SERVIR Eastern and Southern Africa John Kiema Director Technical Services, RCMRD 13-15 June 2017 Sunyani Ghana

Outline Introduction to RCMRD Objectives of SERVIR-E&SA Services Areas/Thematic Work Examples of some Projects

Who is RCMRD?

RCMRD’s Core Mandate Capacity Development Project Implementation Advisory Services Research and Development Repair, Servicing and Calibration of Mapping Equipment Data and Information Dissemination

SERVIR Network RCMRD SERVIR Hub since 2008

SERVIR E&SA Project Goal To increase the ability of African institutions to apply geospatial technologies in the societal benefit areas identified by Global Earth Observations (GEO), improve the resilience of the region to the impacts of climate change and ensure land use management reduces greenhouse gas emissions.

Key Focus during SERVIR E&SA Phase II SOCIETY SCIENCE SPACE SERVICE The next 5 years of SERVIR will be strongly shaped by: 1. Society oriented innovations; 2. Science based innovations; 3. Space science supported innovations; 4. Service to society driven innovations. We can confidently affirm that RCMRD is better placed to drive SERVIR in ESA along those strategic directions.

SERVIR-ESA Results Framework

Service Area/Thematic Work Agriculture & Food Security Ecosystems & Landscapes Water & Related Disasters Weather & Climate

Frost Monitoring & Mapping Problem Massive losses in the tea industry due to frost. The service predicts the likelihood of frost events in vulnerable areas. Milestones Development of 72hr forecasts; Daily Frost Monitoring; Development of the frost data collection mobile application and training stakeholders on its use. Increasing stakeholder network 7 new tea companies. Collaboration with APA insurance in development of a weather based tea insurance index. http://apps.rcmrd.org/frost/maps/ Estimated Frost Occurrence on 30th January 2017 Problem Massive losses in the tea industry due to frost prompted the development of the service which predicts the likelihood of frost events in vulnerable areas, using remotely sensed MODIS data and WRF Numerical Weather Prediction (NWP) model outputs to create of an algorithm for frost detection and prediction. Product Users: Kericho County Government; Kenya Tea Research Foundation; James Finlays; Sireet Tea; Eastern Produce of Kenya (EPK) Saraboit Tea; EMROK Tea; DL Koisagat Tea; Kapsimotwa Tea; Farmers Product Users: Kericho County Government; KTRF; Large-scale tea farmers; Small-scale farmers

Disaster Preparedness: New Hazard & Vulnerability Atlas “The atlas will show areas that are at risk, in terms of hazards. The idea is to prepare communities so that they should know the vulnerable areas.” Mr. Bernard Sande, Principal Secretary – Dept. of Disaster Management Affairs (DODMA). QUOTE Input data: 1. Exposure (LST –modis; forest fires – modis; rainfall – trmm); 2. Sensitivity (poverty levels, population, infant mortality); 3. Adaptive Capacity Malawi suffers from serious flooding

Flood Damage Mitigation: Nzoia Basin Nzoia Basin has serious flooding problems Kenya Government is using SERVIR CREST Tool, to estimate flood height & extent for DYKE CONSTRUCTION – SAVING LIVES! The crest tool, using EO data is being used by national government in kenya to estimate flood height & extent for dyke construction … tool also being used in Rwanda, Uganda & Namibia; coupled routing & excess storage model: input data: trmm, DEM-SRTM

Lake Victoria Basin: Water Quality Modis for water quality operational system; landsat for landcover changes – effectively being able to link water quality degradation with land use changes … Linking land use changes (climate & development) to water quality

Land Cover Mapping for GHG Inventorying Land cover maps developed for 9 countries Personnel trained = over 120 Networking = 50 institutions Maps being used by countries to report to UNFCCC Due to the capacity built by SERVIR, Kenya with support of the Clinton Foundation has engaged SERVIR/RCMRD to do the LC MAP for Kenya. Input data: Landsat series, High resolution imagery …

Applying VI Mapping for Ecosystem Analyses No. Vulnerability Component Factor Possible Indicator 1   Exposure Rainfall Annual rainfall variability (CV) Occurrence (frequency) of drought and floods over the years Temperature Monthly min & max temperature CV for min & max 2 Sensitivity LULC LULC dynamic NDVI NDVI dynamic Human population Population dynamic Animal population Level of protection (NP, GCA, NR, GR, FR) Wild animal & Livestock population dynamics Topography Slope (%) Fire Fire frequency and intensity 3 Adaptive capacity Protected areas Conservation status (conservancies, wildlife management areas, community based conservation development) LUP Presence/absence of LUPs

User Engagement L-R (Uganda, Rwanda, Kenya, Tanzania)

Thank you!