CARPE DIEM

Centre for Water Resources Research

NUID-UCD

Contribution to Area-3

Dusseldorf meeting

26th to 28th May 2003

AREA-3

• WP 9 : Assessment of the bias, spatial pattern and temporal variability of errors in the different sources of areal precipitation estimates.

• WP 10 Optimal use of radar, NWP and rain gauge data in precipitation forecasts for improving flood forecasts in urban and rural catchments.

WP 9 : Assessment of the bias, spatial pattern and temporal variability of errors

in the different sources of areal precipitation estimates

• New/ongoing 1: SMHI

• New/ongoing 2 : CWRR-NUID

Swedish catchment

0 m

5 0 m

1 0 0 m

1 5 0 m

2 0 0 m

2 5 0 m

3 0 0 m

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4 0 0 m

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5 0 0 m

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0 10 20 30 40 50 60 70 80 90 100 k m

Catchment area: 4295 km2

MAP: ~700 mm

Location of rainfall stations

Precipitation estimates• Interpolated station observations (PTHBV)

– Based on all available stations, corrected for observation losses.

– Optimal interpolation.

– Frequencies of wind direction and wind speed included in the description of the topographic influence.

– Spatial resolution 4x4km2. Temporal resolution 24 hours.

– Period with data 1961-2002.

• Radar estimates– Spatial resolution 2x2 km2. Temporal resolution 3 hours.

– Evaluation so far only made for accumulated 24 hour precipitation.

– Period with data 2000-2002.

• Hirlam forecasts– Spatial resolution 22x22 km2. Temporal resolution 6 hours.

– Evaluation so far only made for accumulated 24 hour precipitation (6-30hours).

– Period with data 2002.

Results-1• Comparison of radar and interpolated

station data 2000-2002– Higher spatial resolution and realistic spatial variability

for single days in radar data

– Technical problems cause non-realistic local spatial gradients in radar data.

– Systematic deviations both spatially and temporally - could be explained by technical problems and/or physiographic factors.

Results-2• Comparison of HIRLAM forecasts and

interpolated station data 2002– No obvious systematic deviations.

– Higher spatial variability in HIRLAM forecasts.

– Hirlam tends to generate small rainfall events during dry periods.

Investigation of bias - monthly catchment precipitation

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P (

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J F M A M J J A S O N D

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mm

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J F M A M J J A S O N D

2001

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J F M A M J J A S O N D

2002In terpolatedRadarH irlam

Investigation of bias - spatial distributionAnnual precipitation 2002

562

621

549

633

484

574 644582

660

635

529

597

350 m m

400 m m

450 m m

500 m m

550 m m

600 m m

650 m m

700 m m

Interpolated Radar

Hirlam

Comparison of daily catchment precipitation 2002

0 5 10 15 20 25interpolated (m m /day)

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HIR

LA

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y)

0 5 10 15 20 25in te rpo la ted (m m /day)

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rad

ar

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y)

Spatial distribution of daily precipitation - examples

20m m

30m m

40m m

50m m

60m m

70m m

80m m

90m m

100m m

Interpo la ted

R adar - sca ling factor 0 .79

10m m

20m m

30m m

40m m

50m m

60m m

70m m

80m m

In terpo la ted

R adar - sca ling factor 0 .67

2000-07-13 2000-07-19

Grid by grid comparison of radar and interpolated data

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100 200 300 400 500

Grid node altitude (m.a.s.l.)

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aily

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nce

(m

m)

-1

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J F M A M J J A S O N D

Month

Mea

n d

aily

dif

fere

nce

(m

m)

Deviations versus altitude

data edinterpolationprecipitat radar

Daily ratios

Deviations versus season

computed for each grid and then

classified by altitude and month respectively. Values from 2002.

Time series of daily catchment precipitation and runoff - examples

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Pre

cip

itatio

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y) In terpolatedRadar

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off

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O bservedS im ulated

July 2000

July 2000

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cipi

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H IR LAM

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ay)

June 2002

June 2002

Future work-1

• Further analysis of deviations between radar and interpolated station data

• Runoff simulations with different precipitation estimates – analysis of the effects of the higher temporal and spatial

variability of radar data

Future work-2

• Development of methods for combining radar and point observations in an optimal way– Evaluation of methods for handling the uncertainty in

radar data (caused by technical problems)

CWRR-NUID Catchment

• Dargle river catchment (100 km2)

• East coast of Ireland

• Mainly rural

CWRR-NUID Planned Activities

• Data collection for Case Study (Dargle catchment)

• Implementation of TOPKAPI Model for Dargle catchment ( together with PROGEA/Prof. Todini)

• Sensitivity studies

Data Collection

• Rain gauges (4) in operation, tipping bucket type (tips every 0.2 mm)

• Water level recorders (11) in operation, Ott Thalimides or Optalimes (1 mm resolution, 5 minute data interval)

• Archiving radar data from Met Eireann radar at Dublin airport (1 km CAPPI, 15 minute intervals, 1 km resolution)

TOPKAPI Model Structure

Comments

• Dry summer

• Wet winter with many significant storms, at least 10 significant events in period September to December alone.

• Good possibilities for hydrological model evaluation.

Implementing Topkapi: to dos from Colchester

• To do : Add baseflow component and calibrate model. (completed)

• To do : Do sensitivity analysis and evaluations (adding data from 2003)

Topkapi Calibration- summer/winter 2002

Topkapi Calibration- detail

Additional work• Analysis and integration of archived radar

data. (commenced)

• Inclusion of HIRLAM output. (to be commenced when storms to be analysed are chosen)

• Refining rating equations for catchment gauges (on-going)

WP 10 Optimal use of radar, NWP and rain gauge data in precipitation forecasts for improving flood forecasts in urban

and rural catchments.

• Commenced January 2003

• Builds on results and data from WP9