Drought forecasting,Mapping hydrological drought hazard, Mapping meteorological drought hazard, Mapping agricultural drought hazard

N/A

None

eng

Africa

Africa

Drought forecasting,Mapping hydrological drought hazard, Mapping meteorological drought hazard, Mapping agricultural drought hazard

Data requirements to use: None;

Data used: For the historic reconstruction, the model is forced by meteorological data derived from a blending of reanalysis (NCEP/NCAR) and gridded observation-based datasets including: the CRU TS3.1 monthly precipitation and temperature data set, the NASA TRMM, the monthly gridded precipitation and temperature data set of Willmott and Matsura, and the GPCP monthly data set (Sheffield et al., 2006). For the real-time monitor, the VIC model is forced by a mixture of observations and modeled/remotely sensed meteorology to produce updates of water cycle variables. Daily mean wind speed and daily maximum and minimum temperatures are taken from NOAA’s GFS analysis fields, which ingest data from multiple sources including remote sensing and in-situ observations in real-time (Parrish and Derber, 1992); this is a reliable source for real-time data over large-scales.

Daily precipitation comes from the NASA TRMM Multi-satellite Precipitation Analysis (TMPA) data set (Huffman et al., 2007) when available, otherwise from GFS. Both TMPA and GFS are bias-corrected to ensure consistency with the historical meteorological data set.;

Data format: Not relevant;

Data access: website;

Concise description:

Based on macro scale hydrologic modeling, the system ingests available data to provide a real-time assessment of the water cycle and drought conditions, and puts this in the context of the long-term record back to 1950. The data is made available online for drought research and operational use to augment on-the ground assessments of drought. The monitoring system comprises two parts:

i) First, a historic reconstruction (1950 – 2008) of the water cycle forced by a merged reanalysis/ observational meteorological data set; this forms the climatology against which current conditions are compared.

ii) Second, a real-time monitoring system (2009 – present) driven by remote sensing precipitation and atmospheric analysis data that tracks drought conditions in near real-time.

Indices included:

Meteorology: Precipitation, max temperature, min temperature, wind; Hydrology: soil Moisture, soil moisture anomaly (from remote sensing), evaporation, surface runoff, baseflow, streamflow; Drought indices: SPI-1, SPI-3, SPI-6, SPI-12, NDVI (from remote sensing), Streamflow percentiles, and Drought index. The Drought index is calculated by determining the percentile of the daily average of relative soil moisture at each grid cell with respect to its empirical cumulative probability distribution function provided by the historical simulations (1950 – 2008)

eng

African Flood and Drought Monitor

ISO 19115:2003/19139

1.0

http://stream.princeton.edu/AWCM/WEBPAGE/interface.php?locale=en