Can you provide an example of how DMAP is used in the papers?

It has two parts. First how the DMAP (Drought Monitor And Prediction) tool is utilized in academic papers and the second is the link to some research papers:

Data Input & Index Calculation: Researchers input climate data into DMAP, which could be in the form of Excel files containing data for all stations or gridded datasets in NetCDF format. The tool then calculates various drought indices, such as the KBDI (Keetch-Byram Drought Index), which requires daily Tmax (maximum temperature) and rainfall data.

Drought Severity Assessment: Using customizable thresholds for each index, DMAP allows researchers to determine the severity of drought conditions1. For instance, the SPI (Standardized Precipitation Index) might use a threshold of less than -0.99 to indicate drought severity

Predictive Modeling: DMAP can import outputs of General Circulation Models (GCMs) to assess future drought conditions3. This feature enables researchers to predict and project potential drought occurrences, aiding in preparedness and planning

Graphical Representation: The tool provides informative graphs like line, columnar, and Boxplot representations, which help visualize the progression and impact of drought events across different time scales

These functionalities of DMAP are highlighted in academic papers to showcase its effectiveness in drought monitoring and prediction, aiding researchers in making informed decisions based on comprehensive data analysis.

Here are some research papers that have utilized the DMAP (Drought Monitor And Prediction) tool for drought monitoring:

PDSI - Palmer Drought Severity Index

• Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data: This paper focuses on the estimation of meteorological drought indices using both reanalysis precipitation data and observed data from weather stations
• Predictive value of Keetch-Byram Drought Index for cereal yields in a semi-arid environment: This study explores the use of the Keetch-Byram Drought Index, calculated through DMAP, to predict cereal yields in semi-arid regions
• Rainfed wheat yield prediction using economical, meteorological, and drought indicators through pooled panel data and statistical downscaling: This research uses DMAP to predict wheat yields by incorporating drought indicators along with economic and meteorological data
• Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: This case study in Fars Province, Iran, employs DMAP to predict meteorological drought using the Palmer Drought Severity Index and the Statistical Downscaling Model

You can find these papers and more on our Dropbox here. These papers highlight the application of the DMAP tool in various contexts of drought research, showcasing its versatility and effectiveness in drought assessment and prediction. For more detailed information, you can access the papers through the provided