International Centre for Integrated Mountain Development

Kathmandu, Nepal

Mandira Singh Shrestha , Pradeep Dangol, Guna Nidhi

Paudyal, Shahriar Wahid, Gautam Rajkarnikar

Real-time flood outlook in the Narayani

basin

International conference on climate change

innovation and resilience 12-14 January 2015 Kathmandu, NEPAL

Presentation outline

• Distribution of disasters in Nepal

• Model framework

– Process

– Model platform

• Data available

• System of flow forecasts

• Dissemination

• Example of 2014 floods

• Challenges and road ahead

Distribution of disasters in Nepal:

1983-2010

Source: DWIDP Disaster Bulletin, 2011

About 35% of people killed are due

to floods and landslides

Monsoon 2014 in Nepal

• From June through September 2014 – Killed 265

– Missing 256

– Injured 157

– Affected 200,000

– Displaced 34,000

• Need for timely and reliable forecast and

early warning

Photo source: NRCS

Photo source: NRCS

• Setup of a real-time flood outlook for GB Basin

• 21 nodes have included for flood outlook in GB- basin with 3 days lead

time

• Focus on major rivers of Nepal with real-time data and in collaboration

with DHM

HYCOS: Regional flood information

system

Karnali Narayani

Koshi

Study area: Narayani basin

MIKE platform used to develop flood

outlook

HD Hydrodynamic

AD Advection-Dispersion

ST Sediment Transport

WQ Water Quality

RR Rainfall-Runoff

FF Flood Forecast

DA Data Assimilation

MIKE CUSTOMISED decision support tool - Real time Modelling

Process based models

Hydrologic Model

NAM (lumped and conceptual)

Hydrodynamic Model

Catchments/Watershed River/Channel

Flood forecast nodes

Modelling framework

Dynamic data

Observed data

Data Process Setup of model

Static data

Model Calibration and validation of

Hydrological & Hydrodynamic

model

Satellite data

Topographic Data

• Catchment area

• Elevation

• River network

• River cross section

• Structures/controls

• Model parameters

Meteorological forcing

Historical data

Real time observed/satellite data

QPF Meteo-Forecast

Forecasting Model

in Real time Platform

Dissemination Flood Information

through websites/ email/ SMS

Data/ tools used for modeling

STRM images

Topography

Rainfall

(Bangladesh,

Bhutan & Nepal)

Observed data

ARC/GIS

TRMM Rainfall

(3B42) & (RT)

Satellite data

Software/ tool

MODIS Snow

accumulation

Google earth

Temperature (Nepal)

Discharge

(Bangladesh,

Bhutan & Nepal)

APHRODITE

Temperature

(V1204)

Global ET(GDAS)

• Excel,

• Visual Basic script

• Python script GFS Rainfall/Temp

Topographic data preparation

SRTM 90m DEM

- Delineate catchment

- Generate river network and profile

- Process cross section

Mean average weightage calculation

Thiessen polygon method for observed data

Zonal statistical method of catchment for satellite data

• Data availability > 90%

• Annually homogeneously fit

Calibration of model

Kali Gandaki river

Narayani river at Devghat

Water balance at Narayani river, Devghat

TRMM bias-correction

Linear regression in each cell point,

Between monthly values in years

2000 – 2013

Slope = 0.74

R2 = 0.8

HD simulation comparison –

at outlet of basin

TRMM 3b42 (RT)

TRMM 3b42

TRMM 3b42 bias

Observed Discharge

~50km

System of flow forecast

Hindcast

(Observed RT

data, TRMM

(RT), NASA

Quantitative

Precipitation

Forecast

(GFS, NOAA)

~25km

Hindcast Forecast

Time of Forecast

Dissemination of information

(www.icimod.org/floodoutlook)

Tabular view of forecast

Results of forecast exercise in

August 2014

Performance

Evaluation of flood forecast on the Narayani

Dissemination of information

Challenges and road ahead

• Availability of data - Topographic data (X-section)

• Selection of forecast data for (Amplitude and phase error) Ex:

• RIMES (Daily, Grid – 9 X 9 km)

• GFS (3 hourly, Grid – 50 X 50 km)

• Define “Alert” and “Danger” levels at given locations

• Further development and refinement of the flood outlook.

Thank you