Drought Monitoring at Assets Level

Download Product Sheet

Product Category

• Land use
• Land cover
• Natural Disaster
• Climate Change
• Coast Management 

• Marine
• Earth's Surface Motion

Financial Domains

• Investment management 
• Risk Analysis 
• Insurance management 
• Green finance

User requirements 

UN12: Analysis of potential risks in specific regions.

UN13: Need to geo-map clients.

UN14: Need to screen the feasibility of projects against different hazards criteria.

UN37: Projection of risk to portfolio assets into future.

UN42: Need to monitor the impact of droughts on assets.

Description

Drought is a natural disaster or climatic phenomenon characterized by an extended period of abnormally low precipitation, resulting in a significant water deficit in a particular region. It leads to reduced water availability, soil moisture depletion, and a range of negative impacts on agriculture, ecosystems, water resources, and human communities. Considering the extensive range of factors that can cause and effects that can result from droughts, droughts are classifies into four main categories 1) Meteorological Drought 2) Agricultural Drought 3) Hydrological Drought 4) Socioeconomic Drought. As drought has complex environmental impacts and can affect numerous ecosystems components in parallel, it is important to monitor droughts using different indicators which correspond to each type of droughts. Standardised Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) can be used as indicators for Meteorological Drought. Vegetation Condition Index (VCI) and Combined Drought Index (CDI), which is based on FAPAR anomaly, soil moisture anomaly, and SPI can be used to monitor Agricultural Droughts. For hydrological drought, indices such as Standardised Groundwater Index (SGI), Standardised Streamflow Index (SFI), and Integrated Drought Index (IDI) can be used. Beside using such indicators for drought monitoring and drought damage assessment, seasonal forecast of some of these indices can be generated. 

Spatial Coverage Target

Asset Level

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

Meteorological Drought: SPI is generated based on precipitation data which can be obtained from satellite-based precipitation products such as CHIRPS pr reanalysis products such as ERA5-Land.  The SPI is calculated by comparing the accumulation of precipitation on a location over a period, to the same period through the historical precipitation data at that location. SPEI differs from SPI by calculating precipitation and Evapotranspiration to indicate the severity of drought based on water demand besides the amount of precipitation. Therefore, it is essential to calculate potential evapotranspiration (POT) which requires a lot of data such as precipitation, land temperature, wind speed and direction, and surface pressure. Such information can be acquired from ERA5-Land.

Agricultural Drought:  VCI evaluates the current NDVI about the range of values seen during the same period in preceding years.  The VCI provides a sense of where the observed value falls about the extreme values (minimum and maximum) from prior years. NDVI data can be obtained based on the desired spatial coverage. Moderate spatial resolution NDVI (300m) based on Sentinel-3 can be obtained directly from Copernicus Land Service, on the other hand, high spatial resolution NDVI (10m) can be calculated from Sentinel-2. CDI is calculated based on SPI, FAPAR anomaly, and Soil moisture anomaly. FAPAR anomaly can be calculated based on FAPAR data from Copernicus Land Services, and Soil moisture anomaly can be calculated also using Soil Water Index data from Copernicus Land Service.

Hydrological Drought: SGI and SFI can be calculated using in-situ data. IDI combines data from various sources (some of them are described above) and uses statistical methods or models to produce a single index value that represents the overall drought situation.

Input data sources

Optical:   Sentinel-2

Radar: N.A

Reanalysis products:  ERA5-Land

Satellite-based products: CHIRPS, Copernicus Global Land Service, MODIS.

Supporting data:  Stream flow and groundwater in-situ data.

Accessibility

Sentinel-2 & Copernicus Global Land Service: freely and publicly available from ESA.

CHIRPS: freely and publicly available from the Climate Hazard Centre in the University of Santa Barbara.

ERA5-land: freely and publicly available from ECMWF.

MODIS:  freely and publicly available from NASA.

Spatial resolution

Sentinel-2: 10 m

Copernicus Global Land Service: 300 m

CHIRPS: 0.05°

ERA5-land: 0.1°

MODIS: 250 m

Frequency (Temporal resolution)

Sentinel-2: 6 days

Copernicus Global Land Service: 10 days

CHIRPS: Daily

ERA5-land: Hourly

MODIS: 16 days

Latency

Sentinel-2: ≤ Day

Copernicus Global Land Service: ~ 3 days

CHIRPS: ~ 45 days

ERA5-land: ≤ Day

MODIS: 1-2 days

Geographical scale coverage

Globally

Delivery/ output format

Data type: Raster

File format: GeoTIFF, NetCDF, HDF

Accuracies

Thematic accuracy: 70-90% (depends on the input data sources)

Spatial accuracy: 1.5-2 pixels of input data

Constraints and limitations

• Low spatial resolution of precipitation data
• Lack of in-situ data for stream flow and groundwater data
• Lack of validation due to the lack of in-situ data

User's level of knowledge and skills to extract information and perform further analysis on the EO products.

Skills: Essential

Knowledge: Essential

P21: Drought monitoring at the assets level

Download the gap analysis product sheet

Maturity score

Mean: 2.6

STD: 0.62

Constraints and limitations

·  Cloud presence to calculate vegetation indices.

·  The impact on assets for drought is complex as the spatial and temporal scales of the events are different.

Relevant user needs

UN12: Analysis of potential risks in specific regions.

UN13: Need to geo-map clients.

UN14: Need to screen the feasibility of projects against different hazard criteria.

UN37: Projection of risk to portfolio assets into the future.

UN42: Need to monitor the impact of droughts on assets.

R&D gaps

·  Low spatial resolution of climate data

·  In some specific cases, the data comes later than needed (limitations in latency)

·  Limitations in the availability of adequate stream flow and groundwater data which are the inputs to calculate the indicators of hydrological drought.

·  Lack of validation due to the lack of in-situ data

Potential improvements drivers

·  Additional data on vulnerability and exposure of assets is required to evaluate the impacts of some perils/hazards.

·  Higher spatial and temporal resolution of EO input data

Utilisation level review

Utilisation score

Mean: 2.71

STD: 0.70

No utilisation

Low utilisation

·  Higher cost of using the commercial EO product.

·  Unawareness of the existence of commercial EO products with better specifications.

·  The complexity of monitoring the steps of the hydrological cycle using only EO.

Medium utilisation

·  Unawareness of the existence of the best available commercial EO product with better specifications .

·  Variability of the product by location.

High utilisation

Critical gaps related to relevant user needs

Guideline gap

UN37: Projection of risk to portfolio assets into the future.

UN42: Need to monitor the impact of droughts on assets.