Famine Early Warning System Network (FEWS NET) Venkatesh Merwade Kristi Shaw

FEWS NET US government partnership to improve food security in 17 drought prone countries in Africa Objective: establish effective, sustainable, and African led security and response planning networks to decrease vulnerability of at risk groups Partners with USGS, USAID, Chemonics, Foreign Agriculture Service, NASA, National Oceanic and Atmospheric Administration (NOAA)

FEWS NET Website ( Monthly reports publicly available Technical reports including vulnerability assessments, food insecurity, and World Food Program (WFP) reports Weather monitoring using remote sensing (for warning and hazard analysis) Price monitoring (food access, commercial price changes, supply/demands) Assimilates daily rainfall images (NOAA) Databases on remote sensing, rainfall (RAINMAN), agriculture production (AGMAN), and market prices (PRICEMAN) through a branch of FEWS NET,the Africa Data Dissemination Service (ADDS) Mozambique Flood Hazard Information (

Mozambique Flood Hazard Information Interactive Basin Excess Rainfall Maps (delineate above avg. rainfall regions) Stream Flow Model Dynamic Map and Hydrographs Developed by USGS Earth Resources Observation Systems Data System (EROS) Simulates stream flow for 3000 basins Physically-based catchment hydrological model includes GIS module for input/data preparation and rainfall-runoff simulation models Under development…output will be hydrographs, dynamic basin maps, and publicly available direct access to a map service that updates flood risk maps to improve user functionality Flood Risk Monitoring Model and Hydrographs

FEWS Flood Risk Monitoring Model

Introduction and Brief Description of the Model Development and simulation of model is synonymous with stream flow model Rainfall-runoff model produces surface and sub-surface runoff, an upland headwater basins routing module, and a major river routing module Runoff prediction module Groundwater Zone Active soil layer  Upper thin layer (evaporation, transpiration, and percolation)  Lower layer (transpiration and percolation) Runoff producing mechanisms Precipitation excess Rapid subsurface flow Baseflow Routing in river reaches-- Muskingum-Cunge channel scheme

NoDataSource 1. the USGS HYDRO1K database (derivative of the USGS digital elevation database) 2. the USGS global land cover characteristics database 3. the FAO digital soil map of the worldhttp:// 4. a daily version of the NOAA 10-day rainfall estimate (RFE) images 5. Daily potential evapotranspiration fields as input to the Penman-Monteith equation. Can be obtained using Global Data Assimilation System (GDAS) climate fields Input Data

Input (cont.) HYDRO 1K Basin boundaries and stream networks Embeds topological information in digit Grid cell resolution- one kilometer USGS Global Land Cover Characteristics Database Map projections- Interrupted Goode Homolosine and Lambert Azimuthal Equal Area 1-km nominal spatial resolution derived thematic maps produced through the aggregation of seasonal land cover regions are included in each continental data base FAO Soil Map characterization of the hydraulic properties of the earth's surface original scale is 1:5,000,000 NOAA RFE estimates of gross precipitation input to each basin prepared from METEOSAT thermal infrared images and ground based rainfall stations 0.1 degree latitude/longitude grid 10-km resolution

Output Daily Prediction Hydrographs ability to illustrate trends in river flow magnitude and persistence Buzi, Licungo, Limpopo, Lugenda, Luchulingo, Lurio, Messalo, Olifants, Runde, Save, and Zambezi rivers sample hydrograph for Limpopo, Malbane Disclaimers has not been calibrated (nor validated) with observed data effects of dams on stream flow timing and magnitude have not been included model is still under development and these hydrographs provided online are experimental

Output-- Hydrograph