Drought Risk Data Catalog
Drought Risk Data Catalog (Deltares)
Continents, countries, sea regions of the world.
This newsletter can be used to obtain information of seasonal meteorological drought forecasts for Angola, Botswana, Democratic Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe.
Extent: Angola, Botswana, Democratic Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe
N/A / near real-time
Data requirements to use: None; Data used: N/A; Data format: pdf; Data access: website; Concise description: Every season the SADC issues a newsletter with the expected climate conditions for the SARCOF region in Southern Africa for the following season. This includes the probabilities of normal, above-normal, and below-normal rainfall in 10 distinct regions in Southern Africa Indices included: N/A
This modelling software can be used to simulate groundwater flow and estimate groundwater supplies.
Application: Mapping hydrological drought hazard
Data requirements to use: N/A; Data used: N/A; Data format: N/A; Data access: N/A; Concise description: IWFM is a water resources management and planning model that simulates groundwater, surface water, stream-groundwater interaction, and other components of the hydrologic system. A unique feature of IWFM is the land use based approach of calculating water demand. IWFM simulates stream flow, soil moisture accounting in the root zone, flow in the vadose zone, groundwater flow, and stream-aquifer interaction. Agricultural and urban water demands can be pre-specified, or calculated internally based on different land use types. Water re-use is also modeled as well as tile drains and lakes or open water areas. Another notable feature of IWFM is a 'zone budget' that includes subsurface flow computations across element faces.
This platform can be used to obtain information of drought risk to the vegetation for Africa on a monthly basis.
Type of Drought: Meteorological, Agricultural Drought
Application: Mapping drought risk to agriculture and livestock, Mapping drought risk to population
2001 / near real-time
Data requirements to use: None; Data used: MODIS MOD13C1 Terra CMG dataset, TRMM 3B42 daily data; Data format: PNG; Data access: Web Mapping; Concise description: In the broad context of drought Early Warning, ITHACA's efforts are oriented towards the development of an automated system (Drought Monitoring), able to monitor relevant environmental variables thus enabling the detection and characterization of vegetation stress conditions. Different parameters are integrated into a simplified drought risk model which considers environmental data related to vegetation production and rainfall. In particular, the near real-time monitoring of satellite derived vegetation indexes allows the early detection of water stress conditions of vegetation, and the monitoring of derived phenological parameters. The latter, coupled with the evaluation of precipitation conditions, leads to a near real-time assessment of the vegetation productivity expected at the end of the considered growing season. The timely detection of critical conditions in vegetation health and productivity allows to spot agricultural areas with increased potential of crop failure or food crisis risk. Indices included: SPI-3, SSID, DWI
Socioeconomic Drought Vulnerability Index (SDVI)
This index measures the vulnerability of individual countries to socioeconomic drought.
Type of Drought: Socio-economic Drought
Application: Mapping socio-economic drought vulnerability
Data requirements to use: N/A; Data used: N/A; Data format: N/A; Data access: N/A; Concise description: This index measures the vulnerability of individual countries to socioeconomic drought. It is formulated on the consideration that higher GDP contributions from non-agricultural sectors, lower percentage employment in the agricultural sector and higher crops diversity will collectively lower a country’s chances of developing socioeconomic drought when meteorological drought occurs.Three sub-indices, namely, the Income Diversity Index (IDI), Employment Diversity Index (EDI) andthe Crop Range Index (CDI), make up the composite Socioeconomic Drought Vulnerability Index.
Data requirements to use: N/A; Data used: N/A; Data format: N/A; Data access: N/A; Concise description: MODFLOW is the USGS's modular hydrologic model. MODFLOW is considered an international standard for simulating and predicting groundwater conditions and groundwater/surface-water interactions. Originally developed and released solely as a groundwater-flow simulation code when first published in 1984, MODFLOW's modular structure has provided a robust framework for integration of additional simulation capabilities that build on and enhance its original scope. The family of MODFLOW-related programs now includes capabilities to simulate coupled groundwater/surface-water systems, solute transport, variable-density flow (including saltwater), aquifer-system compaction and land subsidence, parameter estimation, and groundwater management.
IMWI South Asia drought monitoring system
This platform can be used to obtain information of weekly to monthly meteorological drought forecasts for South Asia.
Data requirements to use: None; Data used: N/A; Data format: N/A; Data access: N/A; Concise description: Provides SPI index values and eight-day and monthly drought extents Indices included: SPI
This index measures the vulnerability to socioeconomic drought.
Data requirements to use: Agricultural water use, Total water use, Irrigated area, Average precipitation, Population density, GDP per capita, Agricultural value added/GDP %, Energy use, Population living below USD 1.25 PPP day-1, Adult literacy rate, Life expectancy at birth, Government effectiveness, Institutional capacity, population without access to improved water, Refugees, Fertilizer consumption,Water infrastructure; Data used: N/A; Data format: N/A; Data access: N/A; Concise description: The Drought Vulnerability Index (DVI) can be applied locally or spatially and with different aggregation levels of the input data. The intermediate components can be evaluated independently, allowing comprehensive interpretation of the strengths and weaknesses of each system. The sequential steps taken for the quantification of the DVI are: (a) select variables that are relevant; (b) normalize the variable values with respect to some common baseline; (c) combine the sub-component variables within each category by weighted averages; and (d) quantify DVI as the weighted average of the components. The scores of the DVI range on a scale of 0 to 1, with the total being generated as the average of each component. Each component of the DVI can be viewed as a dimension. Before calculating the overall DVI, an indicator for each of the dimensions needs to be computed. The four dimensions were named as Renewable Natural Capital, Economic Capacity, Human and Civic Recourses and Infrastructure and Technology. All of these categories contain multiple indicators and were aggregated using equal weights.
Standardized Snowmelt and Rain Index (SMRI)
This index can be used to determine and characterizehydrological drought hazards related to snow melt. (source WMO and GWP, 2016)
Not relevant / Not relevant
Data requirements to use: Streamflow data, daily precipitation and daily temperature data. Gridded data were used in the initial study of MRI.; Data used: N/A; Data format: N/A; Data access: N/A; Concise description: With methods similar to SPI, SMRI takes into account both rain and snow deficits and the associated impact to streamflow, including precipitation stored as snow. SMRI is most widely used as a complement to SPI.
This index can be used to determine and characterize agricultural drought hazard. (source WMO and GWP, 2016)
Application: Mapping agricultural drought hazard
Data requirements to use: Remotely sensed data, which are then compared to known surface plots of various vegetation.; Data used: N/A; Data format: N/A; Data access: N/A; Concise description: SAVI is similar to NDVI – spectral indices may be calibrated in such a way that the variations of soils are normalized and do not influence measurements of the vegetation canopy. These enhancements to NDVI are useful because SAVI accounts for variations in soils.
IGAD Climate Prediction and Applications Centre
This platform can be used to obtain meteorological drought hazard information on a near real-time monthly basis for Ethiopia, Kenya, South Sudan, Sudan, Uganda, Somalia, Eritrea, Tanzania.
Application: Mapping meteorological drought hazard
Extent: Ethiopia, Kenya, South Sudan, Sudan, Uganda, Somalia, Eritrea, Tanzania
Data requirements to use: None; Data used: The monitoring products for the IGAD region are: - NDVI anomaly (updated for the last two months), Vegetation Anomaly, SPI (updated for the last two months), The long term average rainfall is presented (1981-2015),Rainfall deviation from long-term dataset; Data format: N/A; Data access: Website- Arcgis web; Concise description: Knowledge management products including databases, Geoportals and Geospatial applications are developed at IGAD Climate Predictions and Applications Centre in order to organize, document, visualize and share (some of) data to member states and partners. This point station data is blended with the satellite data to enhance the quality of the products for the region Due to sparse station networks, the blending of data with satellite improve the spatial representativeness of the areas . The station data are also useful to verify, calibrate and validate some of the climate parameters generated by ICPAC scientists. The website has applications for decision makers such as: Food security integrated phase classification (IPC) Indices included: Standardized Precipitation Index (SPI), NDVI anomaly
SADC, registry@sadc.int. Tel: +267 3951 863
California Department of Water Resources
California Department of Water Resources, IWFMtechsupport@water.ca.gov
Information Technology for Humanitarian Assistance, Cooperation and Action (ITHACA)
Information Technology for Humanitarian Assistance, Cooperation and Action (ITHACA), info@ithacaweb.org
USGC, Chris Langevin (langevin@usgs.gov), Edward Banta (erbanta@usgs.gov), or Richard Winston (rbwinst@usgs.gov)
IGAD Climate Prediction and Applications Centre, N/A
Chris Langevin, Edward Banta, Richard Winston