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.