The government of Rwanda through MIDIMAR has established a National Early Warning System protocol and framework for responding to Rapid Onset Disasters in the country. This framework identifies the role and responsibility of key agencies and sectors in times of disasters. It also identifies the official source of information such as information related to meteorological, hydrological and geological disasters as The Rwanda Meteorological Agency, RNRA Hydrology department and RNRA Geology and Mines department respectively. SpIDeRR gives access points to a wide range of spatial data and information that will complement the activities of the identified agencies in their DRR roles as contributors to the national early warning system. Initial focus will be on drought, floods and forest fires with access to data and information from satellite sources such as MODIS, NPP, TRMM/GPM and direct links to MeteoRwanda Climate Map Rooms.
The maproom is a collection of maps and other figures that monitor climate and societal conditions at present and in the recent past. The maps and figures can be manipulated and are linked to the original data. Even if you are primarily interested in data rather than figures, this is a good place to see which datasets are particularly useful for monitoring current conditions.
The near real-time (NRT) active fire locations (MCD14DL) are processed by LANCE using the standard MODIS MOD14/MYD14 Fire and Thermal Anomalies product. Each active fire location represents the center of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel. Data older than the last 7 days, can be obtained from the Archive Download Tool; the tool provides NRT data and, as it becomes available, it is replaced with data extracted from the standard MCD14ML fire product. FIRMS also offers monthly MODIS Burned Area (MCD45) images through Web Fire Mapper.
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. Spanning 50°S–50°N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. As of February 12th, 2015, version 2.0 of CHIRPS is complete and available to the public.Estimating rainfall variations in space and time is an important aspect of drought early warning and environmental monitoring. An evolving dryer-than-normal season must be placed in historical context so that the severity of rainfall deficits may be quickly evaluated. However, estimates derived from satellite data provide areal averages that suffer from biases due to complex terrain which often underestimate the intensity of extreme precipitations events. Conversely, precipitation grids produced from station data suffer in more rural regions where there are less rain gauge stations. CHIRPS was created in collaboration with scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in order to deliver reliable, up to date, and more complete datasets for a number of early warning objectives (such as trend analysis and seasonal drought monitoring).
Floods are one of the most catastrophic natural disasters in East Africa, impacting human lives and infrastructure. A flood prediction system could reduce these losses. Towards this end, SERVIR and partners developed a raster-based distributed hydrologic model, CREST (Coupled Routing and Excess STorage), for the Nzoia basin, a sub-basin of Lake Victoria—a service that has since extended to eight countries in East Africa.The CREST distributed hydrological model is a hybrid modeling strategy that was recently developed by the University of Oklahoma (http://hydro.ou.edu) and NASA Goddard Space Flight Center scientists. CREST simulates the spatiotemporal variation of water and energy fluxes and storages on a regular grid with the grid cell resolution being user-defined, thereby enabling global- and regional-scale applications. The scalability of CREST simulations is accomplished through sub-grid scale representation of soil moisture storage capacity (using a variable infiltration curve) and runoff generation processes (using linear reservoirs). The CREST model was initially developed to provide online global flood predictions with relatively coarse resolution, but it is also applicable at small, regional scales.