DMAP Tool (Drought Monitor And Prediction)
We have utilized DMAP in several academic papers:
Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data Predictive value of Keetch-Byram Drought Index for cereal yields in a semi-arid environment Rainfed wheat (Triticum aestivum L.) yield prediction using economical, meteorological, and drought indicators through pooled panel data and statistical downscaling Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran
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You can access sample input files for download here: DropBox
What is DMAP (Drought Monitor And Prediction) software?
Drought is considered one of the most impactful natural hazards, causing widespread damage and affecting numerous people (Wilhite, 1993). Monitoring drought events is essential for mitigating their adverse effects. Drought indices play a vital role in this process by providing quantitative measures that characterize drought severity. These indices assimilate data from various variables, such as precipitation and evapotranspiration, into a single numerical value (Zargar et al., 2011).
A reliable drought index should possess several key capabilities. Firstly, it must accurately quantify the severity of drought, enabling a better understanding of its intensity. Additionally, the index should be capable of detecting the onset and cessation of droughts, allowing for timely early warning systems. Moreover, the index plays a crucial role in monitoring water resources for prospective planning and management, ensuring effective utilization during periods of water scarcity. By utilizing robust and comprehensive drought indices, we can enhance our ability to respond to drought events, minimize damages, and safeguard both the environment and the well-being of communities affected by this challenging natural phenomenon.
With this powerful tool, you have the flexibility to input climate data in various formats. You can provide climate data as an Excel file containing data for all stations. Alternatively, you can work with CMIP5/CMIP6 data or any gridded dataset in NetCDF format, expanding the range of data sources available for analysis.
Given the complexity and time-consuming nature of calculating different drought indices, researchers often seek comprehensive software solutions. Drought monitoring involves three main types of drought: meteorological, agricultural, and hydrological droughts. The DMAP (Drought Monitor And Prediction) software comes to the aid, offering the ability to calculate diverse drought indices for each of these three types:
SPI (Standardized Precipitation Index, McKee et al. 1993, 1995
DI (Deciles Index), Gibbs and Maher, 1967
PN (Percent of Normal Index), Willeke et al. (1994)
CZI (China-Z Index), Wu et al. (2001)
MCZI (Modified CZI), Wu et al. (2001)
EDI (Effective drought Index), Byun and Wilhite (1999)
RAI (Rainfall Anomaly Index), van Rooy (1965)
ZSI (Z-score Index), Palmer (1965)
PDSI (Palmer Drought Severity Index), Palmer (1965) and Dehghan et al., 2020
PHDI (Palmer Hydrological Drought Index), Palmer (1965)
SPEI (Standardized Precipitation Evapotranspiration Index), Vicente-Serrano et al., 2010 and Salehnia et al., 2020
RDI (Reconnaissance Drought Index), Tsakiris and Vangelis, 2005.
ARI (Agricultural Rainfall Index), Nieuwolt, 1981
SMDI (Soil Moisture Deficit Index), Narasimhan and Srinivasan, 2005
ETDI (Evapotranspiration Deficit Index), Narasimhan and Srinivasan, 2005
SWSI (Surface Water Supply Index), Garen, 1993
SDI (Streamflow Drought Index), Nalbantis and Tsakiris, 2009
In the Drought Index tab of the DMAP software, users can easily calculate various drought indices by simply selecting the desired index. The software then performs the necessary calculations, providing valuable insights into drought conditions within the study area. To further aid in drought monitoring, DMAP offers a range of informative graphs, including line, columnar, severity of drought, and Boxplot representations. These graphs allow users to observe drought events across different time scales, facilitating a comprehensive understanding of the drought's progression and impact.
For those interested in predicting future drought events, DMAP offers an invaluable capability. Users can import the outputs of General Circulation Models (GCMs) into the tool, allowing them to calculate and assess any desired drought index for the future period. By utilizing this feature, researchers can make informed predictions and projections, enhancing preparedness and planning for potential drought occurrences.
The DMAP software thus serves as a powerful and versatile tool, providing researchers and users with essential resources to monitor, analyze, and predict drought events effectively within their study areas.
Watch Drought Course Videos: Drought Lesson
PDSI - Palmer Drought Severity Index
How can I use DMAP tool to predict drought?
To effectively harness the capabilities of the Drought Monitoring and Prediction (DMAP) tool for forecasting drought, a systematic approach is necessary. Firstly, acquiring future climate projections is imperative, typically sourced from global climate models such as CMIP5 or CMIP6 , spanning an extensive timeframe up to the year 2100. These models provide invaluable insights into potential climate scenarios, encompassing changes in temperature, and precipitation patterns.
Once the future climate projections are obtained, the next step involves inputting these data into the DMAP tool. This is the computation of various drought indices, which serve as quantitative measures of drought severity and duration. The DMAP tool leverages these drought indices to generate predictive models that forecast drought occurrences over the specified timeframe.
By assimilating historical climate data with future climate projections, the tool can discern emerging drought patterns and anticipate potential drought events with a degree of confidence. Moreover, the tool enables users to customize parameters and thresholds based on regional characteristics and specific applications, enhancing its adaptability and relevance across diverse geographical regions.
Type of input file in DMAP tool
In the DMAP tool, users can work with different types of input files to facilitate efficient drought monitoring and prediction. The following are the main types of input files supported by DMAP:
Excel Files: Users can input climate data in Excel file format. This option is particularly useful for providing data from various weather stations or observation points within the study area.
CMIP5/CMIP6 Data: DMAP also supports the input of climate model data from the CMIP5 and CMIP6 projects. This enables users to incorporate global climate model outputs into their analysis and predictions.
Gridded Datasets in NetCDF Format: DMAP offers compatibility with gridded datasets in NetCDF (Network Common Data Form) format. This format is widely used for storing large-scale climate data on a grid, making it ideal for regional or global analyses.
By providing these diverse input options, the DMAP tool ensures users can easily access and utilize the data sources most relevant to their research or monitoring objectives. This versatility enhances the tool's effectiveness in addressing various drought-related challenges and enables users to make informed decisions based on robust and comprehensive data.
Calculation of each index in DMAP Software
In DMAP, we extracted the equations for each index from the original paper that introduced the intent index, along with all the relevant details. The equations used were thoroughly explained in these papers. The primary references for each index are listed in the reference section below.
It's important to note that each index requires specific variables and time scales. For instance, the KBDI index requires daily Tmax (maximum temperature) and Rainfall data. Therefore, when you input daily rainfall and Tmax data into the tool, the KBDI index will become active on the "Drought Index" tab, allowing you to assess drought conditions accordingly.
Severity of Drought in DMAP
In DMAP, users can determine the severity of drought using a customizable threshold, which varies for each index. For instance, in the case of the SPI (Standardized Precipitation Index), the typical threshold might be less than -0.99 or can also be set to zero. On the other hand, for the KBDI (Keetch-Byram Drought Index), the threshold could be set to values greater than 200 or 250.
When utilizing DMAP to calculate drought severity for the SPI index (values falling below the specified threshold), the following steps are followed:
Calculate S0 = SPI - border, where "border" is the specified threshold value.
Identify drought events as instances where S0 is less than zero.
Each drought event starts when S0 first becomes less than zero.
Each drought event continues as long as S0 remains less than zero.
Calculate the duration of each drought event, which corresponds to the number of days between the start and end of the event.
Determine the severity of each drought event by calculating the cumulative sum of the absolute values of S0 during that event, i.e., the sum(|S0|) between the start and end of the drought event.
Tips and Points
In the DMAP software tool V2.0, input files can be in various formats, including xls, xlsx, txt, and csv. Also, support for NetCDF (nc and nc4) format is added. Additionally, a new feature allows extracting data for a specific point or area from a NetCDF file. If a region is selected, the tool calculates the mean value for that region.
DMAP includes a useful option to convert the unit of data in NetCDF files. For instance, it can convert flux values to mm values, which is particularly beneficial for models like CMIP5 or CMIP6.
It's important to note that input data can be in daily, monthly, or yearly timescales. However, if yearly data is entered, the software won't compute drought index values on a monthly scale.
Some indices, such as KBDI and EDI, require daily data. If monthly or yearly data is used as input, the calculation of these indices won't be possible, and the corresponding icons will be deactivated.
The DMAP V2.0 software is capable of computing 19 drought indices related to meteorological, agricultural, and hydrological droughts. Each index has specific requirements regarding input data.
In the station list of the Drought Monitor And Prediction (DMAP) tool, there are 6 input items available. However, the number of inputs required depends on the selected index. For all rain-based indices such as SPI, DI, CZI, MCZI, etc., you only need to enter the names of the stations if you choose not to use the Netcdf file as input. However, if you decide to use the Netcdf file, you will also need to provide the latitude and longitude of the stations. When calculating the PDSI index, you will need to enter the name, latitude, Surface Soil, and Available Water Capacity. For the KBDI Index, you will need to input the name and Field Capacity. If you choose to extract data from Netcdf files using the list of stations, you will need to enter both the latitude and longitude along with the names of the stations to facilitate the data extraction process.
The first tab in the tool allows users to enter variables as a time series (daily, monthly, or yearly). The number of input variables depends on the index. For all rainbased you need to enter just rainfall.
Depending on the selected index, drought calculations can be performed at various time scales, including daily, monthly, yearly, and moving averages. But the moving average is not active for all indexes.
The tool offers two items for evapotranspiration: calculate it by ThornThwaite method(you will need Tempratore and latitude), and manual input. This calculation will do in PDSI and SPEI_by_T. For computing the SPEI index, users have two options: one using ThornThwaite and Hamon methods for evapotranspiration, and the other using user-entered evapotranspiration data.
When calculating SPEI with computed evapotranspiration(SPEI1), users only need to provide the value of Latitude in decimal numbers and temperature as Tave in the Input time series. For KBDI calculation, the user must input the value of Field Capacity (FC) in mm.
For calculation of PDSI and if you apply the amount of computed evapotranspiration, then you need to fill three items, including Surface Soil water in mm(usually is 25mm), Available Water Capacity (AWC) in mm, and Latitude in decimal number.
In the case of RDI calculation, users have the option to select the checkbox for "Standardized RDI."
The DMAP V2.0 software includes a graph plotting panel with three types of graphs: BoxPlot, Linear, and Columnar. Users can customize their graphs by selecting colors or using options like H-Line (horizontal line) and Grayscale.
Quick Guide
I-
Go to the "Input Excel File" tab and open the list of stations, loading the corresponding latitudes and longitudes.
Depending on the index you want to calculate, enter the required values for each station as follows:
PDSI --> Input SS, which signifies the available water capacity of the surface layer for each site (typically in mm, often around 25mm), and AWC, indicating the available water capacity of the underlying layer for each site (in mm).KBDI --> Input Field CapacitySPEI by T --> Input latitude
You can consider the SS values as 25mm
Select the tab corresponding to the variable you need. By inputting each variable, one or several indexes will become calculatable:
Rainfall --> SPI, CZI, ZSI, EDI(if your data is daily), PN, DI, RAI, MCZIRainfall and Tave --> PDSI, PHDI, SPEI1(In this indexes calculate PET by Tave)Rainfall and Tmax --> KBDI. Your data(Rainfall, Tmax) should be in dailyRainfall and PET --> SPEI, PHDI, RDI, ARI, PDSI1PET and AET --> ETDISoil Moisture --> SMDIStream Flow --> SWSI, SDI
Select your Excel file and the corresponding sheet. Manually set the column headers, or use the "Auto-Fill Col-Head" button. If using the auto-fill option, ensure all column headers are correctly selected, and manually select the "Date" column.
The name of stations in the list should be same as in the first row of your data
After you click on "Auto-Fill Col-Head" check the selection of all column header
Select the "Date" column manually.
Once the loading is complete, proceed to calculate the index.
II-
Go to the "Input NetCDF File" tab, select your NetCDF files, and verify the names of variables, units, and calendar information. If needed, you can view the NetCDF content using the "View Database" option.
Depending on your preferences, choose one of the following options:
If you have stations in the list, use the first RadioButton.
If not, input the stations manually in each table or provide a file with station details. Alternatively, use the "Use a Region" RadioButton, entering the start and end latitudes and longitudes for your region. The tool will create a station list based on the cells, considering each cell as one station.
Save the data. If you have loaded the variable in the first tab, it will be replaced. For saving, select the name of the variable and the unit converter. If no conversion is needed, choose "Multiply by 1."
When the loading is complete then you can calculate the index in "Drought Indices" tab.
Regional Drought Monitor
References:
Byun H R, Wilhite D A. 1999. Objective quantification of drought severity and duration. Journal of Climate, 12(9): 2747-2756.
Garen DC, 1993. Revised surface-water supply index for western United States, J. Water Resour. Plann. Manage. 1993.119:437-454.
Gibbs, W.J., and Maher, J.V. 1967. Rainfall Deciles as Drought Indicators, Bureau of Meteorology bulletin, No. 48. Commonwealth of Australia: Melbourne; 29.
McKee T B, Doesken N J, Kleist J. 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. Anaheim, CA: American Meteorological Society, 179-184.
McKee T B, Doesken N J, Kleist J. 1995. Drought monitoring with Multiple Time scales. In: Proceeding of the 9th Conference on Applied Climatology. Dallas, TX: American Meteorological Society, 233-236.
Nalbantis, I., and Tsakiris, G. 2009. Assessment of hydrological drought revisited. Water Resour Manage. 23:881-897
Nieuwolt S, 1981. Agricultural droughts in Peninsular Malaysia. Malaysian Agricultural Research and Development Institute, Serdang, p: 16.
Narasimhan, B., and Srinivasan, R. 2005. Development and Evaluation of soil Moisture Deficit index and Evaporation Deficit Index for Agriculture of Drought Monitoring, Agricultural and Forest Meteorology, 133-69-88.
Palmer WC, 1965. Meteorological drought: US Department of Commerce, Weather Bureau Washington, DC, USA. 45, 58.
Salehnia N, et al., 2017. Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. J Arid Land (2017) 9(6): 797-809. Drought AgMerra
Tsakiris G, and Vangelis H, 2005. Establishing a Drought Index incorporating evapotranspiration. European Water. 9/10:3-11
Van Rooy MP, 1965. A rainfall anomaly index independent of time and space. Notos 14:43-48
Vicente-Serrano SM, Beguerra S, and Lopez-Moreno JI, 2010. A Multi-Scalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index - SPEI. Journal of Climate 23(7):1696-1718, DOI: 10.1175/2009JCLI2909.1
Wilhite DA, 1993. The enigma of drought. Drought Assessment, Management, and Planning: Theory and Case Studies. Kluwer Academic Publishers, Boston, Ma. pp. 3-15.
Willeke G, Hosking J R M, Wallis J R, et al., 1994. The national drought atlas. In: Institute for Water Resources Report 94-NDS-4. U.S Army Corp of Engineers, CD-ROM. Norfolk, VA.
Wu H, Hayes M J, Weiss A, et al., 2001. An evaluation of the Standardized Precipitation Index, the China-Z Index and the statistical Z-Score. International Journal of Climatology, 21(6): 745-758.
Zargar A, Sadiq R, Naser B, Khan FI, 2011. A review of drought indices. Environ. Rev. 19: 333-349 (2011). Doi: 10.1139/A11-013
The license of this tool is applicable for one year of using and you can renew it by pay 20% of the price for the new year.