Remote Sensing Applications for Drought Monitoring
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1 Remote Sensing Applications for Drought Monitoring Amir AghaKouchak Center for Hydrometeorology and Remote Sensing Department of Civil and Environmental Engineering University of California, Irvine
2 Outline
3 Space-Based Observations Satellite Observations: Rainfall Estimation Center for Hydrometeorology and Remote Sensing, University of
4 Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) PERSIANN System Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Center for Hydrometeorology and Remote Sensing, University of
5 Satellite Data Ground Observations Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Feedback PERSIANN System Estimation Global IR ANN Products Hourly Global Precipitation Estimates (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 Error Detection Hourly Rain Estimate Quality Control GPCC & CPC Gauge Analysis Merged Products - Hourly rainfall - 6 hourly rainfall - Daily rainfall - Monthly rainfall Merging Gauges Coverage Center for Hydrometeorology Center for Hydrometeorology and Remote Sensing, and Remote University Sensing, of California, University of
6 Real Time Global Data: Cooperation With UNESCO Center for Hydrometeorology and Remote Sensing, University of
7 PERSIANN Satellite Product On Google Earth & App App: RainMapper Center for Hydrometeorology and Remote Sensing, University of
8 PERSIANN Extensions: Climate-Related PERSIANN-CDR Center for Hydrometeorology and Remote Sensing, University of
9 01/01/ /02/ /03/ /04/ /05/1983../../.../../.../../. 12/24/ /25/ /26/ /27/ /28/ /29/ /30/ /31/2012 PERSIANN-CDR A 30-Year, Daily, 0.25o, Global Center for Hydrometeorology and Remote Sensing, University of Precipitation Climate Data Record
10 PERSIANN-CONNECT Exploratory Analysis b) Search For A Single Storm c) Search For All Storms During 2001: 106 Events :00: :00:00 Center for Hydrometeorology and Remote Sensing, University of
11 Global Integrated Drought Monitoring and Prediction System (GIDMaPS)
12 GIDMaPS: Global Integrated Drought Monitoring and Prediction System Meteorological Drought Agricultural Drought
13 GIDMaPS: Global Integrated Drought Monitoring and Prediction System Meteorological Drought Agricultural Drought Meteo-Agricul. Drought
14 GIDMaPS: Global Integrated Drought Monitoring and Prediction System
15 Drought Definition and Indicators Different drought indices based on different climate variables (e.g., Precipitation, soil moisture): Standardized Precipitation Index (SPI) Standardized Soil Moisture Index (SSI) Standardized runoff Index (SRI) Palmer Drought Severity Index (PDSI) Precipitation (D-Scale) Jan 2012 Soil Moisture (D-Scale) Jan 2012
16 Multi-Index Drought Monitoring Standardized Precipitation Index (SPI) pspi P( X x) SPI 1 pspi Standardized Soil moisture Index (SSI) pssi 1 P( Y y) SSI pssi Multivariate Standardized Drought Index (MSDI) pmsdi P( X x, Y y) MSDI (Hao and AghaKouchak, 2013): Standardized index similar to SPI Improves drought onset detection A multi-index for composite meteorological -agricultural drought monitoring MSDI 1 pmsdi Where: X: accumulated precipitation; Y: accumulated soil moisture; φ: standard normal distribution
17 Multi-Index Drought Monitoring Sample time series of the 6-month SPI, SSI and MSDI for a grid cell in Texas (Location: longitude 100 W and latitude 30 N).
18 1-Month SPI and SSI Derived Using NASA MERRA-LAND Precipitation and soil moisture Data. Multi-Index Drought Monitoring
19 Multi-Index Drought Monitoring
20 GIDMaPS: Global Integrated Drought Monitoring and Prediction System Fraction of the global land in D0 (abnormally dry), D1 (moderate), D2 (severe), D3 (extreme), and D4 (exceptional) drought condition (Data: Standardized Precipitation Index data derived from MERRA-Land).
21 GIDMaPS: Global Integrated Drought Monitoring and Prediction System a) Area under moderate drought
22 GIDMaPS: Global Integrated Drought Monitoring and Prediction System Empirical PDF Matching Mean-Fields Bias Removal Bayesian-Based Correction Algorithm Parametric Fitting Correction Real-Time Data Global Precipitation Climatology Project + ~18 Months Real-Time Data ~ 32 Years Satellite-Based Rainfall Data where G and S denote GPCP and real-time satellite data (here, PERSIANN and TRMM- RT). The conditional probability P(G S) indicates the likelihood of the measurement G given the satellite observation S Present AghaKouchak A., and Nakhjiri N., 2012, A Near Real-Time Satellite-Based Global Drought Climate Data Record, Environmental Research Letters, 7(4), , doi: / /7/4/
23 9-18 Months Real-Time 1-, 6-Month Forecast GIDMaPS: Global Integrated Drought Monitoring and Prediction System Prediction component is based on a drought persistence model which requires historical observations. The seasonal drought prediction component is based on two input data sets (MERRA and NLDAS) and three drought indicators (SPI, SSI and MSDI). Ai+1(1)= Si-4+ Si-3+ Si-2 +Si-1+ Si +S(1)i+1 Ai+1(2)= Si-4+ Si-3+ Si-2 +Si-1+ Si +S(2)i+1... May (1-month lead) May June (1-month lead) July (1-month lead) July (1-month lead) ~ 30 Years Satellite-Based Rainfall Data August (1-month lead) June (1-month August (1-mont Ai+1(m)= Si-4+ Si-3+ Si-2 +Si-1+ Si +S(m)i NOW
24 Improving Early Drought Detection Using Satellite Observations
25 Integration of AIRS Data into GIDMaPS Precipitation (MERRA) Soil Moisture (MERRA) Relative Humidity (AIRS Data)
26 Integration of AIRS Data into GIDMaPS Precipitation (MERRA) Soil Moisture (MERRA) Relative Humidity (AIRS Data)
27 Integration of AIRS Data into GIDMaPS Precipitation (MERRA) Soil Moisture (MERRA) Relative Humidity (AIRS Data)
28 Integration of AIRS Data into GIDMaPS Precipitation (MERRA) Soil Moisture (MERRA) Relative Humidity (AIRS Data)
29 Integration of AIRS Data into GIDMaPS
30 Integration of AIRS Data into GIDMaPS
31 Integration of AIRS Data into GIDMaPS Probability of drought detection (i.e., fraction of detected drought) when Drought Onset (DO) based on SRHI is less or equal to that of SPI (a), mean lead time based on SRHI relative to SPI (months)(b).
32 Summary GIDMaPS provides both monitoring and prediction capabilities based on multiple data sets and drought indicators. MSDI leads to a composite product based on the joint distribution of precipitation and soil moisture. It can be used for multi-index drought assessment. Real-time PERSIANN Precipitation data sets can be used for near real-time drought monitoring and improving initial conditions for drought prediction. PERSIANN-CDR data offers daily long-term data sets that can be used for drought monitoring and assessment. There are opportunities to integrate the Atmospheric Infrared Sounder (AIRS) relative humidity data into GIDMaPS to improve drought early detection.
33 Research Team: Present and Recent Past S. Sellars Center for Hydrometeorology and Remote Sensing, University of and many more
34 References AghaKouchak A., and Nakhjiri N., 2012, A Near Real-Time Satellite-Based Global Drought Climate Data Record, Environmental Research Letters, 7(4), , doi: / /7/4/ Damberg L., AghaKouchak A., 2014, Global Trends and Patterns of Droughts from Space, Theoretical and Applied Climatology, 117(3), , doi: /s Golian S., Mazdiyasni O., AghaKouchak A., 2014, Trends in Meteorological and Agricultural Droughts in Iran, Theoretical and Applied Climatology, doi: /s Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System,Scientific Data, 1:140001, 1-10, doi: /sdata Tabari H., AghaKouchak A., Willems P., 2014, A perturbation approach for assessing trends in precipitation extremes, Journal of Hydrology, 519, , doi: /j.jhydrol
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