adjustment of global gridded precipitation for orographic effects jennifer adam

Adjustment of Global Gridded Adjustment of Global Gridded Precipitation for Orographic Precipitation for Orographic

EffectsEffects

Jennifer Adam

OutlineOutline

1. Background

2. Approach

3. Application over North America

Global Gridded PrecipitationGlobal Gridded Precipitation

• Spatial Interpolation of Gauge Measurements over Land Areas

The Orographic Effect on The Orographic Effect on PrecipitationPrecipitation

Interpolation from Valley GaugesInterpolation from Valley Gauges

*

PRISMPRISM((PParameter-elevation arameter-elevation RRegressions on egressions on

IIndependent ndependent SSlopes lopes MModel)odel)

• 2.5 minute• Topographic

facets• Regresses P

against elevation on each facet

ApproachApproach

1. Select Correction Domain and “Slope Bands”

2. Select Set of Basins that overlap with Correction Domain (must be gauged)

3. Determine “Actual” Basin Average Precipitation using Sankarasubramanian (2002)

4. Determine Scaling Ratios for each “Slope Band”

5. Apply the Scaling Ratios to an Existing Gridded Precipitation Dataset

Outline of StepsOutline of Steps

Select Correction DomainSelect Correction Domain

1. Identified according to slope:

- slopes calculated from 5-minute DEM

- aggregated to half-degree

2. Set Slope Threshold

- 6 m/km (somewhat arbitrary)

3. Break Correction Domain into Slope Bands

- six bands in correction domain

SlopeSlope

> 6m/km

> 12m/km

> 18m/km

> 24m/km

> 30m/km

> 36m/km

Correction DomainCorrection Domain

1. Select Correction Domain and “Slope Bands”

2. Select Set of Basins that overlap with Correction Domain (must be gauged)

3. Determine “Actual” Basin Average Precipitation using Sankarasubramanian (2002)

4. Determine Scaling Ratios for each “Slope Band”

5. Apply the Scaling Ratios to an Existing Gridded Precipitation Dataset

Outline of StepsOutline of Steps

World Streamflow Gauge World Streamflow Gauge StationsStations

1. Select Correction Domain and “Slope Bands”

2. Select Set of Basins that overlap with Correction Domain (must be gauged)

3. Determine “Actual” Basin Average Precipitation using Sankarasubramanian (2002)

4. Determine Scaling Ratios for each “Slope Band”

5. Apply the Scaling Ratios to an Existing Gridded Precipitation Dataset

Outline of StepsOutline of Steps

“In mountainous areas, the best precipitation maps are derived by distributing streamflow back on the watershed and correcting for

evapotranspiration.”-Harry W. Anderson

Water BalanceWater Balance

GQEPdt

dS

QEP

In General:

Long term mean over watershed:

“Q” obtained from streamflow measurements

Problem: how to estimate “E”

1. Use VIC Model to Estimate “E”- relies on good precipitation estimates

- relies on empirical parameters

2. Relate “E” to Potential Evapotranspiration (PET)- no need to rely on precipitation

Alternatives for Estimating Alternatives for Estimating EvapotranspirationEvapotranspiration

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 0.5 1 1.5 2 2.5 3PET/P

E/P

Budyko (1974) CurveBudyko (1974) Curve

EnergyLimited

MoistureLimited

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 0.5 1 1.5 2 2.5 3PET/P

E/P

Budyko (1974) CurveBudyko (1974) Curve

EnergyLimited

MoistureLimited

P

PETf

P

E

1. Semi-empirical relationship

2. Independent of energy and water balance equations.

3. Mean discrepancy between the E/P ratio calculated from curves and that derived by water balance amounts to 10% (Budyko and Zubenok, 1961).

4. Applies to river basins of “considerable” size – runoff dictated by climatic factors

5. Are other variables important?

Discussion of Budyko CurveDiscussion of Budyko Curve

1. Milly (1994):

- soil plant-available water holding capacity, various seasonality parameters

2. Zhang et al. (2001): - plant-available water coefficient, w (2.0 for

forests, 0.5 for pasture and up to 1.0 for mixed vegetation)

3. Sankarasubrumanian and Vogel (2002):

- soil moisture storage capacity

Improvements to Budyko CurveImprovements to Budyko Curve

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 1 2 3PET/P

E/P

budyko gamma=1.5gamma=1.0 gamma=0.5

Sankarasubramanian et al. (2002) Sankarasubramanian et al. (2002) CurvesCurves

γφ,fP

E

Obtaining PrecipitationObtaining Precipitation

P

PETφ Where = Aridity Index

P

bγ Where = Soil Moisture Storage Index

γφ,fP

Q-P

max(S)max(E)bmax

max(S) = Soil Moisture Storage Capacity

PETP PET,

PETP P, max(E)

Estimating PETEstimating PET

• Temperature/Radiation-Based Methods:

1. Priestley-Taylor Method

2. Hargreaves Method

-Solar Radiation determined from:

a. VIC output

b. latitude, julian day, solar declination

• Combination Method (Penman):

- VIC output

Annual PET (1999)Annual PET (1999)(Hargreaves Method)(Hargreaves Method)

Dunne & Willmott (1996)Dunne & Willmott (1996)Soil Moisture CapacitySoil Moisture Capacity

1. Select Correction Domain and “Slope Bands”

2. Select Set of Basins that overlap with Correction Domain (must be gauged)

3. Determine “Actual” Basin Average Precipitation using Sankarasubramanian (2002)

4. Determine Scaling Ratios for each “Slope Band”

5. Apply the Scaling Ratios to an Existing Gridded Precipitation Dataset

Outline of StepsOutline of Steps

For Each Basin:

averaged over the same period of years

(as determined by the stream flow records)

Calculate Average Scaling RatiosCalculate Average Scaling Ratios

est

actave P

PR

Slope Bands in BasinSlope Bands in Basin

0

12

3

45

6

Create Scaling Ratios for each Create Scaling Ratios for each Slope Band (for each basin)Slope Band (for each basin)

est

actave P

PR Calculate Basin Average Scaling Ratio:

6

0sss

6

0ssave PrPR

Constraint:

6

0ss

654321

P

6P5P4P3P2PPdSolution:

1Rave

1,2,...60s for 1d

srs ,

Problem Set-Up:

Example for a Single BasinExample for a Single Basin

35P 80,P 100,P 260,P

1.4R

3210

ave

0.41-1.4

0.83580100260

3(35)2(80)100d

4.5r 4.0,r 3.0,r

2.5,r 2.0,r 1.5,r 1,r

654

3210

By Definition May Not Be Valid!

Also Works if Rave < 1

1. Separate Basins into Regions that are Climatologically/Hydrologically Similar

2. For each Slope Band (1 through 6), find an average scaling ratio for each region by weighting the scaling ratios for the basins within that region:

Weighting of Scaling RatiosWeighting of Scaling Ratios

...WWW

...rWrWrWr

s,3s,2s,1

s,3s,3s,2s,2s,1s,1aves,

i

i2,i1,is, Tcells

N

6

NW 3

Number of Valid Slope BandsNumber of Cells

within a Slope Band

1. Select Correction Domain and “Slope Bands”

2. Select Set of Basins that overlap with Correction Domain (must be gauged)

3. Determine “Actual” Basin Average Precipitation using Sankarasubramanian (2002)

4. Determine Scaling Ratios for each “Slope Band”

5. Apply the Scaling Ratios to an Existing Gridded Precipitation Dataset

Outline of StepsOutline of Steps

Application over North AmericaApplication over North America

1. Break the Continent into “Correction Regions”

2. Choose Streamflow Stations

3. Calculate Precipitation Scaling Ratios

4. Apply to Gridded Precipitation

Correction RegionsCorrection Regions

5

6

1

27

8

10

43

11

12

1314

9

1. Climate Classification

2. Basin Boundaries

3. Location of Streamflow Gauges

Koppen Climate ClassificationKoppen Climate Classification

Hydro1k Basin LevelsHydro1k Basin Levels

Level I

Level III

Level II

Level IV

1. Years of Operation

2. Drainage Area

3. Location4. Nesting5. Degree of

Management

Gauge Station Selection CriteriaGauge Station Selection Criteria

Porcupine River

Liard River

S. Sasketchewan River

Arkansas River

Missouri River

Santiago River

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Rave 0 1 2 3 4 5 6

Final ThoughtsFinal Thoughts

1. First application of this kind on a global scale

2. Problems will arise in regions where there are few or no streamflow gauge stations (Himalayas).

3. Sensitivity to delineation of “Correction Regions”

4. Much fine-tuning left to be done!