Global Integrated Drought Monitoring and Prediction System. GIDMaPS

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1 Global Integrated Drought Monitoring and Prediction System GIDMaPS

2 Global Integrated Drought Monitoring and Prediction System GIDMaPS and Center for Hydrology & Remote Sensing

3 Authors: Amir AghaKouchak Zengchao Hao Navid Nakhjiri

4 Disclaimer: The Global Integrated Drought Monitoring and Prediction System (GIDMaPS) is provided 'as is' without any endorsement made and without warranty of any kind, either express or implied. While we strive to ensure that the information provided on GIDMaPS is accurate, no guarantees for the accuracy of information and the original input data are made. The GIDMaPS data and information can only be used at your own discretion and risk and with agreement that you will be solely responsible for any damage and that the authors and their affiliate institutions accept no responsibility for errors or omissions in the GIDMaPS data, information and documentation. In no event shall the authors, developers or their affiliate institutions be liable to you or any third parties for any special, direct, indirect or consequential damages and financial risks of any kind, or any damages whatsoever, resulting from, arising out of or in connection with the use of the GIDMaPS data and information. The user of the GIDMaPS agrees that the GIDMaPS data and information are subject to change without notice, and that the data and information may not be up-to-date. Finally, GIDMaPS provides information on large-scale and broad-scale conditions. Regional and local conditions may vary.

5 Global Integrated Drought Monitoring and Prediction System (GIDMaPS) Version 2 (Updated 5/2/2013) INTRODUCTION The Global Integrated Drought Monitoring and Prediction System (GIDMaPS) is a drought monitoring and prediction system that provides near real-time drought information based on multiple drought indicators and input data sets ( The below figure shows a snapshot of the GIDMaPS interface.

6 The user can select year, month, input data and the choice of drought index and retrieve the drought information. GIDMaPS has to components: (a) drought monitoring; and (b) drought seasonal prediction. INPUT DATA The system retrieves precipitation and soil moisture from model simulations and remote sensing observations including the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-Land; Reichle et al., 2011, 2012), NASA North American Land Data Assimilation System (NLDAS; Kumar et al., 2006), NASA Global Land Data Assimilation System (GLDAS; Peters-Lidard et al., 2007) and UCI s Global Drought Climate Data Record (GDCDR; AghaKouchak and Nakhjiri, 2012). GDCDR combines real-time PERSIANN satellite data (Sorooshian et al., 2000; Hsu et al., 1997) with long-term GPCP (Adler et al., 2001) observations. The below table summarizes the available input data, source and the spatial resolution of observations.

7 Input Data Set ID Source Resolution NASA Modern-Era Retrospective analysis for Research and Applications Reichle et al., Precipitation and MERRA NASA 2/3 x 1/2 Soil Moisture North American Land Data Assimilation System - Kumar et al., Precipitation and Soil Moisture NLDAS NASA o Global Drought Climate Data Record - AghaKouchak and Nakhjiri, Precipitation GDCDR UCI 0.5 o Global Land Data Assimilation System (GLDAS) - Peters- Lidard et al., Precipitation and Soil Moisture GLDAS NASA 1 o DROUGHT INDICATORS Different drought indices have been developed and applied for drought monitoring and prediction. The Standardized Precipitation Index (SPI, McKee et al., 1993) is commonly used for meteorological drought monitoring and has been adopted as an important monitoring tool to detect the early emergence of drought. The standardization concept of the SPI can also be applied to other variables, such as soil moisture (i.e., Standardized Soil Moisture Index, SSI - Hao and AghaKouchak, 2013a). The performance of different variables differs in detecting the drought onset, persistence, and termination. The differences in the physical bases of drought-related variables make it difficult, if not impossible, to develop a successful drought monitoring and prediction tool based on one single variable (or index), such as precipitation. The use of a single index to indicate the diversity and complexity of drought conditions and impact is one of the major limitations to drought monitoring (Wilhite, 2005). For this reason, GIDMaPS provides drought information based on two univariate drought indicators and one multivariate drought index: (a) SPI; (b) SSI; and (c) Multivariate Standardized Drought Index (MSDI, Hao and AghaKouchak, 2013a) which combines precipitation and soil moisture in a probabilistic manner. Because precipitation is efficient in detecting the drought onset and because soil moisture is reliable in indicating the drought persistence, the MSDI can reliably capture both the drought onset and persistence. Drought Indicator ID Reference Standardized Precipitation Index SPI McKee et al., 1993 Standardized Soil Moisture Index SSI Hao and AghaKouchak, 2013a Multivariate Standardized Drought Index MSDI Hao and AghaKouchak, 2013a,b

8 GIDMaPS DROUGHT MONITORING COMPONENT GIDMaPS includes a monitoring component based on four input data sets and three drought indicators. The input data sets include MERRA, GLDAS, GDCDR and NLDAS from which MERRA and NLDAS update each month automatically. GLDAS and GDCDR update once the data become available. Three drought indicators are built-in in the monitoring component: SPI, SSI and MSDI. The user can select the choice of drought indicator from the Index drop-down menu. Click on load to display the results. The original data can be downloaded from GIDMaPS by clicking on Download.

9 As an example, the following figure displays global drought conditions based on GIDMaPS for October 2012 (input data: MERRA-Land). One can see that GIDMaPS clearly shows the 2012 U.S. drought.

10 The monitoring component of GIDMaPS presents drought information based on five categories of drought types: D0 (abnormally dry), D1 (moderate drought), D2 (severe drought), D3 (extreme drought), and D4 (exceptional drought) - see Svoboda et al., The five drought categories correspond to the following five ranges of the SPI, SSI and MSDI: -0.5 to -0.7 (D0), -0.8 to -1.2 (D1), -1.3 to -1.5 (D2), -1.6 to -1.9 (D3), and -2.0 or less (D4). As another example, the below figure shows global drought conditions based on GDCDR for September The figure clearly shows the Amazon, Russia and the Horn of Africa droughts that were major events of 2010 (see red circles in the figure).

11 GIDMaPS DROUGHT PREDICTION COMPONENT The seasonal prediction component is based on a 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). The theoretical concept is similar to the baseline probability method for drought forecasting proposed by Lyon et al., 2012 which is based on the inherent persistence of the SPI. In addition to SPI, GIDMaPS employs the SSI and MSDI (Hao and AghaKouchak, 2013b) for seasonal drought forecasting. Our preliminary results show that SSI and MSDI have better kills in describing droughts primarily because they exhibit higher persistence relative to SPI. The sampling method and probability estimation methods are different than Lyon et al., For details, the interested readers are referred to (Hao and AghaKouchak, 2013c). The drought prediction model provides the empirical probability of a D1 (or severer) drought: It should be noted that the predictions can also be presented in terms of likelihood based on the above probabilities. For example, (a) drought likely to persist ( 70% probability) (b) drought very likely to persist ( 90% probability) (a) drought extremely likely to persist ( 95% probability)

12 The below figure shows and example of GIDMaPS prediction (top) and observation (bottom). The figure displays the global D1 drought probability using the MERRA data and SSI for April 2013 (1 month lead) see the top panel. The bottom panel shows observed drought conditions in April 2013 using MERRA data (not used for prediction). It is worth mentioning that GIDMaPS does not provide any information on potential drought development if initial conditions do not indicate drought.

13 Currently, GIDMaPS only provides the drought probability, but one can generate likelihood maps using the provided drought probability using the scale mentioned above. The following figure displays an example of 2 months lead predicted drought likelihood of persistence for March 2013 using GIDMaPS (left) using the NLDAS monthly data products, and the closest available US Drought Monitor (USDM) observations (right). As shown, the GIDMaPS prediction is consistent with the USDM data. It should be noted that the USDM data includes observations and inputs from many variables including subjective inputs from climatologists on the ground (human input) that cannot be mathematically accounted for. Also, the USDM does not provide forecasts and only shows the latest conditions based on observations. For this reason, GIDMaPS and USDM are not expected to be identical. The figures show that the general patterns of drought persistence form GIDMaPS and drought conditions from USDM are consistent which indicates that GIDMaPS can be used for probabilistic drought prediction.

14 REFERENCES Adler et al, 2003, The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979 present), J. Hydrometeorology, 4, AghaKouchak A., and Nakhjiri N., 2012, A Near Real-Time Satellite-Based Global Drought Climate Data Record, Environmental Research Letters, 7(4) Hao, Z. and A. AghaKouchak (2013a). Multivariate Standardized Drought Index: A Multi-Index Parametric Approach for Drought Analysis. Advances in Water Resources, 57, 12-18, doi: /j.advwatres Hao, Z. and A. AghaKouchak (2013b). A Multivariate Multi-Index Drought Monitoring Framework. Journal of Hydrometeorology. in revision. Heim, R. R. (2002). A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society. 83(8): Hsu K, Gao X, Sorooshian S and Gupta H 1997 Precipitation estimation from remotely sensed information using artificial neural networks J. Appl. Meteorol Kumar, S. V., and Coauthors, 2006: Land Information System: An interoperable framework for high resolution land surface modeling. Environ. Model. Software, 21, Lyon, B., M. A. Bell, M. K. Tippett, A. Kumar, M. P. Hoerling, et al. (2012). Baseline probabilities for the seasonal prediction of meteorological drought. Journal of Applied Meteorology and Climatology. 51(7): McKee, T. B., N. J. Doesken and J. Kleist (1993). The relationship of drought frequency and duration to time scales. Eighth Conference on Applied Climatology, Am. Meteorol. Soc., Anaheim, CA. Peters-Lidard, C., P. Houser, Y. Tian, S. Kumar, J. Geiger, S. Olden, L. Lighty, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, W. E.F., and J. Sheffield, 2007: High-performance Earth system modeling with NASA/GSFC s Land Information System. Innovations in Systems and Software Engineering, 3(3), Reichle, R. H., R. D. Koster, G. J. De Lannoy, B. A. Forman, Q. Liu, et al. (2011). Assessment and enhancement of MERRA land surface hydrology estimates. Journal of climate. 24(24): Sorooshian S, Hsu K, Gao X, Gupta H, Imam B and Braithwaite D 2000 Evolution of the PERSIANN system satellite-based estimates of tropical rainfall Bull. Am. Meteorol. Soc

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