Integrated Flood Analysis System (IFAS)

Transcription

1 Global Flood Alert System (GFAS) Streamflow Integrated Flood Analysis System (IFAS) For local flood forecasting and warning using satellite-based rainfall and global GIS databases for poorly gauged basin anywhere in the world Kazuhiko FUKAMI International Centre for Water Hazard and Risk Management under the auspices of UNESCO (ICHARM), Public Works Research Institute (PWRI), Japan

2 Integrated Flood Analysis System IFAS Toolkit to implement Global Flood Alert System (GFAS) Streamflow

3 Example of Satellite-based rainfall data disclosed without cost through internet Product name 3B42RT CMORPH QMORPH GSMaP_NRT Developer and provider NASA/GSFC NOAA/CPC NOAA/CPC JAXA/EORC Coverage N60 - S60 Resolution Resolution time 3 hours 3 hours 0.5 hour 1 hour Time lag 10 hours 15 hours 2.5 hours 4 hours Coordinate system Historical data Dec Dec WGS nearest past 2 days Dec. 2007~ Sensors TRMM/TMI Aqua/AMSR- E AMSU-B DMSP/SSM/I IR Aqua/AMSR-E AMSU-B DMSP/SSM/I TRMM/TMI IR TRMM/TMI Aqua/AMSR-E ADEOS-Ⅱ/ AMSR SSM/I IR AMSU-B

4 GSMaP_NRT JAXA, JST-CREST (Prof. Ken ichi OKAMOTO, Osaka Pref. Univ. et al.) ICHARM/PWRI 4

5 Adjustment by rainfall-area movement pattern Present 1hour later 2 hour later 3h additional rainfall Underestimate + + = Present 1hour later 2 hour later 3h additional rainfall Correct + + = Yoshiki Shiraishi, ICHARM 2008 Quickly moving pattern = Small spatial variance of cumulative rainfall On a certain scale Selection of grids to characterize spatial variance S Slowly moving pattern = Large spatial variance of cumulative rainfall On a certain scale 2 n X = 1 k i k t= 0 i n 1 = n i = 0 x Rc = X c () t : k hours (1) 2 ( X X ) : n = 4 ( 2 ) c i { S ( ) } (3) m = ln k + n m( Sn, k) (4) 5

6 1.Background 2.Correction 3.Analysis of Morakot 4.Conclusion Comparison Fig.1 3 hour-rainfall(-2009/08/08 03:00(UTC)) (Left:Ground gauged, Center_L:GFAS Center_R:GSMaP Right:Corrected GSMaP) -999 ~ 0 0 ~ ~ ~ ~ ~ ~ 100 > 100 1GSMaP uses several satellite sensors which aren t used in GFAS, so the rainfall distribution is different from each other. 2GSMaP is basically underestimated because GSMaP didn t get the information from satellite microwave radiometer during the peak heavy rainfall. 6

7 1.Background 2.Correction 3.Analysis of Morakot 4.Conclusion Comparison Observed Total Rainfall in Taiwan [mm] (2009/08/06 18:00 ~ 08/10 09:00 (UTC)) GFAS Total Rainfall in Taiwan [mm] (2009/08/06 18:00 ~ 08/10 09:00 (UTC)) GSMaP Total Rainfall in Taiwan [mm] (2009/08/06 18:00 ~ 08/10 09:00 (UTC)) 1: : : Fig.1 Total rainfall(2009/08/06 18:00 ~ 08/10 09:00)(UTC) (Left:Ground gauged, Center_L:GFAS Center_R:GSMaP Right:Corrected GSMaP) -999 ~ 0 0 ~ ~ ~ ~ ~ ~ ~ 2000 > GSMaP is underestimated. However, rainfall area which concentrates on south part of Taiwan is similarly distributed. 2Comparing corrected GSMaP with ground gauged rainfall, corrected GSMaP is better than GSMaP row data. 7

8 Global Precipitation Measurement (GPM) Current Observation System: TRMM and other orbital Satellites, and 5 Geostationary Satellites Core Satellite Dual Frequency Radar Multi Frequency Radiometer Observation of rainfall with more accurate and higher resolution Adjustment of data from constellation satellites JAXA (Japan) Dual frequency Radar, Rocket NASA(US) Satellite Bus, Micro-wave gauging measurement 8 Constellation Satellites Satellites with Micro-wave Radiometers More frequent Observation Cooperation : NOAA(US),NASA(US),ESA(EU), China, Korea and others Earth heating Phenomena Study of Climate Change Improvement of forecasting system Global Observation every 3 hours IWRM Flood Forecasting Forecasting of crop productivity

9 Integrated Flood Analysis System IFAS Toolkit to implement Global Flood Alert System (GFAS) Streamflow

10 Design concept of IFAS 1. To prepare interfaces to get satellite-based rainfall data in addition to ground-based rainfall data, to secure the worldwide availability of input data for flood forecasting/analysis system. 2. To adopt two types of distributed-parameter hydrologic models, the parameters of which can be estimated as the first approximation based on globally-available GIS databases to secure the worldwide availability of hydrologic models for flood forecasting/analysis. 3. To implement GIS analysis modules in the system to set up the parameters for the flood forecasting/analysis model, therefore no need to depend on external GIS softwares. 5. To prepare a series of easy-to-understand graphical user interfaces for data input, modeling, runoff-analysis, and displaying the outputs. 6. To distribute the executable program, free of charge, from the ICHARM/PWRI website 10

11 Use of Satellite Information 1 ) Satellite-based rainfall data TMPA-3B42RT (NASA) QMORPH/CMORPH (NOAA) GSMaP (JST/CREST, OPU, JAXA etc.) Global coverage : between 60N-60S latitude Mesh size: 0.25 degrees (25km) or 0.1 degree (10km) 3) Adjustment of Satellite Rainfall 1. Self adjustment within observations 2. Physically-based statistcal adjustment Automatic Data Extraction for area of interest Provided by NASA, NOAA, JAXA and IFNet-GFAS through the Internet for free 2) Ground-based rainfall data Observed local data (The source of local ownership of forecasts) Data assimilation with ground observations 4. Regional model + AGCM + Satellite observations

12

13 Modeling function DEM data GTOPO30 (USGS) Create a basin boundary automatically automatically IFAS creates a basin boundary and a river channel network use of digital elevation data. Create a river channel network Landuse data GLCC (USGS) Set parameter automatically IFAS estimates parameter based on the classification that we set up beforehand.

14

15 PWRI-Distributed Hydrological Model (PDHM) Suzuki et.al, 1996 ~ Ver. 2.0 Runoff Analysis YHyM/BTOP Model Ao et. al, 1998 ~ Ver. 1.4 (Coming Very Soon) Easy calibration for parameters Tested in many basins in Japan Well-known in journal papers in the world Suitable for flash floods in small and Tested Easy parameter calibration medium basins Tested in a variety of climatic and < a few tens of thousands square hydrologic conditions of the world kilometers Suitable also for seasonal floods in large continental basins Multi Runoff Analysis Engines

16

17 Display Results (IFAS-PDHM) Hydro-graph Plan view of river discharge Graph of tank water level and discharge Plan view of satellite-based rainfall

18 Example of outputs overlaid on Google Earth 図 -6 Google earth 上での表示 ( 川内川 ) ( 計算結果 ( 上段タンク水位 ) の平面図表示 ) 18

19 TRAINING WORKSHOP FOR THE GLOBAL FLOOD ALERT SYSTEM (GFAS) VALIDATION 3-8 Oct, 2008 JAPAN Purpose of the training course To build capacities to undertake hydrological prediction/forecasting in relatively ungauged basins using satellite-based rainfall. Participants Ethiopia, Zambia, Cuba, Argentina, Bangladesh, Guatemala, Nepal (7countries) Program Remote Sensing of Precipitation from Space (JAXA) Historical evolution of flood management system in Japan Introduction of Global Flood Alert System Operating procedures for IFAS Validation method of satellite-based rainfall Current conditions and problems in each country Validation plans using IFAS

20 Experimental application to actual basins Membramo River Basin in Republic of Indonesia Pasig River Basin in Republic of the Philippines A=499km2 A=78,992km2 Un-gauged basin Awash River Basin in Ethiopia A=66,308km2 Barak River Basin in Bangladesh A=31,026m2

21 IFAS : local ownership of flood forecasts System Training Global Data Local Data Low accuracy High accuracy