Water-centric Inter-linkages Some Case Studies in Sri Lanka S.B Weerakoon 1 - Water with Floods, Climate, Energy Cho Thanda Nyunt 2 - Water with Climate Change Yoshimitsu Tajima 3 - Water with Coastal Environment 1 University of Peradeniya, Sri Lanka 2 University of Tokyo, Japan 3 University of Tokyo, Japan
Rainfall Forecasting and Flood Inundation along the Lower Reach of Kelani River Basin under Changing Climate S.B Weerakoon 1 , Srikantha Herath 2 , Gouri De Silva 1 1 Department of Civil Engineering, University of Peradeniya, Peradeniya, Sri Lanka 2 United Nations University, Shibuya-ku, Tokyo, Japan
Elevation distribution Flood inundation in Colombo and suburbs (DEM) create heavy economic damages 4
Kelani River Basin Region – Wet Zone Total Basin Area – 2,230 km 2 Uppermost Elevation – 2,250 m Length of the River – 192 km Average Annual Rainfall – 2,400 mm Peak flow – 800-1500 m 3 /s Vegetation cover Upper basin – Tea, rubber, grass and forest Lower basin – heavily urbanized
1. Weather forecasting for flood warning Rainfall forecasting using downscaling of 72 hr climate model data by Weather Research and Forecasting (WRF) model - to provide early warning on rainfall and floods
Application of Weather Research and Forecasting model (WRF model) Nesting option – 135/45/15/5 𝑙𝑛 (4050 ×4050 𝑙𝑛 / 1530 ×1395 𝑙𝑛 / 465 ×465 𝑙𝑛 / 245 ×260 𝑙𝑛 ) with input resolution of 10 minutes
Input data downloaded from NCAR web site
WRF – short term rainfall forecasting Calibration – 21 st November 2005 Validation – 27 th and 28 th April 2008, and 30 th , 31 st May and 1 st June 2008 Justification Mean Absolute Model Error percentage 𝑵𝑵𝑵𝑵 % = 𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻 𝑺𝑻𝑻𝑺𝑺𝑻𝑻𝑻 − 𝑷𝑷𝑷𝑻𝑷𝑷𝑻𝑻 𝑺𝑻𝑻𝑺𝑺𝑻𝑻𝑻 × 𝟐𝟐𝟐𝟐 𝑷𝑷𝑷𝑻𝑷𝑷𝑻𝑻 𝑺𝑻𝑻𝑺𝑺𝑻𝑻𝑻
Calibration and validation Calibration Difference between WRF predictions and observed rainfall for the date 21 st November 2005 Validation Difference between WRF predictions and observed rainfall for the date 27 th April 2008
Validation Difference between WRF predictions and observed rainfall for the date 31 st May 2008 Difference between WRF predictions and observed rainfall for the date 1 st June 2008
2. Flood inundation analysis under changing climate • Climate pattern up to 2099 under A2 and B2 Emission Scenarios of AR4 by – Statistical Downscaling Model • Inundation modeling in the lower Kelani basin using FLO-2D model
Study area for rainfall analysis (about 2200 km 2 ) Study area for rainfall – runoff simulation (about 1700 km 2 ) Study area for flood analysis (about 500 km 2 ) Hanwella Source: Department of Irrigation, Sri Lanka
Meteorological data Hydrological data Data collection Topographic data Rainfall forecast for future under both A2 and B2 scenarios (SDSM) Calibration and Analyze forecasted verification of rainfall data HEC-HMS Generate inflow at the (FLO-2D) upstream for future Forecasting future flood conditions according to future rainfall 14
Flood inundation model (FLO-2D) Parameters Grid size – 250 m Channel and catchment characteristics such as , Infiltration Manning’s coefficient Channel roughness Manning's coefficient according to land use Elevation Distribution (DEM) Grid and boundary of catchment Channel shape and dimensions
3 day rainfall for upper basin 3 da day ra rain infall / / Return (mm) mm) Peri riod / d / (yr yr) A 2 B 2 50 50 391 383 100 100 429 420 Daily rainfall for lower basin Dail ily ra rain infall / l / Return (mm) mm) Peri riod / d / (yr yr) A 2 B 2 50 50 425 377 100 100 476 417
Calibration and validation – with respect to discharge hydrograph at the D/S gauging station (Nagalagam Street) and flood inundation maps Justification Normalized Objective Function (NOF) Nash – Sutcliffe efficiency R 2 NS Percentage bias ( δ b ) Fraction of the domain classified correctly by the simulations (F) 𝐺 = 𝑇 𝑝𝑝𝑝 ∩ 𝑇 𝑛𝑝𝑛 × 100 𝑇 𝑝𝑝𝑝 ∪ 𝑇 𝑛𝑝𝑛 17
Calibration Observed and simulated flow during 2005 flood 18
Validation Observed and simulated flow at during April-May 2008 flood Event 𝑂𝑂𝐺 𝑆 2 𝑂𝑇 𝜀 𝑐 November 2005 0.09 0.98 6.98% April-May 2008 0.14 0.97 10.37% 19
Validation HEC–HMS was used to compute inflow into the lower basin at the upstream Time series of observed and simulated flow at the D/S gauging station during May 2010 flood F = 73% Observed Inundation extent (from DMC data) Simulated Inundation extent
Results Inundation extents due to 50 year Inundation extent correspond to 50 year return period rainfall under A2 scenario return period rainfall under B2 scenario Inundation extents due to 100 year Inundation extent correspond to 100 year return period rainfall under A2 scenario return period rainfall under B2 scenario
Water Resources in Sri Lanka
2012 MahaweliBasin Kelani Basin Source: CEB
Source: CEB in kt eq. CO2/TWh About 140 MHPs with 1000 974 778 778 350MW capacity at present 800 600 contribute 6.2% 400 200 15 Goal- Renewable energy to 1 0 supply 10% by 2016 Hydro with Reservoir Hydro Run-of- S1 Diesel Coal Heavy Oil River
Source: CEB Run-of-river MHP/SHP provides lowest contribution during dry period Operation of multi-purpose large reservoirs has great impact on energy generation
For the Kelani River Basin Forecasting of weather for early warning systems Inundation extents and high risk areas of inundation by rainfalls of 50, 100 year return periods under both A2 and B2 scenarios in Colombo were investigated For hydro-energy Integrated water management under changing climate is important.
Devon waterfall ( Upper Kotmale Subbasin ) 7th July 2008, Weerakoon
Adaptation strategies Levee and detention basin • A levee of 1.0 m height and 10 km long from the downstream • Detention reservoirs; several marshy lands were identified from land use maps and converted in to detention reservoirs. 10km long Levee Developed marshy lands as detention reservoirs
Results – Inundation extents under (c) 50 year return period rainfall under A2 scenario Reduces average risk about 65% 50 year return period rainfall under B2 scenario Reduces the average risk about 40%
Results – Inundation extents under (c) 100 year return period rainfall under A2 scenario Reduces the average risk about 32% 100 year return period rainfall under B2 scenario Reduces the average risk about 25%
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