An application of numerical weather forecast and satellite rainfall prediction to flood
forecasting
TINH DANG NGOC
National Centre for Hydro-Meteorological Forecasting,HMS, MONRE, 4 Dang Thai Than, Hanoi, Vietnam
tinhdangngoc@fpt.vn
Abstract In the last years, numerical weather forecast, satellite rainfall estimation andpredictions were applying successfully in the world, becoming more accurate and improved.
This case study presents an initial results of using Mike11 software with application ofnumerical weather forecasts (HRM, JMA, ECMWF), satellite (NOAA-USGS, TRMM)
rainfall estimation and prediction for flood forecasting on the Red river system in Vietnam.
Due attention should be given to the representativeness of the point rainfall locations in viewof topographic effects. Development of properly calibrated rainfall-runoff and river routing
models for the sub-basins. The tools to be selected should allow for multiple data assimilation
options to improve the forecasts. Analysis in a similar manner, extended also toinvestigations for the sub-basins separately, to increase accuracy and extent lead-time offorecasts to five days.
Key words forecasting; flood; numerical weather forecast; satellite rainfall prediction
During the last years, hydrological-hydraulic models, numerical and satellite rainfall
estimation, prediction have being applied in the world and Vietnam, getting more improved
and accurate results. With an availability of having good flood forecasting commercial
models, numerical, satellite rainfall prediction products, an improvement and lead-time
extension of hydrological forecasting become more realistic. This study presents an initial
results of using Mike11 software with application of numerical (HRM, JMA, ECMWF),
satellite (NOAA-USGS, TRMM) rainfall estimation and prediction for flood forecasting at
the Red - Thai Binh river system.
RAINFALL PREDICTION DATA
Comparison of rainfall prediction data HRM, JMA and ECMWF
Carried out comparison of weather forecasts to 48 hours from HRM, JMA and ECMWF forthe period of 15 June until 30 September 2004. In a first analysis of the data area-average
rainfall has been compared for the domain 16.75o-26.875o N, 99.375o-110.625o E for HRM
and JMA, and 16.75o-26.25o N, 99.75o-108.25o E for ECMWF. Both areas contain the Red
river basin. Results are shown in figures 1 and 2. Fig.1 presents the forecasts for 0-24 hours
ahead. Figure 2 presents the forecast for 24 - 48 hours ahead. It shows that the ECMWF
precipitation forecasts are, on average, significantly lower than HRM an JMA forecasts, as
can also be seen from Table 1. The peak rainfall events seem to coincide very well, especially
if one compares ECMWF with JMA.
Numerical rainfall forecasts
For weather forecasting the National Centre for Hydro-meteorological Forecasting (NCHMF)
makes use of the High-resolution Regional Model (HRM), originally developed by theDeutsche Wetter Dienst (DWD). The HRM model takes its initial and boundary conditions
from the global model GME (DWD) every 3 hours from the internet. HRM runs with two
resolutions:Area-average precipitation (mm)30.0

HRM 0-24 hours
JMA 0-24 hours
ECMWF 0-24 hours

25.0

20.0

15.0

10.0

5.0

0.0
15/06/2004

15/07/2004

15/08/2004

15/09/2004
DateFig. 1 Area-average rainfall for the domain 16.75o-26.875o N, 99.375o-110.625o E (HRM andJMA) and the domain 16.75o-26.25o N, 99.75o-108.25o E (ECMWF) over the time period 1506-2004 until 30-09-2004. The lead time of the forecasts is 0-24 hrs.
Area-average precipitation (mm)30
HRM 24-48 hours
JMA 24-48 hours
25

ECMWF 24-48 hours

20

15

10

5

0
15/06/2004

15/07/2004

15/08/2004

15/09/2004
DateFig. 2 Area-average rainfall for the domain 16.75o-26.875o N, 99.375o-110.625o E (HRM
and JMA) and the domain 16.75o-26.25o N, 99.75o-108.25o E (ECMWF) over the time
period 15-06-2004 until 30-09-2004. The lead time of the forecasts is 24-48 hrs
Table 1 Area and time-average rainfall in mm for the domain 16.75o-26.875o N,
99.375o-110.625o E (HRM and JMA) and the domain 16.75o-26.25o N, 99.75o108.25o E (ECMWF) over the time period 15-06-2004 until 30-09-2004.
Time

HRM

JMA

ECMWF

0-24 hrs.

8.2

10.1

7.6

24-48 hrs.

10.7

8.0

5.7

(a) a coarser resolution with grid spacing 0.25o (28 km) for the domain 5oS-35oN, 80.25o130.25oE) for 20 vertical layers, and(b) a finer resolution with grid spacing 0.125o (14 km) for the domain 7.25o-27oN, 97.375o117.25oE) for 31 vertical layers. With HRM forecasts on wind, temperature, humidity andrainfall are made for the next 72 hrs. The performance of the HRM model is not satisfactoryyet. Monsoon patterns are well forecasted but systems from the sea are not. Since the latterproduce the rainfall in the flood season the following performance pattern is observed:• heavy rains are always underestimated, and• lighter rains always overestimated.
It is suspected that the assimilation of observations in GME for this part of the world is not
optimal, so the boundary conditions delivered by GME are inadequate. It is intended to
improve this by taking the boundary conditions in future from the AVN/NCEP global model,
which is foreseen for late 2005.
Since 2001 also every 6 hrs weather forecasts for the next 72 hrs are obtained from
JMA of Japan. This provides rainfall data at a coarser 1.25ox1.25o grid (140 x 140 km). Its
rainfall forecasts are generally much better. Therefore, the HRM and JMA forecasts havebeen combined to see if this results in better flow forecasts, with calibrating coefficient k =RJMA/RHRM
The European Centre for Medium-Range Weather Forecasts (ECMWF) delivers
weather forecasts for the whole world on a grid-basis, up to 10 days ahead. The grid of thedata covers the domain 16.75o-26.25o N, 99.75o-108.25o E.
Numerical daily rainfall forecasts were collected for the period 15 June until 30
September from above mentioned sources.
Satellite rainfall estimation, prediction
Collection from NOAA-USGS satellite rainfall estimation with calibration to surface
synoptic stations from GTS and 3-day rainfall forecasts by MM5; 7-day forecast GFS by
AVN/NCEP. TRMM (Tropical Rainfall Measure Mission) data with 3-hour cumulative
rainfall.
APPLICATION OF HYDROLOGICAL - HYDRAULIC MODELS
Used NAM and hydrodynamic model Mike11 for flood forecasts at the Red river system with
rainfall prediction from numerical weather forecasting products. The Red river basin witharea 169000 km2 divided into 27 sub-basins.
The NAM model was calibrated with observed rainfall and discharge records of the years
2004. Runoff calculated from rainfall by NAM linked as inflow from tributaries or upper
reaches or lateral flow into main stream by Muskingum routing. Flow routing in main stream
was made by hydrodynamic Mike11. Calibrated results of hydrological and hydraulic models
are shown in figures 3-5.
From these figures it is observed that a reasonable fit is only obtained for the Lo at Vu Quang.
For the Da at Hoa Binh upstream (reservoir inflow) the model strongly overestimates thepeak flows, whereas for the Thao the opposite applies (Table 2). This reflected in the values
for the Nash-Sutcliff criterion, defined by equation (1):N∑ (Ysim ,inNS = 1 − i =N1∑ (Yobs ,i− Yobs ,i )2(1)− Yobs )2

i =1

where: nNS = Nash-Sutcliff criterion;
Ysim = simulated discharge
Yobs = observed discharge;
Yobs = average of observations

Calibration of NAM-model: Da at Hoa Binh u/s
16000

14000

12000

Simulated

3

Discharge (m /s)

Observed
10000

8000

6000

4000

2000

0
23/4/04

13/5/04

2/6/0422/6/04

12/7/04

1/8/04

21/8/04

10/9/04

30/9/04

20/10/04

Fig. 3 Calibration of NAM model for Da basin at Hoa Binh
Calibration of NAM-model: Thao at Phu Tho
5000
4500
4000
Simulated

Discharge (m /s)

3500

Observed

3

3000
2500
2000
1500
1000
500
0
23/4/04

13/5/04

2/6/0422/6/04

12/7/04

1/8/04

21/8/04

10/9/04

30/9/04

20/10/04

Fig.4 Calibration of NAM model for Thao basin at Phu Tho
Calibration of NAM-model: Lo at Vu Quang
6000

5000

Observed

3

Discharge (m /s)

Simulated
4000

3000

2000

1000

0
23/4/04

13/5/04

2/6/0422/6/04

12/7/04

1/8/04

21/8/04

10/9/04

30/9/04

20/10/04

Fig. 5 Calibration of NAM model for Lo-Gam-Chay basin at Vu Quang
The values for nNS found for Da at Hoa Binh, Thao at Phu Tho and Lo at Vu Quang arepresented in Table 2 together with the standard error and the fraction of the varianceexplained by the model R2.

Table 2 Quality of NAM model calibration
Location
Da at Hoa Binh
Thao at Phu Tho
Lo at Vu Quang

Standard error
855 m3/s
440 m3/s
443 m3/s

R2
0.70
0.41
0.68

nNS
0.23
0.37
0.68

So, observed rainfall at gauge points that scarcely distributed, un-optimal or
unrepresentative for small sub-basins may be the one of main causes impacted on
calibration of NAM parameters. In other side, it is necessary to determine, calibrate theparameters, boundary and initial conditions for small sub-basins in the Lo, Thao rivers
basins.
Numerical forecasted rainfall from HRM, JMA and ECMWF used as inputs for NAM
and Mike11 models, applied and calibrated for flood season of 2004 year. Results at somesites are shown in tables 3, 4, 5 and figures 6 - 9.
Table 3 Comparison of flow forecast errors using HRM and JMA rainfall data, lead time
1 day
No error correction
StandardnNS
error
HRM JMA
HRM JMA
Hoa Binh 1043
909
0.66
0.62
Phu Tho
440
435
0.34
0.36
Vu Quang 511
564
0.48
0.47
Average
665
636
0.49
0.48
StationWith error correction
StandardnNS
error
HRM JMA
HRM JMA
596
637
0.84
0.82
231
240
0.81
0.81
297
374
0.84
0.82
375
417
0.83
0.82

Table 4 Comparison of flow forecast errors using HRM and JMA, lead time 2 days
StationHoa Binh
Phu ThoVu Quang
Average

No error correction
StandardnNS
error
HRM JMA HRM JMA
845
979
0.62
0.56
434
469
0.39
0.28
515
599
0.47
0.41
598
682
0.49
0.42

With error correction
Standard
nNS
error
HRM
JMA HRM JMA
778
793
0.67
0.72
333
280
0.60
0.75
442
349
0.63
0.80
518
474
0.63
0.76

Hoa Binh reservoir inflow HRM-forecasts
10000
Forecasted 1 day ahead
Forecasted 2 days ahead
Difference 1 - 2 days

8000

3

Discharge (m /s)

6000

4000

2000

0

-2000

-4000
23/4/04

13/5/04

2/6/0422/6/04

12/7/04

1/8/04

21/8/04

10/9/04

30/9/04

20/10/04

Fig. 6 Forecasted (1 and 2 days ahead) inflow to Hoa Binh reservoir and theirdifference; based on HRM and using error corrections to simulated flows
T h a o a t P h u T h o , H R M -fo r e ca s ts
5000
F o r e ca s te d 1 d a y a h e a d
F o r e ca s te d 2 d a ys ah e ad
D iffe re n c e 1 - 2 d a ys
4000

3Discharge (m /s)

3000

2000

1000

0

-1 0 0 0
2 3 /0 4 /04

1 3 /0 5 /04

0 2 /06 /0 4

2 2 /0 6 /0 4

1 2 /0 7 /0 4

0 1/0 8 /0 4

2 1/0 8 /0 4

1 0/0 9 /0 4

3 0 /0 9 /04

2 0 /1 0 /04

Fig. 7 Forecasted (1 and 2 days ahead) flows in Thao river at Phu Tho and theirdifference;
based on HRM and using error corrections to simulated flows
L o a t V u Q u a n g , H R M -fo r e c a s ts
5000
F o re c a s te d 1 d a y a h e a dfo re c a s te d 2 d a y s a h e a d
D iffe re n c e 1 - 2 d a ys

4000

3

Discharge (m /s)

3000

2000

1000

0

-1 0 0 0
2 3 /0 4 /0 4

1 3 /0 5 /0 4

0 2 /0 6 /0 4

2 2 /0 6 /0 4

1 2 /0 7 /0 4

0 1 /0 8 /0 4

2 1 /0 8 /0 4

1 0 /0 9 /0 4

3 0 /0 9 /0 4

2 0 /1 0 /0 4

Fig. 8 Forecasted (1 and 2 days ahead) flows in Lo river at Vu Quang and theirdifference; forecasts based on HRM rainfall data and using error corrections to simulatedflows

1-day volumes
900

800
Observed
Forecast

700

Volume Mm

3

600

500

400

300

200

100

0
23/4/04

13/5/04

2/6/0422/6/04

12/7/04

1/8/04

21/8/04

10/9/04

30/9/04

20/10/04

Fig. 9 - One day forecasted and observed inflow volumes to Hoa Binh reservoir
Table 5 Overview of quality indicators of inflow volume forecasts for Hoa Binh
reservoir (R2 = fraction of explained process variance, R = correlation coefficient, nNS =Nash-Sutcliff criterion)
Lead
time
(days)
1
2
3
4
5

R2

R

nNS

0.87
0.82
0.77
0.73
0.67

0.93
0.91
0.88
0.85
0.82

0.82
0.81
0.76
0.70
0.61

DATA ASSIMILATION TECHNIQUE FOR FORECAST
Flood forecasting systems are typically constructed from a range of hydrological - hydraulicmodels that are applied in the prediction of discharges and stages in the river system, as afunction of observed meteorological conditions and possibly short or medium term forecastsof these. This system consists of 4 modules, such as Real time data acquisition for observed
meteorological and hydrological conditions; Hydrologic and hydraulic models for simulation;
Forecast of meteorological conditions and Updating and data assimilation.
Data assimilation or updating is a feedback system where the process models in second
module are conditioned using the information on the current state of the system modelled.
These process models can be considered as a set of equations containing parameters and statevariables, where state variables are transient in time, and the parameters are generally held
constant at some value determined in the calibration of the model prior to application in thereal time environment. The primary goal of data assimilation is to guarantee an up to daterepresentation of the state variables in model terms. This state is then used as an initial state
for subsequent forecasts.
Data assimilation and updating procedures may be categorised in four different approaches:
Updating of input variables; Updating of model state variables (the Kalman filter that is wellproven for linear systems and the Extended Kalman filter for non-linear systems); Updatingof model parameters; Updating of model outputs or error correction.

In this study use of error correction: Linear regression equations, based on the observed andforecasted output during the entire forecasting period, have been established for each leadtime. In addition an error term is added to the simulation result, by making use of the factthat at least for the shorter lead times the forecast errors are serially correlated. Hence, thefollowing procedure is applied:
Qt + n∆t = a + b.QNAM ,t +∆t ( Pt + n∆t , Pt + ( n −1) ∆t , Pt + ( n − 2) ∆t ,...) + c(Qobs ,t − Q forecast ,t )(2)where: n∆t = forecast lead time
QNAM = discharge forecasted by NAM model
P = precipitation; a, b, c = regression parameters
So, with above flood forecasting tools, it is possible to produce 2-day flow forecast at upperstream sites in the Red river system. Forecasting error may be caused by many reasons likescarcity of rain gauge points; their un-presentativeness; areal mean rainfall not yet taken into
account of orographic effects in view of the topography; not optimum calibration ofhydrological and hydraulic models. To increase an accuracy and lead time of flood forecasts,
must to create a reliable (consistency checked) set of rainfall, evaporation and runoff data of
the hydro-meteorological stations in Da, Thao and Lo-Gam Chay basin for at least 3 recent
years to be used for calibration and verification of the forecasting model.

CONCLUSIONS AND RECOMMENDATIONS(a) It is available using Mike11 software with weather forecasts (numerical and satellite) for
lead-time extension and accuracy improvement of hydrological forecasting.
(b) Compare the observed areal rainfall data derived from point rainfall with estimates fromradar and satellite and one day forecasts, and when needed adjust the areal rainfall prior
to model calibration. Due attention should be given to the representativeness of the point
rainfall locations in view of orographic effects.
(c) Develop properly calibrated rainfall-runoff and river routing models for the sub-basins.
The tools to be selected should allow for multiple data assimilation options to improve
the forecasts.
(d) Analyze in a similar manner, extended also to investigations for the sub-basins
separately, to increase accuracy and extent lead-time of forecasts to 5 days.
(e) Analyze the characteristics of the forecast error as a function of lead time, making a basefor error calibration and forecasting evaluation.