Page 48 - CIWA Water Data Revolution Overview Report

Data obtained from RS can be used to address issues in transboundary WRM that are difficult to
address using other methods given the necessity for a broad array of sensors which are well-
maintained in order to collect such data. As depicted in Figure 2, RS data can be used to
characterize or collect information on the following water cycle attributes for water management
applications: precipitation; evaporation and evapotranspiration (ET); soil moisture; vegetation
and land cover; groundwater; surface water; snow and ice; and water quality.

Figure 2: Data and information on water cycle attributes obtained from remote sensing.

In the context of Africa, data on all these attributes, except for snow and ice in rare cases, are
crucial for managing water issues. Several key data analytics of interest for transboundary water
management can be conducted using exclusively RS data, thus making these types of analyses
possible across regions lacking ground-based observation. For example, the extent which is
impacted by a flood can be predicted using data collected on the following attributes obtained
using only RS: precipitation, soil moisture, surface water, and elevation. Table 1 describes the RS
data types relevant for various water resource management analytics. Moreover, several sources
of RS data are available at free or low cost with large spatial coverage, making RS an attractive
data source for resource-constrained areas. For information on available RS data, please refer to
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Earth Observation for Water Resources Management and Disrupting Hydroinformatics . To
browse additional RS datasets, please view the Land Processes Distributed Active Archive Center
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(LP DAAC) data catalog and Earth Engine data catalog .

Table 1: RS data collected on water cycle attributes relevant for various water management applications. This table
describes the link between the data collected from RS and the analytical application for which it can be used. For
example, in order to map the extent of a flooded area, an understanding of the rate of precipitation in the area must

9 World Bank Group. (n.d.). "Disrupting" HydroInformatics: An Interactive E-book. Hydroinformatics eBook. Retrieved April 15, 2022, from
https://spatialagent.org/HydroInformaticsEbook/index.html.
10 “Data.” LP DAAC - Data, https://lpdaac.usgs.gov/data/.
11 “Earth Engine Data Catalog | Google Developers.” Earth Engine Data Catalog, Google, https://developers.google.com/earth-
engine/datasets.

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