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Drought Monitor And Prediction

What is DMAP (Drought Monitor And Prediction) software?

Among natural hazards, drought is known to cause extensive damage and affects a significant number of people (Wilhite 1993). To reduce the damage from drought, it is crucial to monitor this event. Drought indices are quantitative measures that characterize drought levels by assimilating data from one or several variables such as precipitation and evapotranspiration into a single numerical value (Zargar et al. 2011). A reliable index must be able to quantify drought severity, detect drought beginning and end times for early warning systems, monitor and prospective water resources planning.

Since calculating different indices are sometimes sophisticated and time consuming, so researchers need a comprehensive software. As we know, there are three main drought types, namely meteorological, agricultural, and hydrological droughts. The DMAP (Drought Monitor And Prediction) software can calculate different drought indices in three different types of drought that are listed in following:

1- Meteorological drought

A: Rain Based-drought indices (Salehnia et al., 2017):

B: Other meteorological drought indices:

2- Agricultural drought indices

3- Hydrological drought indices

In the monitor phase in DMAP (Drought Monitor And Prediction) software, through selecting every index, the user can calculating it and then by available graphs (line, columnar, and Boxplot), the user can monitor the happened drought event in various time scale in the study area. In the prediction phase, the user by importing the downscaled outputs of GCMs models in DMAP tool, he/she can calculate every index the he wants for future period.

Type of input file in DMAP (Drought Monitor And Prediction) tool:

In DMAP the input file can be in different format files, namely csv, xls, xlsx, and also nc (NetCDF). This is a unique characteristic and due to this feature, users can easily import and browse his fie, without any concern. Another benefit of this software is the positioning of data in columns. In this software, the ordering of data in columns is not important, and the software recognizes the location of the data according to the input column header. This feature is not considered in other existing software that compute only a few indexes. So the user is having trouble, in such tools, therefore DMAP solve the problem and the user by selecting the header of each column can easily determine the order of them.

Calculation of each index in DMAP (Drought Monitor And Prediction) software:

In DMAP the equations of each index were extracted from the origin paper that it presents the intent index and all details of it. All the used equations were clarified in these papers. The main papers of each index are listed in the reference section in following.


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

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