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Created: | Jun 05, 2024 at 8:19 a.m. | |
Last updated: | Jun 20, 2024 at 4:38 p.m. | |
DOI: | 10.4211/hs.b6d1d853b82247e1aeaf907ac254c99e | |
Citation: | See how to cite this resource | |
Content types: | File Set Content Single File Content |
Sharing Status: | Published |
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Abstract
We present a global dataset of flash drought events, meticulously compiled using the Soil Moisture Volatility Index (SMVI), a cutting-edge tool initially applied within the United States. This dataset marks a significant expansion of the SMVI methodology to a global context, offering an essential resource for comprehensively understanding and predicting rapidly evolving drought phenomena. Characterized by detailed information on the onset, duration, and severity of each event, the dataset covers a wide array of climatic zones, thus providing a diverse and inclusive global perspective. A key feature of this dataset is the integration of atmospheric variables, which sheds light on the meteorological factors driving and influencing flash droughts. Such integration allows for an in-depth exploration of the complex interplay between soil moisture and atmospheric conditions, enhancing our understanding of drought dynamics.
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.txt
##################################### Soil Moisture Volatility Index (SMVI) ##################################### ##########Global Inventory########### ##################################### #Corresponding Author: Mahmoud Osman# ####Email-1: mahosman01@gmail.com#### ####Emain-2: mahmoud.osman@jhu.edu### #####Last Update: December 2023###### ##################################### SMVI data is in this inventory is based on the published studies of flash droughts over the contiguous United States (Osman et al. 2021 & 2022) with slight modifications and enhancements. Description of data: -------------------- - The main directory has 2 sub-directories: 1- FD_Events, 2-Composites, and 1 single .csv file: LonLat.csv. - Note! Each sub-direcory has 2 more subdirecories: 1-CSV, and 2-NetCDF for the convenience of the user any of the 2 formats can be used. 1-FD_Events: It contains the detected flash drought events as described in (Osman et al. 2021) with some modifications as following: - A flash drought is identified at a gridpoint when the following conditions are fulfilled: 1- The 5-day running average (1-Pentad) RZSM fall below the 20-days running average RZSM. 2- The crossover occurs below the 20th percentile RZSM of the corresponding day's long-term record. 3- The previous conditions are persistent for at least 4-pentads period. 4- The grid-point's average temperature during the detected period is more than Zero C (273 K). 5- The grid-point's computed average Bowen's ratio during the detected period is between 0.2 and 7. - In this version multiple flash drought events are allowed to be detected up to 6 events per year, sorted by onset date of event - Events 15 days or less apart are considered to be 1 event, and merged into one. - Severity of the event is quantified according basedon RZSM deficit during the identified flash drought event according to Eq-1 in Osman et al. (2022). - File header goes as: fstdate#: Date of onset of event #n lstdate#: Date of recovery (end of rapid intensifiaction) of event #n SV#: Severity of event #n VEGID: Landcover class number according to GLDAS dominant vegetation type in CLSMF2.5. 2-Composites: It contains some associated atmospheric standardized anomalies (derived from ERA5 dataset) at onset, pre-onset and recovery stages as discussed in (Osman et. al 2022). - There is a separate file for each variable/year/event#. - the file naming goes as: SMVI_GLDAS_E[event#]_[variable name]_[Year].csv - Variables are: ARAIN: Total Liquid Precipitation EVP: actual evapotranspiration PRESS: surface pressure RZSM: root-zone soil moisture TMP: 2-m above ground temperature VPD: vapor pressure deficit WS: 10-m above ground wind speed PEVPR: potential evapotranspiration - File header goes as: On01: The 5-days average (1-pentad) value for the variable's anomaly 1-pentad (5-days) before Onset date. On02: The 5-days average (1-pentad) value for the variable's anomaly 2-pentads (10-days) before Onset date. On03: The 5-days average (1-pentad) value for the variable's anomaly 3-pentads (15-days) before Onset date. On00: The 5-days average (1-pentad) value for the variable's anomaly at "Onset" date. Re00: The 5-days average (1-pentad) value for the variable's anomaly at "Recovery" date. Re01: The 5-days average (1-pentad) value for the variable's anomaly 1-pentad (5-days) after Recovery date. On3Pn: The 15-days average (3-pentads) value for the variable's anomaly 3-pentad (15-days) back from Onset date (i.e. average from Onset to Onset-15d). - LonLat.csv: contains the corresponding Longitude and Latitude for each row in all files. - The NetCDF direcories are created using a Pyhthon script developed by Mahdad Talebpour, that can be accessed from: https://github.com/mahdadt/SMVI_GFD_Conversion/ ---------------------------------------------- ### Sample snippet to read csv data in R: #Load Libraries list.of.packages <- c("maps","maptools","ncdf4","data.table","RColorBrewer","Cairo",'latticeExtra','lattice') lapply(list.of.packages, library, character.only = TRUE) color_palette <- c("dodgerblue2", "#E31A1C","green4","#6A3D9A","#FF7F00", "gold1", "skyblue2", "#FB9A99", "palegreen2","#CAB2D6", "#FDBF6F", "gray70") nlon <- 1440 #Longitude size (as GLDAS) nlat <- 600 #Latitude size (as GLDAS) YYYY <- 2017 #Selected year n_reg <- nlon * nlat csv_in<- fread(file= paste(OUT_DIR,'/FD_Events/SMVI_GLDAS_Summ_table_SMVI_',YYYY,'_n',n_reg,".csv",sep='')) fd_YY <- as.data.frame(csv_in[,'fstdate1'],colClasses = c("date")) #just selecting the a vector that contains the date of the first deteceted FD event to plot fd_YY_Mon <- month(fd_YY[,]) month_matrix <- array(fd_YY_Mon, dim = c(nlon, nlat)) var_MAT <- array(fd_YY, dim = c(nlon, nlat)) #Converts the vector to matrix of size nlon x nlat month_names <- month.abb wld <- maps::map('world',plot=F) wld <- data.frame(lon=wld$x, lat=wld$y) Plt<-levelplot(month_matrix, row.values = lon, column.values = lat, xlab = 'Longitude', ylab = 'Latitude', ylim = c(-60, 90), xlim = c(-180, 180), main = paste(YYYY), col.regions = color_palette[1:12], at = seq(0, 13), colorkey = list(at = seq(1, 13), labels = list(at = seq(1.5, 12.5), labels = month_names)), scales = list(x = list(at = seq(-180, 180, by = 20)),y = list(at = seq(-60, 90, by = 20))), panel.grid = list(col = "lightgray", lty = "dotted"))+ xyplot(lat ~ lon, wld, type='l', lty=1, lwd=1, col='black') png(filename=paste("SMVI_GLDAS_E1_OnsetMon_",YYYY,".png",sep=''), width = 7000, height = 3500,res=300,type='cairo') print(Plt) dev.off() #image(month_matrix) #Or comment the "Plt" line and uncomment this to do a quick visualization for data References: ----------- Osman, M., B. Zaitchik, J. Otkin, M. Anderson, M. Talebpour (2024). SMVI Global Flash Droughts Dataset. HydroShare. https://doi.org/10.4211/hs.b6d1d853b82247e1aeaf907ac254c99e . http://www.hydroshare.org/resource/b6d1d853b82247e1aeaf907ac254c99e Osman, M., Zaitchik, B. F., Badr, H. S., Christian, J. I., Tadesse, T., Otkin, J. A., & Anderson, M. C. (2021). Flash drought onset over the contiguous United States: sensitivity of inventories and trends to quantitative definitions. Hydrology and Earth System Sciences, 25(2), 565–581. https://doi.org/10.5194/hess-25-565-2021. Osman, M., Zaitchik, B. F., Badr, H. S., Otkin, J., Zhong, Y., Lorenz, D., et al. (2022). Diagnostic Classification of Flash Drought Events Reveals Distinct Classes of Forcings and Impacts. Journal of Hydrometeorology, 23(2), 275–289. https://doi.org/10.1175/JHM-D-21-0134.1.
Related Resources
This resource updates and replaces a previous version | Osman, M., B. Zaitchik, J. Otkin, M. Anderson (2024). SMVI Global Flash Droughts Dataset, HydroShare, http://www.hydroshare.org/resource/642ff72592404a17bb85a8a92b4dbcd6 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
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National Science Foundation (NSF) | PREEVENTS Track 2: Collaborative Research: Flash droughts: process, prediction, and the central role of vegetation in their evolution | 1854902 |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
Name | Organization | Address | Phone | Author Identifiers |
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Hamada Badr | Amazon Web Services |
How to Cite
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