Image_5_The Drought Resilience Impact Platform (DRIP): Improving Water Security Through Actionable Water Management Insights.PNG

Millions of people living in the drought-prone Horn of Africa face an increasing threat from a lack of safe, reliable, and affordable water year-round as droughts become more severe and frequent. Drought emergencies emerge when reduced rainfall conspires with limited community water service capacity to cause dramatic reductions in access to water for people, livestock and agriculture. Drought-driven humanitarian emergencies can be prevented if groundwater is reliably made available at strategic locations during cycles of water stress. The Drought Resilience Impact Platform (DRIP) is an initiative combining early detection and planning with proactive groundwater management to ensure water availability, thus enabling drought-prone communities to become effective managers in the prevention of these humanitarian crises. It replaces reactive and expensive short-term assistance measures, like water trucking, with a framework for drought resilience. DRIP links in situ sensors deployed in East Africa with remote sensing data to improve estimates for rainfall and groundwater availability, and it will also develop a localized model for water demand forecasting. These indicators support the operation and maintenance of strategically selected groundwater borehole systems, thereby helping to support water delivery during dry and drought seasons. DRIP can be used to support pay-for-performance contracting, ensuring that water asset management is incentivized. DRIP is presently monitoring the water supplies of about 3 million people in East Africa. This paper presents DRIP's current web-based functionality, which uses several custom and commercial tools, and its applications, including rainfall-adjusted indicators of water pump functionality in Kenya and Ethiopia. Future work includes experimental and statistical characterization of the impact of these capabilities on water security, and the development of forecasting capabilities. This work is supported by NASA, United States Agency for International Development, and the National Science Foundation. The views expressed in this paper do not necessarily reflect the views of the United States Agency for International Development or the United States Government.