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CEOP-AEGIS Periodic Report #1

CEOP-AEGIS Periodic Report #1



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    CEOP-AEGIS Periodic Report #1 CEOP-AEGIS Periodic Report #1 Document Transcript

    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 PROJECT PERIODIC REPORTGrant Agreement number: 212921Project acronym: CEOP-AEGISProject title: Coordinated Asia-European long-term Observing system of Qinghai – TibetPlateau hydro-meteorological processes and the Asian-monsoon systEm with Ground satelliteImage data and numerical SimulationsFunding Scheme: CP-SICADate of latest version of Annex I against which the assessment will be made: 25/08/2009Periodic report: 1st x 2nd ! 3rd ! 4th !Period covered: from 1/5/2008 to 31/10/2009Name, title and organisation of the scientific representative of the projects coordinator1:Prof.dr. Massimo Menenti Faculty of Aerospace Engineering, TU Delft, Delft, The NetherlandsTel: +31 15 2784244Fax: +31 15 278348E-mail: M.Menenti@tudelft.nlProject website2 address: http://www.ceop-aegis.org/1 Usually the contact person of the coordinator as specified in Art. 8.1. of the grant agreement2 The home page of the website should contain the generic European flag and the FP7 logo which are available in electronicformat at the Europa website (logo of the European flag: http://europa.eu/abc/symbols/emblem/index_en.htm ; logo of the 7thFP: http://ec.europa.eu/research/fp7/index_en.cfm?pg=logos). The area of activity of the project should also be mentioned. Page 1 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Declaration by the scientific representative of the project coordinator1 1 I, as scientific representative of the coordinator of this project and in line with the obligations as stated in Article II.2.3 of the Grant Agreement declare that: ! The attached periodic report represents an accurate description of the work carried out in this project for this reporting period; ! The project (tick as appropriate): x has fully achieved its objectives and technical goals for the period; ! has achieved most of its objectives and technical goals for the period with relatively minor 3 deviations ; ! has failed to achieve critical objectives and/or is not at all on schedule . 4 ! The public website is up to date, if applicable. ! To my best knowledge, the financial statements which are being submitted as part of this report are in line with the actual work carried out and are consistent with the report on the resources used for the project (section 6) and if applicable with the certificate on financial statement. ! All beneficiaries, in particular non-profit public bodies, secondary and higher education establishments, research organisations and SMEs, have declared to have verified their legal status. Any changes have been reported under section 5 (Project Management) in accordance with Article II.3.f of the Grant Agreement. Name of scientific representative of the Coordinator1: .Prof. Dr. Massimo Menenti. Date: ....21..../ ...12......./ 2009...... Signature of scientific representative of the Coordinator1:3 If either of these boxes is ticked, the report should reflect these and any remedial actions taken.4 If either of these boxes is ticked, the report should reflect these and any remedial actions taken. Page 2 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Project Title Coordinated Asia-European long-term Observing system of Qinghai – Tibet Plateau hydro-meteorological processes and the Asian-monsoon systEm with Ground satellite Image data and numerical Simulations CEOP AEGISThematic Priority: ENV.2007. Improving observing systems for water resource managementStart Date of the Project: 1 – May – 2008 Duration: 48 months Report Title 1st Periodic Report May 1st 2008 – October 31st 2009 Massimo Menenti1, Li Jia2 and Jerome Colin3 1 Faculty of Aerospace Engineering, TU Delft, Delft, The Netherlands, 2 Alterra, Wageningen University and Research Centre, Wageningen, The Netherlands 3 Image Sciences, Computing Sciences and Remote Sensing Laboratory, University of Strasbourg, Illkirch, FranceDate: December 21st 2009Version: 1.0 Page 3 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Coordinator contact detailsProf.dr. Massimo MenentiE-mail: M.Menenti@tudelft.nlWeb site: www.lr.tudelft.nl/olrsTelephone: +31 15 2784244 Fax: +31 15 278348Deputy coordinator details:Dr. Li JiaE-mail: li.jia@wur.nlWeb site: http://www.alterra.wur.nl/UK/Telephone: +31 317 481610 Fax: +31 317 419000Contractors involvedBENEFICIARY BENEFICIARY NAME BENEFICIARY COUNTRY DATE DATE EXITNUMBER SHORT NAME ENTER PROJECT PROJECT1 CO Université de Strasbour LSIIT UDS France 1 482 CR International Institute for Geo- ITC The 1 48 information science and Earth Netherlands Observation3 CR ARIES Space ARIES Italy 1 484 CR University of Bayreuth UBT Germany 1 485 CR Alterra - Wageningen University ALTERRA The 1 48 and Research Centre Netherlands6 CR University of Valencia UVEG Spain 1 487 CR Institute for Tibetan Plateau ITP China 1 48 Research – Lhasa, Tibet8 CR China Meteorological CAMS China 1 48 Administration – Beijing9 CR Beijing Normal University– BNU China 1 48 Beijing11CR University of Tsukuba – UNITSUK Japan 1 4812 CR WaterWatch WAWATCH The 1 48 Netherlands13 CR Cold and Arid Regions CAREERI China 1 48 Environmental and Engineering Research Institute – Lanzhou, Gansu14 CR University of Ferrara UNIFE Italy 1 4815 CR Institute of Geographical IGSNRR China 1 48 Sciences and Natural Resources Research CAS – Beijing16 CR Institute for Remote Sensing IRSA China 1 48 Applications CAS – Beijing17 CR Future Water FUWATER The 1 48 Netherlands18 CR Delft University of Technology TUD The 12 48 Netherlands19 CR National Institute of Technology NIT India 12 48 Page 4 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1CO = Coordinator CR = Contractor 1. Publishable summaryCEOP AEGIS 1st Periodic Report: May 1st 2008 – October 31st 2009Summaryhttp://www.ceop-aegis.org/ObjectivesThe goal of this project is to:1. Construct out of existing ground measurements and current / future satellites an observingsystem to determine and monitor the water yield of the Plateau, i.e. how much water is finally goinginto the seven major rivers of SE Asia; this requires estimating snowfall, rainfall, evapotranspirationand changes in soil moisture;2. Monitor the evolution of snow, vegetation cover, surface wetness and surface fluxes andanalyze the linkage with convective activity, (extreme) precipitation events and the Asian Monsoon;this aims at using monitoring of snow, vegetation and surface fluxes as a precursor of intenseprecipitation towards improving forecasts of (extreme) precipitations in SE Asia.Work PerformedThe project started with a kick-off meeting held in Beijing on May 1st – 5th 2008 attended by 65participants. In preparation of the meeting all partners were requested to define more precisely theircontribution and roles. This material provided a good basis for a productive meeting. A project mailinglist system was established to handle internal communication, given the complexity of the consortium.The 1st Annual Progress Meeting was held in Milano, Italy on June 29th through July 3rd, including ajoint workshop with the CEOP High Elevation Initiative (HE) and an internal businness meetingdedicated to a review of progress and to the preparation of the 1st Periodic Report. The meeting wasattended by 30 participants. In preparation of the meeting all partners were requested to prepare anoverview presentation for each Work Package.The material prepared for the meetings is available on the project web site. To date there are 112registered Team Members.During the 1st six months period work focused on three main objectives:1. Define the work plan and detailed contributions of partners;2. Perform local experiments and collect first data for validation of algorithms and models;3. Review and improvements of algorithms and models.Ad.1. In order to identify more precisely roles and responsibilities all partners were requested toelaborate further the work plan now included in the Description of Work. This includes now a moreprecise description of (sub)-tasks and of elements of contractual deliverables with individualresponsibilities clearly identified.Ad.2. Field experiments were carried out during the reporting period as described under “MainResults” belowAd.3 Work towards improvement of retrieval algorithms, process models and land-atmospheric modelsadvanced in several directions. This included collection and preparation of data sets acquired by space-and airborne platforms to test algorithms and models, numerical experiments to document theperformance of algorithms and process models and improvement of algorithms and models in thosecases where the causes of poor performances was known already. More details are provided under Page 5 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1“Main Results” below.During the 2nd six-months period work focused on five main objectives:1. Finalize and implement Grant Agreement, including accession of partners;2. Perform local experiments and collect first data for validation of algorithms and models;3. Review and improvements of algorithms and models.4. Design, development and use of atmospheric and water balance models;5. First analyses of time series of drought and flood indicatorsAd.1. The Grant Agreement was completed and signed on December 4th 2008. Accession forms weresigned by all partners except Partner NIH. As explained below, the National Institute of Technology,Rourkela, will replace NIH and carry out all planned tasks.Ad.2. Field experiments were carried out during the reporting period and data analysis started asdescribed under “Main Results” below. Work concentrated on the analysis of ground measurements onland – atmosphere interactions collected at the permanent observatories on the Plateau.Ad.3 Work towards improvement of retrieval algorithms, process models and land-atmospheric modelsadvanced towards the implementation of specific improvements emerged in the previous period. Thisincluded development of new procedures to deal with complex terrain in radiative transfer models andretrieval algorithms, new algorithms for the retrieval of land surface temperature and radiative fluxes atthe surface and preparation of data sets on precipitation measurements with rain radars.Ad.4 Work advanced both on the assessment of connections between land surface conditions withconvective activity and precipitation events and on the design of the regional water balance model tointegrate all observations for the entire Plateau.Ad. 5 Work was also initiated on the analysis of time series of satellite data towards the early detectionof anomalies in land surface conditions and early warning on droughts and floods. Because of the needfor extended data records, this element of the project relies on existing data sets, besides the onesgenerated by the project. During this 6-months period work concentrated on development ofprocedures for the detection of anomalies, based on a moving window analysis and comparison withthe climatology of the land surface variables under consideration. Different indicators were evaluated.During the 3rd six-months period work focused on the same five main objectives as in the 2nd six-months period:1. Finalize documents for the amendments of the Grant Agreement, including accession of newpartners;2. Perform local experiments and collect first data for validation of algorithms and models;3. Improvements of algorithms and models.4. Development and use of atmospheric and water balance models;5. First analyses of time series of drought and flood indicators.Ad.1.The access of two new partners, i.e. NIT and TUD required a significant amount of time andwork. Progress of the project was monitored through a series of Skype conferences, the 1st AnnualProgress Meeting and additional working meetings in 2009: Beijing August and October, Lanzhou inAugust and Roorkee in September.Ad.2. Field work intensified during this period. In addition to the normal operation of theobservatories, new instruments were installed to improve observations of radiative and turbulent heatfluxes and to characterize the size distribution of rain droplets, necessary to improve accuracy ofretrievals by rain radars (see Main Results below).Ad.3 Work towards improvement of retrieval algorithms was focused on atmospheric correction ofsatellite measurements in the VNIR-SWIR, TIR and microwave regions. This included dealing withretrieval of Land Surface Variables using data acquired by the new satellites HJ-1B (China) and IRS(India). The new algorithms developed in the previous period were applied to generate time series ofSnow Covered Area and Snow Water Equivalent. The development of a new data processing systemfor Surface Energy Balance analyses based on the combination of satellite measurements with PBL Page 6 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1fields generated by the GRAPES NWF model was completed.Ad.4 Significant advances have been achieved towards analysis of land – atmosphere interactions withatmospheric models and towards regional modeling of the Plateau water balance. A full forecast runwas performed for the entire year 2008 with the system GRAPES. A study of the sensitivity MonsoonConvective Systems (MCS) to land surface conditions was carried out with the model WRF at theUniversity of Tsukuba and the prototype water balance model of the Qinghai –Tibet Plateau wasimplemented at a 5 km x 5 km spatial resolution and applied to obtain daily rainfall excess and riverflow over the entire domainAd.5 Several results became available on different indicators relevant to drought and flood earlywarning. Work focused on two parallel streams: improving algorithms and analyzing available timeseries of satellite data. A new version of the HANTS algorithm was released and a new model tocompute daily EvapoTranspiration (ET) was developed and applied. Time series of satellite data onLand Surface Temperature (LST), photosynthetic activity (EVI, fAPAR) and soil wetness wereanalyzed to document inter-annual variability, detect anomalies and evaluate them as precursorindicators for drought and flood early warning.Main ResultsField experiments During the 1st Reporting Period the existing system of Plateau observatories wasimproved by adding several instruments: gauges to measure total precipitation above 6000 m, twoLong Path Scintillometers, three disidrometers to measure the size distribution of water droplets, foursets of radiometers to measure the four components of the radiative balance and one suntracker tomeasure direct irradiance.Several Co-Investigators participated in a major RS experiment covering an entire watershed on thenorthern rime of the Plateau: the WATER project provided invaluable detailed data to improve andvalidate several algorithms to be used within CEOP-AEGIS. Collection of soil moisture andtemperature measurements at the Maqu site for the validation of algorithms to retrieve soil moisturecontinued. An expedition to the the Yamdruk-tso lake basin and Qiangyong Glacier was carried out.The NaimonaNyi ice core was processed in cold room.The first eddy-covariance measurements of turbulent flux densities became available after qualitycharacterization and gap filling. The analysis of the data collected at the NamCo observatory revealeda significantly higher number of free convection events in the monsoon period. The results have beenpublished in JGR. An approach to upscale flux measurements to the grid scale of meso-scale modelsand remote sensing data was developed.Work towards improvement of retrieval algorithms, process models and land-atmospheric modelsadvanced in several directions:- Collection and preparation of several data sets comprising multi-spectral, multi-angularradiometric data;- Evaluation of land – atmosphere models- Review and preparation of codes of radiative transfer models of the soil-vegetation system- Improvement and generalization of multi-scale model of land surface energy balance;- Estimation and mapping of land – atmosphere heat and water exchanges with ASTER multi-spectral radiometric data for the areas surrounding the ITP observatories on the Plateau;- Preparation of microwave radiometer data (AMSR-E) for the evaluation of soil moistureretrieval algorithms;- Improvement of model to characterize the diurnal cycle of Land Surface Temperature usingFeng Yun infrared data and use of the CLM to relate the diurnal LST cycle to soil moisture- Improvements in the meso-scale land-atmospheric model GRAPES of CMA; preliminary casestudies performed and hypotheses identified;- Preparation of data sets for the evaluation of candidate water balance models; evaluation ofsnow-melt-runoff models using MODIS and AMSR-E satellite data; Page 7 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1- Preparation of MODIS time series (LAI/fAPAR, Vis, and LST) for entire China;- Improvements in the algorithms to detect and predict anomalies in vegetation development;- Case studies on drought events combining ground and satellite data;2nd period- Analysis of sample data set HeiHe basin with simultaneous multi-angular, multi-spectral andlidar observations of vegetation canopies- Topography correction inserted in the RT modeling system for the VNIR, SWIR and TIRspectral ranges.- Development of a simple model to describe the thermal directional radiation in rugged terrain;- A topographic correction algorithm for albedo retrieval in rugged terrain was developed.- Development of a preliminary algorithm to calculate land surface temperature from AMSR-Edata;- Developing the concept of a new radiative transfer model, capable of simulating the seasonalchanges of canopy structure;- Development of new version of MSSEBS (vers. 2.0.2) SEB algorithm;- Development of algorithm for regional estimation of net radiation flux;- Determination the surface albedo, surface temperature, vegetation fractional cover, NDVI,LAI and MSAVI over whole Tibetan Plateau;- Implementation of a radiative transfer soil moisture retrieval method using ASCAT data- Comparison of in situ data collected by Maqu soil moisture monitoring network with AMSR-EVUA-NASA satellite soil moisture products;- Collected the soil moisture and temperature data of 20 SMTMS, and replaced 4 temperatureand moisture probes;- Processing the raw precipitation radar data in the Tibetan Plateau and provide the griddedprecipitation data for case studies;- Final revision of paper on the nighttime monsoon precipitation over the TP was submitted toJMSJ and accepted in March- Simulation of daily snow cover using daily and eight-day MODIS snow cover products andmeteorological observation;- Analysis of glacier and lake changes using observed data and RS data in the Nam Co Basin;.3rd periodDevelopment of algorithms and retrieval of canopy structure from airborne LIDAR;- Development of algorithm for atmospheric corrections of AMSR-E (microwave);- Generalized procedure for atmospheric correction based on an ensemble of MODTRANsimulations;- Automation of procedures to generate LST from MODIS data;- Implementation and first tests on generic algorithm for retrieval of LAI and fCover;- Development of new algorithm to retrieve LST from HJ-1B (China) and IRS (India) data;- Development of new Angular & Spectral Kernel based BRDF model for the normalization ofdata acquired with different angular and spectral configurations;- First test of SEB algorithm combining satellite data for land surface observations and PBLfields generated with high resolution atmospheric model (GRAPES);- Evaluation against turbulent heat flux measurements of SEB estimates based on ASTER data;- Mosaic of rain-rates observed with rain radars over the Plateau have been generated anddelivered to other investigators for calibration of algorithms based on satellite data;- Improved algorithm for retrieval of snow covered area from MODIShas been developed and evaluated against observations at higher spatial resolution ( TM);Design, development and use of atmospheric and water balance models. Page 8 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 12nd period- The first numerical experiments with the GRAPES land – atmosphere modeling and data assimilationsystem were performed and evaluated:- Sensitivity experiments of different soil initial conditions on the development of convectionsby using 2-km resolution of GRAPES_Meso- Detection of Meso-scale Convective System (MCS) on the TP was done for the passed sixyears using METEOSAT-IR data- Preparing GIS files for hydrological modeling, including boundary, DEM. Slope, aspect,stream network.-Model selection and algorithm comparison report for Plateau water balance monitoring tool wascompleted3rd period.- The system GRAPES of CMA has been applied to generate forecasts for the entire year 2008;- A study on the sensitivity of MCS to land surface heating has started using the WRF numericalmodel at the Univ. Tsukuba;- Gridded climate data have been used to compute the water balance of the Headwaters of theYellow River Basin and to compute potential ET;- The prototype of the Qinghai – Tibet Plateau distributed water balance model has beenimplemented and applied to compute for the year 2000 daily water balance for each 5 km x 5 km gridand water routing; model riverflow at seven selected sections is being compared with observations;-Model parameterization of glaciers mass balance is being applied to the Zhadang glacier; in- depthcase – study including the use of satellite data is in progress;Analyses of time series of drought and flood indicators2nd period-Available satellite data were retrieved, time series were constructed and first analyses wereperformed:-Algorithm development on drought monitoring by time series analysis of anomalies in several landsurface parameters;-Using time series of VTCI AVI, VCI and TCI as indicators for the estimation of the droughtimpacts;-Analysis of time series meteorological data (air temperature and precipitation, wind speed, airhumidity, solar radiation, etc)-Development of soft computing techniques based on ANN and Fuzzy logic model for real time floodforecasting3rd period- A new version of the HANTS algorithm for time series analysis of satellite data has beenreleased;- A multi-annual MODIS data set covering the Plateau and surrounding regions has been createdafter improved cloud screening and used to compute at-surface net radiation in addition to LST, EVIand fAPAR;- Analysis of a 25 years climatology of AVHRR LST and NDVI has been completed;- Time series of SPI and VTCI have been generated and used as an indicator for droughtforecasting;- A new ET model has been applied to evaluate potential yield loss in the winter 2008;- A first evaluation of AMSR-E time series as an indicator of soil wetness and to detect(positive) anomalies has been completed for the Plateau and Northern India;Expected Final Results Page 9 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Data base containing ground observations, satellite data and higher level products, hydrologic and atmospheric model fields for the period 2008 – 2010 over the Qinghai – Tibet Plateau. System to generate daily streamflow in the upper catchment of all major river in SE Asia gridded to 5 km x 5 km.Potential Impact and Use of Results Implementation and demonstration of an observing system of water balance and water flow on and around the Qinghai – Tibet Plateau will provide to all countries information on water resources and the role of the Plateau in determining weather and climate in the region. 2. Project objectives for the periodThe goal of this project is to:1. Construct out of existing ground measurements and current / future satellites an observing system todetermine and monitor the water yield of the Plateau, i.e. how much water is finally going into the seven majorrivers of SE Asia; this requires estimating snowfall, rainfall, evapotranspiration and changes in soil moisture;2. Monitor the evolution of snow, vegetation cover, surface wetness and surface fluxes and analyze thelinkage with convective activity, (extreme) precipitation events and the Asian Monsoon; this aims at usingmonitoring of snow, vegetation and surface fluxes as a precursor of intense precipitation towards improvingforecasts of (extreme) precipitations in SE Asia.During the first year of the project, emphasis in all WP-s will be on review tools, experimental protocols,algorithms and models. On this basis, the elements of the investigations next step will be identified in detail: thefirst detailed description of new retrieval algorithms will be available, data analysis protocols will be agreed,modelling experiments will be designed and the organization of data base will be consolidated.During the second year of the project, work will be focused on the Algorithms Theoretical Basis Documents andpotential progresses towards community model to determine land-atmosphere energy and water fluxes withmulti-spectral satellite images. First analysis of datasets with candidate algorithms and models will be presented,with preliminary results on time series analysis of Plateau water balance, droughts and floods indicators. Page 10 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 3. Work progress and achievements during the periodPlease provide a concise overview of the progress of the work in line with the structure of Annex I of the Grant Agreement.For each work package -- except project management, which will be reported in section 3.5--please provide thefollowing information: • A summary of progress towards objectives and details for each task; • Highlight clearly significant results; • If applicable, explain the reasons for deviations from Annex I and their impact on other tasks as well as on available resources and planning; • If applicable, explain the reasons for failing to achieve critical objectives and/or not being on schedule and explain the impact on other tasks as well as on available resources and planning (the explanations should be coherent with the declaration by the project coordinator) ; • a statement on the use of resources, in particular highlighting and explaining deviations between actual and planned person-months per work package and per beneficiary in Annex 1 (Description of Work) • If applicable, propose corrective actions. Page 11 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.1 Work progress in WP 1 and achievements during the period " A summary of progress towards objectives and details for each task Task 1.1 The in-situ data has been collected in the observation network of the GAME/Tibet and CAMP/Tibet and the Mt. Everest station(QOMS), the Nam Co station(NAMOR) and the Linzhi Station(SETS) of the TORP(Tibetan Observation and Research Platform) and Namco site of Tip( formally KEMA Station of TiP). Four components radiation system were set up at the sites of D110, MS3608, Namco area, and Lhasa branch of ITP (formally Yakou of Namco). Field trip to the Yamdruk-tso lake basin and Qiangyong Glacier was performed. Precipitation, lake water and river water samples has been collected at 3 stations in this basin for isotope analysis in the laboratory in Beijing. Glacier shallow ice cores were drilled at 6100m of the glacier to rebuild the annual precipitation data in high elevation region. Daily atmospheric vapor samples were collected at Lhasa and are still on going. Fig.1 to Fig.4 are the sites layout and the stations of this WP. (a) (b) Fig.1.1 The geographic map and the sites layout during the GAME/Tibet and the CAMP/Tibet. (a) GAME/Tibet; (b) CAMP/Tibet. Fig.1.2 The instruments in Mt.Everest station, Namco station and Linzhi station of ITP/CAS Page 12 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Fig.1.3. Sites of the four components radiation system over the Tibetan Plateau. The seasonal and inter-annual time scale of the exchange of surface heat flux, momentum flux, watervapour flux, surface and soil moisture over the different land surfaces of the Tibetan Plateau, and the structurecharacteristics of the Surface Layer (SL) and Atmospheric Boundary Layer (ABL) were analyzed in the last oneand half year. The aerodynamic and thermodynamic variables were determined over the different land surfacesof the Tibetan Plateau. The characteristics of precipitation and atmospheric water vapour transport over andsurrounding the Tibetan Plateau area were analyzed.Task 1.2: A technical report was prepared for the documentation of the flux calculation procedure in order to provideall users of flux data the necessary information. Furthermore, within an UBT field trip to the Tibetan Plateau(June-August 2009) a workshop was held from June 29th to July 1st for participants of ITP and CAREERI aboutthe usage of the UBT software packages for EC data post processing, footprint and QA/QC techniques. Thisensures a uniform data processing for all ground truth EC stations related to CEOP AEGIS, which is the task ofITP and CAREERI according to the data policy rules.Task 1.3:In order to apply detailed footprint analysis for the EC stations, all necessary site information to prepare therequired land use maps were collected for Bj, Namco and Qomolangma site during the UBT field trip in summer2009. Detailed footprint analysis already exists for Namco in late 2005 and from Oct 2005 up to Sept 2006, but Page 13 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1has to be refined with actual flux data. Missing site information for Linzhi station will be gathered during a postworkshop excursion in July 2010 right after the CEOP workshop in Lhasa and the calculation of the footprintanalysis starts as soon as the flux data is available.Task 1.4:The gap filling will be processed following a procedure developed by Ruppert et al., but an extension to thelatent heat flux has to be made, for which data from Tibetan Plateau are necessary. The procedure starts as soonas the flux data is available.Task 1.5:In order to find an adequate path for LAS measurements at Qomolangma site possible solutions wereinvestigated during the UBT field survey in summer 2009. The LAS system was set up in Mt.Everest(Mt.Qomolangma) station in November, 2009 (Fig.4).Afterwards a preliminary footprint report was elaboratedexamining the possible paths and hinting at the optimal solution. The results were documented within a specialreport, the selected path and its respective footprint is shown in Fig.5. Fig.4 The LAS system in Mt.Qomolangma(Mt.Everest ) StationFig.5: Selected path (solid red line) for the LAS measurements at Qomolangma site with source contributions for a footprint “climatology” of the expected wind distribution, unstable stratification, zm = 20m.A set of LAS was installed and aligned in Naqu BJ station (31°227.18"N, E91°5355.36"E) in July, 2009, Naquarea of Tibet. The underlying surface of observation site is alpine meadow. The effective height and path lengthis 8.63 m and 1560m, respectively. Combined with the measurements of Eddy Covariance system (EC) andAutomatic Weather Station (AWS), the performance of LAS under Tibetan plateau environment has beenchecked. Page 14 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Fig.6 The LAS system in Naqu BJ stationTask 1.6:A first error analysis of flux data was given in a technical report. This will be updated as soon as the flux data isavailable.Task 1.7:For tasks 1.6 and 1.7 a footprint scheme is currently developed by UBT and will soon be published in a peerreviewed journal. A foundation for this scheme was elaborated within a Master thesis, for a description seesection results. Furthermore, a experiment was performed nearby the Namco Station (Fig.7). The investigationscover EC, energy balance and soil moisture measurements for a period from June 26th to August 8th and was setup directly at the shoreline of a small lake, pre-located to the Namco lake. This measurements will be used tovalidate the footprint related upscaling scheme and serve for parameterization of fluxes above lake surface andKobresia mats. A documentation of the experiment is now available. Fig.7. Turbulence measurements at Namco lake Page 15 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 • Highlight clearly significant results1. Underlying surface roughness lengths under the quality control of observation were determined Eddy covariance flux data collected from ITP/CAS three research stations (Qomolangma station, Namcostation and Southeast Tibet station-Linzhi station) on the Tibetan Plateau are used to analyze the variation ofmomentum transfer coefficient (CD), heat transfer coefficient (CH), aerodynamic roughness length (z0m), thermalroughness length (z0h) and excess resistance to heat transfer (kB-1). All the data was checked under thequality control firstly. The monthly average surface roughness, bulk transfer coefficient and excess resistance toheat transfer at all three sites are obtained. Momentum transfer coefficient (CD) is quite changeable during theday but relatively stable and lower in the night. The parameter kB-1 exhibits clear diurnal variations with lowervalues in the night and higher values in the daytime, especially in the afternoon. Negative values of kB-1 are oftenobserved in the night for relatively smooth surfaces on the Tibetan Plateau. Page 16 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 (a) (b) Fig. 8 Frequency distribution of ln(z0m) at Nam Co station in September(a) and October(b) Page 17 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 (a) (b) (c)Fig. 9 The diurnal variations of observed excess resistance to heat transfer (kB-1)at Qomolangma station(a), Namco station(b) and Southeast Tibet station(c) in March2 Variation characteristics of radiation of the wetland surface in the Northern Tibetan Plateau Based on the observed data at Automatic Weather Site(AWS) of MS3478 in the typical wetland of northernTibetan Plateau from March 2007 to February 2008. The seasonal mean diurnal, seasonal and annual variationfeatures of the radiation budget components were analyzed in this paper. The results indicated that in springdiurnal variations of both global solar radiation and the reflective radiation were larger than in other seasons, andtheir annual variations were double-peak-shaped, but the phases were different. The distributions of both thediurnal variation and the annual variation of the earth surface long-wave radiation were unsymmetrical. Annualvariation of the earth effective radiation was of bimodal pattern. One peak corresponded to March and April,when frozen soil melted, while the other to October, when froze soil froze. Net radiation mainly concentrated inMay, June and July, accounting for 40.14% of the total, indicating that in late spring and early summer theregions surface had obtained the largest net energy, which played a decisive role for the formation of terrestrialheat and the heating of the atmosphere.3. Analysis onpotential evapo-transpiration and dry-wet condition in the seasonal frozen soil region ofnorthern Tibetan Plateau This study was based on the observed data at Automatic Weather Site(AWS) of MS3478 in the seasonalfrozen soil region of northern Tibetan plateau from March 2007 to February 2008.The variation characteristics ofpotential evapotranspiration (PE) was analyzed based on Penman-Monteith method recommended by FAO. Thecontributions of dynamic, thermal and water factors to PE were discussed. Meanwhile, the wet-dry condition ofthat region was further studied. The results indicated that daily PE was between 0.52mm and 6.46mm in thewhole year. In summer evaporation was the most intensive, and from May to September monthly PE was over100mm. In November, there was a clear mutant. Annual potential evapotranspiration was 1037.83mm. Insummer, thermal evapotranspiration was much more significantly than dynamic evapotranspiration; in winter itwas on the contrary. In addition, drought and semi-drought climate lasted for a long time while semi-humidclimate short. The effect of water and dynamic factors on PE varied considerably with the season. Soil moisturewas not the main factor affected PE.4.Up-scaling scheme was developedThe location of the footprint function varies in time due to changing wind direction and atmospheric stability.Therefore the footprint of atmospheric measurements does not only affect data quality but also Page 18 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1representativeness of the observed data for the grid level. A scheme to overcome this drawback is indevelopment and will work in principle as shown in figure 3. Fig.10. Upscaling scheme for turbulent flux data from heterogeneous landscapes5.Free convection events at Nam Co site of the Tibetan Plateau were found and analyzed The spatial and temporal structure in the quality of eddy covariance (EC) measurements at Nam Co site isanalyzed, by using the comprehensive software package TK2 together with a footprint model, and the highquality turbulent flux data have been obtained for the investigation of free convection events (FCEs). Theresearch of FCEs at Nam Co site indicates that the generation of FCEs not only can be detected in the morninghours, when the diurnal circulation system changes its previously prevailed wind direction, but also can betriggered by the quick variation of heating difference between different types of land use during the daytimewhen clouds cover the underlying surface or move away. FCEs at Nam Co site are found to occur frequently,which can lead to the effective convective release of near ground air masses into the atmosphere boundary layer(ABL) and may strongly influence its local moisture and temperature profiles and its structure. Page 19 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Fig. 11. The distribution (a) and frequency statistics (b) of free convection events (FCEs) times at Nam Co site.6. Diurnal variation of sensible heat flux were very clear Careful data processing and quality control of LAS has been performed in Naqu BJ station. The comparisonof sensible heat flux measurement by LAS and EC are plotted in Fig12, which shows the similar variationbetween LAS and EC measurement. Page 20 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Fig 12 Comparison of sensible heat flux measurement by LAS and EC (2009.08.01-2009.08.28) Page 21 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Page 22 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 3.2 Work progress in WP2 and achievements during the period WP2 aims to develop algorithms to retrieve surface parameters from a broad family of multi-spectral and/or multi-angular radiometric data and produce a consistent data set over the region of Tibetan Plateau. # Instrument and validation A multispectral canopy imager (MCI) was developed for the field measurements of forestry canopy LAI. It can capture image pairs in three different wavelength bands at arbitrary zenithal and horizontal directions. The MCI image pairs can be used to discriminate the sky, leaves, cloud and woody components. As a result, this instrument is capable of measuring the woody area index which is very important in field LAI measurements. In the Heihe river field campaign which was taken in June 2008, MCI was used to get the directional clumping index and woody-to-total area ratio. Finally, the LAI values were obtained in several locations after consider the correcting of the clumping effects and woody components. # Model development A Whole Growth Stages (WGS) model was developed for simulating the directional reflectance of the row planted canopy across the whole growth stages. Based on a series of simplifications and assumptions, we gave out an analytical expression to describe the spatial regular fluctuation of LAVD of row planted wheat canopy. We found that the LAVD of the vegetal row is approximately negative correlation to the distance from the centre of the row. Then we put forward a suit of calculation scheme to estimate the directional gap fraction which well considering the spatial regular fluctuation of LAVD within row-planted wheat canopy. In our new model, only 4 input parameters are needed, including LAI, the ratio of row width to height, the ratio of row space to height, row direction. A new angular & spectral kernel model was developed to describe the BRDF characteristics for most of the land covers. Compared with the semi-empirical kernel-driven model used by AMBRALS (Algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface) which was employed in the MODIS (Moderate Resolution Imaging Spectra Radiometer) albedo/BRDF product, the component spectra were combined into the kernel functions instead of kernel coefficients. Then the kernels were expressed as function of both the observed geometry and wavelength. As a result, the kernel coefficients are independent of wavelength in this new model. That characteristic enables the broad band conversion to be a linear combination of the new integral kernels which is much simple and efficient. A model describing thermal directional radiation was established for the rugged terrain. By parameterization of sky-view factor and terrain configuration factor, the emitted radiance was parameterized as a linear composition of the contributions of radiance from vegetation and soil, taking into account the coupling between vegetation-soil, vegetation-vegetation and soil-vegetation interactive processes. # A generic inversion algorithm In order to enable the application of the method to several satellite sensors, the observation model SLC (soil- leaf-canopy) was extended for applications in the thermal domain, and the MODTRAN interrogation technique was extended to this domain as well. In addition, look-up table (LUT) techniques were optimized in order to allow for efficient image simulations under various conditions. This means that for angular interpolations of the sun-target-sensor geometry only a limited size of the LUTs is required. Topographic effects were included by considering slope and aspect angles to be obtained from a DEM (digital elevation Page 23 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 model) of the area. Slope and aspect are used to estimate the fractions of solar and sky spectral irradiance in the optical and thermal domains. A unified equation was derived to describe the TOA radiance as a function of surface and atmospheric parameters in the optical and thermal domains with the incorporation of topographic effects. The MODTRAN interrogation technique was extended into the thermal domain as well, and several MODTRAN outputs were identified with physical quantities of four-stream radiative transfer theory. # Topography and scale effect correction for albedo products One coarse scale pixel includes many tilted micro-areas, which have different slopes and aspects. Its directional reflectance is affected by these micro-areas and their shadows. An equivalent smooth surface directional reflectance was introduced for a virtual surface of the coarse scale pixel, which was assumed to be smooth so that there were no micro-area topography effects. A scale effect correction factor was defined to correct the topography and scale effect. This factor is only dependent on DEM and the geometry of sun and sensor. The topography and scale effect correction algorithm includes three steps: (1) Setting up a database for pixel-average slope and aspect angle for each pixel of 500m grid and 5km grid, and scale effect correction factor for each 5km pixel; (2) Correcting the pixel level topography effect for 500m directional reflectance, using slope and aspect angles; (3) Correcting the pixel level, as well as subpixel level, topography effect for 5km directional reflectance, using slope, aspect angles and the scale effect correction factor. # A priori knowledge based LAI inversion The a priori knowledge of LAI was obtained by three ways: (1) Getting the relationship between a multidirectional averaged NDVI and LAI by simulation using a BRDF model (eg. SAILH model); (2) Developing the empirical crop growth model by the regression of a LOGISTIC equation and the field measured LAI data sets; (3) Developing a priori LAI trend from several years’ MODIS LAI product. All of this a priori information was used in the inversion of radiative transfer models to get the temporal continuous and robust LAI. Both of the MODIS and MISR data were used in the inversion to improve LAI product. # Angular effect correction of fractional vegetation cover Under the assumption of that a remote sensing pixel is mixed by vegetation and background, a simple directional fractional vegetation cover (FVC) model was developed based on Beer-Lambert law. The variables in this model can be got by using the MODIS images in 16 days and high resolution HJ-1 images The Scaled Trust-Region Solver for Constrained Nonlinear Equations (STRSCNE) algorithm was used to retrieve the variables. A vegetation growth model was introduced to constrain the relative worse quality of HJ data in a temporal scale. The different spectral responds of MODIS and HJ were also compared with spectrums of typical surface class. Uncertainty was assessed by error propagation theory and field experiments. # LST inversion using polar satellite data A review of existing algorithms to retrieve land surface emissivities (LSE) and land surface temperatures (LST) has been carried out. This review has allowed the selection of the needed algorithms to retrieve LSE and LST, which includes the preliminary determination of several parameters such as NDVI (Normalized Difference Vegetation Index), FVC (Fraction of Vegetation Cover), total atmospheric water vapour content, as well as carrying out cloud tests, image atmospheric and geometric correction. In the absence of the MODIS – CEOP-AEGIS dataset, these algorithms are being implemented on the data acquired by the Global Change Unit at the University of Valencia (Spain), in order to obtain a near-real estimation of LSE and LST. The completion of this process is expected during the next reporting period. In a second step, this processing chain will be adapted to the Tibet area in order to process the MODIS – CEOP-AEGIS dataset. Page 24 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 The algorithm of daytime 150m LST product was proposed by using the HJ-1 dataset over the Tibet Plateau. A view angle dependent single channel LST algorithm has been developed for correcting atmospheric and emissivity effects for all land cover types. • Highlight clearly significant results (3 pages) # Multispectral canopy imager (MCI) and its use in woody-to-total area ratio determination The MCI, which mainly comprises a near-infrared band camera, two visible band cameras, filters and a pan tilt, was developed to measure clumping index, woody-to-total area ratio and geometrical parameters of isolated trees (figure 1). Two typical sampling plots (Plots 1 and 5) which were covered by Picea crassifolia were selected for the estimation of woody-to-total area ratio and its directional change in Heihe river basin, China. The clumping index and woody-to-total area ratio values of the forest canopy were got at eight zenith angles (from 0 to 70° in increments of 10°) using MCI images based on gap size distribution theory (figure 2,3). Figure 1. Illustration of the multispectral canopy imager (MCI).Erreur ! Des objets ne peuvent pas être créés à partir des codes de champs de mise en forme.Erreur ! Des objets nepeuvent pas être créés à partir des codes de champs de mise en forme. Figure2. Clumping indices at Plot 1 (a) and Plot 5 (b).Erreur ! Des objets ne peuvent pas être créés à partir des codes de champs de mise en forme.Erreur ! Des objets nepeuvent pas être créés à partir des codes de champs de mise en forme. Figure3. The woody-to-total area ratio of Plot 1 (a) and Plot 5 (b). The detailed description of the equipment and the method can be found in the following paper: Page 25 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Jie Zou, Guangjian Yan, Lin Zhu and Wuming Zhang, Woody-to-total area ratio determination with a multispectral canopy imager (MCI), Tree Physiology, 2009; doi: 10.1093/treephys/tpp042. # Unified modelling of TOA radiance for the generic inversion algorithm A unified equation was derived to describe the TOA radiance as a function of surface and atmospheric parameters in the optical and thermal domains with the incorporation of topographic effects. This equation reads:where and are the viewing factors associated with illumination from the sun and the sky, respectively.They are given by ,where and are terrain slope and aspect, respectively.The four terms in square brackets are the ones associated with: • Atmospheric path radiance in both domains • Adjacency effects in both domains • Sky irradiance contributions in both domains for the target • Direct solar bi-directional and thermal direct target contributionsNote, that emissivities are represented here by their associate reflectance equivalents and(hemispherical and directional emissivity). # Time series LAI mapping over Heihe river basin Page 26 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 The developed variational assimilation method was implemented and some results on LAI values for the whole year of 2008 over Heihe River Basin are presented in Figure 4. It shows the regional LAI mapping results from the time series MODIS reflectance data acquired over this area in 2008 with the spatial resolution of 500m. As seen, temporal variation of the LAI values in this region is reasonable. And the spatial variability is consistent with the vegetation cover map in this area. Figure 4 LAI inversion results in the middle of Heihe River area. # Emissivity measurements and data preparation Several papers have been published regarding different topics of LST from polar satellites such as: (1) José A. Sobrino, Cristian Mattar, Pablo Pardo, Juan C. Jiménez-Muñoz, Simon J. Hook, Alice Baldridge, and Rafael Ibañez. 2009. Soil emissivity and reflectance spectra measurements. Applied Optics, Vol. 48, Issue 19, pp. 3664-3670. This work present a laboratory procedure to characterize the emissivity spectra about several soil samples collected in diverse suite of test sites in Europe, North Africa, and South America from 2002 to 2008. Here, we presented a cross calibration with in-situ measurements and further application to thermal remote sensing. This work presents a methodology to characterise the emissivity values of a given soil sample, additionally, the soil emissivity values analyzed here were presented for all polar satellites which have thermal sensors. (2) C. Mattar, J.A. Sobrino, Y.Julien, J.C. Jiménez-Muñoz, G. Soriá, J. Cuenca, M. Romaguera, V. Hidalgo, B. Franch, R. Oltra. 2009. Database of atmospheric profiles over Europe for correction of Landsat thermal data. Proceedings of the 33rd International Symposium on Remote Sensing of Environment. (in press) This work presents a new vertical profile data base for correct thermal remote sensing images. In this case we focused our work to provide useful information to correct Landsat thermal images. However, the data base could be used for other remote sensing sensors. # Spectra normalization of HJ and MODIS data Difference of spectral responds of HJ and MODIS sensors should be considered in FVC retrieval, though MODIS and HJ sensors have overlapped region in spectral respond functions (figure 5). Many reflectance spectrums of leaves and soils were selected from spectrum library of ENVI software. The mean values were Page 27 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 computed for the two sensors (table 1). Scattering plot of 4 bands in figure 6 didn’t exhibit much difference for HJ and MODIS. Figure 5. Relative spectral respond function of MODIS and HJ-1 bands used in FVC retrieval Table 1. Mean reflectance of typical land covers with HJ and MODIS relative spectral response Reflectance of typical leaves and soils conifer deciduous Grass and soil arbre Blue HJ-1 0.0704562 0.07849 0.08478 0.077605 MODIS 0.0621984 0.065187 0.071822 0.064877 Green HJ-1 0.100901 0.132595 0.135229 0.139566 MODIS 0.114949 0.149223 0.14475 0.12815 Red HJ-1 0.075 0.119595 0.129705 0.204328 MODIS 0.071389 0.110964 0.12425 0.195855 Near- HJ-1 0.51273 0.683053 0.517343 0.281649 infrared MODIS 0.525689 0.692068 0.534383 0.300353 Scattering plot of reflectances Blue Green Red NIR Figure 6. Reflectances of HJ-1 and MODIS signal corresponding to typical land cover types # Development of a quantitative remote sensing products inversion system Page 28 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 A quantitative remote sensing products inversion system is being developed for the parameters products generation. It is composed of 5 sub-systems, including database, data pre-processing, products inversion, validation, and visualization. (1) Database subsystem takes charge of the data management and data flow of the whole system. All the other sub-systems will be connected together by database without physical connection between the 4 sub-systems; (2) Data pre-processing subsystem will process all the incoming remotely sensed data into standard data products. The pre-processing procedures include cross radiometric calibration, geometric correction, projection transferring, gridding, and cloud screening; (3) Products inversion subsystem is a products “pool” which is composed of 22 geo and bio parameters and system users will make their own product producing workflow. The subsystem will be producing products through the workflow instantaneously or routinely; (4) Validation subsystem will validate the inversion products based on the predefined methods routinely or by users’ convenience; (5) Visualization subsystem is a visual interface which provides users with data management, image display environment, image and graphic processing, terrain analysis, statistics analysis, and annotating. Page 29 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Page 30 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 3.3 Work progress in WP 3 and achievements during the period Summary of progress towards objectives, per task: Task 3.1 (ALTERRA, ITC, BNU, CAREERI, TUD): local validation of algorithms with ground eddy covariance measurements at footprint scale and cross-comparison of approaches to turbulent flux partitioning. The remote sensing based algorithm for flux calculation to be evaluated in this task can be applied at local scale (S-SEBI, SEBS) or at a larger (meso) scale (SEBS, MSSEBS). They all follow the approach proposed by Menenti and Choudhury (1993) stating that for a given net radiation value, and for homogeneous atmospheric conditions, the surface temperature is related to the ratio between actual and potential evaporation. Both methods require physical properties of the surface extracted from remote sensing to characterize the surface radiative balance (albedo, surface temperature, emissivity) and vegetation structure (fractional cover, Leaf Area Index). Also they differ in the way to define wet and dry boundaries in terms of normalized surface to air temperature gradient, they all require some basic meteorological information. Therefore the contribution of UDS in this task consisted in: i. identify remote sensing products available to conduct SEB calculation for areas and periods of time where reliable ground measurement data were available; ii. gather and post-process meteorological data to be used as forcing conditions in the SEB schemes The remote sensing products used to conduct the algorithm comparisons are Modis images acquired by Terra. The reasons are: i. the adequate spatial and temporal resolution of the sensor; ii. the panel of adequate products; iii. ad hoc products from WP2 are not available at this stage of the project. The products and dates are summarized in the tables bellow. The candidate dates were selected on the basis of global cloudiness on the Plateau.April 2003 15th and 25thMay 2003 28thOctober 2003 17th and 23rdNovember 2003 8th and 11thProduct Variable Spatial resolution Temporal resolutionMOD11A1 LST/Emissivity 1km DailyMCD43B3 Albedo 1km 16 daysMOD13A2 Vegetation index NDVI 1km 16 daysMOD15A2 LAI 1km 8 days The characterization of the state of the Planetary Boundary Layer is based on the output from the Meso-scale Numerical Weather Prediction Model GRAPES developed by the Chinese Academy of Meteorological Sciences, partner in this project. The following variables were extracted from GRAPES simulations covering the entire Plateau at a resolution of 30 km and 30’ time step: Variables extracted at the height of the Atmospheric Boundary Layer: • ABL height • Air temperature • Specific humidity • Wind speed • Air pressure Variables needed at 2 meters: • Specific humidity Page 31 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 • Air pressure UDS prepared two set of inputs centered on the validation site called BJ, with either 400x400 km or 100x100 km extent. The processing consisted in: • extraction of MODIS products, re-projection and spatial re-sampling of albedo, LST with corresponding acquisition time layer, NDVI, LAI • extraction of GRAPES outputs from GrADS raw files, geo-processing of layer variables to the same resolution and coverage as MODIS products • creation of time-composite PBL layers to associate adequate GRAPES field to MODIS LST following MODIS LST acquisition time • extraction of SRTM Digital Elevation Data for the selected scene to calculate PBL elevation This dataset was used to perform S-SEBI, SEBS and MSSEBS calculations, tests and comparisons (see next section). Task 3.2 (UDS, ALTERRA, ITC, ITP, BNU): generalize SEB calculation at a high spatial resolution and on a regional extent. On such an extent, local towers cannot be used to define boundary conditions. The MSSEBS (Colin, 2006) approach enables to link ground variables at a high spatial resolution (typically 30 meters) with Atmospheric Boundary Layer (ABL) state at a proper resolution related to the typical ABL length scale. Generalize SEB calculation on the entire Plateau lead to several conceptual and technical challenges: • the combination of high resolution remote sensing products with medium (meso) resolution NWPM outputs in a single calculation scheme, combining physical variables whose meaning is closely related to their inherent scale, as to be taken into account in the algorithm implementation • the use of high (1km) resolution remote sensing products over the Plateau lead to significant amount of data (e.g. 1,400 x 1,700 km grid means 2.4E6 calculation nodes, for n variables and j time steps with n > 25 and j >> 100). • the use of NWPM with different spatial and temporal resolution, geo projection, supposes to have a powerful pre-processing procedure to mix various data sources in a single model input set of layers • the probable occurrence of data unavailability (clouds…), data inconsistency (NaN, error code) supposes to have a flexible enough implementation to manage with various situations with a minimum of manual work These considerations lead to the prototyping and current development of a new SEB framework, with the following characteristics: • core algorithms are separated from I/O procedures; external I/O procedures can be extended without any modification of the algorithms to allow the use of new data sources • efficient object oriented python coding based on Numpy and SciPy math libraries for fast processing of numerical arrays; multi-core computation capability; fully open source based and cross-plateform • XML based configuration, with HTML/PhP user interface (under development) • powerful geo-processing library GDAL embedded • self-diagnosis capability for fast analysis of mass of log files At this stage of the project, this code is under development, with evaluation of a beta version. The first stable version will be described in details in the Algorithm Theoretical Basis Document to be delivered on milestone M2. The resulting products will be made available to WP8 partners, and as a new product in the database of the project to be registered to GEOSS. Page 32 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Figure 1: SEB framework chart Task 3.3 (UDS, ALTERRA, ITC, ITP) : The same MSSEBS approach is used with low resolution satellite images (Feng Yun-2) and NWP model outputs over the entire plateau. These low resolution fluxes maps can be validated from spatially integrated maps obtained in Task 3.2. (nothing at this stage of the project) Significant results The aim of the calculations performed with the 2003 dataset is to perform a cross-comparison of algorithms and a validation with ground measurements. The candidate algorithm of UDS is the Multi-Scale Surface Energy Balance System (Colin, 2006). This is a single source SEBI based scheme designed to process radiative balance, PBL stability and external resistances at appropriate scales as regards the physical meaning of key variables (e.g. roughness length for momentum and heat, stability functions in the atmospheric boundary layer…), to produce evaporative fraction maps. The soil heat flux is computed following vegetation fraction data, and the total diurnal evaporation is computed with a locally fitted model of net available energy for turbulent flux. The sensible heat flux is calculated as the residual of the energy balance. Page 33 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Figure 2: example of results for Nov 11th 2003: (top left) PBL forcing from GRAPES, values are allocated following the acquisition time of the LST; (top right) MODIS products; (bottom left) Sensible heat flux map from MSSEBS; (top right) Latent heat flux map from MSSEBS.For the 1x1 km pixel where the Bijie site is located is, for Nov. 11th 2003 at 11:06, the latent heat flux calculatedwith MSSEBS is 7.6 W.m-2 , and the sensible heat flux is 143.1 W.m-2, while ground values of latent heat fluxmeasured at respectively 10:30 and 11:30 range from -14.3 W.m-2 to -55.5 W.m-2, and the corresponding sensibleheat flux ranges from 91.4 W.m-2 to 200.0 W.m-2.Since the latent heat flux from MSSEBS is of the order of magnitude of the model uncertainty (Colin 2006), theevaporation can be considered as almost negligible. Moreover, as the ground measurement values used here aresensor values, a comparison with a 1 km resolution pixel would require further analysis of the spatial meaning ofthe measures.This first experiment gives important information for the preparation of the next phase of the project: • whatever the date of the year, even a limited scene is affected by clouds. The SEB framework has to be able to deal with missing values in mathematical processing, and gap filling technics to be implemented in WP2 will probably be critical to provide a continuous flow of inputs for the time series processing phase to come Page 34 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 • also these experiments are based of GRAPES simulations, GRAPES usually provide analysis data, ie. at a fixed 6 hour time step. This is of consequence as regard the acquisition time of LST products. An additional step may be required to derive LST at a GRAPES time step from the remote sensing products.This first experiment has several significant limitations: • no data were available to conduct a dual-source calculation • validation data were only available for one point, and local meteorological conditions only allowed to use one of the selected dates • ground measurement data used for validation didn’t passed through detailed quality and footprint analysisTherefore a new validation experiment was initiated with a selection of 3 different sites located in very differentparts of the Plateau, using 4 sets of 10 days of data in January, April, July and October 2008. This set ofvalidation data was made available late September 2009 by WP1 partners. MODIS products were collected, andGRAPES simulations still have to be performed at the time of writing this report. Therefore it is asked that thetarget delivery time of deliverable de 3.1 “Review of selected existing algorithms and models on local, regionaland Plateau scales data sets” is set to December 20th to allow for the completition of this analysis. Page 35 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 3.4 Work progress in WP 4 and achievements during the periodSummary of progressTask 4.1: Review and inter-comparison of available algorithms and products (microwave backscatteringcoefficient, microwave emissivity and land surface temperature diurnal cycle) (ITC, CAREERI, BNU,IGSNRR) This task is completed and report is writtenTask 4.2: Collection of consistent continuous in-situ soil moisture measurements at regional scale ofselected sites on the Tibetan plateau measurements which will include soil moisture (including soiltemperature, vegetation parameter, soil texture and land surface roughness) at two sites (Maqu-grassland,and Naqu tentatively) (CAREERI, ITC) Task 4.2 has been completed and Deliverable 4.1 has been distributed. CARRERI and ITC have installed in May-July 2008 an extensive soil moisture and soil temperature monitoring network in the water source region of the Yellow River to the South of Maqu city, on the border between Gansu and Sichuan province, in China (33°30’-34°15’N, 101°38’-102°45’E). The network consists of 20 stations monitoring the soil moisture and temperature at different depths (from 5 to 80 cm deep) every 15 minutes. The network covers an area of approximately 40 km*80 km, where the elevation ranges between 3430 m and 3750 m a.s.l (north-eastern edge of the Tibetan Plateau). To ensure complete data continuity, the data are downloaded twice per year by CAREERI: at the beginning of the monsoon season (in May) and at the end (in October). A specific calibration of the probes has been carried out for the soil type of Maqu area, increasing the accuracy of the soil moisture measurements from 6% to 2%. The quality of the data downloaded from Maqu monitoring network has been checked by evaluating their consistency in time and space and by comparing their trends with meteorological data and with soil moisture satellite products. A clear consistency and a good agreement have been found. The calibrated data collected at all the stations and at all available depths are reported in an Excel file and a detailed technical report has been attached to the data. Both of them have been delivered to the project teams. Task 4.3: Development of a satellite sensor independent system for the soil moisture combined retrieval algorithms (ITC, CAREERI, BNU) This task is in progress. A retrieval model is developed for ASCAT data which will be combined with passive microwave data in the course of the project.Task 4.4: Estimation of soil moisture from Geostationary Satellite (GS) data (optical remotely senseddata) (IGSNRR)In order to develop method of estimate soil moisture based on geostationary satellite data using the diurnalvariation of LST derived and global radiation (shortwave). Following investigations were conducted during thistime: 1. Construction of land surface diurnal temperature cycle model and the ellipse relationship between LST and solar shortwave radiance.In geostationary satellite observation system, there are adequate images to describe land surface temperaturevariation under clear sky condition. In generally, land surface temperature diurnal variation can be expressed as aharmonic term in daytime and an exponential term during the nighttime. This two-part semi-empirical diurnaltemperature cycle (DTC) model has used by Göttsche and Olesen (2001), Schädlich et al. (2001) and Jiang et al.(2006). In our work, we chose the model applied in Jiang (2006). 2. Land surface temperature simulation with land surface model (i.e. Common Land Model ) Page 36 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1In order to validate some assumption and analyze the method mentioned above, simulation data is an easy andfast way. In our simulation, Common Land Model was selected to simulate land surface temperature underdifferent environment conditions in clear air condition. During the simulation, soil type and land cover type wereusually set to be constant. Then we modeled the land surface temperature variation under different percentvegetation cover varying from 0%- 100% with a step of 10% and soil volumetric water content varying from0%-50% with a step of 5%.Several parameters were extracted from the land surface temperature daily cycle like maximum temperature,minimum temperature, daily temperature amplitude, temperature morning raising rate and so on. Correlationanalysis was conducted here to analyze the relationship of there parameters with soil water content and percentvegetation cover. The results showed that land surface temperature is a complex variable. It is influenced notonly by soil water content, but also is greatly influenced by surface land cover type and percent vegetation cover.As an interface between land and air, Land surface has strong energy and material exchange processes. In orderto understand the degree of soil water content’s influence on land surface temperature, the other factors shouldbe eliminated firstly. 3. Organization and implement field experiment in Lang fang experimental base.Beside land surface model simulation, we also organized a field experiment in Lang fang experimental base inHe bei province, China. In order to measure the atmosphere and soil data, such as air temperature, wind velocity,soil volumetric water content, we purchased an Automatic Weather Station and Time Domain Relectometers(TDR). Meanwhile, land surface temperature was measured by infrared thermometer. Down-welling globeradiation and net radiation were also recorded using Solar Radiometer.The experiment was implemented from 17th Oct. to 5th Nov. 2008 for 20 days. Three sites were executedsimultaneously with three soil types (sand, watered local soil and non-watered local soil). 4. In-situ measurement data analysisFrom the experiment, many data was collected. Fig. 3.4.1 shows the observed records of soil surfacetemperature, wind speed and air temperature at 2 Meter height of 5 days.Fig.3.4.1 Sample of observed data during the experiment Page 37 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1From the observed data, we analyzed the soil temperature raising rate related to the Net Surface ShortwaveRadiation (NSSR) during the morning time, and the temperature falling rate related to NSSR or Net SurfaceRadiation (NSR) during the afternoon time. 5. Abnormal surface nocturnal cooling effect analysisFrom the in-situ measurements and satellite data of MSG SEVIRI, we found that there exists an abnormal risingof the change of the soil temperature in the nocturnal cooling process. Nocturnal surface intense cooling mayresult in the inversion of the atmospheric temperature and water vapor. In order to study the abnormalphenomenon, we analyze and simulate the changes of surface temperature under different atmosphericconditionsTask 4.5: A data product of the plateau using different sensors simultaneously (AMSR-E, ASCAT,SMOS) (BNU, ITC) Up to October 2009, we had collected all of the satellite observation data and ancillary data used for retrieval, including AMSR-E Level 2A, Level 3 brightness temperature data, SRTM 90m DEM data, MODIS IGBP land cover map, and surface freeze/thaw status data, etc. Available ground surface emission models were evaluated and compared in detail, on this basis, a forward simulation system was established. It uses Qp model to calculate the emission of rough soil surface, and !-" model to consider the vegetation effects. Through simulation analysis, the crucial inversion methods were determined. A multi-channel temperature estimation algorithm using AMSR-E was selected to obtain the surface temperature. The new developed microwave vegetation Indices (MVIs) was used to eliminate the vegetation effects. And a soil moisture index developed from Qp model was put forward to minimize the effects of surface roughness. When the above methods were used in the soil moisture retrieval, some good results were achieved, and further results are still in progress.Task 4.6: Validation results and documentation of uncertainties (CAREERI, BNU) There is no progress made so far and is in accordance with project plan.Significant resultsCollection of consistent continuous in-situ soil moisture measurements at regional scaleOne of the objectives of the CEOP-AEGIS project is to develop a soil moisture retrieval algorithm based on thesimultaneous use of active and passive microwave satellite data. The developed algorithm is sensorconfiguration independent and is able to incorporate data of present and future satellite data, such as AMSR-E,ASCAT and SMOS. The long term and large scale products obtained applying the developed algorithm over theTibetan Plateau, will be extremely important to understand the links between Monsoon system, precipitationpatterns and soil moisture.For this reason, extensive soil moisture monitoring networks are required to obtain ground information whichcan be compared to the retrieved soil moisture products, in order to evaluate their consistency.To tackle this validation problem, CARRERI and ITC have installed in July 2008 an extensive soil moisture andsoil temperature monitoring network in the water source region of the Yellow River to the South of Maqu city(Gansu province, China). The network consists of 20 stations monitoring the soil moisture and temperature atdifferent depths (from 5 to 80 cm deep) every 15 minutes. The network covers an area of approximately 40km*80 km.The area selected for the installation of an extensive soil moisture monitoring network is located to the South ofMaqu city, on the border between Gansu and Sichuan province, in China. The network is at the north-easternedge of the Tibetan Plateau (33°30 -34°15’N, 101°38’-102°45’E) and at the first major meander of the YellowRiver, where it meets the Black river. It covers the large valley of the river and the surrounding hills (Figure Page 38 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.4.2), characterised by a uniform land cover of short grassland used for grazing by sheep and yaks. In this areathe elevation ranges between 3430 m and 3750 m a.s.l.The installation of the soil moisture and soil temperature monitoring stations started in May 2008 with thestations CST_01-05 and was concluded at the end of June 2008 with all the other stations. Therefore since July2008 the complete network is operative.The network covers an area of approximately 80 km*40 km and the locations have been selected in order tomonitor the area extensively at different altitudes and for different soil characteristics.During the installation, soil samples were collected in order to analyse bulk density, particle size distribution andorganic matter content. The samples for particle size and organic matter were collected at a depth between 5 and15 cm. A laser scanner (Mastersizer S Ver. 2.18 by Malvern Instruments Ltd.) was employed to estimate the soilparticle size distribution and the standard method for the organic matter content. Soil sample rings (aluminiumcylinders of known volume) were collected at 5 cm depth and oven dried at 105°C to estimate the bulk density(i.e. dry soil mass in a known volume). When the soil profile showed a variation at deeper layers, the samplecollection and the analyses were repeated for the second horizon as well.Figure 3.4.2 Maqu area, Yellow River valley and location of the 20 soil moisture and soil temperature stations of thenetwork.Each network station consists of one Em50 ECH2O datalogger (by Decagon), which is recording the datacollected by two to five EC-TM ECH2O probes (by Decagon) able to measure both soil moisture and soiltemperature.EC-TM ECH2O probe consists of 3 flat pins 5.2 cm long. It is a capacitance sensor measuring the dielectricpermittivity of the soil surrounding the pins. The dielectric permittivity is then converted in volumetric soilmoisture according to a standard calibration equation. The soil temperature is measured using a thermistorlocated on the same probe. Page 39 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Figure 3.4.3 Installation procedureA specific calibration of the probes was needed for the soil type of Maqu area. Therefore soil samples werecollected and laboratory calibrations were carried out (see following paragraph).For the installation, a deep hole in the soil was dug and the probes were installed on one of the hole walls, atdifferent depths and with the pins in horizontal direction. Then probes and datalogger (closed in a box) werecompletely buried (see Figure 3.4.3).EC-TM ECH2O probes estimate the volumetric water content of the soil by measuring the dielectric constant ofthe soil. However the dielectric properties of the soils depend on soil texture and salinity. Decagon hasdetermined a generic calibration equation (applied by default by the datalogger), which is valid for all finetextured mineral soils with an accuracy of approximately ± 3%. This accuracy can be increased to 1-2%,performing a soil-specific calibration. For this reason about 5-6 kg of soil were collected in each location at adepth of about 5-15 cm (as well as at deeper layers, in case the soil profile was different) in order to carry out alaboratory specific calibration, following the instruction guide provided by Decagon.Figure 3.4.4 Results of the soil specific calibration of ECH2O probes Page 40 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1In conclusion, the calibration (Fig.3.4.4) has led to a decrease of the rmse between the volumetric soil moisturemeasured by the rings and that measured by the probes from 0.06 to 0.02 m3/m3. Page 41 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.5 Work progress in WP 5 and achievements during the periodEstimation of precipitation over the Plateau and surrounding zones with optical and microwaveobservationsThe objective of this WP was twofold: to provide multisensor and multiplatform observation of precipitationover the Plateau, and to get a deeper understanding of cloud and precipitation processes ongoing over this area.The temporal development of the activities identified as the first step the set-up of a reliable strategy to providequantitative precipitation measurement. This was achieved during the first reporting period of the project: theweather radar data have been pre-processed to provide the project with a quality controlled 3D precipitationdataset over the project target area.On the other side, two studies were completed indicating that prevailing synoptic scale trough is one of a keyindicator to establish unique precipitation system over the Tibetan Plateau. Other activities are in their firstdeveloping phase, and did not yet achieved significant results, as planned in the DoW document.In the next pages a more detailed description of the activities is presented task by task.Summary of progress.Task 5.1: To observe the cloud and precipitation microphysics processes in Tibetan Plateau andsouthwestern China by cloud Doppler radar, movable X and C band dual linear polarization radar. Ahydrometeors classification algorithm will be applied to retrieve the 3D microphysical cloud structure.The radar observation has started in the sites operated by CAMS: the radar network and rain gauge information,analyze the ground blockage for radar in Tibetan and Qinghai Province. The results show that the radars inTibetan are blockage by around mountain severely, the radar coverage is limited. The radar in Qinghai provincecan be used to precipitation estimation with rain gauges. A fuzzy-logic based algorithm for hydrometeor phaseclassification with polarimetric radar has been developed by CAMS. A small network of three X-banddisdrometers (PLUDIX) is planned by UNIFE (with the assistance of ITP-CAS) and the installation will becompleted in November 2009.Task 5.2: To develop the QC and mosaic algorithms for operational Doppler radar network. Thedisdrometric data will be used in radar QC and for radar calibration if disdrometers instruments areavailable.Research work on radar data quality and reflectivity remap and mosaic has been carried on by CAMS, and thealgorithm for 3 D mosaic. A the fuzzy logic based algorithm is used to detect the anomalous propagation andground clutter; four interpolation approaches are used to remap raw radar reflectivity fields onto a 3D Cartesiangrid with high resolution, and three approaches of combining multiple-radar reflectivity fields are used. Thealgorithm has been used to process the radar data and provide 3D data to the other partners of WP5.In particular, the raw precipitation data in Tibetan and the gridded precipitation data were provided by CAMS toUNIFE for two case studies. for period of 18 June 2008-19 June 2008 and 18 July 2008-20 July 2008, withspatial and temporal resolution (0.01°#0.01°#0.5km#5min)Finally, CAMS processed radar data and provided 3D reflectivity data to WP5. Grid Reflectivity in Qinghaifrom 18 July 2008 -21 July 2008 were product, the radar data in Tibetan from 18 June 2008 to19 June 2008, 18July 2008 to 20 July 2008 were provide.The data of three X-band disdrometers will made available by UNIFE for the period 1 November 2009 – 30October 2010, to improve the quantitative radar rainfall products. Page 42 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Task 5.3: To analyze the meso-scale structures and processes of precipitation systems in Tibetan Plateau andsouthwestern China by operational Doppler radar network in China and satellite (e.g. cloud products of MODIS).The precipitation distributions with different algorithms will be compared in case studies.UNIFE carried out an inventory of satellite precipitation estimation techniques, including both physical andstatistical approach and considering microwave (AMSR-E, SSM/I-SSMIS and AMSU), visible-infrared (MODIS,AVHRR, Meteosat, FY-2C), and blended techniques. The characteristics of different techniques were analyzedto select the more suitable ones for application over the Tibetan Plateau. The events proposed by CAMS wereselected as case study for the early application of selected techniques.UNITSUK completed an analysis of the meso-scale structures and processes of precipitation systems andidentification of the indicators for the rainfall processes in Tibetan Plateau (TP) and southwestern China, and theresults will be summarized in the next section.Task 5.4: To use the rain maps obtained by the ANN technique along two main lines: improve the performanceof floods and drought warning systems, and analyze long term (seasonal) rainfall pattern.IGSNRR performed an inventory of Satellite Rainfall Estimation approaches and studied the theory of ArtificialNeural Network (ANN) and application in satellite rainfall estimation. The MATLAB software is considered forANN implementation. A first satellite dataset (June 2007 to September 2007) has collected and processed: FY-2C satellite images (provided by the National Satellite Meteorological Center of China at 5Km spatial resolutionand hourly temporal resolution) and Gauge data (purchasing from the National Satellite Meteorological Centerof China) at hourly temporal resolution as well.An ANN technique is implemented and tested with gauges data by IGSNRR, and the preliminary results will besummarized in the next section.UNIFE started to apply an ANN technique developed for MODIS data and focused on mid-latitude, to the casestudies over the Tibetan Plateau.Task 5.5: To retrieve the precipitation with Doppler radar, satellite data and rain gauges in mountain region.The retrieval of precipitation fields from radar and rain gauges has started (see task 5.2), while the satelliteapproach is still in its preliminary phase (see also Tasks 5.3, 5.4 and 5.8).Task 5.6: To obtain the distribution of Precipitable Water Vapor (PWV) in Tibetan Plateau and itsadjoining area by GPS receiver.This task is not yet started by CAMS.Task 5.7: To obtain the indicators of the rainfall process in Tibetan Plateau and southwestern China by analyzingthe change of PWV.UNITSUK carried on a study on the relevance of water vapor transportation processes, using reanalysis data andnumerical weather prediction output. Results of this study will be summarized in the next section.Task 5.8: To improve the current combined precipitation estimation technique with the radiometer(TMI) and PR with the simulation database developed above and inclusion of the effects of topography over thePlateau; Also here we will correct the satellite estimation of precipitation using the ground rain gauge data in thealgorithm, and validate the inversion scheme with ground observation.For this task UNIFE planned to apply a rainfall retrieval scheme that works on conical scanner data (SSM/I-SSMIS, AMSR-E, TMI). The algorithm is based on a cloud radiation database constructed as follows. A cloudprofile data set is assembled by means of cloud resolving model outputs (the Non-hydrostatic Modeling Systemof the University of Wisconsin is used to this end), then a radiative transfer algorithm is applied to simulate theradiances upwelling from the modeled cloud profiles. When a set of satellite radiances is measured from a givensensor, the database is searched for the cloud profile whose simulated radiance better match the observed ones.This algorithm is currently applied in different regions with encouraging results.Significant results Page 43 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 12.1 Delivery of radar 3D precipitation gridded data (CAMS)A remarkable result of the first 18 months of WP5 activity is the production of a radar derived precipitationproduct gridded on a 3D grid (mosaic). The used techniques and the developed algorithms are described in thedeliverable (D5.1) about radar data pre-processing issued by CAMS, responsible for the radar data.2.2 Studies on moist processes over Tibetan Plateau (UNITSUK)To reach the general WP5 objective of improving the understanding of cloud, water vapor, and precipitationprocesses of mountain area in Tibetan Plateau (TP) and Southwestern China, two relevant results were achievedby the analysis of the meso-scale structures and processes of precipitation systems and identification of theindicators for the rainfall processes in TP.At first, water vapor (WV) transportation processes into the TP during monsoon season was examined byreanalysis data and numerical simulation, and found that synoptic scale troughs separated by the TP playedprimarily rules to intrude the mid-troposphere WV and converge over the southeast on the TP. The systematicintrusion occurred at the same time with Indian monsoon breaks. Numerical simulation also indicated thatdaytime valley winds play secondary function to bring WV into higher elevations over the southern slopes of theTP even prevailing with passing of troughs. Those evidences need to be verified by in-situ observation data thatare planned to be archived by the WP5 partners in the later stages. The results are published in the Journal ofMeteorological society of Japan by Sugimoto et al. (2008).On the TP, there are two types of convection processes, one is the thermal convective activities during daytimeand the other is the stratified nocturnal system. During the first stage, we are more focused on the analysis ofnighttime precipitation system. Frequent occurrence of the nocturnal precipitation was often reported andidentified as a key parameter to control morning soil moisture amount which could feedback the next day’sstarting of convections. However, there were few studies treated the mechanism of nighttime precipitation onthe TP. Composite analysis of the in-situ observation data and re-analysis data showed that precipitation afterthe mi-night frequently occurred with easterly winds provided by an anti-cyclone located in the north-west of theTP through successive days. Numerical simulation revealed that the anti-cyclone was formed by the plateauscale topographic effect with a traveling of mid-latitudes baroclinic wave, which caused synoptic scaleconvergence zone over the central PT and activate convections by dissolving near-surface convective instability.It can be explained that such convergence would be dissipated during daytime due to prevailing of strongthermal convections and associated sub-grid scale local circulations. The results are published in the Journal ofMeteorological society of Japan by Ueno et al. (2009).Those two studies indicated that prevailing synoptic scale trough is one of a key indicator to establish uniqueprecipitation system over the TP. Accurate prediction of the location and development of troughs with its year-to-year variability would be required to assess long-term water cycle trends. Research activities regarding to thedevelopment of meso-scale convective systems, that links to severe weather prediction, have been conducted asa part of WP7 and explained there.2.3 ANN implementation to retrieve precipitation from FY-2C data (IGSNRR)Preliminary results were also reached with the use of an Artificial Neural Network (ANN) rainfall estimationtechnique. Several ANN types have been considered for application: in this work a three-layer feedforwardneural network (TLFNN) was implemented. Output layer has only one neuron since the objective is to estimatethe precipitation value associated to a certain pixel(i,j), otherwise, a log-sigmoid and “purelin” activationfunction is used for the first hidden layer and the second hidden layer respectively. In table 1 the input of theTLFNN are listed. Lots of researches show that Tibetan Plateau was mainly rainy in summer, so in this work we focused onsummer season. The performance of a NN depend on the choice of network model, architecture and parameters Page 44 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1(weights and bias). Therefore, when the network model is chose, the performance of the NN configurationdepend on the choice of the number of neuron of layers and the training. As a result of we just got the data ofsatellite images and gauge, several configurations of feed-forward networks have been trained only selectedsatellite images form the phases sequence of 1 and 7 June 2007. A further improvement in the performance of acertain neural configuration is expected next we will use wider set of input patters, representative of differentmeteorological situations is provided to the network in the training phase. In addition, the configuration of feed-forward network will be done lots of experiments, choosing the best as we need.Table 5.1. Input variables for TLFNN modelIPA The ratio of the intensity IR3 and the sum of brightness temperature IR1(IR3/(TBBIR1+TBBIR2)) IR2INCREMENT The infrared brightness temperature IR1 increment of the pixel hourly adjacent interval (TBBIR1) The infrared brightness temperature of the pixel The grad of brightness temperature of 3#3pixel window centered on the target pixelLAT The latitude of infrared brightness temperature IR1 of the pixelLONG The longitude of infrared brightness temperature IR1 of the pixel The mean of brightness temperature of the 3#3pixel window centered on the target pixel The standard deviation of brightness temperature of the 3#3pixel window centered on the target pixel The mean of brightness temperature of the 5#5pixel window centered on the target pixel The standard deviation of brightness temperature of the 5#5pixel window centered on the target pixelAs figure 5.1 and table 5.2 below show, the accuracy in this experiment is not high, reasons may be amongthese: 1) a too short phase used for training (1 and 7 June 2007); 2) the number of neuron of layers is not thebest; 3) the choice of phase for training is not typical; 4) the performance of TLFNN should be compared withmore statistical data with gauge; 5) other reasons to be investigated in the next months. Page 45 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Fig 5.1.The difference of Gauge and TLFNN on 8/26,2007(00:00~23:00)Table 5.2. Comparing of Samples statistics of Gauge and TLFNN on on 8/26,2007(15:00) Rainfall[mm] Gauge TLFNN No rain 31 25 0~2 14 12 2~10 5 14 >10 2 1ReferencesSugimoto S., K. Ueno and W. Sha, 2008: Transportation of water vapor into the Tibetan Plateau in the case of apassing synoptic-scale trough. J. Meteor. Soc. Japan, 86, 935-949.Ueno K., S. Takano and H. Kusaka, 2009: Nighttime precipitation induced by a synoptic-scale convergence inthe central Tibetan Plateau. J. Meteor. Soc. Japan, 87, 459-472. Page 46 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.6 Work progress in WP 6 and achievements during the periodSummary of progressWP6 aims at the estimation of glaciers and snow melt-water on the QTP. During the period from May 1st 2008to October 1st 2009, works progressed toward the objectives and scheduled tasks as planned. In the first level,the algorithms for snow/ice and frozen soil properties retrievals and products, that major on snow cover/fractioncover, snow water equivalent(SWE), soil freeze/thaw status, have been reviewed and intercompared. With theabove works as base, the prototypes for mapping snow cover/fraction cover, SWE retrieval and soil freeze/thawstatus classification are primarily developed and their daily products has been generated and validated.In addition, we are developing a high-resolution meteorological dataset, which will be used to drive the land dataassimilation system and derive soil parameters. Moreover, the mass balance observation for Zhadang glacier andhydrological as well as meteorological observations in this region have been carried out as the first step toevaluate glacier area and volume on QTP. Finally, climate effects on glacier mass balance was modelled using“positive degree day factor” method and a new snowmelt model (Binggou Snowmelt Model (BSM)) which cancouple remote sensing data and meteorological observation was established using snow energy balance method.Task 6.1: Review and intercomparison of available algorithms for snow/ice properties (fraction cover,water equivalent) retrievals and products (CAREERI, BNU, TUD)(1) A review has been performed of the available algorithms for snow/ice properties. The focusedalgorithms are the ones for snow cover/fraction cover mapping, SWE retrievals by passive microwave remotesensing and soil frozen/thaw status identification.(2) Satellite datasets both of the optical, such as the MODIS and microwave like SMMR and SSM/I havebeen collected. Besides, the global products of snow cover and SWE on QTP has been validated by some in situobservations now available to us and by the results of high resolution images like TM. Due to the forgone workand the want of more general assessment, some validation on snow cover mapping products has been conductedin Xinjiang Province as for reference. The drawbacks of NSIDC global snow cover products and the necessaryof developing a new algorithm for retrieving snow depth on QTP have been identified.The Dept. of Remote Sensing of Delft UT contributes to two tasks within WP 6. For both tasks, data of theGLAS full waveform laser ranger on board of the ICESat mission has been used. GLAS collected world widealong-track elevations between 2003 and 2009 with a decimeter vertical accuracy. It has no side-lookingpossibilities, therefore sampling over Tibet is strongly hampered by its near polar orbit, resulting in an acrosstrack distance between adjacent tracks of 70 km. ICESat records the full waveform return signal, which meansthat the signal return resulting from the convolution of the outgoing signal with the vertical structure in the ~70m footprint is sampled at 15 cm vertical resolution. From this return signal an elevation is estimated using auniform method.Ongoing work links the shape of the GLAS full waveform returns from GLAS data over the NyainqentanglhaMountains to land cover and glacier characteristics. It is considered to what extend the full return signal can bedirectly used to decided on properties of the surface. In a first study, a distinction is made between returns fromwater (Namtso lake), rock and glacier. In a second study, variations in return signal within a glacier (Bare ice,snow, debris) will be considered.Using ICESat repeated laser range data it is possible to analyze changes in elevation along track during themission lifetime from 2003 and 2009. However, tracks are only repeated up to a few 100 m. Therefore directmonitoring of glacial elevation changes is challenging. It has been demonstrated however, (Figure 6.2), thatICESat is very suited to obtain elevation changes over many Tibetan lakes. In a next step the links between Page 47 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1glaciers (CAREERI glacier mask) and lakes (MODIS water mask) will be established using GIS techniques inorder to, at least partly, relate lake elevation changes to meltwater equivalents.Task 6.2: Developing a prototype for mapping snow cover extent and Snow Water Equivalent (SWE)(CAREERI, BNU)(1) We have developed a new daily snow cover mapping algorithm by: 1) improving the NSIDC snowcovering algorithms and 2) combining MODIS-Terra and MODIS-Aqua data on QTP. Further more, a methodfor mapping fractional snow cover from MODIS data on QTP has also been proposed. The new snow covermapping algorithm can provide daily snow cover products at 500-m resolution on QTP. The new snow coveralgorithm employs the CIVCO topographic correction, a grouped-criteria technique using the NormalizedDifference Snow Index (NDSI) and other spectral threshold tests and image fusion techniques to identify andclassify snow on a pixel-by-pixel basis.(2) We Modified the Chang snow algorithm to make it suitable for snow depth retrieval on QTP usingSMMR and SSM/I remote sensing data and snow depth data recorded at the China national meteorologicalstations. We further analyzed the accuracy and uncertainty of the new snow product generated by the modifiedChang algorithm. The daily snow depth datasets in China from 1978/1979 to 2005/2006 has been produced, andtheir spatial and temporal characteristics analyzed primarily.(3) We have developed a new decision tree algorithm to classify the surface soil freeze/thaw states. Thealgorithm uses SSM/I brightness temperatures recorded in the early morning. Three critical indices areintroduced as classification criteria—the scattering index (SI), the 37 GHz vertical polarization brightnesstemperature (T37V), and the 19 GHz polarization difference (PD19). And the discrimination of the desert andprecipitation from frozen soil is considered, which improve the classification accuracy. Long time series ofsurface soil freeze/thaw statuses can be obtained using this decision tree, which potentially can provide a basicdataset for research on climate and cryosphere interactions, carbon cycles, hydrological processes, and generalcirculation modelsTask 6.3: Evaluating glacier area and volume on the Qinghai-Tibet Plateau (CAREERI, BNU, ITP, TUD).(1) Observation of mass balance for the Zhadang glacier;(2) Hydrological and meteorological observation in the Zhadang glacier area(3) Modelling climate effects on glacier mass balance using “positive degree day factor” methodAs it is relatively easy to estimate changes in the glacier area using readily available image data, Delft UT hasfocused on novel methods aiming at analyzing volume changes using different types of topographic satellitedata. First SAR data can be applied in two essentially different ways to obtain glacial flow velocity fields. Onemethod is image based, and obtains velocities by matching images obtained from different moments. Thismethod has been successfully applied by others on e.g. Baltoro. The other method, InSAR, exploits phasedifferences between different acquisitions. This method has been used to obtain a flow velocity map of Rongbukglacier, on the North side of Everest.Topographic coverage of all Tibetan glaciers could be obtained using photogrammetric techniques applied onstereo data from e.g. the ALOS/PRISM instrument. Efforts are ongoing to optimally profit from the informationcontents in the ALOS/PRISM imagery. First results at the border area between China, India and Nepal showDTM processing using standard software is possible, but is hampered by low texture on snow, and shadoweffects and low visibility in deep valleys. A next step will consider custom made software to overcome part ofthese problems. Page 48 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Task 6.4: Providing soil parameter data sets for the entire plateau from a microwave land dataassimilation system from 2008 to 2010(1) We are developing a high-resolution meteorological dataset, which will be used to drive the land dataassimilation system and derive soil parameters. The forcing data includes six components: shortwave radiation,longwave radiation, precipitation, pressure, wind, air temperature, and air humidity. Several data sources areused for the development: in situ data from China Meteorological Administration (CMA), precipitation fromGPCP, and other components from Princeton University. At this moment, we are focusing on the shortwaveradiation. Preliminary results shows the accuracy of radiation can be significantly improved compared withcurrently available forcing data.(2) To facility the land data assimilation system, we have taken a number of soil samples, with a totalweight of 80 kg. The soil samples are being tested at Institute of Tibetan Plateau Research to measure itshydraulic and thermal parameters. The output of laboratory experiments would provide a basis to evaluate landsurface models and the data assimilation system.Task 6.5: Estimation of glaciers and snow meltwater. (CAREERI, BNU):Snow energy balance method was utilized to set up a new snow model - Binggou Snowmelt Model (BSM) - withremote sensing data and meteorological observation. Snow distribution, snowmelt and snow sublimation weremodelled by BSM in 2008 snow season in Binggou watershed.Significant results1. On Snow Cover Mapping and SWE Retrieval:We proposed a modified algorithm to retrieve the snow depth on QTP from SMMR (1978 to 1987) and SSM/I(1987 to 2006), and analyzed the spatial and temporal variations of snow depth over whole QTP. The snowdepth products were generated based on the new algorithm from SMMR and SSM/I during the period from 1978to 2006 on the whole QTP.We have developed a new daily snow cover mapping algorithm based on which daily snow cover products at500-m resolution on QTP has been generated. In addition, a prototype for snow fraction cover mapping has beenproposed.A decision tree algorithm was developed to identify the surface soil freeze/thaw states by taking the influence ofthe desert and precipitation into account. The more reliable SI was introduced into this decision tree instead ofSG to identify the scatterers. The average accuracy of the classification result was 87%, which was validatedagainst the 4 cm deep soil temperature observations. Most misclassifications occurred when the soiltemperatures were near the soil freezing point and during the transition period between the warm and coldseasons. A grid-to-grid Kappa analysis was also conducted to evaluate the consistency between the map of theactual number of frozen days obtained using the decision tree classification algorithm and the map ofgeocryological regionalization and classification in China. The results showed that the overall classificationaccuracy was 91.7%, while the Kappa index was 80.5%. Both validation results show that this new decision treealgorithm based on SSM/I brightness temperature can produce a long time series of surface soil freeze/thawstatus from the launch of SSM/I in 1987 until now with an accuracy capable of providing a dataset to analyze thetiming, duration and areal extent of surface soil freeze/thaw status for the research on climate and cryosphereinteractions, carbon cycles, and hydrological processes in cold regions. Page 49 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Fig. 6.1 Actual number of frozen days in China for the period from Oct. 1, 2002 to Sep. 31, 20032. On Glacier Area and Volume EvaluationObservations in the Zhadang glacier indicate the parameterization of mass balance with annual precipitationamount is insufficient to describe the response of glaciers to climate change. Seasonal concentrations ofprecipitation strongly influence glacier mass balance, especially in monsoon regions with summer precipitationclimate.Using mass balance and meteorological data in the ablation season of the year 2007 and 2008 in Zhadangglacier, degree-day factors have been obtained for snow (5.3 mm•d-1• -1) and ice (4.0~14.0 mm•d-1• -1 atdifferent altitudes with an average of 9.2 mm•d-1 -1). Degree-day factors for the Zhadang glacier drop withelevated altitude, though there are no significant changes along with time. Mass balance in 2006/2007 and2007/2008 of Zhadang Glacier is estimated using degree-day model. The simulated mass balance in 2006/2007is -393.2mm w.e., and 289.7mm w.e. for the year 2007/2008.3. On Soil Parameters Dataset for Whole QTP from Microwave Assimilation SystemWe have accomplished the validation of the land data assimilation system, and this work has been published byJournal of Hydrometeorology, Vol. 10 (3) (Yang et al., 2009; see the attachment). The auspice of CEOP-AEGISis clearly acknowledged. This study testifies the capability of a new microwave land data assimilation system(LDAS) for estimating soil moisture in semi-arid regions, where soil moisture is very heterogeneous. Thissystem assimilates the AMSR-E 6.9 GHz and 18.7 GHz brightness temperatures into a land surface model Page 50 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1(LSM), with a radiative transfer model as an observation operator. In order to reduce errors caused byuncertainties of system parameters, the LDAS uses a dual-pass assimilation algorithm, with a calibration pass toestimate major model parameters from satellite data and an assimilation pass to estimate the near-surface soilmoisture.4. Validation data of soil moisture were collected in a Mongolian semi-arid region. Results show that (1)the LDAS-estimated soil moistures are comparable to area averages of in situ measurements, though themeasured soil moistures were highly variable from site to site; (2) the LSM-simulated soil moistures show lessbiases when the LSM uses LDAS-calibrated parameter values instead of default parameter values, indicating thatthe satellite-based calibration does contribute to soil moisture estimations; (3) compared to the LSM, the LDASproduces more robust and reliable soil moisture when forcing data become worse; the lower sensitivity of theLDAS output to precipitation is particularly encouraging for applying this system to regions where precipitationdata are prone to errors.Figure 6.2 Tibetan lake level trends between 2003 and 2009 as captured from ICESat data5. On Glacier and Snowmelt Water EstimationBinggou Snowmelt Model (BSM) was designed to model snow water equivalent and runoff, and the results werevalidated by measured snow depth. Daily snow cover observation was simulated by a utilization of daily andeight-day MODIS snow cover products and meteorological observations. With the simulated SWE, snowmeltprocesses at both the point-scale and basin scale were analyzed in detail. The net energy input was negativebefore snowmelt occurrence and heat eradiated from snowpack in this period. With solar azimuth changed,shortwave radiation enhanced and air temperature increased, energy input into snowpack increased and resultedas three large-scale snowmelt processes. Meteorological measurement, field observation and daily runoff datawere used to validate the simulation results by BSM. The results were in agreement well with three differentobservations but with some problems because: 1) the point-scale measurement could not be represented by gridsimulation; 2) snow cover was not recognized well sometimes and 3) frozen and thaw soil was not consideredproperly.6. Glacier flow and mass balance Page 51 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1The analysys of ICESAT, ALOS/PRISM and ENVISAT/ ASAR data led to the following results: $ Lake level changes of over 100 Tibetan lakes between 2003 and 2009 (Fig.6.2) $ Subdivison of lake level changes into major river basins $ Determination of corresponding changes in water volume $ Flow velocity map of Rongbuk glacier $ Construction of ALOS/PRISM DTM using standard software (Border area China/India/Nepal) using ICESat ground control points $ Validation ALOS/PRISM DTM using ASTER-GDEM and independent ICESat elevations. Page 52 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.7 Work progress in WP 7 and achievements during the periodSummary of progressNumerical Weather and Climate Prediction modeling system”K. Ueno (University of Tsukuba) and X. Shen (Chinese Academy of Meteorological Sciences )3.7.1 Progress for detailed analysis of the relationship between Plateau land surface processes, monsoononset and intense precipitation with a coupled land-atmosphere meso-scale model by UNITSU In the WP7, University of Tsukuba (UNITSU) team is mainly focused on the 1nd objectives “Detailed analysisof the relationship between Plateau land surface processes, monsoon onset and intense precipitations with acoupled land – atmosphere meso-scale model”. Based on the analysis results, UNITSU is also responsible fornominating candidate cases of precipitation systems for CAMS team to assess the land-surface effects andimproving lead time in the forecasts of intense precipitations by the numerical sensitivity studies. Then, our teamfollowed three steps in the analysis during 2008-2009. First, a target period was set through January to September in 2008 which covered seasonal transition of land-surface condition in spring and monsoon onset periods. The year of 2008 corresponded to “Asian MonsoonYear”, and JICA intensive observations were also conducted, that provided good opportunity to archive multiplein-situ data sets to validate products by WP7. WP7 is basically planning to use land-surface data produced byother working packages. However, the products have not been ready at this moment. Then, UNITSU archivedexisting global data sets, such as satellite and re-analysis data products by agencies as listed below, and havestarted assessments;1) MODIS/Terra Snow Cover 8-day L3 Global 500m Grid, Ver. 5 (snow cover) Jan.-Sep 2008, 8-day composited , 500m 500m National Snow and Ice Data Center ;NSIDC (http://nsidc.org)2) AMSR-E Level 3 Daily Soil moisture, Ver. 6 (Soil moisture) Jan.-Sep. 2008, Daily, 0.25 *0.25 Japan Aerospace Exploration Agency; JAXA (http://www.eoc.jaxa.jp/iss/index.html)3) JRA25 ( Dec. 2004)+JCDAS (Jan. 2005 ) (Geopotential height, Air temperature, Specific humidity, Dewpoint depression, Zonal wind, meridional wind, Cloud water content, Sea level pressure) Jan. 1990- Dec. 2008, 6-hourly, 1.25 *1.25 Japan Meteorological Agency- Central Research Institute of Electric Power Industry(http://jra.kishou.go.jp/)l JCDAS takes over the same system as JRA25, and the data assimilation cycle is extended up to thepresent (More detail information is shown in a web site of JRA25 ).4) Global Precipitation analysis; GPCP (precipitation; mm/day) Jan. 1997-Apr. 2008 (May-Sep. 2008 unreleased yet), Daily, 1 *1 NASA/Goddard Space Flight Center; GSFC (http://precip.gsfc.nasa.gov/)5) METEOSAT7 (IR; This satellite has only 10.5-12.5 µm ) Jan.-Sep. 2008, Hourly, inhomogeneous distributed data (about 5 km at sub-satellite point) EUMETSAT (http://www.eumetsat.int)6) FY2 (IR1=10.3-11.3µm, IR2=11.5-12.5µm, IR3=6.3-7.0µm, IR4=3.5-4.0µm) Jan.-Sep. 2008, Hourly, 0.04 *0.04 , 44.6E 164.6E,60S 60N Center for Environmental Remote Sensing (CEReS) 4 Virtual Laboratory (http://www.cr.chiba-u.jp/~4vl/wiki/wiki.cgi)7) Global Surface Summary of Day; GSOD (observation data at ground surface) Jan.-Dec. 2008, daily (only in China) National Climatic Data Center (NCDC); NOAA (http://www.ncdc.noaa.gov/cgi-bin/res40.pl?page=gsod.html) Page 53 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Secondary, seasonal transition was examined by JRA25 data and meso-scale convective systems (MCS) wereautomatically identified using the METEOSAT data followed by the method of Evans and Shemo (1996).Global data sets showed that there were several periods for plateau-scale snow cover during winter and normalonset of monsoon in the middle of June. Then, we focused on the strong convective activities in the warmseason, and extracted the MCS occurrence and movement automatically. Occurrence of the MCS wascoincident with intra-seasonal variability in the mid-latitudes, and dominated in the two areas over the TP, suchas south and southeast. In the periphery of the TP, MCS tended to occur along the south of Himalayas, Assam,and over the extending mountain zone from the southeastern TP, and they were mostly activated during thenight. The MCS was divided into two groups according to the synoptic conditions, such as 1) strong surfaceheating conditions with prevailing of upper high-pressure system (Tibetan High) without effects of mid-latitudesdisturbances or troughs, and the other is 2) the severe weather cases in the lee-side of TP associated with troughsor apparent fronts without the Tibetan High. Candidate periods for each condition are listed as follows, andhandled to CAMS team;Thirdly, we examined the influence of TP for development of MCS by numerical simulations for some cases oftwo types. Numerical simulation in the UNITSUK was designed by Weather and Research Forecast (WRF)model with three nesting domains, and non parameterization with two-way interactive simulation was conductedwith 4 km resolution in the last domain. Occurrence of MCSs in the model was well corresponded withMETEOSAT images, and processes of development was diagnosed. Tentative results were introduced at thejoint international conferences of IAMAS/IAPSO/IACS in Montréal, Canada by Sugimoto and Ueno (2009) andUeno (2009).ReferencesShiori SUGIMOTO and Kenichi UENO, 2009: The effect of synoptic and land-surface conditions for precipitation processes over the Tibetan Plateau, MOCA-09, the IAMAS/IAPSO/IACS 2009 Joint Assembly, Abstract No. 402, July, Montreal, Canada.Kenichi UENO, 2009: Mountain weather modification in the Tibet/Himalayas, MOCA-09, the IAMAS/IAPSO/IACS 2009 Joint Assembly, Abstract No. 514, July, Montreal, Canada.3.7.2 Progress for improving lead time in the forecasts of intense precipitations by CAMS2.1 Brief introduction of GRAPES_Meso The GRAPES is a unified NWP model with 3/4DVAR data assimilation system, which is the abbreviation ofGlobal/Regional Assimilation and PrEdiction System. The main features of GRAPES include: (1) fullycompressible equations with hydrostatic/non-hydrostatic option; (2) the semi-implicit and semi-Lagrangian time-stepping method; (3) height terrain-following coordinate in the vertical and latitude-longitude sphericalcoordinate in the horizontal; (4) scalar advection by piece-wise rational method; (5) fully physical package. The meso-scale version of GRAPES is utilized in WP-7. Its main characteristics are listed up in the followingtable. Flux-form equations of water substances Piece-wise rational method + volume- Page 54 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 remapping SL for scalar advection Dynamics Reference atmosphere based on the initial field Effective topography NOAH LSM + Simple initialization Xu and Randall Diagnostic cloud Betts-Miller-Janjic Cumulus CAMS mixed-phase microphysics Physics Effect of slope on surface radiationSignificant resultsRe-run of GRAPES_Meso with 15km horizontal resolution for 2008 has been finished, and verification ofrainfall forecast over Tibet was conducted. Figure 1 gives the time series of forecasted and observed 24hraccumulated rainfall. As shown, GRAPES_Meso can well capture most of the rainfall events occurred in Tibetduring the period from April to September of 2008. This encourages us to further investigate the possible role ofunderlying surface-atmosphere interaction on convective activities in the future work. averaged area 70~105 E 22.34~40 NFig.7.1: Time series of forecasted and observed 24hr accumulated rainfall averaged over area (70~105 E22.34~40 ). Unit is mm.(2) Through case study, investigation of effect of different complexity LSM on convective initiation has beenconducted. In the study, SLAB and NOAH (developed by Oregon State University) land surface models (LSMs)were employed to understand the impacts of different land surface processes on the initiation of convectiveactivities. A locally-developed convection case occurred on August 2, 2003 in Jiangxi Province of China wasselected. Figure 7.2 shows the simulated (fig. 7.2.b-e) and observed rainfall (fig. 7.2.a). Clearly, the simulatedrainfall by using complex NOAH LSM exhibits closer to the observations. Page 55 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 (a) (b) (c) (d) (e)Fig.7.2: (a) observed 24-h precipitation, (b) simulated 6-h precipitation from 00 to 06 UTC 02 Aug 2003 in color scheme byNOAH and by SLAB (c), simulated 12-h precipitation (00-12UTC) by NOAH (d), and by SLAB (e). Unit: mm. The boxshows the observed precipitation band over north-east Jiangxi. Further analysis show that regional convective precipitation is extremely sensitive to the land surfaceprocesses. The NOAH model applied in the study had a rational simulation of the initiation of convectiveactivities, while the SLAB model produced a retarded initiation of convective activities by 1-2 hours, implyingthat NOAH is good at describing surface sensible and latent heat. Soil temperature and moisture have a directimpact on the distribution of surface sensible and latent heat. Distribution of surface sensible heat flux, in turn,affects the development of boundary layer. The development of boundary layer affects the onset of localcirculations, by altering the stability of thermal-dynamic structures of the boundary layer, and by directlyaffecting the initiation of convective activities. NOAH made a quick response to the increased sensible heat flux Page 56 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1form nighttime to daytime. Booming sensible heat flux facilitated the fast development of the boundary layer,with correspondingly enhanced convective available potential energy (CAPE), creating the needed conditions forkicking off convective activities. A rational and detailed land surface process is extremely important fornumerical modeling, especially for the initiation and development of a strong local convective system under theweak synoptic forcing. 3 Systematic evaluation of effects of LSM on daily rainfall forecast for a successive heavy rainfall event wasconducted. The successive heavy rainfall has been occurred over the Huai River basin during the period fromend of June to July 11 in 2007. The successive 24-hour rainfall forecast by using GRAPES_Meso with one-waynested 16km 6km and 2km resolutions shows the obvious improvements were found in simulations of locationand intensity of heavy rainfall by using NOAH LSM. The threat score (TS) of precipitation becomes larger thanthat by using the simple SLAB. Page 57 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.8 Work progress in WP 8 and achievements during the periodSummary of progress Task 8.1: Evaluation of water balance calculation approaches FutureWater, ArieSpace and IGSNRR have assessed a number of water balance calculation approaches on their usability as water balance monitoring systems. Several criteria were used to rank the different approaches: • The spatial land surface schematisation of the model should be detailed in such a manner that integration with E.O. products is warranted. • The model should recognize direct forcing of daily grids of ET, precipitation and snow cover • Model outputs include all components on the water balance at sufficient level of temporal and spatial detail. This is specifically true for top soil moisture, which will be used for validation. • The model includes algorithms related to snow melt, infiltration, surface runoff, drainage, percolation and groundwater base flow. • The model should be able to incorporate glacial melt in a lumped mode. • The model should be able to deal with permafrost processes. • The model needs to include a (horizontal) routing component that allows accumulation of runoff across the Tibetan plateau. • The Tibetan plateau includes number of large lakes and artificial reservoirs. The model routing component needs to be able to take into account storage variation in lakes and artificial reservoirs and the resulting delay in water yield. • The model source code must be accessible and customizable. Based on these criteria a number of candidate models were evaluated. These models include SWAT, HBV, LARSIM, GRAPES and PCRGLOB-WB. The analysis showed that all the models, except SWAT and GRAPES, have similar algorithms for soil water balance and runoff. SWAT is based on a different method and it has probably a slightly more advanced routing, based on Muskingum approach and GRAPES has a more advanced soil water balance module but no routing. Most model require source code changes to allow forcing by remote sensing (precipitation, evapotranspiration, snow), except for PCRGLOB-WB which already has an inbuilt option for pre-described actual evapotranspiration. Some changes are still required to include snow cover forcing. GRAPES, LARSIM and PCRGLOB-WB are raster based, but GRAPES has no routing component. LARSIM and PCRGLOB-WB are efficient, open source and with very close links to the original developers which allows straightforward customizing in this project context. Based on this analysis it was decided that the PCRGLOB-WB is used as water balance monitoring tool for the entire plateau, but that at local scale other models are tested as well. In particular the HIMS model that is developed at IGSNRR. This model will be developed simultaneously with the plateau model for the upper Yellow river catchment and can be used to validate the plateau model. The results of the evaluation of the water balance approaches are reported in (1). Task 8.2: Water balance and run-off calculation over the entire Plateau Page 58 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 FutureWater has setup a first concept version of the water balance monitoring system of the Tibetan plateau based on the PCRGLOB-WB model. The model setup at a high spatial resolution (5km) and simulated the complete hydrological cycle on a daily step. In each cell the vertical flow of water through four compartments (canopy and three soil compartments), soil and canopy are fed by rainfall and snowmelt and depleted by evapotranspiration. Runoff and groundwater base flow are transferred to the to the drainage network and routed along the digital elevation model. Discharge is calculated from the kinematic wave approximation of the Saint-Venant equation. A schematic overview of the plateau model including the data requirements is shown in.Figure 8.3 Schematic overview of the Tibetan plateau water balance model Currently the model is forced by public domain reanalysis data based of the ERA40 dataset (evapotranspirtation and temperature) and on TRMM data (precipitation). As the project progresses the model will be forced and validated with datasets from the other WPs. For the coming period a number of conceptual improvements and additions are planned: • Evaluate and possibly modify soil water algorithm (e.g. compare with HIMS) • Incorporate reservoirs in routing scheme • Incorporate model for glacier melt • Forcing with data from other CEOP-AEGIS WPs • Further detail soil and vegetation parameterization based on RS datasets • Validation with soil moisture Page 59 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Task 8.3 : Water balance and climate change IGSNRR has collected geographic information (Fig.8.2) including DEM (digital elevation model), river network, land use and land cover, soil type of the entire plateau has been prepared for running the water balance model. Long-term climate and hydrologic data of the Qing-Tibet Plateau has also been collected for the period 1960 to 2000 from 89 stations (Fig.8.3). Spatial interpolation has been processing to provide 10 10km climate dataset for the entire plateau considering the location and the elevation of the grids. The sample dataset of the Headwater of the Yellow River Basin is around 1.33GB. The details of the dataset are as followed: (1) Spatial Resolution: 0.1 degree, 1254 grids; (2) Temporal Resolution: Daily; (3) Periods:1960 2001; (4) Climate variables: " Mean Daily Temperature: oC " Maximum Daily Temperature: oC " Minimum Daily Temperature: oC " Vapor Pressure: hPa " Air Pressure: hPa " Wind Speed at 2m: m/s " Sunshine Duration: hours Figure 8.2 DEM and land use/cover of the Qing-Tibet Plateau Page 60 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Figure 8.3 Climate stations of the entire plateau and the sample dataset of the Headwaters of the Yellow River BasinSignificant results According to the climate data collected, the long-term climate and hydrologic changes of the plateau have been detected using the Mann-Kendall method. The results have shown that potential evapotranspiration, wind speed and solar radiation tends to decrease during the past 40 years, while temperature and vapour pressure deficit tends to increase. The increasing rate of temperature was about 0.28oC/10a (Fig.8.4). The spatial patterns of the change showed that temperature (Tmax, Tmin, Tmean), relative humidity (RH) and precipitation (P) increased in most part of the plateau, while potential evapotranspiration (ET0 and ETpan), wind speed (U) and sunshine duration (Shour) decreased. The vapour pressure deficit (VPD) increased in the north part of the plateau while decreased in the south part (Fig.8.5). Page 61 of 98
    • (0C/y) 0.028SyearTmean (kPa/y)197019751980198519901995200022.533.544.5slope: (m/s/y)19701975198019851990199520000.40.420.440.460.480.5slope:0.00028NSyearVPD 0.017SyearU2 (MJ/m2/y)19701975198019851990199520001.522.53slope: 1.43SyearRn (mm/y)19701975198019851990199520003200325033003350slope: 4.57SyearETpan (mm/y)1970197519801985199019952000150016001700180019002000slope: 1.05SyearET0 (0C/y)197019751980198519901995200092094096098010001020slope: 0.028SyearTmean (kPa/y)197019751980198519901995200022.533.544.5slope: (m/s/y)19701975198019851990199520000.40.420.440.460.480.5slope:0.00028NSyearVPD 0.017SyearU2 (MJ/m2/y)19701975198019851990199520001.522.53slope: 1.43SyearRn (mm/y)19701975198019851990199520003200325033003350slope: 4.57SyearETpan (mm/y)1970197519801985199019952000150016001700180019002000slope: 1.05SyearET0 197019751980198519901995200092094096098010001020slope: CEOP-AEGIS (GA n° 212921)Page 62 of 98 Figure 8.4 Long-term climate changes of the Qing-Tibet Plateau Periodic Report no. 1
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Figure 8.5 Spatial patterns of long-term climate change in the entire plateau (1960-2001) To assess the impacts of climate and land surface change on streamflow, an approach based on the concept climate elasticity has been proposed assuming that: (8.1) Page 63 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 where Q is streamflow, P and E0 are precipitation and potential evapotranspiration respectively representingdominant climate factors on hydrological cycle, V is a factor that represents the integrated effects of catchmentcharacteristics on streamflow. Following Eq.8.1, changes in streamflow due to changing climate and catchmentcharacteristics can be approximated as: (8.2)where , , and are changes in streamflow, precipitation, potential evapotranspiration, andcatchment characteristics respectively, with , and . On theassumption that the land surface factors are independent of the climate factors, Eq.8.2 can be rearranged as: (3a) (3b) (3c)where , are changes in streamflow due to climate change and land use/cover change respectively. InEq.8.3a, can be estimated from observed streamflow records, thus if or is known, theframework can be used to separate the effect of climate change from that of land use/cover change onstreamflow. The effect of climate change on streamflow ( ) was assessed using the concept of climateelasticity of streamflow defined as: (4)Thus, Eq.8.3b can be rewritten as: (5)where and are elasticity of streamflow with respect to precipitation and potential evapotranspiration.The case study on the Headwaters of the Yellow River Basin (HYRB) have shown that land use change isresponsible for about 74.6% of the streamflow reduction in the 1990s, while climate change contributed to25.4% of the reduction. The climate elasticity appears to have an inverse relationship with runoff coefficient, butpositive relationship with aridity index, showing that the drier the catchment, the more sensitive streamflow iswith respect to precipitation change. References1. Immerzeel, W. et al., Model selection for the Tibetan plateau water balance monitoring system (CEOP AEGIS report, Strassbourg, 2009), pp. 1-59.2. Zheng, H., L. Zhang, R. Zhu, C. Liu, Y. Sato, and Y. Fukushima (2009), Responses of streamflow to climate and land surface change in the headwaters of the Yellow River Basin, Water Resour. Res., 45, W00A19, doi:10.1029/2007WR006665.Paper Published:Zheng, H., L. Zhang, R. Zhu, C. Liu, Y. Sato, and Y. Fukushima (2009), Responses of streamflow to climate and Page 64 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1landsurface change in the headwaters of the Yellow River Basin, Water Resour. Res., 45, W00A19,doi:10.1029/2007WR006665. Page 65 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.9 Work progress in WP 9 and achievements during the periodSummary of progressTask 9.1: Identification of study areas and ground data collection both in China and India (Alterra, BNU,IRSA, NIH).Information on past drought events, damage on agriculture resulted by drought both in China and India werecollected and reviewed. Table 9.1 gives the summary of severe drought events in the past, while Fig 9.1 is thesummary of drought prone areas in India.Pilot areas in both countries are preliminarily identified according to above information. The pilot area in Chinawill be the North Plain – part of Yellow River basin (for instance Henan province) and southwest area – part ofYongtz river basin (Sichuan-Chongqing ). In India the pilot area will be Ganga River basin.Historical meteorological data (air temperature and humidity, precipitation etc) were collected and analyzed overthe pilot areas. This ground dataset will also be taken as reference in the development and evaluation ofalgorithms for anomaly detection by using satellite data (different land surface parameters) to ensure systematicanalysis on drought events over study areas.Part of GIS data (for instance shape files of boundaries of administrative areas at country, province and countylevels) have been collected over China. The further GIS data are under collection. Figure 9.1: (Left) Natural hazards areas in India; (Right) key vulnerable river basins in India. Page 66 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Task 9.2: Vegetation dynamic monitoring through long-term time series of satellite observations forChina and India (UVEG, NIH, IRSA)A bibliographical review of methods for monitoring vegetation has been carried out. The climate and vegetationchanges undergone by the Tibetan plateau during the past decades have been identified. Pathfinder AVHRR datahave been downloaded and checked for consistency. These data have been resized to the study area. Algorithmshave been identified and implemented in IDL language in order to obtain NDVI (Normalized Difference andVegetation Index) and LST (Land Surface Temperature) parameters. The whole data base is presently beingprocessed at the Global Change Unit of the University of Valencia.Deviation: - Analysing the linkage of spatial and temporal vegetation dynamics with changes in climate systems and water resources on the Tibetan Plateau and surrounding areas over a long-term period (UVEG). - correlation between drought on the Plateau atmospheric circulation and precipitation on Tibetan Plateau and surrounding areas. (UVEG/NIH/IRSA).These two sub-tasks will be done together with task 9.3 once the analysis on rainfall anomalies are completed. Page 67 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Table 9.1 Summary of severe drought events in the past over China. Year Region Type Duration Drought Damage southern areas of Yunnan, the northeast An area of 4.349 million hectares of crops were affected, with an area of Autumn, winter regions of Guizhou, the eastern and 0.94 million hectares of crop failure. 51.04 million people were affected, 2010 and spring 2009.10-2010.04 southern parts of Guangxi, Sichuan and 16.09 million people were facing water shortage. A direct economic loss drought Chongqing of 19.02 billion yuan An area of 3.635 million hectares of crops were affected, with an area of South China (Hunan, Jiangxi, 0.472 million hectares of crop failure. 35.93 million people were 2007 Guangdong, Guangxi, Guizhou and Autumn drought 2007.09 -2007.12 affected, 5.796 million people were facing water shortage. A direct Fujian) economic loss of 8.52 billion yuan An area of 3.776 million hectares of crops were affected, with an area of 0.686 million hectares of crop failure. 66.47 million people were 2006 Chongqing and Sichuan Summer drought 2006.06-2006.08 affected, 15.37 million people were facing water shortage. A direct economic loss of 22.27 billion yuan Province-wide drought, serious drought for the past Heilongjiang 40 years. Drought area of the crop field is more than 1.53 Jilin million hectares. Middle and western of the major grain producing areas were affected by serious drought, and an area Northeast China (Heilongjiang, Jilin, Liaoning 2003 Spring drought 2003.2-2003.5 of 2.454 million hectares dry land crop field was Liaoning and Inner Mongolia) affected. Soil moisture of crop field is poor, lots of turning Inner Mongolia green period pasture dead, cattle weights had a dramatic decline. An area of 6.6 million hectares of crops were Total affected North China (Inner Mongolia, Hebei, An area of 48.76 million hectares of crops and grassland were affected, 2000 Shanxi, Hehan, Gansu, Hubei, Liaoning, Spring drought 2000.02-2000.05 with an area of 5.813 million hectares of crop failure. Jilin and Heilongjiang) the area of affected crop field in North China was June almost 20 million hectares Summer and the area of affected crop field in North China was 1997 North China 1997.06-1997.10 July Autumn Drought almost 26.67 million hectares an area of more than 6.67 million hectares of planted October winter wheat affected Jiangsu, Anhui, Hubei, Shanghai, The area of affected crop was up to 30 million hectares, more than 27 Zhejiang, Hehan, Sichuan, Hunan, 1994 Summer drought 1994.06-1994.08 million people and more than 26 million live-stocks were facing water Jiangxi, Shaanxi, Shanxi, Hebei and problem. Shandong 1991 North China (Shaanxi, Hebei and Winter Drought 1990.10-1991.2 An area of 4,840,000 square kilometers was affected Page 68 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Beijing) Yellow River and Huaihe River region (Hubei, Henan, the middle and lower The area of affected crop field in North China was almost 20.67 million 1988 reaches of Yangtze River, Sichuan, Summer Drought 1988.06-1988.08 hectares. Guizhou, Liaoning, Shandong, Jilin, Heilongjiang and Inner Mongolia) North China (central North China Plain, The affected area of crop field in Henan province was more than 4 Loess Plateau, Inner Mongolia, Hinggan 1986 Summer drought 1986.06-1986.08 million hectares. In Shanxi province, the affected area was more than 2 League, Henan, Shanxi, Shandong, million hectares, which accounted for 77% of total planted field. Hebei, Shaanxi, Sichuan and Gansu) Page 69 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Significant results(1) Characteristics of Drought in the pilot area Henan province in China based on SPI analysis Henan Province is located in the middle of the North China Plain (Fig. 9.2), covering an area of about167,000 square kilometers which ranges from latitude 31°23!to 36°22! and longitude 110°21! to 116°39!. Itexhibits a transitional climate including the north subtropical humid monsoon climate and the warmtemperate semi-humid climate with average annual precipitation from 600 to 1000 mm, and the altitudereduces from west to east. Rainfall in this region occurs mainly in summer through the monsoon wind; non-monsoon rainfall is limited and irregular. Henan province is the largest food producer in China, however, dueto the transitional geographical environment and climatic conditions, precipitation becomes so variable thatdrought often occurs and spreads over large areas. Drought is the main natural disaster for agriculturalproduction in Henan Province. Figure 9.2 The location of Henan Province, China. The Standardized Precipitation Index (SPI) was used for the identification of drought events and toevaluate drought severity in Henan Province, China, in which the SPI is calculated from monthly rainfalldata of 16 meteorological stations from 1952 to 2001. The variation of monthly averaged precipitation inHenan Province is presented in Fig. 9.3, which shows a typical monsoon climate precipitation pattern, withrainfall concentration during the summer months, and a very dry winter. Figure 9.3 Seasonal variation of monthly mean precipitation in Henan Province in China. Page 70 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 The drought severity classes are defined in Table 9.2. In order to estimate the drought frequency indifferent seasons, we think that drought event happened if there is at least 1 month of SPI less than -0.5 in aseason. Fig. 9.4 shows the spatial pattern of drought frequency based on SPI value calculated at 1-monthtime scale from 1952 to 2001. Table 9.2 Drought severity based SPI. Figure 9.4 Spatial pattern of drought frequency in Henan Province based on SPI calculated at the time scale of 1 month.Influenced by the seasonal movement of West Pacific Subtropical High and Siberian High, Henan Provinceis vulnerable to the drought events. The drought frequency is high in spring, summer and autumn. Spring is atransition season and the inter-annual variability of precipitation is high, which caused the drought frequencyabove 50% in the whole province and it is higher in the north. In summer, with the movement of the WestPacific Subtropical High from the south to north, Henan province gets into the rainy season. However, thefrequently happened abnormity of West Pacific Subtropical High movement brings an extremely largeinstability of the rainfall in each month, which caused the high drought frequency in summer, and it is above60% in most of Henan province. The drought frequency is highest in west area and lowest in east area inautumn. In winter, the climate in Henan province is controlled by the Siberian High, the precipitation and the Page 71 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1inter-annual variability of rainfall is lowest, the drought frequency decreases and it ranges from less that 20%in north area to above 60% in south area. The sensitivity of SPI value to the precipitation changes with time scale of SPI. The shorter time scalesquantify more upper soil water, so there is a great fluctuation when the precipitation changes. The longertime scales reflect the state of subsoil moisture, surface and subsurface water resources, only long period ofrainfall abnormality can make SPI begin to fluctuate, which is reasonable for drought monitoring, especiallylong term drought. Therefore, we also choose a long time scale (12 months) SPI to analyze the temporalvariations of drought in Henan Province, and more detailed drought information is gotten from short timescale SPI. Fig. 9.5 shows the temporal change of the SPI calculated at different time scale in Zhengzhou station.In this figure, the red line indicates the SPI value of -0.5, which is the threshold between drought and nodrought. (a) (b) (c) (d) Figure 9.5 Temporal change of SPI calculated at different time scale in Zhengzhou station (a) 1 month; (b) 3 months; (c) 6 months; (d) 12 months. Page 72 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Eight drought events are identified in Zhengzhou in the 12-month-scale SPI series (Fig. 9.5(d), Table 9.2),which are the period 1952-1954, 1959-1962, 1965-1969, 1981-1982, 1986-1989, 1991-1992, 1997-1998 and2001 respectively. For the 3-month and 1-month scale (Fig. 9.5(a), (b)), the SPI values fluctuate frequently.From 1952 to 1954, there are two deep SPI valleys, whose values are lower than -2, indicating that extremedrought occurred (Fig. 9.5(d)). During this period, the short time scaled SPI values also show the conditionsof water loss, although there are obvious fluctuations. SPI values at 1 month scale indicate that there is littleprecipitation in summer of 1952, autumn of 1953 and spring of 1954, which led to severe loss of surfacewater from 1952 to 1954. The drought continued until the continuous rainfall to supplement the cumulativeloss of surface water in the summer of 1954.Persistent drought occurred from 1959 to 1962 in Zhengzhou (Fig. 9.5(d)), with maximum intensity in thesummer of 1960. The SPI values at short time scales indicate that almost every month of the precipitationwas less than the average, leading to increasingly heavy accumulated losses of water and reaching its peak atthe summer of 1960.From 1965 to 1969, another drought event occurred, and the intensity was less than before. What is more,Zhengzhou experienced wet conditions in some months (Fig. 9.5(d)).There was almost no long period drought occurred during 1970’s. However, the drought frequency increasedagain during 1980’s and 1990’s. The drought events at 1981-1982, 1986-1989, 1991-1992 and 1997-1998can be observed from Fig9.5(d). Table 9.2 Long-term droughts from 1952 to 2001 in Zhengzhou, China. Maximum intensity Time of maximum Drought period Duration (SPI) intensity 1952.1 1953.5 17 -2.79 1952.7 1959.7 1961.7 25 -2.66 1960.5 1965.7 1967.6 24 -1.88 1965.9 1968.6 1969.6 13 -2.44 1968.8 1986.7 1987.6 12 -1.78 1987.4 1988.6 1989.4 11 -1.21 1988.11 1991.7 1992.6 12 -1.54 1992.3 1997.7 1998.4 10 -1.69 1998.1(2) Vegetation dynamic monitoring through long-term time series of satellite observations for Chinaand India Within the review of methods for monitoring vegetation, an NDVI based method has been developedand evaluated, which is described in: Sobrino, J. A. & Julien, Y. (in press). Global trends in NDVI derived parameters obtained from GIMMSdata, International Journal of Remote Sensing, in press. This method allows the yearly determination of various NVDI based parameters regarding bothvegetation statistics and phenology, which can then be studied interannually in order to retrieve vegetationchanges. On a different topic, in order to complete the NDVI and LST time series, which present some gaps dueto atmospheric contamination or instrument failure, a methodology has been developed to interpolateparameter missing values: Julien, Y. & Sobrino, J. A. (in press). Comparison of cloud-reconstruction methods for time series ofcomposite NDVI data, Remote Sensing of Environment, in press. At the time of the redaction of this report, two thirds of the whole Pathfinder database have beenprocessed for estimation of NDVI and LST parameters. Figure 1 shows an example of LST for the wholeworld and figure 2 for the Tibet area. Page 73 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Figure 2. Example of global LST map for 21 July 1995. Figure 3. Example of Tibet LST map for 21 July 1995. Page 74 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.10 Work progress in WP10 and achievements during the periodSummary of progressThis workpackage started with some difficulty due to the withdrawal of the National Institute of Hydrology,India. All tasks, with identical work content and deliverables, were taken over by the National Institute ofTechnology, Rourkela by May 1st 2009.. The back log has been recovered, but there has been some obviousproblems with the coordination of the Work Package. This notwithstanding, the most critical objectives havebeen achieved, namely the development of an algorithm for time series analysis of a satellite-based indicatorof wetness conditions, potentially useful for flood early warning and the synthesis of available information toidentify flood prone areas in China and India. Work in China has advanced significantly towardsdevelopment of models useful to map fllood hazard and study the propagation of floods with the support ofsatellite data.Task 10.1. Flood early warning with wetness indicator derived from low-resolution microwave satellitedata (ULP, BNU)The theoretical basis of the algorithm to determine open water, flooded land, moist soil and vegetation byinterpreting the difference in horizontal vs, vertical polarization of brightness temperature at 37 GHz, !T37has been developed. The algorithm makes use of known characteristic values !T37 of open water and baredry soil in combination with time series analysis (see De.10.1) to determine flooded and moist area byinverting a linear mixing model with time-dependent parameters.In the linear mixing model used by Sippel et al. [1994, 1998], Hamilton et al. [1996], the differencebrightness temperature of vertical and horizontal polarization for each end-member is constant during anentire year time series. This assumption is possibly correct in tropic zone, because tropic plants do not showvery large seasonal changes. However, for subtropical and temperate plants, the seasonal changes ofvegetation canopy and leaf area index are very large, especially for the cropland. The structure and watercontent changing of vegetation is significant in area without flood (Choudhury, 1990). Thus, the linearmixing model needs to be modified to account for the variation of the vegetation.AMSR-E on board the Aqua satellite measures radiation at six frequencies in the rage 6.9 – 89 GHz, all dualpolarized, with a constant incident angel of 55˚ since May 2001. 36.5 GHz polarized data from AMSR-E atlocal solar time of 1:30 PM is used in this research, of which footprint is 14km by 8km.A first time series of microwave radiometer data has been constructed using AMSR-E measurements.The information of 10 major floods in Yangtze River basin since 1980 have been collected, which will beused to analyst correlation between surface wetness indicator from satellite data and occurrence of flood forearly flood warning.The case – study summarized under “Significant results”demonstrates the use of microwave data to deriveflood prone areas, as well as flood extent in space and time. The satellite flood identification system used inthis study uses data from the microwave sensors TRMM (rainfall) and AMSR-E (soil moisture). The SRTMshuttle mission provided digital elevation data.Task 10.2. Real time flood forecasting using data from the atmospheric-hydrologic network (NIT,BNU)A real time flood forecasting model, which is named as XiAnJiang Model, has been developed. In this modelthree water sources, including surface runoff, interflow and groundwater runoff, are considered. Consideringthe uneven of rainfall and difference of underlaying surface condition in a large basin, runoff is calculated insub-basins with same rainfall and underlaying surface. In order to test the modle, it has been used toforecasting flood of HuaiHe River in China.A method to forecast floods using hydrological data, remote sensing data and other ancillary data and aArtificial Neural Network Approach has been developed and evaluated in India. For this purpose Collectionof rainfall data and hydrological data of Kosi basin and in the Gandak river basin both tributaries of the River Page 75 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Ganga. Maps needed for the numerical simulation experiments on flood forecasting have been collected orproduced when necessary using remote sensing data and integrated in a GIS application.Task 10.3. Mapping and visualizing flood inundation, flood risk using combined satellite data andhydraulic models (BNU, NIT)The Chang Sha channel segment of Xiang Jiang River is selected as study area in Hunan Province of China.The study area is located in the south of Dong Ting Lake. It is approximately located between 112 20 to113 20 E and 27 50 to 28 30 N. The historical hydrologic data, including water level anddischarge of the observation station, have been collected for analysis of the development trends through thetime series analyzing method. In order to obtain precise DEM, we have bought 12 scenes of scale 1:10,000topography maps in the study area, which have been digitized into ArcGIS vector format. Beside, someremote sensing images, population data, and economic data of the study area have also been collected. Allthese data are base for flood risk mapping study in the next step.Significant resultsA flood identification model has been made that includes two components: flood risk mapping using soil moisture: ! (x, y, t) flood risk mapping using soil moisture and local rainfall: ! (x, y, t), P (x, y, t)AMSR-E soil moisture data provides time series of soil moisture. Satellite measurements of land surfacemicrowave emissivity such as AMSR-E describe the day-to-day variation of top soil moisture conditions.Changes in the hydrological system are reflected in the soil moisture values. Land with suddenly rising soilmoisture values may be prone to flooding. Both absolute values and the time series of top soil moisture willbe used to provide a first identification of flood risk. Basically two aspects of soil moisture development intime are important: (1) absolute soil moisture values in comparison to previous years; and (2) changes of soilmoisture in time (d!/dt).Integration with rainfall data further refines the flood identification system. Rainfall data will be derivedfrom the Tropical Rainfall Measuring Mission (TRMM) satellite. TRMM rainfall provides information onthe location, quantity, intensity and timing of rainfall. TRMM rainfall is an extra source of informationmeasured independently of AMSR-E soil moisture for the flood model. The TRMM rainfall algorithm 3B42(trmm.gsfc.nasa.gov/3b42.html) that provides 3-hourly rainfall data at 25 km resolution is most useful forlocal rainfall and rainfall-runoff monitoring.Rainfall observations fall into two components: local rainfall and rainfall in upstream catchments. Largeamounts of rainfall can cause flooding locally, but can also cause flooding downstream in the basin.Figure 10.1 Bi-monthly soil moisture tolerance of two periods. When floods occurr the absolute moisture values exceedthese values. Page 76 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1In China, work on preparing reference information to evaluate the new algorithm based on the polarizationdifference in the 37GHz brightness temperature has concentrated on the Yangtze basin and the data collectedinclude for each flood the time of occurrence, intensity and location (Figure 10.2).Figure 10.2 The locations of 10 major floods in Yangtze River basin since 1980; data on time of occurrence, intensityand location are accessible through a GIS application.In India, work on preparing reference information to evaluate the new algorithm based on the polarizationdifference in the 37GHz brightness temperature has concentrated on the Ganga basin (Fig. 10.3). Collectionof the river flow data required significant efforts and are considered a critical information resource for thenext stages of the project. Page 77 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Fig.10.3 Mean flow, seasonal variation and lean flow in summer for selected basins in the Ganga BasinThe new XiAnJiang Model is being evaluated using data of the HuaiHe River in China (Figure 10.4).Comparison of calculated with observed river flow iindicates a good agreement.Figure 10.4. Forecast of floods of HuaiHe River in China with XiAnJiang Model; model vs, observed discharge show ininsetData collection for the case studies on flood propagation has been focusing on the Chang Sha channelsegment of Xiang Jiang River (Yangtze Basin), because this area is subject to frequent and severe floods andthe opportunity to understand better the driving factors of flooding in the catchment of the Dong Ting Lake(Fig.10.5). Page 78 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Fig. 10.5 The Chang Sha channel segment of Xiang Jiang River in Hunan Province, China; the domain of the model todescribe flood propagation is shown (red). Page 79 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.11 Work progress in WP 11 and achievements during the periodSummary of progressThis work-package started with some delay due to the difficulty to find a proper balance between work ondissemination of project results through scientific events and in general international initiatives (e.g. GEO)and true capacity buidling activities. In addition proper contacts with stakeholders organizations had to beestablished, primarily in China. We had also underestimated the time and effort needed to make the CEOP –AEGIS contribution to GEO visible. This all required a significant re-thinking of the work plan, timewiserather than content – wise.Task 11.1 Dissemination of project resultsGEO: CEOP-AEGIS activities contribute to four societal benefits areas identified within GEOSS, ie. theReduction and Prevention of Disasters area, the Climate Change area, the Water Management area and theWeather Forecasting area. In order to build up an observing system as a pilot of GEO system of systems forwater resources management, an important element is the dissemination of project information and resultsand the involvement of stakeholders.To successfully disseminate the knowledge gained in the project and make our contribution to GEOSSvisible, many activities are conducted in parallel: ! The organisation and contribution to international conferences and workshop ! The contribution to GEO activities ! The creation of communication media, ie. a website, paper and electronic brochures, multimedia content ! The dissemination of knowledge through courses and training for representative stakeholders and for young scientistsThe work done and related outcome is described in detail in the Report De 11.1Workshops and conferences.Coordination meeting CEOP-AEGIS (CA) and CEOP-High Elevation (HE), June 29th – July 3rd 2009,Hotel Ibis, Via Finocchiaro Aprile 2, 20124 MILANO, ITALYThis meeting was organized to establish cooperative links with research and capacity building program ledby CNR, Italy EvK2 CNR. This program has a permanent high elevation observatory in Nepal and iscarrying out interdisciplinary research and capacity building projects in the high elevation regions of Nepaland Pakistan.Task 11.2 Training sessions focus on human capacity buildingThe training program has been developed and it is included in De 11.1.The objective of the advanced course is to train the participants from Asia of all the aspects related to in-situand earth observation, retrievals and modeling of land surface processes and land-atmosphere interactionswith emphasis on the Tibetan plateau. The course will include theory, instruments, validation, retrievals andmodeling and applications. Practical sessions will be oragnised with hands-on exercises with data collectedin different WPs. Lecturers are experts responsible for tasks in each WP.Task 11.3 Tailored capacity building1. Coordination meeting on Satellite based flood monitoring system of pilot areas of China and India, IndianInstitute of Technology Roorkee from September 12th to 14th 2009This meeting was organized as a part of a UK – India Workshop on Water Resources Management underClimate and Environment Change to inform a community focusing on hydrology and water managementabout CEOP – AEGIS objectives and work. Page 80 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Significant resultsCapacity Building web-page:An element of the project web-site dedicated to dissemination and capacity building has been designed(Fig.11.1).Figure 11.1 Content of the Capacity Building pages on the CEOP-AEGIS websiteThis diagram summarizes the on-going dissemination, training and capacity building activities of the project.The web site is also to be used to make widely available any output of the project relevant to capacitybuilding (see pages “Teaching and demonstration materials”).ConferencesOverviews of CEOP – AEGIS objectives and progress were presented at the following InternationalMeetings: - CEOP Implementation Meeting held in Geneva from the 15th to 18th Septembre 2008 - Reinforcing Europe’s contribution to, ISRSE-33 side event, May 5, 2009 PALAZZO DEI CONGRESSI, STRESA, ITALY - WCRP/GEWEX Melbourne, August 2009These meetings had a considerable impact towards improving international awareness of CEOP – AEGISscope and establishing effective linkages with related international initiatives, relevant to CEOP – AEGIS Page 81 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1from the point of view of either scientific issues, i.e. water cycle, land-atmosphere interactions and earthobservation or area of interest, i.e. SE Asia, or both.The dissemination of information through the Internet being highly effective and in practice mandatory, theCEOP-AEGIS Project Office bought the ceop-aegis.org domain name for an initial duration of 4 years. Theproject website was constructed on an open source Web 2.0 Content Management System architecture callMODx, and associated with a complete mailing list system. The domain name, the website and the mailinglists rely on shared hardware plate-forms hosted by the University of Strasbourg. The website is designed tohost public information, news and material, as well as private content for registered users (ie. projectparticipants and stakeholders). The figure below shows the website map.Figure: Diagram of the website structure. The green part is open to public access, while the orange and blue ones are subject to registration.The impact of the website can be approached by statistics on the activity on the www.ceop-aegis.org portal,summarized in the following table.Table: Statistics of www.ceop-aegis.org for 2009 and 2010.*for 2009 statistics were only available for thelast three months of the year (2nd version of the website). Single visitor Visits Pages viewed Hits Bandwidth2009* 333 812 24,596 60,498 498.67 Mo2010 2,430 4,588 26,174 50,157 11.39Go Page 82 of 98
    • CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Beside the website, a mailing list system is maintained by UDS to facilitate the communication within eachwork-package, working-groups and lead scientific contacts. Moreover, efforts are made to encourage remotecommunication through teleconference tools.GEO MeetingsCEOP – AEGIS have been attending the following GEO meetings in the reporting period:GEO CBC and STC Meeting HannoverGEO European Project Workshops Bruxelles, Istanbul, Athens, Page 83 of 98