UCCRTF backs training for improved early flood warning in Central Viet Nam

UCCRTF backs training for improved early flood warning in Central Viet Nam

By Trang Dinh and Bas Stengs

Modernizing flood forecasting and warning often comes with the requirements of knowledge transfer and expertise enhancement for forecasters, decision makers, and the residents in local communities. To ensure that the Flood Forecasting and Warning System that is being built for Hoi An city and VGTB Basin — a major catchment in Viet Nam—is able to operate effectively, an extensive collaborative modelling and training programme was held from July 2019 to February 2020, with support from the Urban Climate Change Resilience Trust Fund (UCCRTF).

The on-the-job training program was held in Tam Ky, Quang Nam, the mid-central province of Vietnam under ADB Grant 0462-VIE[1]: Urban Environment and Climate Change Adaptation Project. Key deliverables of the project are: 

  1.  a Flood Forecasting and Warning System (FFWS);
  2. supporting the Provincial Hydrological and Meteorological Centre;
  3. a Decision Support System (DSS); and,
  4.  supporting the Provincial Steering Committee for Disaster Prevention, Search and Rescue (PSCDPSR).​
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Officers of Standing Office of Provincial Steering Committee for Natural Disasters Prevention, Search and Rescue participating in the Decision Support System training, February/2020, in Tam Ky, Quang Nam Province. Credit to Dang Thi Kim Nhung, Emergency communication & Management Specialist of Vietnam Institute of Water Resources Planning.

The project, underway since March 2018, is led by a consortium of Deltares (Netherlands), HaskoningDHV Nederland B.V. (Netherlands), SUEZ Consulting (SAFEGE) (France) and the Institute for Water Resources Planning (Vietnam).  The FFWS and DSS for the Vu Gia-Thu Bon river system, was considered to be one of the most urgent (non-structural) project measures. The FFWS system is designed to improve the procedures for flood warning and timely evacuation, while the DSS enables the analysis of both structural and non-structural measures regarding flood management, and the study of water shortage problems and salinity intrusion during dry periods.

The project applied a state-of-art flood early warning system, called “Delft FEWS” – an open, flexible, free-of-charge software package developed by Deltares, to the Vu Gia Thu Bon river basin. This was paired with an upgraded MIKE river basin modelling package and a new Delft3D marine model to create an integrated FFWS.

​Training to ensure long term sustainability

The goal of the training is to ensure the long-term sustainability of the FFWS, by building the capabilities of system developers and operators. A collaborative approach was deployed through a series of technical on-the-job training sessions, allowing participants to gain knowledge and know how to operate and maintain the FFWS and DSS in the future. The specific objective of the technical working and training sessions was to train the staff in using the calibrated models and operate the FFWS and DSS, and to teach them the process of building, calibrating and maintaining the systems.

​One participant, Mr. Truong Xuan Ty, Chief of Standing Office of the Provincial Steering Committee for Disaster Prevention, Search and Rescue, said “we currently don’t use any forecasting software. If we can better understand the flood forecasting and flood warning models, by using the FFWS+DSS, this will greatly improve the efficiency of the decision making and will speed up the warnings to the communities”.

​A total of nine training sessions were delivered to end users such as the Provincial Hydro-Met Centre (PHMC), the Mid-Central Regional Hydro-Met Centre (RHMC) and the Provincial Steering Committee for Disaster Prevention, Search & Rescue (PSCDPSR). The training was divided into two main components: (i) Catchment and river model development and (ii) Delft-FEWS flood early warning system configuration and operation.

​The on-the-job training was organized at the end users’ location in Tam Ky city, Quang Nam province. Priority was given to the group of potential operators: forecasters from PHMC and RHMC and technical officers from PSCDPSR, by delivering intensive instructions and knowledge ensuring as much interaction as possible between trainers and trainees. Characteristics of the training sessions included: 

  • Each technical session introduced a specific topic providing expertise on applicable tools, software, features, required data, know-how to self-configure and operate the models through various practical exercises.
  • Demo versions pre-configured for the project were provided for demonstration and practice during and after each session.  
  • The demo versions were updated to reflect comments and requests from end users during and after each session. Agile work plans for the following sessions were arranged together with the end-users at the end of each session, to incorporate the user needs as much as possible.

​The training was designed and lead international consultants. Because most local officers are not fluent in English, language barriers were a considerable challenge. To overcome this, a Vietnamese user interface was developed for both software systems and the training was delivered in Vietnamese by local trainers.

​With the final training session held in February this year, a recap of the complete training was done with lots of room for interaction, by means of a Q&A session and a variety of user-selected practical exercises, such as the Delft-FEWS basic configuration and river catchment model set-up. The fact that most exercises were completed with little or no support from the trainers proved that the local skills on modelling, flood forecasting and warning had significantly improved through the concept of “learning by doing”. 

​For further information about the project and the training, please contact the authors: Bas Stengs (Bas.Stengs@deltares.nl) or Trang Dinh (Trang.Dinh@deltares.nl).


This article was originally posted on the Asian Development Bank Livable Cities Blog.
Cover image by Ngocnb at Vietnamese Wikipedia

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