1、NASA SCIENCE MISSION DIRECTORATE APPLIED SCIENCES PROGRAM_Earth Science for Disaster Management Detecting Tropical Floods and Landslides using NASA-based Precipitation InformationEvaluation ReportSeptember, 2006Goddard Space Flight CenterEarth Science Exploration Serving SocietyAccelerating the real
2、ization of economic and societal benefits from Earth science,information, and technolo2Detecting Tropical Floods and Landslides using NASA-based Precipitation InformationEXECUTIVE SUMMARY:This project uses NASA-based data sets of precipitation and land surface characteristics for use in quasi-global
3、 flood and landslide DSTs (developed in part by USGS/NOAA partners for USAID and NOAA/NWS use) for use in disaster management, response, preparedness and mitigation activities around the globe. The key NASA data set is the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Anal
4、ysis (TMPA) global rainfall map, which is produced by using TRMM to calibrate, or adjust, the estimates from other satellite sensors, and then combining all the estimates into the 3-hr TMPA final analysis. The TMPA is a TRMM standard product at fine time and space scales and covers the latitude band
5、 50N-S for the period 1998 to the delayed present. A real-time version of the TMPA merged product was introduced in February 2002 and is available on the NASA TRMM web site (http:/trmm.gsfc.nasa.gov).The second key data set is the digital elevation model (DEM) data from the NASA Shuttle Radar Topogr
6、aphy Mission (SRTM; http:/www2.jpl.nasa.gov/srtm/) The 30m spatial resolution provide by SRTM data are used to derive topographic factors (slope, aspect, curvature, etc.) and hydrological parameters (flow direction, flow path, etc.). In addition, MODIS global land cover data are used as a surrogate
7、for vegetation and land use types. The highest resolution of the MODIS land cover classification map is 250-meter. The MODIS Land Cover Product describes the geographic distribution of the 17 land cover types based on an annual time series of observations.Data set inputs are evaluated as part of the
8、 development of the landslide susceptibility map. The global landslide susceptibility map is finally classified into six categories: 0-water bodies or permanent ice and snow; 1-very low; 2-low; 3-moderate; 4-high; 5-very high susceptibility. The very high and high susceptibility categories account f
9、or 2.8% and 18.6% of land areas. The results clearly demonstrate the hot spots of the high landslide potential regions: the Pacific Rim, the Alps, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, and parts of the Middle East and Africa. Early validation results of the TMPA indic
10、ate reasonable performance at monthly scales, while at finer scales the TMPA is successful at reproducing the surface-observation-based histogram of instantaneous precipitation over land, as well as reasonably detecting large daily events. Requirements for landslide and flood DSTs have been identifi
11、ed and evaluated as part of the development of the preliminary landslide effort. The result indicates that both the precipitation information and the land surface data sets are of value for input into the DST and the new NASA (and other) data will allow the landslide DST to be successful. Because th
12、e same data sets will be used for the flood DST and have the same requirements as to precision, resolution and access, the conclusion also holds 3for the flood DST. Significant progress has been made in this project preparing for movement into the design and implementation stages. This will be accom
13、plished over the next six months as the final design for the modifications to the flood and landslide are made and the required software developed and implemented. Verifying and validating the NASA data will then take place and benchmark activities will initiate. A global flood and a global landslid
14、e forecast system using NASA input for improvement appears to be a very realizable goal.1. IntroductionFloods and landslides associated with heavy rainfall account for the largest number of natural disasters and affect more people than any other types of natural disasters around the world. Various U
15、.S. national agencies (e.g., USAID), NGOs (Non-Government Organizations, e.g., International Red Cross) and international organizations (e.g., UNESCO) require real-time, global information on existing and potential natural disasters for response, preparedness, planning and mitigation. NOAA and the U
16、SGS also require real-time, global information for their forecast and hazard models. While over most of the developed world conventional observations (e.g., ground-based radars and rain gauges) are useful for the prediction, detection and monitoring of floods and related landslides, in the developin
17、g world, especially in the tropics, these conventional observations are sparse or unavailable. The goals of the project are:a) Provide NASA-based data sets of precipitation and land surface characteristics for use in quasi-global flood and landslide DSTs (developed in part by USGS/NOAA partners for
18、USAID and NOAA/NWS use) for use in disaster management, response, preparedness and mitigation activities around the globe. This application activity will be built on the solid foundation of the substantial NASA work already done in producing quasi-global precipitation information in real-time. b) Su
19、pport partners in implementing the improved DSSs and verify and validate the usefulness of the NASA-based data sets and benchmark the resulting, improved DSSs in cooperation with USGS, NOAA and USAID. 2. Overview of DSTThe work in this project applies quasi-global satellite-based precipitation infor
20、mation, combined with satellite-based surface information (e.g., elevation,) and hydrological models to provide critical information as input into Decision Support Tools (DSTs) used by these organizations. Examples of such DSTs are the Asia Flood Network (AFN) and the Famine Early Warning System (FE
21、WS) developed by USGS/NOAA and used by USAID. USGS landslide DSTs based on precipitation thresholds are also used. Regional application to NWS AWIPS DSTs will also be explored. This project plays a key role in expanding the geographic coverage of these DSTs from national and regional to quasi-global
22、. This rapidly changing area can be greatly enhanced by the use of various 4NASA data sets including precipitation (from TRMM, Aqua-AMSR and other satellites), elevation (from Shuttle Radar Topography Missions SRTM), land-use and vegetation (from MODIS) and other data sets wholly or partly based on
23、space data.3. NASA Inputs to DSTsPrecipitationA long history of development in the estimation of precipitation from space has culminated in sophisticated satellite instruments and techniques to combine information from multiple satellites to produce long-term products useful for climate monitoring 8
24、. A fine time resolution analysis, such as the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), is the key precipitation data set for this study. The TMPA global rainfall map is produced by using TRMM to calibrate, or adjust, the estimates from other satellit
25、e sensors, and then combining all the estimates into the TMPA final analysis. The coverage of the TMPA depends on input from different sets of sensors. First, precipitation-related passive microwave data are collected by a variety of low-Earth-orbit satellites, including the TRMM Microwave Imager (T
26、MI) on TRMM, Special Sensor Microwave/Imager on Defense Meteorological Satellite Program (DMSP) satellites, Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) on Aqua, and the Advanced Microwave Sounding Unit B (AMSU-B) on the National Oceanic and Atmospheric Administrati
27、on (NOAA) satellite series. The second major data source for the TMPA is the window-channel (10.7 micron) infrared (IR) data that are being collected by the international constellation of geosynchronous-Earth-orbit satellites, which provide excellent time-space coverage (half-hourly 4x4-km-equivalen
28、t lat./long. grids) after merged by the Climate Prediction Center of the National Weather Service/NOAA. The IR brightness temperatures are corrected for zenith-angle viewing effects and inter-satellite calibration differences. Finally, the research TMPA also makes use of three additional data source
29、s: the TRMM Combined Instrument estimate, which employs data from both TMI and the TRMM Precipitation Radar, as a source of calibration; the monthly rain gauge analysis developed by the Global Precipitation Climatology Centre (GPCC); and the Climate Assessment and Monitoring System monthly rain gaug
30、e analysis. The TMPA estimates are produced in four stages; (1) the microwave precipitation estimates are calibrated and combined, (2) infrared precipitation estimates are created using the calibrated microwave precipitation, (3) the microwave and IR estimates are combined, and (4) rain gauge data a
31、re incorporated. Digital Elevation Model data and its derivatives The basic digital elevation model (DEM) data sets considered in this system include NASA Shuttle Radar Topography Mission (SRTM; http:/www2.jpl.nasa.gov/srtm/) and U.S. Geological Surveys GTOPO30 (http:/edcdaac.usgs.gov/gtopo30/gtopo3
32、0.html). The 30m spatial resolution provide by SRTM data is a major breakthrough in digital mapping of the world, particularly for large portions of the developing world. DEM data are used to derive topographic factors (slope, aspect, curvature, etc.) and hydrological parameters (flow direction, flo
33、w path, etc.).5Global Soil Property InformationGlobal soil property data sets are taken from Digital Soil of the World published in 2003 by the Food and Agriculture Organization of the United Nations (http:/www.fao.org/AG/agl/agll/dsmw.htm) and available in the International Satellite Land Surface C
34、limatology Project Initiative II (ISLSCP II) Data Collection (http:/www.gewex.org/islscp.html). The ISLSCP II data set provides gridded data for 18 selected soil parameters derived from data and methods developed by the Global Soil Data Task, coordinated by the Data and Information System (DIS) of t
35、he International Geosphere-Biosphere Programme (IGBP), and distributed on CD-ROM by the Oak Ridge National Laboratory Distributed Active Archive Center (http:/daac.ornl.gov/). The soil parameters used in this study are soil property information (including clay mineralogy and soil depth) and 12 soil
36、texture classes, following the U.S. Department of Agriculture soil texture classification (including sands, loam, silt, clay, and their fractions). Land cover and land use dataMODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra and Aqua satellites. MODIS is vie
37、wing the entire Earths surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (http:/modis.gsfc.nasa.gov/index.php). The global land cover data from MODIS are used as a surrogate for vegetation and land use types. The highest resolution of the MODIS land cover class
38、ification map is 250-meter. The MODIS Land Cover Product describes the geographic distribution of the 17 IGBP land cover types based on an annual time series of observations.4. Evaluation of Remote Sensing Data SetsSurface Data SetsThe surface data sets (elevation, vegetation, soils, etc.) were eval
39、uated as part of the effort to develop a landslide susceptibility map for the globe. Based upon the geospatial data sets already mentioned, several terrain factors that contribute to landslide occurrences are derived, including elevation, slope, soil types (clay, loam, silt, and sand etc.), soil tex
40、ture, and land use classification. The factors have been downscaled or bi-linearly interpolated to the highest SRTM spatial scale (30m) in this study. Previous studies demonstrated that these geospatial parameters are closely associated with landslide occurrences and found that a combination of elev
41、ation and slope best portrayed landslide susceptibility. In many regions, elevation is approximately a proxy for mean rainfall that increases with height due to orographic effects and high elevation areas are probably preferentially susceptible to landslides because they receive greater amounts of r
42、ainfall than areas at lower elevations. Vegetation on the slope is critical because bare slopes are especially vulnerable to erosion and mass wasting, but slopes with lush, healthy vegetation are far more resistant. In addition, land cover can be classified into five classes: (a) forested land; (b)
43、shrub land; (c) grass land; (d) pasture and cropland, (e) developed land and road corridors, which describe a continuum of increasing susceptibility (e.g., from zero to one) to landslides.6Following the above analysis, we first classified each landslide-controlling factor into various categories. Fo
44、r example, the MODIS land cover types can be assigned susceptibility values from zero to one at the order of increasing landslide susceptibility, respectively. Assignment of landslide susceptibility values for other parameters is based on several empirical assumptions: (1) higher slope, higher susce
45、ptibility; (2) coarse and shallow soil is more susceptible than fine and deep soil; and (3) higher elevation, higher susceptibility. Under assumption (1), for example, the slope map units are given zero susceptibility value for the class of flat slopes and susceptibility value one is assigned to the
46、 class of steepest slopes. After assignment of the numerical values to every landslide-controlling factor, the second step is to generate thematic data layers and to store (overlay) these layers in a GIS system. The last step is to derive the final susceptibility values by performing a weighted line
47、ar combination (WLC) function. WLC is a method where landslide-controlling factors can be combined by applying primary- and second-level weights. Among the five parameters, the slope is the most important factor and soil types and soil texture are also primary-level parameters, while the elevation a
48、nd land cover types are of secondary-level importance. Thus, the preliminary weight determination for the five parameters was chosen as 0.4, 0.2, 0.2, 0.1, and 0.1 for slope, soil type, soil texture, elevation, and land cover types, respectively in this study. The consequent range in susceptibility
49、values is normalized from zero to 100. The larger the susceptibility value, the greater the landslide potential is at that location.Figure 1. Global landslide susceptibility map derived from surface multi-geospatial data. The six landslide susceptibility categories are: 0-Water Bodies, Permanent Snow/Ice; 1-Very Low Susceptibility;2-Low Susceptibility; 3-Moderate Susceptibility; 4-High Susceptibility; 5-Very High Susceptibility.The landslide susceptibility values are then classifi
