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Close water cycle at the river basin scale using remote sensing data (ID. 10649) Prof. Xin Li (李新) Cold and Arid Regions Environmental and Engineering Research Institute, CAS Prof. Harry Vereecken Institute of Bio-and Geosciences (IBG), Jülich Research Centre Germany Chinese Investigators European Investigators • Xin Li (CAREERI) • Harry Vereecken (IBG, G) • Jian Wang (CAREERI) • Kurt Roth (IEP, G) • Jiemin Wang (CAREERI) • Massimo Menenti (TU Delft, NL) • Shaomin Liu (BNU) • Mingguo Ma (CAREERI) • Bob Su (ITC, NL) • Ling Lu (CAREERI) • Henk de Bruin (TU Delft, NL) • Weizhen Wang (CAREERI) • Wolfgang Kinzelbach (ETH, CH) • Chunlin Huang (CAREERI) • Haijing Wang (ETH, CH) • Zhuotong Nan (CAREERI) • Carsten Montzka (IBG, G) • Tao Che (CAREERI) • Patrick Klenk (IEP, G) • Rui Jin (CAREERI) • Xiaoduo Pan (CAREERI) • Shuguo Wang (CAREERI) • Xujun Han (CAREERI) Outline • Objectives and background • Research contents • EO data acquisition plan • Project schedule • Field campaigns and expected data sets • Conclusions and outlook 1.1 Objective • The overall objective of this project is to close the water cycle at river basin scale. • Water budget components including precipitation, evapotranspiration (ET), soil moisture (SM), snow water equivalent (SWE), runoff, and groundwater storage will be estimated using multi-source remote sensing observations and corresponding time series products of these variables will be generated. • These products, in combination with hydrology and land surface modeling will be integrated by data assimilation methods to close the land water budget at basin scale. 4/33 1.2 Background 1.2 Study area China: Heihe River Basin 1.2 Study area Germany: Rur Catchment Grassland test site “Kall” • Eddy Correlation station • Soil moisture sensor network • Soil temperature measurements • Soil CO2 flux measurements X-band Doppler Weather Radar Agricultural test site “Selhausen” Forest test site “Wüstebach” • Eddy Correlation station • Soil moisture sensor network • Soil temperature measurements • Groundwater monitoring • Runoff and solute monitoring • Soil CO2 Flux measurements • Eddy Correlation station • Soil moisture measurements • Soil temperature measurements • Soil CO2 flux measurements • Ground-based remote sensing • LIDAR 2. Research contents • Estimation approaches on precipitation, ET, SM, SWE and groundwater storage; • Corresponding time series products for these variables; • Closing water budget through integration of remote sensing products, hydrological modeling, and a land data assimilation system. 8/33 2.1 Precipitation Input for precipitation products • • • WRF-25km: TRMM/GPM WRF-1km: Doppler-radar(200-250m) WRF-200m: Rain gauges 2.2 Evapotranspiration General roadmap 2.3 Soil moisture Time series SAR imagery Backscattering coefficients Retrieval scheme Assuming surface roughness is invariant, vegetation cover grows periodically Temporal dynamics of σ0 with in situ soil moisture (e.g. linear relationship) Sensor configurations, media dielectric properties, vegetation, surface roughness To detect soil freeze/thaw status dependent on temporal dynamics of σ0 , in situ soil moisture and temperature Change detection method Spatial variability of σ0 due to land heterogeneities To apply change detection method to derive soil moisture sequence Forward model, iterative loop, ANN, data assimilation etc. Empirical, semi-empirical, theoretical models Soil moisture 2.3 Soil moisture Downscaling scheme Downscaling algorithms 1km soil moisture 25km Soil moisture validation 1km MODIS NDVI Statistical calculation 1km TVDI 1km MODIS LST SM_fine=f(SM_coarse, TVDI, TVDI_coarse)Observations or model simulations 2.4 Snow water equivalent Snow depth and snow water equivalent retrieval by fusing optical and passive microwave remote sensing data Optical multispectral (Rf) Snow grain size at surface Snow density Snow temperature Passive microwave Tb Microwave transfer model Look-up table (Tb vs Snow properties) Snow depth Snow water equivalent 2.5 Groundwater storage Estimating groundwater storage changes in the Heihe River Basin Inter-annual changes of groundwater estimated by GRACE in the entire HRB Spatial variability of groundwater in the HRB in 2003-2008 2.6 Integration of products and modeling through data assimilation High‐resolution Atmospheric forcing Data Multi‐resolution Data Products Continuous Hydrological Data ET ET HDAS SM SWE DA Algorithm GW Spatial Resolution: 1‐25km Temporal Resolution:1‐30day LSM Hydrological model SM SWE GW Spatial Resolution: 1km Temporal Resolution:hourly 2.6 Integration of products and modeling through data assimilation • Generation of atmospheric forcing data – Using WRF model and measurement data • Assimilation of the retrieved products – Using Multi-scale Ensemble Kalman Filter to fuse the remote sensing products and model estimation • Calibration and validation – Using ground-based, airborne, satellite data • Analysis of water budget – Understanding of complex hydrological processes, e.g. surface and groundwater interaction 3. EO data acquisition plan • • • • • • • ERS ENVISAT GOCE SMOS CRYOSAT-2 Sentinel EarthCARE • • • • CBERS HJ BJ FY • • PROBA ALOS • • • • TerraSAR-X Cosmo-Skymed CoReH2O SMAP 4. Project schedule WP1, present-2013, further improvement of the retrieval algorithms for key hydrological variables based on the outcomes of Dragon 2 period. WP2, 2014-2015, generation of time series of remote sensing data products regarding key water cycle components based on the results obtained from WP1. WP3, 2015-2016, integration of remote sensing products, hydrological modeling through a data assimilation framework to close the basin scale water budget. WP4, data management. 5. Field campaigns and expected data sets 19/33 5.1 In the middle stream China: HiWATER 5.1 One superstation, 16 towers equipped with EC and LAS, and four AMSs established on gobi, desert and wetland China: HiWATER 5.1 China: HiWATER In the upstream 5.2 Germany: TERENO Multi‐scale soil moisture monitoring •Process based modeling of regional water fluxes taking into account multi-sensor and multi-scale observation patterns •Assimilation of multi-scale soil moisture data: • Microwave remote sensing (regional scale) • Cosmic ray probes (footprintscale) • Soil moisture sensor networks (point-scale) 5.2 Germany: TERENO Cosmic Ray monitoring network in the Rur Catchment ⇒ Also important for Heihe catchment to get link between in situ and spaceborne soil moisture observations 5.2 Germany: TERENO Cosmic Ray monitoring network in the Rur Catchment ⇒ Also important for Heihe catchment to get link between in situ and spaceborne soil moisture observations 5.2 Germany: TERENO Multi‐scale ground measurements data for the validation of remotely sensed brightness temperature and soil moisture products Combination of PLMR2 and DLR F-SAR onboard a Dornier DO228 aircraft + IR-camera + Hyperspectral camera • • F‐SAR is able to operate in 4 frequency bands (X, C, L and P) Dual (F‐SAR) channel operation Page 26 • Polarisation: Dual linear (V and H) • Incidence angles: +/‐ 8°, +/‐22°, +/‐ 38° @ pushbroom 5.2 Germany: TERENO Airborne active and passive microwave data fusion for soil moisture retrieval The SMAP active/passive fusion approach Soil moisture (36 km) Soil moisture (9 km) Backscatter (3 km) Das, Entekhabi and Njoku (2011) F-SAR backscatter overlayed with PLMR brightness temperature 6. Training of young scientists • Young scientists will be actively involved in our study for various aspects. • The annual symposium acts as an ideal platform for young scientists to exchange their knowledge. • Experts from both sides will provide joint training of young scientists, short-term visits or to accomplish their PhD dissertation. • Exchange of team members, instrument from both sides in field campaigns. • Coauthorship in international peer-reviewed journals and conference proceedings. 7. • Conclusions and outlook This project inherits the outcomes of our previous Dragon 2 project, and will further improve the studies and applications of remote sensing in hydrology research. • A comparative analysis in the two well-instrumented catchments is an important task for a future global application of the established model-data synthesis product. 7. • Conclusions and outlook To close the river basin scale water cycle by integrating remote sensing, in situ observations and modeling with a land data assimilation framework. • To create time series of remote sensing and assimilated data products for the key water cycle components in both study. • To propel the utilization of mature remote sensing observation methods and examine the innovative technologies in hydrological studies. Xin Li CAREERI/CAS, Lanzhou 730000, China E-mail: [email protected] Web: http://www.westgis.ac.cn water.westgis.ac.cn Harry Vereecken IBG/Jülich Research Centre Forschungszentrum Jülich GmbH, IBG-3, Leo-Brandt-Strasse, 52425 Jülich, Germany Email: [email protected] Thank you!