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An extended multivariate framework for drought monitoring in Mexico Roberto A. Real-Rangel (1,2) , Adrián Pedrozo-Acuña (1) , J. Agustín Breña-Naranjo (1) , and Víctor H. Alcocer-Yamanaka (3) (1) Institute of Engineering, National Autonomous University of Mexico, Mexico City, Mexico ([email protected] ) (2) Program of Master and Doctorate in Civil Engineering, National Autonomous University of Mexico, Mexico City, Mexico (3) National Water Commission Introduction/Background Conclusions References • A reliable drought monitoring system allows to identify regions affected by these phenomena so that early response measures can be implemented (Wilhite, 2000). • Drought monitoring systems around the world have taken advantage of global hydroclimatological datasets derived from remote sense tools (Hao et al., 2014; Sepulcre-Canto et al., 2012; Sheffield et al., 2014; Svoboda et al., 2002). However, its use in Mexico for drought assessment is still incipient (e. g., de Jesús, 2016). • Mexico has the Mexico’s Drought Monitor (MDM), derived from the North American Drought Monitor (NADM), since 2014. Although it has inherited several strengths from the NADM, its main limitation is the scarcity of ground-based data, as well as the subjective criteria to represent the spatial extent of droughts. • MDM has failed to detect some past events. AghaKouchak (2015). DOI: 10.1016/j.advwatres.2014.11.012 AghaKouchak and Nakhjiri (2012). DOI: 10.1088/1748-9326/7/4/044037 de Jesús et al. (2016). DOI: 10.3390/w8080325 Dracup et al. (1980). DOI: 10.1029/WR016i002p00297 Hao and AghaKouchak (2014). DOI: 10.1175/JHM-D-12-0160.1 Hao et al. (2014). DOI: 10.1038/sdata.2014.1 Kao and Govindaraju (2010). DOI: 10.1016/j.jhydrol.2009.10.029 McKee et al. (1993). URL: http://ccc.atmos.colostate.edu/relationshipofdroughtfrequency.pdf Rienecker et al. (2011). DOI: 10.1175/JCLI-D-11-00015.1 Sepulcre-Canto et al. (2012). DOI: 10.5194/nhess-12-3519-2012 Sheffield et al. (2014). DOI: 10.1175/BAMS-D-12-00124.1 Svoboda et al. (2002). DOI: 10.1175/1520-0477(2002)083<1181:TDM>2.3.CO;2 Wilhite (2000). ISBN: 0415168333 Time (layers, T) Objectives Provide an operational framework for drought monitoring in Mexico, based on univariate and multivariate nonparametric standardized indexes. Atmospheric reanalysis Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2; Rienecker et al., 2011). Data I= i lat : 14° ≤ lat ≤ 34° J= j lon : −119° ≤ lon ≤ −86° T= t month : jan/1980 ≤ month < present MERRA-2 temp. coverage Study area Mexico Input/Output data array used D4 D3 D2 D1 D0 W0 W1 W2 W3 W4 Drought intensity map Drier Wetter M < 1 1 ≤ M < 3 3 ≤ M < 6 6 ≤ M < 9 9 ≤ M < 12 M ≥ 12 Drought magnitude map More persistent • The applied framework succeed in detecting observed drought events. • Maps of magnitude are helpful for identifying areas with drought persistence, which can help to define priority regions for aid relief. • While MSDI offer insight on the whole dry spell across the hydrological continuum, the analysis of univariate SI allows to track its propagation. • Further work shall aim at correcting MERRA-2 output with ground-based data in order to improve the results. Methodology MSDI has been extended to three variables associated to droughts (precipitation, runoff and soil moisture) in order to detect the whole dry spell across the hydrological continuum. Drought maps and time series Intensity Magnitude MSDI SSI SRI SPI MERRA-2 PRECTOTLAND BASEFLOW + RUNOFF RZMC PRECTOTLAND: Total precipitation in land BASEFLOW: Baseflow flux RUNOFF: Overland runoff RZMC: Water in the root zone SPI, SRI, SSI and MSDI: nonparametric standardized drought indices ( SI) for precipitation, runoff and soil moisture, and multiple variables (Hao and AghaKouchak, 2013; Farahmand and AghaKouchak, 2015; McKee et al., 1993; Shukla and Wood, 2008). X, Y, Z i,j = x, y, z t i,j :t∈T = − 0.44 + 0.12 = −1 where denotes the empirical probability; i is the rank of non-zero data in ascending order; n is the sample size; is the standard normal distribution function. Data validation Clim. station Mexicali (02033) Stream gauge La Flor (36039) Stream gauge Tepehuaje (24301) Clim. station Cañada Honda (01004) Clim. station Tonalá (07168) Clim. station Los Ídolos (30068) Clim. station Callejones (06003) Stream gauge Bolaños (12484) Stream gauge San Bernardo (09067) Stream gauge Jesús Carranza II (29006) Cañada Honda Mexicali Callejones Tonalá Los Ídolos MERRA-2 Precipitation (×10 -3 mm/month) RMSE=0.02 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 San Bernardo Bolaños Tepehuaje Jesús Carranza II La Flor MERRA-2 Runoff (×10 -3 millions of m 3 /month) 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Results Sample event: Michoacán 2015-present -2.0 -1.0 0.0 1.0 2.0 -2.0 -1.0 0.0 1.0 2.0 SPI 01 SRI 01 -2.0 -1.0 0.0 1.0 2.0 SSI 01 -2.0 -1.0 0.0 1.0 2.0 01/10 07/10 01/11 07/11 01/12 07/12 01/13 07/13 01/14 MSDI 01 February/2017 February/2017 Newspaper (March 15, 2017) “Drought in Cuitzeo Lake is due to natural causes: National Water Commission” http://primeraplananoticias.mx -2 -1 0 1 2 01/15 07/15 01/16 07/16 01/17 p0 p25 p50 p75 p100 Drought intensity time series (MSDI 01 ) Michoacán Michoacán Michoacán MDM drought map NRMSE=19.3% RMSE=0.03 NRMSE=6.0% RMSE=0.05 NRMSE=7.6% RMSE=0.07 NRMSE=8.6% RMSE=0.07 NRMSE=15.3% RMSE=0.08 NRMSE=12.0% RMSE=1.56 NRMSE=110.3% RMSE=0.05 NRMSE=7.3% RMSE=0.37 NRMSE=18.6% RMSE=0.12 NRMSE=43.4%
