This document summarizes efforts to improve riverine flood forecasting in the Lower Mekong Basin. It describes the development of an operational bias correction tool called GPM-BICO to improve the accuracy of near-real-time satellite rainfall data used in the Mekong River Commission's Flood Early Warning System (MRC-FEWS). Testing showed GPM-BICO reduced errors in rainfall data by up to 50%, improving streamflow forecasts. It also describes incorporating the higher resolution CHIRPS-GEFS rainfall forecast product into MRC-FEWS, which provided more accurate forecasts up to 15 days compared to other products like GFS. MRC has begun implementing both GPM-BICO and CH

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Towards more accurate riverine
flood forecasting over the
Lower Mekong Basin
Miguel Laverde (ADPC), Chinaporn Meechaiya (ADPC),
Arjen Haag (Deltares), Martijn Kwant (Deltares)

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SCO NASA
SERVIR-Eastern
and Southern Africa
SERVIR-Hindu Kush
and Himalayas
SERVIR-Mekong
SERVIR-West Africa
SERVIR-Amazonia
Hub Network

4. Bangkok, Thailand
Intergovernmental
organization
Consortium:

5. Thematicareas
Supporting a better riverine and flash
flood forecasting for Lower Mekong
Region
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• The MRC Flood Early Warning System
(MRC-FEWS) is a modular
hydrological- hydraulic model
created to provide short and
medium-term early warnings flood
updates for each member countries.
• Dry season: weekly forecast
• Flood season: Daily forecast

6. Objectives:
• Improve the accuracy of the NRT
satellite-based rainfall product for
Short-term flood riverine forecast.
• Incorporate the state of the art bias
corrected CHIRPS-GEFS for Medium-
term flood riverine forecast.
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7. Operational bias correction
tool for the MRC FEWS system
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Short-term riverine forecast:
Characteristics:
• Python based tool (Open access)
• Working with multiple bias correction
methods
• Adjusted to work with MRC daily
information
• Working operationally or date range
based
• Evaluate the performance based on
R, RMSE and BIAS
https://github.com/Servir-Mekong/GPM-BICO
Available

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Bias correction schemes:
Performance of GPM-BICO
1. Uniform Distribution
Transformation (DT) (Bower,
2004)
2. Spatial Bias corrector (SB):
(Immerzeel (2010)
3. Spatiotemporal Distribution
Transformation
4. Gamma Quantile Mapping (V 1.3)
Period : 2014 – 2018
monsoon seasons
(June – October)
Correction
Validation
129 stations
138 stations

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Results: Hydrological impact
r = 0.85 and NS = 0.81
r = 0.72 and NS = 0.68
R = 0.94 and NS = 0.84
DT
SB
SDTMetrics
• Correlation
coefficient (r)
• Nash (NS)
Improvement:
25% the RMSE and BIAS percentage at annual scale
and almost 50% at monthly scale

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GPM-BICO into FEWS:
The Regional Flood Management and
Mitigation Centre (RFMMC)
Technical training GPM-BICO
27 May 2019,
Phnom Penh,
Cambodia

11. The Global Forecast System (GFS)
precipitation data are provided on
a daily basis by the NOAA Climate
Prediction Center.
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NOAA Daily GFS
Forecast Data
Source: earlywarning.usgs.gov
Medium-term riverine forecast:
• 7 day forecasts of precipitation
• 0.25 degree resolution

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State of the art bias corrected rainfall
forecast product: CHIRPS-GEFS
Source
https://nasaharvest.org
Bias-corrected and downscaled
version of NCEP Global
Ensemble Forecast System
precipitation forecasts.
Daily 5-day, 10-day, 15-day
Forecasts
5 km resolution
CHIRPS-GEFSGEFS

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CHIRPS-GEFS version 2.0
https://www.chc.ucsb.edu/data/chirps-gefs
Daily simulations 15 days rainfall forecast
(Before 5 days latency)
October 1st, 2020 to present are
currently available

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Available in:
https://climateserv.servirglobal.net/
CHIRPS-GEFS in SERVIR-MEKONG

15. Categorical
metrics
Extremal
TemporalSpatial
o Monsoon seasons 2017-2019 (Jun to October)
o 1 – 6 days forecast (0.25 degrees)
o Reference data GPM-IMERG Final version bias
corrected
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Analysis • Root Mean Square Error
• Bias
• Correlation coefficient
• POD
• FAR
• CSI
Standard metrics
Performance of CHIRPS-GEFS and GFS
ERROR METRICS
CHIRPS-GEFS v1 10 days forecast

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Temporal analysis
BIASRMSE
Correlation
coefficient

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POD 1 - FAR CSI
Temporal analysis

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Spatial analysis
GFS CHIRPS-GEFS
POD

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1-FARSpatial analysis
GFS CHIRPS-GEFS

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CSISpatial analysis
GFS CHIRPS-GEFS

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Results: Extremal
CHIRPS-GEFSGFS

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CHIRPS-GEFS into FEWS
Discharge
GFS
CHIRPS-GEFS
Anlong Touk

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Summary
• SERVIR-Mekong supports MRC in accessing the latest technology for near-real-time (NRT)
monitoring and rainfall forecast prediction for the FEWS system
• Results for bias correction GPM-BICO tool showed a reduction up to 50% of the bias and RMSE
errors in NRT IMERG data. Results in the hydrological model suggested that this reduction
considerable improved the streamflow forecast.
• CHIRPS-GEFS rainfall forecast provides a high resolution daily forecast information up to 15
days with 5 km spatial resolution
• In comparison with the GFS, CHIRPS-GEFS displayed the lowest temporal and spatial error with
a longer forecast during monsoon seasons
• This encouraged MRC to implement GPM-BICO and CHIRPS-GEFS into their Flood Forecasting
System in an operational setting to improve the lead time and accuracy of riverine Flood
Early Warning in the Lower Mekong Basin.

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https://github.com/Servir-Mekong/

26. www. servir.adpc.net
miguel.Barajas@adpc.net
chinaporn.m@adpc.net
arjen.Haag@deltares.nl
martijn.kwant@deltares.nl