions for SSURGO simplifications

• Miller and White (1998) used similar assumptions to convert STATSGO polygon data to a 1 km grid and 11 standard layers for the conterminous U.S.

• Efficient processing of large data sets using GRASS, R, K Shell and Perl scripts• Phases 1 and 2 run for each soil survey area and then merged to state and regional domains in Phase 3• Parameters aggregated to ¼, ½, and 1 HRAP resolutions for hydrologic modeling Phase 3

Zhang et al. (2007), in review

Example SSURGO-NLCD Results:

UZTWM

Basic Result

Basic with Gap Filling

STATSGO-STATSGO_GLCC

STATSGO: UZTWM STATSGO_GLCC: UZTWM

Mean: 54 mmMean: 51 mm

STATSGO – STATSGO_GLCCSTATSGO – STATSGO/GLCC Forested Areas

PE and PE Adjustment Factor Grids

PE

Koren, Schaake, Duan, Smith, and Cong (1998)

PE Adjustment

July

Janu

ary

Gridded A-priori Estimates for Two Snow-17 Parameters

Derived from:1. Aspect (500-m DEM)2. Slope 3. Forest Type3. Forest Cover, %4. Anderson (2002)

recommendations for MFMIN, MFMAX (Chapter 7-4)

MFMIN

MFMAX

Flow Direction Grid

Digital Elevation Model and Derivatives (DEMs)“Out-of-the-Box” DEM Analysis

Flow Accumulation

“Out-of-the-Box” DEM Analysis

Streams Stream links

Sub-basins

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2 3

4

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“Out-of-the-Box” DEM Analysis

Customized Algorithms for Analyzing DEMs with Low Accuracy in Flat Areas

Identify flat areas and digitized streams Modify elevation grid

Compute new flow directions from Modified grid

Digital Elevation Models (DEMs) and Derivatives

• NOHRSC Data (CONUS by RFC)– 15 arc-second DEM (resampled from 3 arc-second)– RF1 (1:500,000 stream vectors)– Customized algorithms used to blend DEM and streamline data – Used in IHABBS, ThreshR, CAP, and to derive first-cut HL-RDHM

connectivity files• National Elevation Dataset (NED)

– 1 arc-second (30-m resolution)– Used NSSL derivative products for selected study areas (e.g.

DMIP)– No correction with digitized streams or basin boundaries

• NHDPlus Project DEM Derivatives– Multi-agency effort to develop attributes for National Hydrography

Data set (NHD)– Uses several algorithms to forces consistency between DEM

derivatives, NHD, and that National Basin Boundary Dataset – Not necessarily best algorithms to correct DEMs, but looks to be

the most practical and best available product for basin and stream delineation

Deriving Coarse Resolution (e.g. HRAP) Flow Directions from Higher Resolution DEMs

HRAP grid

Cells flow to the wrong basin

Out-of-the-Box Steepest Descent Algorithm Works Well for High Resolution DEMs but

not for HRAP resolution

Cell outlet tracing with an area threshold (COTAT), Reed (2003)

Using networks derived from high-resolution DEMs improves the results

ABRFC ~33,000 cells

MARFC ~14,000 cells

• OHD delivers baseline HRAP resolution connectivity, channel slope, and hillslope slope grids for each CONUS RFC on the basis of higher resolution DEM data.

HRAP Cell-to-cell Connectivity Examples

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3

Must choose this cell to get only subbasin 3, losing cells in the red box.

2 km resolution allows more accurate delineation of subbasin 3

Distributed Model Resolution Impacts the Accuracy of Basin Representation

1: 2258 km2

2: 619 km2

3: 365 km2 HRAP ½ HRAP

Drainage Area Delineation Accuracies

• Open squares represent errors due to resolution only. • Black diamonds represent errors due to resolution and connectivity.• We correct for these errors by adjusting cell areas in HL-RDHM implementations.• Both higher resolution input DEMs and use of finer resolution distributed models (e.g. ½ HRAP) can be used to increase accuracy

Delineated from an HRAP Network Derived from 400-m Flow Directions

Delineated directly from DEM resolution

Representative Slopes Are Extracted from Higher Resolution DEMS(North Fork of the American River (850 km2))

Slopes from 30-m DEM

Hillslope Slope (1/2 HRAP Resolution)Average = 0.15Slopes of all DEM cells within the HRAP pixel are averaged.

Main Channel Slope (1/2 HRAP Resolution)Average = 0.06Channel slopes are assigned based on a representative channel with the closest drainage area.

Local Channel Slope (1/2 HRAP Resolution)Average = 0.11

Slope (m/m)

Main

Tributary

Main Channel Slope vs. Local Channel Slope

(1) Slopes of each stream segment are calculated on the DEM grid

(2) Model pixel slopes are assigned from representative segments (DEM cell) that most closely match either the cell’s cumulative or local drainage area.

Segment Slopes (m/m)

Cell slope -> pixel-wise local slopec

Cell slope -> pixel-wise main slopec

Calibration Assistance Program (CAP) • Avenue-based , requires ArcView 3.x with the Spatial Analyst 1.1• V. 1.0, 2000 (Seann Reed, Ziya Zhang, David Wang)• Initially intended to:

– simplify initial parameter estimation for lumped modeling (assumed non-expert GIS user)

– facilitate extensibility and creative exploration for GIS experts

• V. 1.1, 2002: Added tools to automatically define MAPX areas for OFS based on zone or basin polygons (Lee Cajina)

• 2003 – 2007 no updates – AWIPS migrates to Linux so future of ArcView 3.x applications is unclear

• V. 1.2, 2007: Minor enhancements– Updated cover data from NOHRSC (1996-2003) – Two new grids to support the frozen ground model are now provided – Scripts updated to support new grids – Scripts modified to allow most functions to run properly on Windows XP

operating system (not functions that interact with OFS, e.g. MAPX)

• All data in Albers Equal Area Projection (equal area projection makes it easier to compute zone and basin areas)

CAP v. 1.2 Functionality • Derive area-elevation curves

– Export area-elevation to MCP input deck format• Sub-divide basins into elevation zones• Derive elevation-precipitation plots• Compute basin or zonal mean, max, and min values of:

– precipitation (monthly, annual, and seasonal)– potential evaporation (monthly, annual, and seasonal)– potential evaporation adjustment factors– percent forest– percent of each forest type– soil-based estimates for 11 SAC-SMA parameters– Mean annual temperature (C) used in the frozen ground model (TBOT)

• Compute the dominant soil texture in a basin’s upper layer (STXT) used in the frozen ground model

• Display NOHRSC historical snow images from (1990-2003)• Display basin boundaries and defined zones on top of other data layers

(e.g. snow cover, SAC parameters, etc.)• Derive/export geographic information required to run NWSRFS-MAPX

routines (must run on HP)

CAP Example GraphicsSnow Cover Analysis

Forest Cover Analysis

Future of CAP?Needs• Re-engineer CAP to move out of ArcView 3.x.

– Maintain original goals: (1) friendliness for non-GIS experts, (2) extensible for intermediate GIS users.

• Deliver refined a-priori parameter grids as they are developed (no problem)

• Deliver parameter estimation procedures via the new CAP (as opposed to delivering only pre-processed data)

• Many others . . .

Future of CAP?Possible Development Paths• Organize collaborative development project by hydrologists

(‘local application’ in GRASS or ArcGIS?)– PROS: Less expensive, short wait, easily customizable to meet

local needs– CONS: Requires field expertise and high level of coordination (from

where?), risks lack of coordination and multiple versions, informal support

• Push for official AWIPS development project by software engineers– PROS: Would yield a more polished user friendly application,

formal AWIPS support– CONS: Higher cost, longer wait, greater risk of no future

enhancements if funds dry up, may be difficult to get a high enough priority to receive funding

Secant Polar Stereographic Map Projection(Basis for the HRAP coordinate system used in NEXRAD processing and distributed hydrologic

modeling)

• Points are projected from the model earth to the image plane along a straight line drawn from the South Pole• The “secant” image plane intersects the earth at 60 N (the standard latitude, o)

B

Image Plane

A

B

A' B'

A

• Distances between points are elongated relative to true distances at latitudes below o but shortened at latitudes above o, e.g.:

A'B' > AB

South Pole

Elevation View

HRAP grid is specified in the image plane of the polar stereographic map projection:

4017625.4

psterx

hrapx

16017625.4

pstery

hrapy

True Side Lengths and Areas for HRAP Cells at Different Latitudes

Although not ideal for hydrologic modeling, we can readily adjust HRAP cell areas to represent the true area when converting runoff depths to flow volumes.

Latitude LocationTrue Side Length (km)

True Area (km2)

60 Standard Latitude 4.76 22.6850 Winnipeg 4.51 20.3245 Minneapolis 4.36 18.9835 Memphis 4.02 16.1325 Miami 3.63 13.18

Polar Stereographic to HRAP

ESRI Polar Stereographic Projection Example

See also: http://www.nws.noaa.gov/oh/hrl/distmodel/hrap.htm

/*Example Arc/Info projection file/*to go from geographic to polar /*stereographicinputprojection geographic spheroid sphereunits ddparametersoutputprojection polarspheroid sphereunits metersparameters-105 0 060 0 24.5304792 /* stand. latitude (dd mm ss)0.00.0end

**TRICK: Standard latitude is adjusted so that the HRAP earth radius of 6371.2 km can be used instead of the ESRI default 6370.997 km. As of Arc/Info 7.2, ESRI did not support a user defined radius for this projection.

GRASS Input and Output Location Projections

name: Lat/Lonproj: llellps: sphere

name: Stereographicproj: sterea: 1337.784777es: 0.0f: 0.0lat_0: 90.0000000000lat_ts: 60.0000000000lon_0: -105.0000000000k_0: 1.0000000000x_0: 401.0y_0: 1601.0

Earth radius divided by 4762.5 (size of 1 HRAP cell)

HL-RDHM XMRG Grids to GIS and Back

ncols 1060nrows 821xllcorner -1905000.000000yllcorner -7620000.000000cellsize 4762.500000NODATA_value -1.000000

ncols 1060nrows 821xllcorner 1.000000yllcorner 1.000000cellsize 1.000000NODATA_value -1.000000

xmrgtoasc <infilename> <outfilename> <ster|HRAP>

Header output with ‘ster’ option: Header output with ‘HRAP’ option:

Arc/Info: asciigrid/gridasciiGRASS: r.in.gdal/r.out.gdal

asctoxmrg <infilename> <outfilename> <ster|HRAP>

Go to http://www.weather.gov/ohd_files/project-hydrology/index.phpAnd click on ‘dhmworkshop’ link.

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Summary• GIS data and tools provided valuable assistance in estimating

hydrologic model parameters• Because algorithms to derive apriori parameters are complex,

work cannot be done with out-of-the-box GIS functions• Recently, products delivered to the field from OHD are derived

data set rather than data and software• Reasons include

– algorithm complexity (no need for everyone to learn)– lack of a common GIS platform– limited resources

• Efforts to deliver data and programs should be considered in the future (potential added value by field developers and possibility of using better local data sources)

• New CAP should be considered

GIS-based Parameter Estimation for Lumped and Distributed Hydrologic Models

Calibration Assistance Program (CAP) – Arcview 3.x

Prototype Tools Available to RFCs

Parameter GridsHRAP/XMRG

ESRI Grids and Shapefiles

Hydrology Laboratory Distributed Hydrologic Model(HL-RDHM)

ThreshR – ArcView 3.x

In-house Procedures

Tools to derive A-priori Parameter Grids

• ArcView 3.1 w/ Spatial Analyst (HP-UX)

• Arc/Info 7.x (HP-UX)• GRASS 6.2• R Statistical Software• FORTRAN/C/C++

Derived Data Layers

GRASS/ArcView/ArcInfo

Asctoxmrg, xmrgtoasc

Parameter GridsHRAP/ASCII

Edit/displayGrids

ABRFC’s XDMS