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Cycle 5 (2015 Deadline)

Regionalization of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) for Afghanistan

PI: Khadija Jawadi (, Environmental Conservation Specialist Organization of Afghanistan
U.S. Partner: Amir AghaKouchak, University of California, Irvine
Project dates: January 2017 - July 2019

Project Overview:

In the past decades, Afghanistan has encountered several droughts that inflicted terrible damage on groundwater, and surface water resources, agricultural sectors, forests, and pastures. Extreme droughts are expected to occur more frequently in the coming decades, and this may cause major economic losses and social and environmental disasters. The overall objective of this research is developing, validating, and implementing a drought monitoring system for the Amu Darya and Kabul Basins in Afghanistan. The researchers will work to provide reliable information that can be used towards a drought mitigation, risk management, and planning strategy. Regionalization of this model for the Afghanistan basins would promote better monitoring and prediction of droughts and enhance decision making and risk reduction efforts.

This research will build upon the successful development and implementation of previous UNESCO drought monitoring systems and will enhance risk management and drought mitigation by providing consistent and continuous drought information. The system would provide drought monitoring information based on multiple indicators and data sources, including satellite observations and local ground-based information. Providing multi-model, multi-index, seasonal drought prediction information for Afghanistan is an integral part of the implementation. The use of a single index to indicate the diversity and complexity of drought conditions and impacts is one of the major limitations to drought monitoring. For this reason, the system to be developed and tested by this team will provide drought information based on multiple univariate drought indicators and one multivariate drought index.

Summary of Recent Activities:

In the past three months the data gathering team started collecting data for the project which could be described as follow. As the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) require Ground-based information (local data) and Model simulations & remote sensing observations (satellite-based data) for the calibration of the model, the research team started gathering local data including: precipitation, soil moisture and temperature from the two river basins (Kabul and Amu Darya). It contains 10 years period of local data. The team also obtained data of land-cover, land-use, geographic information system (GIS) maps and soil data for the two river basins has been obtained from Ministry of agriculture, Irrigation and livestock (MAIL). They also met with the irrigation and agricultural research deputy of MAIL. The team visited the Ministry of Energy and water deputy and obtained local data including: precipitation, temperature, evaporation and wind data from the two river basin. They also had a discussion meeting with the technical board of Ministry of Energy and Water to describe that this research will help monitor and predict drought and climate change impacts on the two river basins. Finally, the team had a visit with the head of meteorological office of Afghanistan and head of water-supply and hydrology office of Kabul and Mazar-e-Sharif to obtain the other local information about the two river basins.

In the next few months, the team plans to evaluate the available and accessible satellite imagery and remote sensing data from MERRA-Land, NLDAS, GLDAS & PERSIANN, TMPA satellite images. The research will also utilize the merged satellite & including the Global Precipitation Climatology Project (GPCP). The research team also plan to investigate on-site and ground-based data from meteorological stations in the basins especially precipitation and temperature in addition to soil moisture. The research team will also collect imagery and ground level data, create a database and calibrate the model accordingly.

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