Remote Sensing of Water Resources

Transcription

1 Remote Sensing of Water Resources National Disaster Management Institute Yoomi Hur

2 Climate Change Hydrologic Cycle Hydrologic Indices Soil Moisture Evaportranspiration Drought Conclusions

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4 The water cycle is the only way that Earth can be continually supplied with fresh water. The heat from the sun is the most important part of renewing our water supply. 4

5 Soil Moisture Evapotranspiration Drought MODIS-Based ESI Estimation AMSR-E Soil Moisture / August, : :25 Instantaneous Radiation Dry Wet ESI / Spring ESI / Summer : :55 Instantaneous Evapotranspiration (ET) Dry Wet ESI / Autumn ESI / Winter SMOS Retrieval Algorithm

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7 Hydrologic Cycle Soil Moisture Source :

8 Satellite based measurements of soil moisture B Brand New Satellite!!! Accurate observation!!!

9 VUA, AMSR-E Soil Moisture on January, 2007 VUA, AMSR-E Soil Moisture on August,

10 Downscaling application 10

11 Paper : Choi*, M., and Y. Hur (2012). Remote Sensing of Environment, doi : /j.rse

12 European Space Agency (ESA) s Earth explorer mission Lunched on November the 2 nd 2009 Using L-band (1.4Ghz) passive remote sensing Measure volumetric soil moisture 4% error ranges Average spatial resolution 43 km (Grid scale 15 km) SMOS Mission

13 Level 0 Level 1 Level 2 Level 3

14 SMOS L2 Product (Soil Moisture) 2011/02/ /03/01 Computed using Brightness Temperature L-band is corrupted measurements by RFI. (Mickaël etal., 2011) Very few data in Korea. 2011/04/ /05/01

15 SMOS L3 Product Very few data in Korea either. Level 3 product soil moisture (January, 2011.)

16 SMOS L1C Product (BrightnessTemperature(TB)) 2011/02/ /03/01 Level 1c data product contains the brightness temperature. Necessary input to Level 2 Processing. 2011/04/ /05/01

17 SMOS Retrieval Algorithm Backward model Calculate brightness temperature(tb) from estimated soil moisture Match SMOS observed TB and calculated TB Iterate calculation with reestimated SM until difference between observed TB and calculated TB less then error range

18 SMOS Observed Brightness Temperature (August 15,2011) SMOS Calculated Brightness Temperature (August 15,2011) SMOS Observed Soil Moisture SMOS Retrieved Soil Moisture (August 15,2011) (August 15,2011) Patent : Soil moisture retrieval algorithm based on the genetic algorithm using satellite data, application number :

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20 KoFlux Haenam site, Korea < KoFlux Sites, > Evapotranspiration is the water lost to the atmosphere by two processesevaporation and transpiration Apart from precipitation, the most significant component of the hydrologic budget is evapotranspiration Evapotranspiration varies regionally and seasonally; during a drought it varies according to weather and wind conditions 20

21 MODerate resolution Imaging Spectroradiometer (MODIS) multispectral Sensor Onboard Terra (1999) and Aqua (2002) satellites 36 spectral bands in a range of 0.4 to 14.5μm Basic resolution Temporal resolution : 1 day Spatial resolution : 1km X 1km Terra Post processed land/atmospheric products provided by NASA/USGS : high usability 21

22 MODIS 16 Global ET Product Revised and proposed by Mu et al. (2007) based on Cleugh et al. s (2007) Remote Sensing-Penman Monteith (RS-PM) method The land surface ET product represents all transpiration by vegetation and evaporation from canopy and soil surfaces. Spatial distribution of ET is computed globally everyday at 1 km 1 km resolution. 22

23 Penman-Monteith Equation E sa C p ( e sat s (1 r s e) / / r a ) r a λe : latent heat flux (W m -2 ) s : slope of the curve relating saturated water vapor pressure (e sat (Pa)) to temperature (K) (Pa K -1 ) A : available energy (W m -2 ), ρ : air density (kg m -3 ) C p : specific heat capacity of air (J kg -1 K -1 ) e : actual water vapor pressure (Pa), r a : aerodynamic resistance (s m -1 ) γ : the psychrometric constant (Pa K -1 ), r a : aerodynamic resistance (s m -1 ) r s : surface resistance (s m -1 ) Revised RS-PM algorithm - Difficulties of accurate estimation : surface/aerodynamic resistance Using Enhanced Vegetation Index (EVI) to estimate fractional vegetation cover F C EVI EVI max EVI EVI min min F C : fractional vegetation cover EVI : Enhanced Vegetation Index 23

24 Estimation of MODIS-based Evapotranspiration (ET) R net ( 1 ) R sdn R ldn R lup Vegetation Index (LAI, NDVI, ) albedo Air temp. LST Emissivity Solar irradiance MOD13, 15 MOD43 MOD07 MOD11 MOD07 Evapotranspiration!!! 24

25 Spatio-Temporal variation of Evapotranspiration in North-East Asia (monthly) 25

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27 Top worst-disaster in the 20 th century (NOAA, 1999) 1. Drought and famine in China in 1907, toll estimated at 24 million. Millions dead in other drought-related famines in , 1936 and Drought in the Ukraine and Volga region of the Soviet Union in , deaths estimated at 250,000 to 5 Four events in Top 5 were caused by drought! million. 3. Indian drought of , estimates of dead at 1.5 million. In 1900, drought in India blamed for 250,000 to 3 million deaths. 4. Yangtze River flood, China, 1931, 3.7 million killed due to flooding and subsequent disease and starvation. 5. Sahel drought in Africa in , with estimates of dead at 600,000; more than that were died in Bangladesh cyclone in 1970, with 300,000 to 500,000 dead in wind and storm surge. 7. Bangladesh cyclone in 1991, with 138,000 killed. 8. Flooding in Vietnam in 1971, with 100,000 killed. 9. Hurricane Mitch in Central America in 1998, with an estimated 11,000 dead, the region's greatest hurricane loss since Great Iran flood in 1954, with more than 10,000 dead. 11. Typhoon Thelma in 1991 in the Philippines, with 6,000 fatalities. 12. Typhoon Vera in Japan in 1958, with 5,000 dead. 13. Great Smog of London in 1952, 4,000 deaths linked to the smog, that many others to related causes. 14. Iran blizzard of 1972, with about 4,000 people dead. 15. Violent winter storms along the coasts of northern Europe, including the Netherlands and United Kingdom, in 1965, with 2,000 lives lost. 27

28 Drought: w.r.t. durations of drought? 1-, 3-months duration: relatively short durations : When rainfall is not sufficient for a short duration, the soil moisture may be reduced, streams of small catchments corresponding to 2 nd or higher order may dry up, and small agricultural reservoirs begin to show its bottom. These may influence on the agricultural activities. (Agricultural drought) 6-, 12-months duration: relatively long durations : If drought continues for a long time, The water level at the large scale dam may keep dropping. And finally, these may influence on the hydrological cycle, water supply system for tab water and industrial water. (Hydrological drought) 28

29 Drought: drought Indices PDSI (Palmer Drought Severity Index ; Palmer 1965) : Precipitation, Temperature, Soil Moisture SPI (Standardized Precipitation Index; McKee et al. 1993, 1995) : Precipitation data ESI (Evaporative Stress Index; Martha 2007) : Evapotranspiration, Land Surface Temperature SWSI (Surface Water Supply Index; Shafer and Dezman 1982) : Precipitation, Steam flow, Ground water stage SMI (Soil Moisture Index), EDI(Effective Drought Index), RDI (Reclamation Drought Index), CMI(Crop moisture Index). 29

30 - temporal anomalies in evapotranspiration (ET) - anomalously high or low rates of water use across the land surface ET - retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST - a fast response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates capability for capturing early signals of flash drought, brought on by extended periods of hot, dry and windy conditions leading to rapid soil moisture depletion. 30

31 The history of remote sensing based drought index 31

32 ESI - not require precipitation data The current surface moisture state - deduced directly from the remotely sensed LST More robust in regions with in-situ precipitation monitoring. Signatures of vegetation stress - manifested in the LST signal before any deterioration of vegetation cover occurs Indicated in NDVI, TIR-based indices such as ESI can provide an effective early warning signal of impending agricultural drought 32

33 ALEXI-based ESI - includes non-precipitation related moisture signals (such as irrigation; vegetation rooted to groundwater; lateral flows) ALEXI-based ESI - provides an independent assessment of current drought conditions, supplementing precipitation and modeling-based indices Anderson et al., (2007) 33

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36 Dry Wet 36

37 Drought 37

38 National Drought Monitoring System using Satellites Data 38

39 Remote sensing is the observation and establishment of a permanent record of an object without actually touching (Paul, 1980). Satellite based sensors offer the advantages of large area mapping and long term repetitive coverage (Jackson et al, 1996). Remote sensing provides a means of observing hydrological state variables temporally and over large areas (Schmugge et al, 2002). Continuous monitoring with remote sensing 39

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