diff --git a/services/API-service/src/scripts/json/indicator-metadata.json b/services/API-service/src/scripts/json/indicator-metadata.json index ceccf45d1..7e8bf783c 100644 --- a/services/API-service/src/scripts/json/indicator-metadata.json +++ b/services/API-service/src/scripts/json/indicator-metadata.json @@ -33,32 +33,32 @@ "malaria": "An alert is released when two conditions are simultaneously the relative number of malaria cases is anomalous accordance to WHO guidelines, by comparing it to its monthly averages, the second condition is that the absolute number of malaria cases is high and thus likely to require humanitarian intervention." }, "KEN": { - "drought": "

The layer shows each county within the Northern and Eastern Livelihood zones trigged based on two parameters; the 3-month average Vegetation Condition Index (VCI3M) and the 3-month Standardised Precipitation Index (SPI3). An alert is given, if the VCI value drops below 30% with at least a 33% chance of exceedance.  VCI is supplemented by the SPI Forecast from KMD with a threshold value below -0.98 and a 30% probability of exceedance. The lead-time of the forecast is up to 12 weeks. 

Source links: 

Kenya Meteorological Department (KMD),  Regional Centre for Mapping of Resources for Development (RCMRD), and TAMSAT ALERT (University of Reading)

For further information please refer to the EAP

", - "floods": "

The layer shows each county triggered based on two parameters from the 7-days GLOFAS forecast on a daily basis: the return period of the forecasted flood and the probability of occurrence. The trigger will activate when GloFAS issues a forecast of at least 85% probability of occurrence of a 5 year return period flood within the next 7 days. The GLOFAS flood forecast triggers except in the wards where the False Alarm Ratio (RAR) > 0.5.

Source link: https://www.globalfloods.eu

Latest updated: September 2021

" + "drought": "The layer shows each county within the Northern and Eastern Livelihood zones trigged based on two parameters; the 3-month average Vegetation Condition Index (VCI3M) and the 3-month Standardised Precipitation Index (SPI3). An alert is given, if the VCI value drops below 30% with at least a 33% chance of exceedance. VCI is supplemented by the SPI Forecast from KMD with a threshold value below -0.98 and a 30% probability of exceedance. The lead-time of the forecast is up to 12 weeks.

Source: Kenya Meteorological Department (KMD), Regional Centre for Mapping of Resources for Development (RCMRD), and TAMSAT ALERT (University of Reading)", + "floods": "The layer shows each county triggered based on two parameters from the 7-days GLOFAS forecast on a daily basis: the return period of the forecasted flood and the probability of occurrence. The trigger will activate when GloFAS issues a forecast of at least 85% probability of occurrence of a 5 year return period flood within the next 7 days. The GLOFAS flood forecast triggers except in the wards where the False Alarm Ratio (RAR) > 0.5.

Source: Global Foods" }, "MWI": { - "flash-floods": "Not currently available", - "floods": "

The layer shows each administrative area triggered based on two parameters from the 6-days GloFAS forecast on a daily basis at 10:35 CET: the return period of the forecasted flood and the probability of occurrence. The trigger will activate when GloFAS issues a forecast of at least 60% probability of occurrence of a 5 year return period flood within the next 6 days. The GloFAS flood forecast triggers except in the Traditional Areas where the False Alarm Ratio (FAR) exceeds the predetermined maximum value which is 0.5.


Source link: https://www.globalfloods.eu 


Latest updated: August 2022

" + "flash-floods": "No information available.", + "floods": "The layer shows each administrative area triggered based on two parameters from the 6-days GloFAS forecast on a daily basis at 10:35 CET: the return period of the forecasted flood and the probability of occurrence. The trigger will activate when GloFAS issues a forecast of at least 60% probability of occurrence of a 5 year return period flood within the next 6 days. The GloFAS flood forecast triggers except in the Traditional Areas where the False Alarm Ratio (FAR) exceeds the predetermined maximum value which is 0.5.

Source: Global Foods" }, "PHL": { - "dengue": "Administrative divisions that reached alert threshold, in terms of number of potential cases.

See definition at: link to technical documentation", - "floods": "

The layer shows each county triggered based on two parameters from the 3-days GLOFAS forecast on a daily basis: the return period of the forecasted flood and the probability of occurrence. The trigger will activate when GloFAS issues a forecast of at least 50% probability of occurrence of a 5 year return period flood within the next 7 days. The GLOFAS flood forecast triggers except in the manucipalities where the False Alarm Ratio (RAR) >0.5

Source link: https://www.globalfloods.eu

Latest updated: September 2021

", - "typhoon": "

The predicted impact (72 hours before landfall) is more than 10% of houses being totally damaged at municipal level, in at least 3 municipalities. The source for predicted impact is 510 typhoon impact prediction model.

Only municipalities that are included in the EAP can reach a triggered state. For other municipalities all data - such as predicted impact - is visible in the map, but they will never turn in to a triggered state.

" + "dengue": "Administrative divisions that reached alert threshold, in terms of number of potential cases.

See definition at: link to technical documentation", + "floods": "The layer shows each county triggered based on two parameters from the 3-days GLOFAS forecast on a daily basis: the return period of the forecasted flood and the probability of occurrence. The trigger will activate when GloFAS issues a forecast of at least 50% probability of occurrence of a 5 year return period flood within the next 7 days. The GLOFAS flood forecast triggers except in the manucipalities where the False Alarm Ratio (RAR) >0.5.

Source: Global Foods", + "typhoon": "The predicted impact (72 hours before landfall) is more than 10% of houses being totally damaged at municipal level, in at least 3 municipalities. The source for predicted impact is 510 typhoon impact prediction model.

Only municipalities that are included in the EAP can reach a triggered state. For other municipalities all data - such as predicted impact - is visible in the map, but they will never turn in to a triggered state." }, "SSD": { - "floods": "This layer shows the areas (payams) in which the trigger threshold has been reached. These areas are outlined in red on the map. The threshold is defined by two parameters from the 7-days GloFAS forecast: the return period of the forecasted flood and the probability of occurrence, these are updated on a daily basis. The trigger is issued when GloFAS forecasts an occurrence with a probability of at least 60% of a 5 year return period flood in the next 7 days. The GloFAS will not trigger in areas where the False Alarm Ratio (FAR) > 0.35.

Alert threshold source: https://www.globalfloods.eu
Latest updated: September 2021" + "floods": "This layer shows the areas (payams) in which the trigger threshold has been reached. These areas are outlined in red on the map. The threshold is defined by two parameters from the 7-days GloFAS forecast: the return period of the forecasted flood and the probability of occurrence, these are updated on a daily basis. The trigger is issued when GloFAS forecasts an occurrence with a probability of at least 60% of a 5 year return period flood in the next 7 days. The GloFAS will not trigger in areas where the False Alarm Ratio (FAR) > 0.35.

Source: Global Foods" }, "UGA": { - "drought": "

This layer represents the areas in which the trigger threshold has been reached. It is visualised on the map as red outlines around the exposed areas.
The primary trigger mechanism uses rainfall forecasts based on ECMWF before the start of the season. This trigger will provide information with a lead time of up to 3 months. The trigger values for this trigger are for more than 30% of the geographical area of a district (admin level 2) predicting drier than normal (below average rainfall) conditions. The rainfall values are based on the seasonal rainfall forecast issued by ECMWF. The probability of below normal rain should be at least 45% based on probabilistic forecast information provided by ECMWF.

", + "drought": "This layer represents the areas in which the trigger threshold has been reached. It is visualised on the map as red outlines around the exposed areas.

The primary trigger mechanism uses rainfall forecasts based on ECMWF before the start of the season. This trigger will provide information with a lead time of up to 3 months. The trigger values for this trigger are for more than 30% of the geographical area of a district (admin level 2) predicting drier than normal (below average rainfall) conditions. The rainfall values are based on the seasonal rainfall forecast issued by ECMWF. The probability of below normal rain should be at least 45% based on probabilistic forecast information provided by ECMWF.", "floods": "URCS will activate this EAP when GloFAS issues a forecast of at least 60% probability (based on the different ensemble runs) of a 5-year return period flood occurring in flood prone districts, which will be anticipated to affect more than 1,000hh. The EAP will be triggered with a lead time of 7 days and a FAR of not more than 0.5.", - "heavy-rain": "

The alert threshold shows which administrative areas are expecting a large amount of rainfall, exceeding a defined threshold (60 mm).

You can find information about the rainfall forecast in the Rainfall Extent layer.

The defined 1-day cumulative threshold is estimated based on rainfall data collected over a period of 2 years and is provided by https://scg.zednet.co.za.

" + "heavy-rain": "The alert threshold shows which administrative areas are expecting a large amount of rainfall, exceeding a defined threshold (60 mm).

You can find information about the rainfall forecast in the Rainfall Extent layer.

The defined 1-day cumulative threshold is estimated based on rainfall data collected over a period of 2 years and is provided by Zednet." }, "ZMB": { - "drought": "Not currently available", + "drought": "No information available.", "floods": "The trigger is activated if the daily issued GLOFAS forecast reports a water discharge that exceeds the threshold corresponding to a 10y return period flood in one or more GLOFAS stations. The EAP will be triggered with a lead time of 7 days." }, "ZWE": { - "drought": "

The layer shows each province in the country with a drought risk at the end of the growing season (April), and as such determine which provinces are triggered when at least one of their districts is expected to face a +/- 6 year return period drought.

The drought model is to assess a drought prediction skill of the 3-month running average Niño 3.4 values and initiates a drought risk when there is a potential negative crop yield anomaly predicted. The model is developed based on the XGBoost algorithm tested and trained with historical ENSO: Seasonal ERSSTv5 and CHIRPS Rainfall data in relation to historical negative crop yield anomalies in April, which is used as drought impact proxy. Loss of crops, livestock loss, and child malnutrition and stunting are indicated by the ZRCS DRM working group and representatives from IFRC, PNS, and Red Cross Climate Centre (RCCC)  as targeted drought impact

Source links:

" + "drought": "The layer shows each province in the country with a drought risk at the end of the growing season (April), and as such determine which provinces are triggered when at least one of their districts is expected to face a +/- 6 year return period drought.

The drought model is to assess a drought prediction skill of the 3-month running average Niño 3.4 values and initiates a drought risk when there is a potential negative crop yield anomaly predicted. The model is developed based on the XGBoost algorithm tested and trained with historical ENSO: Seasonal ERSSTv5 and CHIRPS Rainfall data in relation to historical negative crop yield anomalies in April, which is used as drought impact proxy. Loss of crops, livestock loss, and child malnutrition and stunting are indicated by the ZRCS DRM working group and representatives from IFRC, PNS, and Red Cross Climate Centre (RCCC) as targeted drought impact.

Source: ENSO: Seasonal ERSSTv5 (1991-2020 base period) 3-month running average in Niño 3.4 (5oNorth-5oSouth) (170-120oWest))

CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations | Climate Hazards Center - UC Santa Barbara.

Source (Crop Yield Data): Izumi, Toshichika (2019): Global dataset of historical yields v1.2 and v1.3 aligned version. PANGAEA, Supplement to Iizumi, Toshichika; Sakai, T (2020): The global dataset of historical yields for major crops 1981–2016. Scientific Data, 7(1)" } } }, @@ -104,44 +104,44 @@ "unit": "no. of people", "description": { "EGY": { - "heavy-rain": "Number of people exposed is calculated by the population living in the rainfall extent area within the governorates currently triggered. The number of people and the rainfall extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

Source Rainfall Extent: Global Ensemble Forecast System (GEFS) is a global weather forecast model produced by the NOAA's National Centers for Environmental Prediction (NCEP). Dozens of atmospheric forecast variables up to 16 days in the future, including precipitation, are available through this dataset.
The Rainfall Extent layer shows areas where forecasted GEFS precipitation occurrence exceeds defined thresholds." + "heavy-rain": "Number of people exposed is calculated by the population living in the rainfall extent area within the governorates currently triggered. The number of people and the rainfall extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Rainfall Extent): Global Ensemble Forecast System (GEFS) is a global weather forecast model produced by the NOAA's National Centers for Environmental Prediction (NCEP). Dozens of atmospheric forecast variables up to 16 days in the future, including precipitation, are available through this dataset.

The Rainfall Extent layer shows areas where forecasted GEFS precipitation occurrence exceeds defined thresholds." }, "ETH": { - "drought": "Number of people exposed is calculated by the population living within the districts currently triggered. The number of people data is derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl", - "floods": "Number of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

Source Flood Extent: The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." + "drought": "Number of people exposed is calculated by the population living within the districts currently triggered. The number of people data is derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "floods": "Number of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "KEN": { - "drought": "

Number of people exposed is calculated by the population living in the county triggered by the exceedance of the droughts alert threshold. The number of people and the drought extent is derived from the below sources.

Source Links:

", - "floods": "

Number of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source link:

" + "drought": "Number of people exposed is calculated by the population living in the county triggered by the exceedance of the droughts alert threshold. The number of people and the drought extent is derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Drought Extent): Based on the SPI3 and VCI3M forecast information: for example, see August 2020 bulletin from Taita Taveta county.", + "floods": "Number of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "MWI": { - "flash-floods": "

This layer shows the estimated rounded number of people potentially exposed per geographic area. The estimate is calculated by determining the population living in the potentially flooded area.


Source: Meta on HDX

", - "floods": "

Number of people exposed is calculated by the population living in the flood extent area within the administrative areas currently triggered. The number of people and the flood extent are derived from the below sources.

Source link:

" + "flash-floods": "This layer shows the estimated rounded number of people potentially exposed per geographic area. The estimate is calculated by determining the population living in the potentially flooded area.

Source (Population Data): High Resolution Population Density Maps", + "floods": "Number of people exposed is calculated by the population living in the flood extent area within the administrative areas currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): peanutButter: An R package to produce rapid-response gridded population estimates from building footprints, version 1.0.0 version 1.0.0. Accessed 15-08-2022. WorldPop, University of Southampton. 2021.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "PHL": { - "floods": "

Number of people exposed is calculated by the population living in the flood extent area within the manucipality currently triggered. The number of people and the flood extent are derived from the below sources.

Source link:

" + "floods": "Number of people exposed is calculated by the population living in the flood extent area within the manucipality currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.Source (Flood Extent): The flood extent maps are generated by the National Operational Assesment of Hazards (NOAH) project.

The 25-year rain return flood hazard maps show low, medium, and high flood hazards represented as yellow, orange, and red respectively. Flood hazards consider take both the height and velocity of the water into consideration and calculates the hazard levels based on the danger they pose to people and structures. Generally, for an average Filipino with a height of 5’ 6”, areas with flood depths from the knee down can be considered to have low hazard levels. Those with flood depths ranging from the knee to neck are considered to have medium hazard levels, and those covered with floods that are higher than the neck have high hazard levels. However, since the flow velocity is also considered, areas that have shallow but fast-flowing flood waters may have a higher hazard level than that denoted by the height of the flood covering it." }, "SSD": { - "floods": "This layer shows the exposed population by number in the triggered areas, It is visualised in shades of purple that are represented in the legend on the bottom left corner of the map when the layer is selected. The number of people exposed reflects the number of people living in the potential flood extent of the triggered selected area.

Population data source: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018).
Flood extent source: The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." + "floods": "This layer shows the exposed population by number in the triggered areas, It is visualised in shades of purple that are represented in the legend on the bottom left corner of the map when the layer is selected. The number of people exposed reflects the number of people living in the potential flood extent of the triggered selected area.

Source (Population Data): WorldPop (School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018).

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "UGA": { - "drought": "This layer shows the exposed population. It is visualised in shaed of purple on the map when triggered. the number of people exposed is calculated by the population living within a triggered district. The number of people data is derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl", - "floods": "Number of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

Source Flood Extent: The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012].", - "heavy-rain": "

Number of people exposed is calculated by the population living in the triggered area.


Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. https://www.ciesin.columbia.edu/data/hrsl

" + "drought": "This layer shows the exposed population. It is visualised in shaed of purple on the map when triggered. the number of people exposed is calculated by the population living within a triggered district. The number of people data is derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "floods": "Number of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012].", + "heavy-rain": "Number of people exposed is calculated by the population living in the triggered area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016." }, "ZMB": { - "drought": "Number of people exposed is calculated by the population living within the districts currently triggered. The number of people data is derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl", - "floods": "Number of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

Source Flood Extent: The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." + "drought": "Number of people exposed is calculated by the population living within the districts currently triggered. The number of people data is derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "floods": "Number of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "ZWE": { - "drought": "

Number of people exposed is calculated by the population living in the droughts alert threshold reached area within the district currently triggered. The number of people and the drought extent is derived from the below sources.

Source links:

" + "drought": "Number of people exposed is calculated by the population living in the droughts alert threshold reached area within the district currently triggered. The number of people and the drought extent is derived from the below sources.

Source (Population Data): Worldpop

Source (Drought Alert Threshold): ENSO: Seasonal ERSSTv5 (1991-2020 base period) 3-month running average in Niño 3.4 (5oNorth-5oSouth) (170-120oWest)). and CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations | Climate Hazards Center - UC Santa Barbara.

Source (Crop Yield Data): Izumi, Toshichika (2019): Global dataset of historical yields v1.2 and v1.3 aligned version. PANGAEA, Supplement to Iizumi, Toshichika; Sakai, T (2020): The global dataset of historical yields for major crops 1981–2016. Scientific Data, 7(1)" } } }, { "countryDisasterTypes": { "PHL": { "typhoon": ["map", "aggregate"] } }, "name": "affected_population", - "label": "Exposed population", + "label": "Eposed population", "icon": "Affected-population-white.svg", "weightedAvg": false, "active": "no", @@ -154,7 +154,7 @@ "unit": "no. of people", "description": { "PHL": { - "typhoon": "

The number of people affected is calculated based on the predicted number of completely damaged houses, which is derived from the typhoon impact predicting model. To derive a methodology to estimate the number of affected people from a predicted number of completely damaged houses, we performed a log fit between the number of completely damaged houses and the number of affected people for past typhoon events using data derived from DROMIC reports. To estimate potential number of affected population this formula is applied to the predicted number of damaged houses.

" + "typhoon": "The number of people affected is calculated based on the predicted number of completely damaged houses, which is derived from the typhoon impact predicting model. To derive a methodology to estimate the number of affected people from a predicted number of completely damaged houses, we performed a log fit between the number of completely damaged houses and the number of affected people for past typhoon events using data derived from DROMIC reports. To estimate potential number of affected population this formula is applied to the predicted number of damaged houses." } } }, @@ -181,29 +181,29 @@ "dynamic": true, "description": { "EGY": { - "heavy-rain": "Percentage of people exposed is calculated by the population living in the rainfall extent area within the governorates currently triggered. The number of people and the rainfall extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

Source Rainfall Extent: Global Ensemble Forecast System (GEFS) is a global weather forecast model produced by the NOAA's National Centers for Environmental Prediction (NCEP). Dozens of atmospheric forecast variables up to 16 days in the future, including precipitation, are available through this dataset.
The Rainfall Extent layer shows areas where forecasted GEFS precipitation occurrence exceeds defined thresholds." + "heavy-rain": "Percentage of people exposed is calculated by the population living in the rainfall extent area within the governorates currently triggered. The number of people and the rainfall extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Rainfall Extent): Global Ensemble Forecast System (GEFS) is a global weather forecast model produced by the NOAA's National Centers for Environmental Prediction (NCEP). Dozens of atmospheric forecast variables up to 16 days in the future, including precipitation, are available through this dataset. The Rainfall Extent layer shows areas where forecasted GEFS precipitation occurrence exceeds defined thresholds." }, "ETH": { - "drought": "Percentage of people exposed is calculated by the population living in within the districts currently triggered. The number of people was derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl", - "floods": "Percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

Source Flood Extent: The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." + "drought": "Percentage of people exposed is calculated by the population living in within the districts currently triggered. The number of people was derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "floods": "Percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "KEN": { - "floods": "

The percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source Link:

" + "floods": "The percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "MWI": { - "floods": "

The percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source Link:

" + "floods": "The percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): peanutButter: An R package to produce rapid-response gridded population estimates from building footprints, version 1.0.0 version 1.0.0. Accessed 15-08-2022. WorldPop, University of Southampton. 2021.

Source (Flood Extent): Flood hazard map of the World - 10-year return period. European Commission, Joint Research Centre (JRC). 2016." }, "PHL": { - "floods": "

The percentage of people exposed is calculated by the population living in the flood extent area within the manucipality currently triggered. The number of people and the flood extent are derived from the below sources.

Source link:

" + "floods": "The percentage of people exposed is calculated by the population living in the flood extent area within the manucipality currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps are generated by the National Operational Assesment of Hazards (NOAH) project.

The 25-year rain return flood hazard maps show low, medium, and high flood hazards represented as yellow, orange, and red respectively. Flood hazards consider take both the height and velocity of the water into consideration and calculates the hazard levels based on the danger they pose to people and structures. Generally, for an average Filipino with a height of 5’ 6”, areas with flood depths from the knee down can be considered to have low hazard levels. Those with flood depths ranging from the knee to neck are considered to have medium hazard levels, and those covered with floods that are higher than the neck have high hazard levels. However, since the flow velocity is also considered, areas that have shallow but fast-flowing flood waters may have a higher hazard level than that denoted by the height of the flood covering it." }, "SSD": { - "floods": "This layer shows the exposed population by percentage in the triggered areas, It is visualised in shades of purple that are represented in the legend on the bottom left corner of the map when the layer is selected. The percentage of people exposed is the proportion of the exposed population In the triggered area out of the total population of the triggered area.

Population data source: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018).
Flood extent source: The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." + "floods": "This layer shows the exposed population by percentage in the triggered areas, It is visualised in shades of purple that are represented in the legend on the bottom left corner of the map when the layer is selected. The percentage of people exposed is the proportion of the exposed population In the triggered area out of the total population of the triggered area.

Source (Population Data): WorldPop (School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018).

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "UGA": { - "floods": "Percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

Source Flood Extent: The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." + "floods": "Percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." }, "ZMB": { - "floods": "Percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

Source Flood Extent: The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." + "floods": "Percentage of people exposed is calculated by the population living in the flood extent area within the districts currently triggered. The number of people and the flood extent are derived from the below sources.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

Source (Flood Extent): The flood extent maps compare six global flood hazard models and one local model. These models are CaMa-UT [Yamazaki D 2011], GLOFRIS [Winsemius H 2013], ECMWF [Pappenberge 2012], JRC [Dottori 2016], SSBN [Sampson 2015], CIMA-UNEP [UNISDR 2015] and local model ATKINS[2012]." } } }, @@ -247,37 +247,37 @@ "unit": "no. of people", "description": { "EGY": { - "heavy-rain": "Population data is aggregated per administrative area, from the following original source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl" + "heavy-rain": "Population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016." }, "ETH": { - "drought": "Population data is aggregated per administrative area, from the following original source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl", - "floods": "Population data is aggregated per administrative area, from the following original source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl", - "malaria": "Population data is aggregated per administrative area, from the following original source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl" + "drought": "Population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "floods": "Population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "malaria": "Population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016." }, "KEN": { - "drought": "

Population data aggregated per administrative area.

Source link: High-Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

", - "floods": "

Population data aggregated per administrative area.

Source link: High-Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

" + "drought": "Population data aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

", + "floods": "Population data aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.

" }, "MWI": { - "floods": "

Population data aggregated per administrative area.

Source link: peanutButter: An R package to produce rapid-response gridded population estimates from building footprints, version 1.0.0 version 1.0.0. Accessed 15-08-2022. WorldPop, University of Southampton. 2021.  https://apps.worldpop.org/peanutButter

" + "floods": "Population data aggregated per administrative area.

Source (Population Data): peanutButter: An R package to produce rapid-response gridded population estimates from building footprints, version 1.0.0 version 1.0.0. Accessed 15-08-2022. WorldPop, University of Southampton. 2021." }, "PHL": { - "floods": "Population data is aggregated per administrative area, from the following original source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. https://www.ciesin.columbia.edu/data/hrsl" + "floods": "Population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016." }, "SSD": { - "floods": "This layer shows the total population in the triggered areas, It is visualised in shades of purple that are represented in the legend on the bottom left corner of the map when the layer is selected.The population data is aggregated from the administrative areas.

Population data source: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018)." + "floods": "This layer shows the total population in the triggered areas, It is visualised in shades of purple that are represented in the legend on the bottom left corner of the map when the layer is selected.The population data is aggregated from the administrative areas.

Source (Population Data): WorldPop (School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018)." }, "UGA": { - "drought": "This layer shows the total population. It is visualised in shades of grey or purple on the map depending on if there's a trigger.

The population data is aggregated per administrative area.

Population source: High-Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

", - "floods": "Population data is aggregated per administrative area, from the following original source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl", - "heavy-rain": "This layer shows the total population. It is visualised in shades of grey or purple on the map depending on if there's a trigger.

The population data is aggregated per administrative area.

Population source: High-Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl

" + "drought": "This layer shows the total population. It is visualised in shades of grey or purple on the map depending on if there is a trigger. The population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "floods": "Population data is aggregated per administrative area.
Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "heavy-rain": "This layer shows the total population. It is visualised in shades of grey or purple on the map depending on if there is a trigger. The population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016." }, "ZMB": { - "drought": "Population data is aggregated per administrative area, from the following original source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl", - "floods": "Population data is aggregated per administrative area, from the following original source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.  https://www.ciesin.columbia.edu/data/hrsl" + "drought": "Population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016.", + "floods": "Population data is aggregated per administrative area.

Source (Population Data): High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-01-2020. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016." }, "ZWE": { - "drought": "Population data is aggregated per administrative area, from the following original source: Worldpop data:" + "drought": "Population data is aggregated per administrative area.

Source (Population Data): WorldPop Estimates of total number of people per grid square broken down by gender and age groupings.

Accessed 07-2020 The mapping approach is Pezzulo, C. et al. Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci. Data 4:170089 doi:10.1038/sdata.2017.89 (2017)" } } }, @@ -303,7 +303,7 @@ "malaria": "Potential number of cases are calculated with the assumtion of a rough proportionality between malaria mosquito enviromental suitability and malaria risk. Then estimating a time lag between optimal malaria mosquito environmental conditions and the peak in number of malaria cases." }, "PHL": { - "dengue": "Number of potential dengue cases, based on dengue risk and demographic data.

Source demographic data: https://data.humdata.org/dataset/philippines-pre-disaster-indicators" + "dengue": "Number of potential dengue cases, based on dengue risk and demographic data.

Source (Population Data): Pre-Disaster Indicators" } } }, @@ -332,13 +332,13 @@ "dynamic": false, "description": { "KEN": { - "floods": "

The flood vulnerability index is a composite index for the context of exposure to floods and the capacity to anticipate, cope with and recover from the impacts of floods. The National Society and Technical Working group selected the following criteria below:

For further information please refer to the EAP

" + "floods": "The flood vulnerability index is a composite index for the context of exposure to floods and the capacity to anticipate, cope with and recover from the impacts of floods. The National Society and Technical Working group selected the following criteria below:

" }, "MWI": { - "floods": "The flood vulnerability index is a composite index for the context of exposure to the hazard and the capacity to anticipate, cope with and recover from the impacts of floods. The vulnerability index is selected with the following criteria below: (including their weight in the total score)." + "floods": "The flood vulnerability index is a composite index for the context of exposure to the hazard and the capacity to anticipate, cope with and recover from the impacts of floods. The vulnerability index is selected with the following criteria below: (including their weight in the total score)

" }, "UGA": { - "floods": "The disaster vulnerability index is a composite index for the context of exposure to the hazard and the capacity to anticipate, cope with and recover from the impacts of floods. The National Society and Technical Working group selected the following criteria below: (including their weight in the total score)
For further information please refer to the EAP. For source links, see each individual layer." + "floods": "The disaster vulnerability index is a composite index for the context of exposure to the hazard and the capacity to anticipate, cope with and recover from the impacts of floods. The National Society and Technical Working group selected the following criteria below: (including their weight in the total score)

" } } }, @@ -376,7 +376,7 @@ "dynamic": false, "description": { "UGA": { - "floods": "Percentage of people living in female headed households.

Source Data: https://unstats.un.org/unsd/demographic/sources/census/wphc/Uganda/UGA-2016-05-23.pdf. Year: 2014." + "floods": "Percentage of people living in female headed households.

Source (Population Data): National Population and Housing Census 2014" } } }, @@ -397,9 +397,9 @@ "dynamic": false, "description": { "ETH": { - "drought": "Under age: vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates.

Source Data: https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates", - "floods": "Under age: vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates.

Source Data: https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates", - "malaria": "Under age: vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates.

Source Data: https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates" + "drought": "Under age: vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates.

Source (Population Data): Ethiopia: High Resolution Population Density Maps + Demographic Estimates", + "floods": "Under age: vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates.

Source (Population Data): Ethiopia: High Resolution Population Density Maps + Demographic Estimates", + "malaria": "Under age: vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates.

Source (Population Data): Ethiopia: High Resolution Population Density Maps + Demographic Estimates" } } }, @@ -418,7 +418,7 @@ "dynamic": true, "description": { "MWI": { - "floods": "Number of Exposed Population U18 is calculated by total target population living in the flood extent area within the administrative areas currently triggered. The target population are those living in the households classified as Poor, Poorer, Poorest and whose household head is below 18 years old.

Source target population: Unified Beneficiary Registration (UBR). Department of Economy Planning and Development, Malawi. Collected and processed in 2022." + "floods": "Number of Exposed Population U18 is calculated by total target population living in the flood extent area within the administrative areas currently triggered. The target population are those living in the households classified as Poor, Poorer, Poorest and whose household head is below 18 years old.

Source (Population Data): Unified Beneficiary Registration (UBR). Department of Economy Planning and Development, Malawi. Collected and processed in 2022." } } }, @@ -437,7 +437,7 @@ "dynamic": true, "description": { "MWI": { - "floods": "Number of Exposed Population 65+ is calculated by total target population living in the flood extent area within the administrative areas currently triggered. The target population are those living in the households classified as Poor, Poorer, Poorest and whose household head is older than 65 years old.

Source target population: Unified Beneficiary Registration (UBR). Department of Economy Planning and Development, Malawi. Collected and processed in 2022." + "floods": "Number of Exposed Population 65+ is calculated by total target population living in the flood extent area within the administrative areas currently triggered. The target population are those living in the households classified as Poor, Poorer, Poorest and whose household head is older than 65 years old.

Source (Population Data): Unified Beneficiary Registration (UBR). Department of Economy Planning and Development, Malawi. Collected and processed in 2022." } } }, @@ -456,7 +456,7 @@ "dynamic": false, "description": { "UGA": { - "floods": "Percentage of people under 8 years old.

Source Data: https://unstats.un.org/unsd/demographic/sources/census/wphc/Uganda/UGA-2016-05-23.pdf. Year: 2014." + "floods": "Percentage of people under 8 years old.

Source (Population Data): National Population and Housing Census 2014" } } }, @@ -475,7 +475,7 @@ "dynamic": false, "description": { "PHL": { - "dengue": "Percentage of people under 9 years of age.

Source demographic data: https://data.humdata.org/dataset/philippines-pre-disaster-indicators" + "dengue": "Percentage of people under 9 years of age.

Source (Population Data): Pre-Disaster Indicators" } } }, @@ -494,10 +494,10 @@ "dynamic": false, "description": { "PHL": { - "dengue": "Percentage of people over 65 years of age.

Source demographic data: https://data.humdata.org/dataset/philippines-pre-disaster-indicators" + "dengue": "Percentage of people over 65 years of age.

Source (Population Data): Pre-Disaster Indicators" }, "UGA": { - "floods": "Percentage of people over 65 years old.

Source Data: https://unstats.un.org/unsd/demographic/sources/census/wphc/Uganda/UGA-2016-05-23.pdf. Year: 2014." + "floods": "Percentage of people over 65 years old.

Source (Population Data): National Population and Housing Census 2014" } } }, @@ -516,10 +516,10 @@ "dynamic": false, "description": { "PHL": { - "dengue": "Percentage of people over 65 years of age.

Source demographic data: https://data.humdata.org/dataset/philippines-pre-disaster-indicators" + "dengue": "Percentage of people over 65 years of age.

Source (Population Data): Pre-Disaster Indicators" }, "UGA": { - "floods": "Percentage of people over 65 years old.

Source Data: https://unstats.un.org/unsd/demographic/sources/census/wphc/Uganda/UGA-2016-05-23.pdf. Year: 2014." + "floods": "Percentage of people over 65 years old.

Source (Population Data): National Population and Housing Census 2014" } } }, @@ -538,7 +538,7 @@ "dynamic": false, "description": { "UGA": { - "floods": "Percentage of households with permanent wall materials; percentage of buildings with (partly) concrete or brick walls.

Source link: https://unstats.un.org/unsd/demographic/sources/census/wphc/Uganda/UGA-2016-05-23.pdf. Year: 2014." + "floods": "Percentage of households with permanent wall materials; percentage of buildings with (partly) concrete or brick walls.

Source (Population Data): National Population and Housing Census 2014" } } }, @@ -573,7 +573,7 @@ "lazyLoad": false, "description": { "UGA": { - "floods": "The COVID-19 Risk Index is a composite index for the context of exposure, vulnerability to COVID and the capacity to anticipate, cope with and recover from the impacts of COVID-19 (a higher percentage indicates a higher risk to COVID-19). The National Society  selected the following criteria below:

ExposureVulnerabilityLack of Coping Capacity
Source link: https://nlrc.maps.arcgis.com/apps/opsdashboard/index.html#/9ca9f0f452b04046b8594a74c31f0c3b." + "floods": "The COVID-19 Risk Index is a composite index for the context of exposure, vulnerability to COVID and the capacity to anticipate, cope with and recover from the impacts of COVID-19 (a higher percentage indicates a higher risk to COVID-19). The National Society  selected the following criteria below:

ExposureVulnerabilityLack of Coping Capacity
Source (Risk Data): COVID-19 Risk Index Uganda." } } }, @@ -879,7 +879,7 @@ "unit": "cases", "description": { "PHL": { - "dengue": "Number of dengue cases per administrative division per year.

Source: https://doh.gov.ph/statistics" + "dengue": "Number of dengue cases per administrative division per year.

Source: https://doh.gov.ph/statistics" } } }, @@ -901,8 +901,8 @@ "lazyLoad": false, "description": { "PHL": { - "floods": "Pantawid Pamilya Beneficiary_Households by Municipality. Data source DSWD, NATIONAL HOUSEHOLD TARGETING OFFICE.

Source Link: HDX : https://data.humdata.org/showcase/philippines-pre-disaster-indicators-dashboard This dataset has been generated by combining PSGC and 4Ps data from DSWDOngoing (updated regularly)", - "typhoon": "

calculated based on the Pantawid Pamilya Beneficiary Households by Municipality.The source for this data is DSWD, NATIONAL HOUSEHOLD TARGETING OFFICE

" + "floods": "Pantawid Pamilya Beneficiary_Households by Municipality.

Source: DSWD, NATIONAL HOUSEHOLD TARGETING OFFICE.

Source: Pre-disaster Indicators Dashboard This dataset has been generated by combining PSGC and 4Ps data from DSWDOngoing (updated regularly).", + "typhoon": "Calculated based on the Pantawid Pamilya Beneficiary Households by Municipality.

Source: DSWD, NATIONAL HOUSEHOLD TARGETING OFFICE." } } }, @@ -924,7 +924,7 @@ "lazyLoad": false, "description": { "PHL": { - "floods": "Roof and Wall types by Municipality. Data source DSWD, NATIONAL HOUSEHOLD TARGETING OFFICE.

Source Link: HDX : https://data.humdata.org/showcase/philippines-pre-disaster-indicators-dashboard This dataset has been generated by combining PSGC and 4Ps data from DSWDOngoing (updated regularly)", + "floods": "Roof and Wall types by Municipality. Data source DSWD, NATIONAL HOUSEHOLD TARGETING OFFICE.

Source Link: HDX : https://data.humdata.org/showcase/philippines-pre-disaster-indicators-dashboard This dataset has been generated by combining PSGC and 4Ps data from DSWDOngoing (updated regularly)", "typhoon": "https://data.humdata.org/showcase/philippines-pre-disaster-indicators-dashboard This dataset has been generated by combining PSGC and 4Ps data from DSWD." } } @@ -945,7 +945,7 @@ "lazyLoad": true, "description": { "PHL": { - "typhoon": "

Total Number of Housing units in each Municipality

" + "typhoon": "Total number of housing units in each municipality." } } }, @@ -965,7 +965,7 @@ "unit": "incidence", "description": { "PHL": { - "dengue": "Number of dengue cases per 10.000.000 people per administrative division per year.

Source: https://doh.gov.ph/statistics" + "dengue": "Number of dengue cases per 10.000.000 people per administrative division per year.

Source: https://doh.gov.ph/statistics" } } }, @@ -984,7 +984,7 @@ "dynamic": false, "description": { "ZWE": { - "drought": "Number of exposed small ruminants (sheep and goats) is calculated by the small ruminants per province within the droughts alert threshold reached area currently triggered. Livestock numbers small ruminants exists of the number of small ruminants multiplied with the Livestock unit (LSU): 0.1 to aggregate livestock from various species (as reference unit 1.0, which is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, without additional concentrated foodstuffs).

Source Links:
Number of small ruminants (sheep and goats) mentioned within the 2nd round crop- and livestock assessment report 2020/2021 season. Published: 21st of April 2021Source assessment:https://fscluster.org/zimbabwe/document/second-round-crop-and-livestock-0." + "drought": "Number of exposed small ruminants (sheep and goats) is calculated by the small ruminants per province within the droughts alert threshold reached area currently triggered. Livestock numbers small ruminants exists of the number of small ruminants multiplied with the Livestock unit (LSU): 0.1 to aggregate livestock from various species (as reference unit 1.0, which is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, without additional concentrated foodstuffs).

Source: Number of small ruminants (sheep and goats) mentioned within the 2nd round crop- and livestock assessment report 2020/2021 season. Published: 21st of April 2021.

Source (Assessment): SECOND ROUND CROP AND LIVESTOCK ASSESSMENT REPORT 2020/2021 SEASON" } } }, @@ -1003,7 +1003,7 @@ "dynamic": false, "description": { "ZWE": { - "drought": "

Livestock numbers cattle exists of the number of cattle multiplied with the Livestock unit (LSU): 1.0  as reference unit to aggregate livestock from various species, which is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, without additional concentrated foodstuffs.

Source Links :

" + "drought": "Livestock numbers cattle exists of the number of cattle multiplied with the Livestock unit (LSU): 1.0 as reference unit to aggregate livestock from various species, which is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, without additional concentrated foodstuffs.

Source: Number of cattle mentioned within the 2nd round crop- and livestock assessment report 2020/2021 season. Published: 21st of April 2021.

Source (Assessment): SECOND ROUND CROP AND LIVESTOCK ASSESSMENT REPORT 2020/2021 SEASON

Source (Livestock): Glossary: Livestock unit (LSU)" } } }, @@ -1047,7 +1047,7 @@ "dynamic": false, "description": { "UGA": { - "drought": "This layer shows the vulnerability index. It is visualised in shades of grey or purple in the map depending on if there's a trigger. The vulnerability index is a copy of the 'flood vulnerabilty index' used in the IBF Floods portal. It is a composite index for the context of exposure to the hazard and the capacity to anticipate, cope with and recover from the impacts. The National Society and Technical Working group selected the following criteria below: (including their weight in the total score)
For further information please refer to the EAP. For sources of the components, see the source layers in the IBF floods portal." + "drought": "This layer shows the vulnerability index. It is visualised in shades of grey or purple in the map depending on if there is a trigger. The vulnerability index is a copy of the 'flood vulnerabilty index' used in the IBF Floods portal. It is a composite index for the context of exposure to the hazard and the capacity to anticipate, cope with and recover from the impacts. The National Society and Technical Working group selected the following criteria below: (including their weight in the total score)
For further information please refer to the EAP. For sources of the components, see the source layers in the IBF floods portal." } } }, @@ -1067,7 +1067,7 @@ "lazyLoad": true, "description": { "ETH": { - "malaria": "Potential cases under 5. Vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates.

Source Data: https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates" + "malaria": "Potential cases under 5. Vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates.

Source (Population Data): Ethiopia: High Resolution Population Density Maps + Demographic Estimates" } } }, @@ -1087,7 +1087,7 @@ "lazyLoad": true, "description": { "PHL": { - "dengue": "Number of potential dengue cases among children under 9 years of age, based on dengue risk and demographic data.

Source demographic data: https://data.humdata.org/dataset/philippines-pre-disaster-indicators" + "dengue": "Number of potential dengue cases among children under 9 years of age, based on dengue risk and demographic data.

Source (Population Data): Pre-Disaster Indicators" } } }, @@ -1110,10 +1110,10 @@ "lazyLoad": true, "description": { "ETH": { - "malaria": "Elderly: vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates" + "malaria": "Elderly: vulnerable population group Ethiopia: High Resolution Population Density Maps + Demographic Estimates" }, "PHL": { - "dengue": "Number of potential dengue cases among people above 65 years of age, based on dengue risk and demographic data.

Source demographic data: https://data.humdata.org/dataset/philippines-pre-disaster-indicators" + "dengue": "Number of potential dengue cases among people above 65 years of age, based on dengue risk and demographic data.

Source (Population Data): Pre-Disaster Indicators" } } }, @@ -1133,7 +1133,7 @@ "lazyLoad": true, "description": { "ZWE": { - "drought": "

Number of exposed cattle is calculated by the cattle per province within the droughts alert threshold reached area currently triggered. Livestock numbers cattle exists of the number of cattle multiplied with the Livestock unit (LSU): 1.0 as reference unit, which is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, without additional concentrated foodstuffs. 

Source Links :

" + "drought": "Number of exposed cattle is calculated by the cattle per province within the droughts alert threshold reached area currently triggered. Livestock numbers cattle exists of the number of cattle multiplied with the Livestock unit (LSU): 1.0 as reference unit, which is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, without additional concentrated foodstuffs.

Source: Number of cattle mentioned within the 2nd round crop and livestock assessment report 2020/2021 season. Published: 21st of April 2021.

Source (Assessment): SECOND ROUND CROP AND LIVESTOCK ASSESSMENT REPORT 2020/2021 SEASON

Source (Livestock): Glossary: Livestock unit (LSU)" } } }, @@ -1153,7 +1153,7 @@ "lazyLoad": true, "description": { "ZWE": { - "drought": "Number of exposed small ruminants (sheep and goats) is calculated by the small ruminants per province within the droughts alert threshold reached area currently triggered. Livestock numbers small ruminants exists of the number of small ruminants multiplied with the Livestock unit (LSU): 0.1 to aggregate livestock from various species (as reference unit 1.0, which is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, without additional concentrated foodstuffs).

Source Links:
Number of small ruminants (sheep and goats) mentioned within the 2nd round crop- and livestock assessment report 2020/2021 season. Published: 21st of April 2021Source assessment:https://fscluster.org/zimbabwe/document/second-round-crop-and-livestock-0." + "drought": "Number of exposed small ruminants (sheep and goats) is calculated by the small ruminants per province within the droughts alert threshold reached area currently triggered. Livestock numbers small ruminants exists of the number of small ruminants multiplied with the Livestock unit (LSU): 0.1 to aggregate livestock from various species (as reference unit 1.0, which is the grazing equivalent of one adult dairy cow producing 3000 kg of milk annually, without additional concentrated foodstuffs).

Source: Number of small ruminants (sheep and goats) mentioned within the 2nd round crop- and livestock assessment report 2020/2021 season. Published: 21st of April 2021.

Source (Assessment): SECOND ROUND CROP AND LIVESTOCK ASSESSMENT REPORT 2020/2021 SEASON" } } }, @@ -1173,7 +1173,7 @@ "lazyLoad": true, "description": { "PHL": { - "typhoon": "

Total Number of completely damaged houses as predicted by 510 typhoon impact prediction model

" + "typhoon": "Total number of completely damaged houses as predicted by 510 typhoon impact prediction model." } } }, @@ -1193,7 +1193,7 @@ "lazyLoad": true, "description": { "PHL": { - "typhoon": "

Probability for a Municipality being with in 50km of the forecasted typhoon track. Source for Typhoon forecast is ECMWF

" + "typhoon": "Probability for a municipality being with in 50km of the forecasted typhoon track.

Source (Typhoon Forecast): ECMWF" } } }, @@ -1214,7 +1214,7 @@ "unit": "kph", "description": { "PHL": { - "typhoon": "

Forecasted 1 minute average maximum wind speed in kilometers per hour for each municipality during the duration of the typhoon event. The source for this forecast data is ECMWF.

" + "typhoon": "Forecasted 1 minute average maximum wind speed in kilometers per hour for each municipality during the duration of the typhoon event.

Source (Typhoon Forecast): ECMWF" } } }, @@ -1235,7 +1235,7 @@ "unit": "mm/day", "description": { "PHL": { - "typhoon": "

24 Hour Precipitation Total extracted from forecast issued by The Weather Prediction Center (WPC) of National Atmospheric Administration, NOAA.

" + "typhoon": "24 hour precipitation total extracted from forecast issued by The Weather Prediction Center (WPC) of National Atmospheric Administration, NOAA." } } }, @@ -1257,7 +1257,7 @@ "aggregateUnit": "MWK", "description": { "MWI": { - "flash-floods": "

This indicator estimates potential economic damage based on global depth-damage functions provided by the JRC (Joint Research Centre). The assessment considers water depth and land use, specifically using urban and agricultural land use classes from the ESA (European Space Agency) WorldCover 2020 dataset. The monetary value is converted from Euro to Malawi Kwacha using a fixed exchange rate.

For more information, refer to the JRC publication here and the ESA WorldCover 2020 dataset here.

" + "flash-floods": "This indicator estimates potential economic damage based on global depth-damage functions provided by the JRC (Joint Research Centre). The assessment considers water depth and land use, specifically using urban and agricultural land use classes from the ESA (European Space Agency) WorldCover 2020 dataset. The monetary value is converted from Euro to Malawi Kwacha using a fixed exchange rate.

For more information, refer to the JRC publication here and the ESA WorldCover 2020 dataset here." } } }, @@ -1279,7 +1279,7 @@ "aggregateUnit": "km", "description": { "MWI": { - "flash-floods": "

This indicator shows the total length of exposed roads (in kilometers) in the potentially flooded area, calculated based on the selected area. The calculation includes all road types, which may exceed what is shown on the map layer.


Source: OpenStreetMap

" + "flash-floods": "This indicator shows the total length of exposed roads (in kilometers) in the potentially flooded area, calculated based on the selected area. The calculation includes all road types, which may exceed what is shown on the map layer.

Source: OpenStreetMap" } } }, @@ -1300,7 +1300,7 @@ "unit": "schools", "description": { "MWI": { - "flash-floods": "

This indicator shows the total number of school buildings in the potentially flooded area, calculated based on the selected area.


Source: Cloud2Street

" + "flash-floods": "This indicator shows the total number of school buildings in the potentially flooded area, calculated based on the selected area.

Source: Cloud2Street" } } }, @@ -1321,7 +1321,7 @@ "unit": "health sites", "description": { "MWI": { - "flash-floods": "

This indicator shows the total number of exposed health sites in the potentially flooded area, calculated based on the selected area.


Source: Cloud2Street

" + "flash-floods": "This indicator shows the total number of exposed health sites in the potentially flooded area, calculated based on the selected area.

Source: Cloud2Street" } } }, @@ -1342,7 +1342,7 @@ "unit": "waterpoints", "description": { "MWI": { - "flash-floods": "

This indicator shows the total number of exposed water points in the potentially flooded area, calculated based on the selected area.


Source: mWater

" + "flash-floods": "This indicator shows the total number of exposed water points in the potentially flooded area, calculated based on the selected area.

Source: mWater" } } }, @@ -1363,7 +1363,7 @@ "unit": "buildings", "description": { "MWI": { - "flash-floods": "

This indicator shows the total number of exposed buildings in the potentially flooded area, calculated based on the selected area.


Source: OpenStreetMap

" + "flash-floods": "This indicator shows the total number of exposed buildings in the potentially flooded area, calculated based on the selected area.

Source: OpenStreetMap" } } }, @@ -1389,7 +1389,7 @@ "lazyLoad": true, "description": { "KEN": { - "drought": "

The Drought Phase Condition identifies a combined analysis from four indicator groups (biophysical, production, access, and utilization type of indicators) that determine the particular drought phase that helps to guide the most appropriate response for that stage in the drought cycle. The drought phase classification is expressed into four drought classess. 

Source link: National monthly drought update published by the National Drought Management Authority (NDM) https://www.ndma.go.ke/index.php/resource-center/national-drought-bulletin

Field monitors collect data in a number of sentinel sites across 23 arid and semi-arid counties. This collected data is complemented by information from other sources, such as Household data collection, community key informants questionnaires, observations, and additional satellite data. For all indicators, the current value is compared with the long-term average for the time of year in order to establish whether it falls within seasonal norms

Latest updated: month, year

" + "drought": "The Drought Phase Condition identifies a combined analysis from four indicator groups (biophysical, production, access, and utilization type of indicators) that determine the particular drought phase that helps to guide the most appropriate response for that stage in the drought cycle. The drought phase classification is expressed into four drought classess.



Source: National monthly drought update published by the National Drought Management Authority (NDM)

Field monitors collect data in a number of sentinel sites across 23 arid and semi-arid counties. This collected data is complemented by information from other sources, such as Household data collection, community key informants questionnaires, observations, and additional satellite data. For all indicators, the current value is compared with the long-term average for the time of year in order to establish whether it falls within seasonal norms." } } }, @@ -1431,7 +1431,7 @@ "lazyLoad": true, "description": { "KEN": { - "drought": "

The Vegetation condition is one of the indicators monitored within the drought early warning system of NDMA as part of the biophysical type of indicator.This layer presents the Vegetation Condition Index VCI3M (3-month averaged VCI) as off . The VCI values return a drought category presented below with the corresponding thresholds. 

Once the VCI3M goes below a threshold of 35, the NDMA triggers a rapid food security assessment and has access to the National Drought Contingency Fund in order to implement its preparedness strategies and contingency plans. 

Source Link: National monthly Drought Update published by the National Drought Management Authority (NDMA) https://www.ndma.go.ke/index.php/resource-center/national-drought-bulletin

Latest updated: month, year

" + "drought": "The Vegetation condition is one of the indicators monitored within the drought early warning system of NDMA as part of the biophysical type of indicator. This layer presents the Vegetation Condition Index VCI3M (3-month averaged VCI) as off. The VCI values return a drought category presented below with the corresponding thresholds.



Once the VCI3M goes below a threshold of 35, the NDMA triggers a rapid food security assessment and has access to the National Drought Contingency Fund in order to implement its preparedness strategies and contingency plans.

Source: National monthly Drought Update published by the National Drought Management Authority (NDMA)" } } }, @@ -1455,7 +1455,7 @@ "lazyLoad": true, "description": { "KEN": { - "drought": "

Livestock body condition is one of the indicators monitored within the drought early warning system of NDMA as part of the production type of indicator. This layer presents the livestock body condition expressed as a score to describe the relative fatness of the herd. The score is ranging from extremely thin to extremely obese on a nine-point scale. The areas that are evaluated are the backbone, ribs, hips, pin bones, tailhead, and brisket


Source link: National monthly Drought Update published by the National Drought Management Authority (NDMA) https://www.ndma.go.ke/index.php/resource-center/national-drought-bulletin 

Latest updated: every month

" + "drought": "Livestock body condition is one of the indicators monitored within the drought early warning system of NDMA as part of the production type of indicator. This layer presents the livestock body condition expressed as a score to describe the relative fatness of the herd. The score is ranging from extremely thin to extremely obese on a nine-point scale. The areas that are evaluated are the backbone, ribs, hips, pin bones, tailhead, and brisket

Source: National monthly Drought Update published by the National Drought Management Authority (NDMA)" } } }