Drought is Africa’s principal type of natural disaster and is at the core of serious threats to the livelihoods of millions of people and the natural resources on which they depend. The economies of many African countries are based on agricultural activities that are controlled mainly by rainfall amounts and distribution; thus, food security in the region is highly vulnerable to impacts of drought.

RHEAS (Regional Hydrological Extremes Assessment System) is a drought assessment and prediction system with a proven hydrologic model at its core coupled with an existing agricultural productivity model. The system benefits from a suite of satellite-based hydrologic products, including soil moisture, precipitation, and evapotranspiration that leads to accurate nowcasts and initial conditions for forecast of drought characteristics, such as onset and recovery probability, cumulative soil moisture deficit, vegetation greenness, and agricultural productivity and yield. Results will be delivered through Geographic Information System (GIS) Web mapping services and because mobile phones are one of the most widely available platforms for information dissemination and communication in rural East Africa, through mobile devices.

This software automates the deployment of nowcasting and forecasting hydrologic simulations, and ingests satellite observations through data assimilation. It allows coupling of other environmental models and facilitates delivery of data products to users via a GIS-enabled database.

The advantages of RHEAS are:

  • Automatically builds the software dependencies and the software itself.

  • Automatically downloads and stores data products.

  • Its modular structure allows the coupling of external earth science models.

  • Allows querying of a spatial database through external tools (Structured Query Language (SQL), GIS software etc.).

  • Parallel execution of model ensembles.

  • Allows the use of vector GIS files to define the domain instead of explicitly defining a bounding box.

  • Can explicitly perform nowcasts and forecasts without the need to run a weather model by downscaling available weather forecast datasets.

This work was done by Konstantinos M. Andreadis, Narendra N. Das, Stephanie L. Granger, and Dimitrios Stampoulis of Caltech for NASA’s Jet Propulsion Laboratory. This software is available for license through the Jet Propulsion Laboratory and you may request a license here. NPO-49821