Ensuring Water in a Changing World

Transcription

1 Ensuring Water in a Changing World Evaluation and application of satellite-based precipitation measurements for hydro-climate studies over mountainous regions: case studies from the Tibetan Plateau Soroosh Sorooshian 1, Kuolin Hsu 1, Phu Nguyen 1, Tiantian Yang 1, Hamed Ashouri 1, Zhongwen Yang 2, Xiaomang Liu 3, and Dan Braithwaite 1 Presented by Amir AghaKouchak 1 1, USA 2 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, China 3 Key Laboratory of Water Cycle & Related Land Surface Process, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China 8th GEWEX Open Science Conference MAY 6-11, 2018 CANMORE, ALBERTA, CANADA

2 Research Team: Present and Recent Past and many more

3 Precipitation Measurement is one of the KEY hydrometeorologic Challenges Having adequate high resolution (time and Space) observations for model Input, Calibration, Testing, and to capture extremes is crucial

4 Precipitation Observations: Which to trust??

5 Number of range gauges per grid box. These boxes are 2x2 degrees (Source: Global Precipitation Climatology Project)

6 Space-Based Observations for Model Testing Satellite Observations: Rainfall Estimation

7 Satellite Data for Precipitation Estimation Geostationary IR Cloud top data minute temporal resolution Passive Microwave (SSM/I) Some characterisation of rainfall ~2 overpasses per day per spacecraft, moving to 3-hour return time (GPM) TRMM precipitation RADAR 3D imaging of rainfall 1-2 days between overpasses ( S-35 N-35 )

8 Satellite Data Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) PERSIANN System Estimation Global IR (CPC, NOAA) High Temporal-Spatial Res. Cloud Infrared Images MW-RR (TRMM, NOAA, DMSP Satellites) MW-PR Hourly Rain Rates (GSFC, NASA; NESDIS, NOAA) Sampling Feedback Error Detection ANN Hourly Rain Estimate Products Hourly Global Precipitation Estimates Quality Control Ground Observations GPCC & CPC Gauge Analysis Gauges Coverage Merged Products - Hourly rainfall - 6 hourly rainfall - Daily rainfall - Monthly rainfall Merging Center Center for Hydrometeorology for Hydrometeorology and Remote and Remote Sensing, Sensing, University University of California, of California, Irvine Irvine

9 PERSIANN Extensions: Climate-Related PERSIANN- CDR (Climate Data Record)

10 PERSIANN -CDR Daily Precipitation Data Data Period: 1983~2017 Coverage: 60 o S ~ 60 o N Spatial Resolution: 0.25 o x0.25 o Ashouri, Hsu et al., BAMS, 2015., &,,,,,

11 PERSIANN Websites and Apps - CHRS Data Portal - CHRS RainSphere - CHRS irain - PERSIANN-CONNECT

12 CHRS Data Portal

13 PERSIANN Extensions: Climate-Related CHRS RainSphere An Integrated System for Global Satellite Precipitation Data and Information

14 CHRS RainSphere Rainsphere.eng.uci.edu Search Location Map Layers Rain Data Type Rain Layers Rain Comparison Rain Statistics and Trends Legend

15 CHRS RainSphere

16 CHRS RainSphere Rainsphere.eng.uci.edu

17 CHRS RainSphere Rainsphere.eng.uci.edu Political Major Countries Watersheds Divisions Basins Rainfall Trends Across Spatial Scales

18 How Do we Judge the level of accuracy of PERSIANN-CDR??

19 Regional Evaluations of PERSIANN-CDR Many Regional Evaluation of PERSIANN-CDR Have Already Been Reported: EA PERSIANN-CDR Pixel correlation Scatterplot of mean RR95p 10mmTOT Rain rate (mm/day) R10mm

20 PERSIANN-CDR Evaluation over China ~1 gauge per 25,000 km 2 ~1 gauge per 5,000 km 2 Monsoon region EA Rain Gauge Distribution Elevation Map Dr. Chiyuan Miao - BNU Gauge data: daily precipitation over East Asia (EA) (Xie et al., 2007) More than 2,200 ground-based stations across China 0.5 resolution Period PERSIANN-CDR: up scaled into the same resolution as EA (0.5 o )

21 Results: Entire China EA PERSIANN-CDR Pixel correlation Scatterplot of mean RR95p R10mmTOT R10mm Miao et al., JHM 2015

22 Miao et al., 2015 conclusion About Tibetan Plateau The results show that PERSIANN-CDR depicts similar precipitation behavior as the ground-based EA product in terms of capturing the spatial and temporal patterns of daily precipitation extremes, particularly in the Eastern China monsoon region. However, the agreement between the data sets in the Tibetan Plateau is not strong. An important factor that may have influenced the results is that the groundbased stations are very sparse. Miao et al., 2015

23 PERSIANN-CDR Evaluation in Tibetan Plateau Upper Yangtze River Basin 138,000 km 2 4 Gauges Upper Yellow River Basin 120,000 km 2 11 Gauges The Evaluation was based on the use of a calibrated hydrologic model and using both gauge and PERSIANN-CDR to see which one produced more accurate Streamflow simulation X. Liu T. Yang Liu et al., HESS 2016

24 PERSIANN-CDR Evaluation in Tibetan Plateau Comparisons of the simulated and observed average monthly streamflow driven by the gauge-based precipitation and PERSIANN-CDR precipitation for the two basins from 1983 to Liu et al., HESS 2016

25 PERSIANN Extensions: Weather-Related CHRS irain An Integrated System for Global Real-time Precipitation Observation

26 Capturing Extreme Precipitation from Hurricane Harvey (Aug )

27 How Trust Worthy are Real-Time Satellite Precipitation Estimates??

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29 Zhongwen Yang

30 Zhongwen Yang et al., 2017 (GRA)

31 Hydrologic Evaluation

32 Who Uses CHRS Products irain.eng.uci.edu

33 Summary Satellite-based precipitation estimates have been evolving and improving in terms of their spatial and temporal resolutions. Two of the PERSIANN products (the Climate-Scale PERSIANN-CDR and the Real-Time high resolution PERSIANN-CCS) were introduced. Results of their evaluations over several watersheds in the Tibetan Plateau were presented. The efficacy of PERSIANN-CDR over two Tibetan watersheds was demonstrated. In the case of the real-time high resolutions PERSIANN- CCS, it is shown that even with limited gauge observations systematic corrections of the remotely sensed estimates improves Center for the Hydrometeorology performance and Remote of Sensing, the University algorithm of California, and Irvine its accuracy

34 Thank You for Listening Somewhere in New Mexico, USA - Photo: J. Sorooshian

35 Evaluation Indices ID Definition Unit RR95p The 95th percentile of annual precipitation on wet days (precipitation 1mm) mm/day R10mmTOT Annual total precipitation when daily precipitation 10mm mm R10mm Annual count of days when precipitation 10mm Days Extreme precipitation indices used in the analysis