Results, cont.

Discussion

Future Directions

Study Watershed and Model

Results

Study Overview

The Role of Roadside Ditch Networks in Short-Circuiting Natural

Hydrologic Pathways: implications for nonpoint source pollution transport

Geospatial mapping with custom dictionary to

characterize ditches. Parameters included:

Soil & Water Research Group Cornell University

H31F-0726

SOIL & WATER LAB

Cornell University

Integrating "poor-man's" ensemble weather risk forecasts into spatial

hydrologic modeling systems for small stream flooding and hazard assessment

Fuka, D.R., Easton, Z.M., Buchanan, B.P., Walter, M.T., and Steenhuis, T.S Biological & Environmental Engineering

Issues in Hydrologic Modeling

Spatial Issues

Introduction Hydrologic modeling at daily or sub daily resolution

requires accurate precipitation data, which is

particularly important when assessing flood risk in

small runoff dominated watersheds. Precipitation gage

data pose a variety of challenges, including the fact

that they are located outside of many watersheds of

interest and thus, may not accurately represent the

actual rainfall in the watershed. We show that by

integrating global forecast products from the

atmospheric modeling community into the hydrologic

models commonly used that we can circumvent some

of the issues with point-of-measure rain gauges.

Traditionally in hydrologic forecasting, a precipitation

forecast is used from a nearby precipitation station on

which the hydrologic model has been calibrated. In

this study, we compare and contrast calibrating a

watershed model (SWAT) using derived statistical

representations of precipitation forecasts from "poor-

man's" ensembles of raw gridded atmospheric models

interpolated to the center of subbasins vs using the

closest precipitation gage measurement and NWS

forecasts for that gage location. In addition, we look at

what scales and radii using direct gridded model

output may introduce equal or less error to watershed

modeling projects than using the closest station.

Closest Station KALB

Assuming nearest real-time reporting station for PPT.

• 30km apart, PPT r2 .23

• Mehta, V.K. et. al. 2004.

Finding a set of stations to run with real-time at 30km is unlikely.

This type of agreement would be a dream situation for us!

US has:

• ~60km spacing between real-time stations

Ethiopia

• 1 semi operational real-time weather station

• From 2-4 recording sites; data in bad photo copies

• 600km-1000km regularly spaced stations in region

Data Quality Issues

We want to develop “live” management tools.

• Waste spreading risk areas.

Has to be applicable world wide.

Need consistent forcing variables (ppt, temp, etc).

We would like to produce short term forecasts.

• Agricultural waste spreading time windows.

Gridded weather models have some skill, thus

substituting short range forecasts centered in

the watershed for weather station data should

better represent conditions in the watershed

than stations at some X distance away.

Question: What is this distance?

TIGGE Ensemble Archive.

• ECMWF

• NCEP

Closest Stations.

• KALB

• KBGM

• KMSV

Variables.

• PPT, as well as Max and Min Temperature.

Date range: 4/1/2007 - 11/1/2008

• 0Z runs, model hours 06 though 30

ECMWF Ensemble.

• .45deg Res. Global.

• 50 perturbations.

• Median from 50 member pt forecast.

NCEP Global Ensemble Forecast System (GEFS).

• 1.0deg Res. Global.

• 20 perturbations.

• Median from 20 member pt. forecast.

ENS PPT

-5

0

5

10

15

20

25

0 20 40 60 80 100 120 140 160

Hrs

PP

T(m

m)

Pert1

Pert2

Pert3

Pert4

Pert5

Pert6

Pert7

Pert8

Pert9

Pert10

Pert11

Pert12

Pert13

Pert14

Pert15

Town Brook watershed.

• Catskill Mountain range.

• New York State.

• 37km catchment of Cannonsiville basin.

• Drains into West Branch of the Delaware River.

• 500m to 1000m elevation.

SWAT 2005, initialized with ArcSWAT.

Landuse

• ~Half Ag, Half Forest

Slope

• Half below 17%

3 Sub-basins

• 44 HRUs

Summer 2007

0

0.5

1

1.5

2

2.5

3

4/29

/07

5/6/

07

5/13

/07

5/20

/07

5/27

/07

6/3/

07

6/10

/07

6/17

/07

6/24

/07

7/1/

07

7/8/

07

7/15

/07

7/22

/07

7/29

/07

8/5/

07

Date

Flo

w

Flow

KALB

ECMWF Ens

NCEP GEFS

Com b. Mean

Summer 2007 KALB

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Measured

Pred

icte

d

Summer 2007 ECMWF Ens

y = 1.139x

R2 =

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Measured

Pred

icte

d

Summer 2007 NCEP GEFS

y = 0.7452x

R2 = 0.6118

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00

Measured

Pred

icte

d

Summer 2007 NCEP ECMWF Combined Mean

y = 1.0294x

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00

Measured

Pred

icte

d

Goals

Hypothesis

Data Overview

Gridded Forecast Models from TIGGE

It appears there is validity to using short

term forecast archives in place of weather

station data to force watershed models.

The distance to the where the model skill

produces the same accuracy as the weather

stations is closer than we thought.

We need to further investigate the

watershed calibrations independent for

individual forcing data sets.

We need to be careful when applying this

method, and should use statistical

summaries of the ensembles sparingly.

Variables from the atmospheric models are

physically linked, and we should likely keep

them that way.

Obligatory Hydrograph

Hr 6-30 Forecasts

Combined Forecast

Continuing the study in a watershed with

larger number of near-by met stations to

determine true value of X.

Repeating the study in Lake Tana Basin,

where current Met records are hard to

obtain.

Studying effect of additional variables

available in models that are not available at

most met stations.

Acknowledgements and Interesting Side Points

Authors would like to acknowledge ECMWF, who supplied the data for this study via the TIGGE online user

interface. http://tigge-portal.ecmwf.int/

All computation for this study was performed in the publicly accessible Amazon EC2 grid.

Authors are looking for collaborators who might wish to run similar studys without the pain of extracting the datasets

themselves.