Panut Manoonvoravong ASEAN Academic Networking in Water, Disaster Management & Climate Change 23 Sept. 2014

Transcription

1 Panut Manoonvoravong ASEAN Academic Networking in Water, Disaster Management & Climate Change 23 Sept. 2014

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3 Locations of early warning stations in the North No Basins Stations 1 Ping Wang 33 3 Sarawin 84 4 Kok 67 5 North Kong 56 6 Yom 52 7 Nan 100 Total 539

4 Early warning installation areas Numbers of early warning stations since 2005 in the north-west Thailand Provinces Early warning stations Effective villages 1. Chiang Mai Chiang Rai Lampang Lampoon Tak Kompang Pet Payao Mae Hon Son แผนท แสดงตำแหน งสถำน เต อนภ ยล วงหน ำ From

5 Instrument installation 1. Hill-side villages: rainfall measurement 2. Bridge side: rainfall & flow rate measurement

6 Instrument set Data processor and remote transferring gear Soil moisture detector Rain gauge Alarm signals

7 Warning signals Light and sound warning system 3 warning levels Watching and following the situation Low sound warning, every 20 minutes Alert Medium sound warning, every 15 minutes Evacuation High sound warning, every 3 minutes

8 Early warning implementation Early warning stations Regional water resources offices Central water resources office Operational officials Incident Information / report of flood - Water level investigation (Max water level record) - Damage investigation Insiders - Bureau of research, development & hydrology - Water crisis prevention centre No incident Information from Web Site Information from radar and met.stations Close Water level and rain fall monitoring Close collaboration with EW officials Outsiders - Department of Disaster Prevention and Mitigation - Sub-district administration organization - Provincial national resources and environment office

9 Conceptual hydro-climatic landslide triggering model (After Crozier, 2010)

10 1. Principles and Rationale Climate change is led by high uncertainties. Earth energy consuming is the major effect of climate change causing the concerned water issues such as season changed, extreme events, etc. Increment or Decrement of annual rainfall affects to severe and frequent flood/ draught. Heavy rainfalls induce consequently to landslides. A study on perspectives of flash-flood/landslide areas induced by climate change needs the future climate scenarios synergizing with the landslide model and forecasted rainfall.

11 2. Objectives 2.1 To evaluate the high risk areas forecasted from mathematic climate change models 2.2 To create the knowledge guideline for handling with flash flood/landslide induced by climate change 2.3 As the fostering data in future climate change for long term planning of concerning sectors 2.4 Future supporting data for policy makers

12 3. Study areas The rainfall measurement led to analyze risky flash flood / landslide was conducted cover 7 basins in the North namely: - Salawin - North Kong - Kok - Ping - Wang - Yom - Nan These areas include totally 16 provinces: Kam phang Pet, Chiang Rai, Chiang Mai, Tak, Nakorn Sawan, Nan, Payao, Pi Chit, Pit Snulok, Mae Hong Son, Petcha Boon, Prae, Lampang, Lampoon, Suko Thai, and Utara Dit

13 4. Methodology

14 Input requirement 1.Rainfall results evaluated from PRECIS (A2&B2) Baseline years from Predicted years from

15 Input requirement 2.Rainfall data from observation stations ( Baseline years from )

16 Input requirement 3. Maps

17 Input requirement 4. Historical flash-flood/landslide incidents

18 Activity (event) Landslide record 11 Rock Groups Landslide Activity NR MM Sum % Sum 1 Carboniferous and Permo-Triassic Granite Rocks Triassic Granite Rock Cretaceous Granite Rock Extrusive and Mafic Igneous Rocks Predominantly Sandstone and Siltstone Predominantly Shale and Mudstone Interbedded Sedimentary Rocks Predominantly Metamorphic Rock Predominantly Carbonate Rock Quaternary deposits Sedimentary Rock on Khorat Plateau Total Stimulated by Human Stimulated by Heavy Rainfall Rock Groups Soralump, et al

19 11 groups of rocks risky to induce landslide in 7 basins (Adapted from Thai geological map scale 1: 250,000 after department of mineral resources)

20 Analysis of appropriate bias adjustment to adjust initial rainfall simulated by PRECIS (Scenario A2 & B2)

21 Evaluation of high potential landslide area due to climate change DynaSlide Se-mi Statistical - DynaSlide Model - Semi Statistical Model

22 DynaSlide Model Pros More precise results Suitable in large-scale areas Cons Complicated, geotechni cal data used Not suitable in smallscale areas Time consuming Higher cost than semistatistical model

23 Antecedence Precipitation Index (API) in soil V T V v V a V w V s air water soil Porosity n Degree of saturation Sr V V V V API nsrt v T w v V T V S V v V w V a T = Total volume = Soil volume = Void = Water volume = Air volume = Thickness of crisis layer (After Soralump & Torwiwat,2008)

24 Se-mi Statistical Method Temporal Antecedence Precipitation Index (API t ) API t = (K t *API t-1 ) + P t API t = API at time t (t) (mm) API t-1 = Previous API (t-1) (mm) P t K t = Precipitation rate (t) (mm) = Regression Constant

25 Recession Constants: K K t = exp(-e t /W) เม อ E t = Evaporation at time เม อ W = Moister content (mm) W = %WHC*D B *soil depth 100 WHC = Water holding capacity (% by weight) D B Soil depth = Total density (g/cm3) = mm

26 Make it Dynamics Rainfall PRECIS %RTL = API t x 100 API cr Landslide Susceptibility Map (Se-mi Statistical) API cr

27 Summary of rainfall prediction results Description PRECIS Scenario A2 PRECIS Scenario B2 Average annual rainfall 58 years ( ) compared with baseline years ( ) Average wet season rainfall 58 years ( ) compared with baseline years ( ) Average dry season rainfall 58 years ( ) compared with baseline years ( ) Average max. daily rainfall 58 years ( ) compared with baseline years ( ) 5% 10% 3% 11% 16% 8% 11% 16% Crisis years of Kok and North Kong basins Crisis years of Ping, Wang, Yom, Nan, and Salawin basins

28 Crisis rainfall years used for semi statistical landslide model PRECIS Scenario A2 B2 Crisis rainfall years

29 A2 the year 2026

30 A2 the year 2035

31 B2 the year 2015

32 B2 the year 2033

33 Thank you for your attention