The impacts of climate change are ever more apparent. The frequency and scale of devastation and destruction of weather hazards are on an increasing trend. According to the latest Intergovernmental Panel on Climate Change Report (IPCC, 2021) climate change is intensifying the water cycle. This will cause more intense droughts in many regions. Moreover, water-related extremes impact the quality of life disproportionately strong. Drought accounts for 25% of all losses from weather-related disasters in the United States of America (Hayes et al., 2012).

Graph showing that >2 bio people live in countries experiencing high water stress
Figure 1: People living in countries that experience high water stress (UN water report 2019)


Drought can cause serious issues in an affected region, triggering famines, communicable disease outbreaks, and crop losses. According to the World Water Development Report 2019, about a quarter of the global population lives in countries experiencing high water stress (see Figure 1). In India, an increase of 3.6% in rural suicide rates during extremely dry growing seasons has recently been documented (Richardson et al., 2020). Among many other life-threatening aspects, drought can be directly related to loss of life stock, crop production, habitat loss and ecosystem degradation (Mfitumukiza, Barasa, and Emmanuel, 2017). 71% of the world’s irrigated areas experience water shortages (see Figure 2).

bar chart that displays % of the worlds irrigated areas experiencing water shortages
Figure 2: Worlds irrigated areas experiencing water shortages, accessed from United Nations Convention to Combat Desertification website (as of 27 September 2021)


Chart that displays % of major cities experiencing water shortages
Figure 3: Cities experiencing water shortages, accessed from United Nations Convention to Combat Desertification website (as of 27 September 2021)

"The sustainability of civilization lies in its ability to tame water scarcity"
-Day et al., 2012

If we don’t close our eyes, there are alarming trends recognisable. Cities running out of water have been increasingly frequenting the news. About one half of the major cities globally experience water shortages, at least periodically (see Figure 3). Drought in Cape Town, South Africa, forced authorities to declare ‘Day Zero’ the day when water supplies of the municipal town would need to be shut off. A recent report (BBC, 2018) listed 11 cities, São Paulo, Bangalore, Beijing, Cairo, Jakarta, Moscow, Istanbul, Mexico, London, Tokyo, and Miami, which will most likely run out of drinking water by 2030. The stress is likely to expand even further if no countermeasures are taken. The World Resource Institute’s (WRI) Aqueduct Project mapped the water stress per country projections for the year 2040 under business-as-usual scenarios (WRI, 2015) (see Figure 4).

Anticipated Water Stress by Country for 2040, from extremely high (>80%) in dark red low (<10%) in light yellow. Source: World Resources Institute (2015)
Figure 4: Anticipated Water Stress by Country for 2040, from extremely high (>80%) in dark red low (<10%) in light yellow. Source: World Resources Institute (2015).


“Drought is a period of unusually persistent dry weather that continues long enough to cause serious problems such as crop damage and/or water supply shortages. Droughts are caused by low precipitation over an extended period” -NASA, n.d.

Unlike other disasters, droughts usually are slow with their onset and provide a window for decision-makers to optimize resources according to the severity. Thus, the ability to predict and project the onset could save human lives, improve life quality, save livestock and vegetation, and reduce economic losses. Flash droughts are an exception to the above written. They occur when low precipitation and heat waves, high winds and/or changes in radiation and are characterized by unusually rapid intensification over sub-seasonal time scales that culminates in drought conditions (Otkin et al. 2021).

There are numerous emerging technologies and a surge in data from new satellite missions. Private firms are using data derived from the Internet of Things (IoT) for augmenting satellite data for hyper-local forecasts. It is important to standardize the varied datasets that could augment our weather forecasts to prevent or mitigate at least some of the challenges that are intensifying with climate change.  


A trajectory of space-based drought monitoring

Predictive monitoring of droughts began with the rise of satellite-aided weather projections, especially with the era of remote sensing, starting with the launch of Sputnik 1 by the Soviet Union in 1957 (Tatem, Goetz, and Hay, 2008). Although the American satellite Vanguard 2, launched in 1959, was designed for earth observation, it had limited capability. It was followed by the launch of the Television Infrared Observation Satellite (TIROS-1) by Nasa in 1960. Equipped with two television cameras and video cameras, TIROS-1 provided meteorologists with the first views of cloud formation as they developed around the world (NOAA, 2016).


A timeline of satellites that led to resultant drought monitoring. Photo Source: NASA
Figure 5: A timeline of satellites that led to resultant drought monitoring. Photo Source: NASA.


A few years later in 1966, the Applications Technology Satellite (ATS-1) was launched which provided half-hourly hemispheric images of the Earth’s surface aiding routine monitoring of weather systems. However, what truly changed weather monitoring was the introduction of the Advanced Very High-Resolution Radiometer (AVHRR) by the National Oceanic and Atmospheric Administration (NOAA) (Tatem, Goetz, and Hay, 2008). AVHRR was able to provide multitemporal measurements on a global scale, which led to the development of the Normalised Difference Vegetation Index (NDVI), a simple mathematical transformation of two spectral bands with a strong relationship to leaf area index and green biomass (Hayes. et al., 2012). 

Figure 6: Top of canopy NDVI released by Joint Polar Satellite Systems (JPSS). Photo Source: JPSS (2021)
Figure 6: Top of canopy NDVI released by Joint Polar Satellite Systems (JPSS). Photo Source: JPSS (2021)


NDVI served as an effective tool for drought monitoring by aiding the assessment of vegetation conditions. The declassification of military satellites paved the way for the use of multispectral sensors (Tatem, Goetz, and Hay, 2008). With the launch of NASA’s Landsat 1 in 1972, practical applications of the electromagnetic spectrum were further realized. These include infrared and visible spectrum at a higher spatial resolution.

Another significant advance in the capability is realized with the introduction of hyperspectral sensors, combining information from various sensor bands, spectrometers, sounding systems, and active microwave systems. This paved the way for synthetic aperture radars (SAR). SAR can sense, through the cloud cover, to establish the altitude, location, and scattering properties of the Earth’s surface (Tatem, Goetz, and Hay, 2008). Satellite Missions with SAR sensors such as Radar Satellite (RADARSAT), European Remote Sensing (ERS), and Japanese Earth Resource Satellite (JERS) have been contributing to significant gains in soil moisture studies (Souza et al., 2018). However, radar signals used in these missions interact with different components of the land surface. An example SAR image can be seen in Figure 7. In contrast, the Soil Moisture Active Passive (SMAP) satellite mission, launched in 2015, uses microwave data that is unaffected by land surface components (NASA, 2020). Figure 8 shows an 8-day composite of SMAP images.

This image was taken by the L band radar in HH polarization (horizontal transmit and horizontal receive) from the Synthetic Aperture Radar on the space shuttle Endeavour in 1996.  Photo Source: (NISAR, n.d.)
Figure 7: This image was taken by the L band radar in HH polarization (horizontal transmit and horizontal receive) from the Synthetic Aperture Radar on the space shuttle Endeavour in 1996.  Photo Source: (NISAR, n.d.)


SMAP observations on global moisture. Source: NASA/NSIDC (2019)
Figure 8: SMAP observations on global moisture. Source: NASA/NSIDC (2019)


Understanding the subsurface movement of water in arid regions could potentially also aid the monitoring and management of water shortages. With radar technology similar to the one used by NASA in the Mars Reconnaissance Orbiter, the Orbiting Arid Subsurface and Ice Sheet Sounder (OASIS) project intends to discover freshwater aquifers in water-stressed regions in the near future. OASIS will make use of sounding radar sensitive to the changes in electrical properties beneath Earth’s surface caused by the difference in signal absorption rate of the underlying area (Jet Propulsion Laboratory, 2020). A similar project was conducted in Kuwait in 2011, using radars mounted on helicopters (NASA, 2011). The proposed satellite deployment improves the scope and scale of aquifer detection and management.

An increase in average temperature results in an increased rate of evaporation from soil and higher evapotranspiration. Thus, indicators such as Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), Gravity Recovery Climate Experiment- Drought Severity Index (GRACE-DSI) and Vegetation Condition Index (VCI) are used to monitor and predict drought occurrences (Aghakouchak et al. 2015).

The following table (Table 1) groups various indices used in drought monitoring per parameter to be observed and further provides information on the underlying satellites/sensors used as well as the advantages and disadvantages of the index.

Table 1: Common Drought Monitoring Indices and Parameters
Table 1: Common Drought Monitoring Indices and Parameters


Complex and diverse conditions of droughts cannot be represented through a single indicator across all dimensions affected by drought (Hayes et al., 2005). A composite indicator incorporating various parameters and indices makes way for a hybrid approach in drought monitoring.

For example, initiated in 1999, USDM (the United States Drought Monitor) converged evidence from in situ data such as stream flows and various indices such as Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) to arrive at classification on the severity of drought conditions. Due to advanced spatiotemporal resolution USDM enabled the implementation of changes in national agricultural policies at a local scale and is currently considered as the state-of-the-art drought monitoring tool (Hayes et al., 2012). The South Asia Drought Monitoring System (SADMS), a prototype drought monitoring system developed in 2015 with funding from WMO, visualises drought using data from remote sensing. The European Drought Observatory is another portal developed by European Commission's Joint Research Centre to assess, monitor and forecast drought (EDO, 2021).

The South Asia Drought Monitoring System (2017)
Figure 9: The South Asia Drought Monitoring System (SADMS, 2017)


With the increasing frequency of extreme weather incidents further improvement in monitoring and managing water resources is necessary. Emerging technologies such as Artificial Intelligence (AI), Machine Learning, IoT and Satellite Internet Connectivity provide a window of opportunity to augment and expand existing space technologies to manage challenges rising with climate change and water scarcity.

Gearing up for a paradigm shift? How satellite communication adds value

Satellite internet connectivity in combination with IoT has the capacity to provide data for a composite indicator for granular weather forecasting even for remote regions. Using a technology called radio occultation, atmospheric variables affecting weather such as temperature, pressure and water vapour can be calculated. Radio waves transmitted and received between ground stations and satellites are refracted and slowed down by atmospheric components. Radio occultation uses this variation in the angle of refractivity to reconstruct atmospheric variables with the help of physical and mathematical models (Sangomla, 2019). Integrating this with data generated from devices like smartphones, CCTV cameras, and other communication devices, start-ups are making hyper-local forecasts. Hyper-local forecasts represent an AI-solution that takes the context of each demand driver within each individual location into account to make accurate forecasts on the most granular level as per (Quinyx, n.d.) The forecast correlates 90% with ground truth data, in comparison to radars that achieve 50% correlation. Furthermore, satellite internet connectivity can facilitate data collection with lower latency and faster distribution of forecasts.

The positive results yielded from a growing number of in situ sensors equipped with IoT technology, along with the capacity to transmit data from remote location via affordable satellite internet connectivity and the capability to process ever increasing volumes of data (inter alia by means of machine learning and other AI) are a strong indication for the need to standardise various data sources. Standardised data (including from IoT sensors) to allow for large data processing are hence a precondition to be able to efficiently process these data and – in the long run – to be able to counter the impacts of climate change. The WMO Integrated Global Observing system was envisioned to provide a framework for integration and sharing of data across various sources, explains WMO website accessed on September 26,2021.

WMO Integrated Global Observing System

Schematic of the Global Observing System, a component observing system of WIGOS (Source: World Meteorological Organization)
Figure 10: Schematic of the Global Observing System, a component observing system of WIGOS (Source: World Meteorological Organization).


An upgrade of the existing WMO Global Space and Ground Based Observation Systems to keep up with advancements in technology was realized with the introduction of the WMO Integrated Global Observing System (WIGOS) in 2016. The system achieved operational maturity in January 2020. WIGOS provides an overarching framework for the evolution of existing observing systems and achieved data compatibility across different formats optimizing weather observations. Components sharing data with WIGOS are the Global Observing System (see Figure 10), the WMO Hydrological Observing system (WHOS) and the observing components of the Global Atmosphere Watch (GAW) and of the Global Cryosphere Watch (GCW). One of the WIGOS building blocks is the WMO Observing Systems Capability Analysis and Review Tool (WMO-OSCAR, see Figure 11) which facilitates a Rolling Requirement Review Process. The Rolling Requirement Review Process is an evaluation tool, comparing the observational requirements with respect to existing capabilities to identify gaps in capacity and to support the planning of integrated global observing systems.

 OSCAR overview listed on WMO OSCAR website as of September 26, 2021
Figure 11:  OSCAR overview listed on WMO OSCAR website as of September 26, 2021.


The solid operational framework provided by WIGOS significantly improved early warning capacities in climate monitoring programmes. drought management. Global coverage and real time processing of integrated data from WIGOS and additional remote data sources contributed to an improved quality of observations (WIGOS Technical Report, 2020-1).

However, many countries still don't have a standardized identifier registration for the WIGOS Station Identifier System. A collective effort is required for full integration of observation systems and, also for including data emerging from IoT equipped sensors. An integrated WIGOS improves the quality of data for processing accurate predictions, shortens response with real time monitoring and provides accurate localized forecasts.


Incidents of water shortages are being reported all over the world. To improve water management operations, accurate (near) real-time information and forecasts are necessary for which standardization of data is critical.

Over the past sixty years, space technologies have contributed significantly to the better management of water resources. Remote sensing and composite indices based on the data produced by e.g., SMAP, GRACE, AVHRR and MODIS aided in early monitoring and prediction of extreme weather events. Further, the ability to observe, the different parameters of developing drought conditions such as precipitation, soil moisture, water storage and evapotranspiration using space technologies is helping effective policy formulations.

With the rise of satellite connectivity, WMO WIGOS improves early warning capability and management of drought even for remote areas. However, the lack of a standard identifier registration of many countries is posing a challenge in the integration of WIGOS across nations.



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