# Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security
## 4 Earth Observations for Water Security: The Data Cube
### Avenues for Implementing an Analysis of Water Security Dynamics through Data Cube
EODC offers accurate measures of water-related hazards, for example, to observe floods and droughts events and to determine human and economic exposure by crossing the spatial extension of events with population and economic datasets. A concrete illustration is the mapping of water extent dynamics across the entire Australian continent [89]. Using EODC, the mapping combines observations of permanent and temporary water bodies at 30 m resolution, with a temporal resolution of 15 days during 27 years. Such information gave insights on presence and frequency of occurrence of surface water across various spatial scales ranging from a few tens of meters about to the entire continent. It gave key information about spatial and temporal extents of significant stream flows and floods. Another example is the PreView Global Risk Data Platform using more than 30 years of EO data to map floods and droughts at the global scale [90]. It allows determining human and economic exposure as well as associated risks [91]. This can give essential information on socio-economic aspects related to natural hazards. These EODC tools could be used to set up experimental research design, and use causal modeling to test our model of change for SES. For instance, the institutional complexity trap has been studied in the case of flood policy in Switzerland by showing an unexpected increase in institutional overlaps and conflicts that should limit regulation efficiency [31]. With the PreView Global Risk Data Platform, we could link this increasing complexity of the Swiss flood regulation with exposure to floods. Learning \begin{table}
\begin{tabular}{l l l} \hline \hline
**Security Criteria (Weights)** & **Indicators (Weights)** & **Spatial and Temporal Scales** \\ \hline \multirow{7}{*}{Availability (45\%)} & Water scarcity index (70\%) & 0.5\({}^{\circ}\) spatial resolution; \\ \cline{2-3}  & & Monthly mean value of 2010 \\ \cline{2-3}  & & 0.5\({}^{\circ}\) spatial resolution; \\ \cline{2-3}  & & Monthly mean value of 2012 \\ \cline{2-3}  & Groundwater depletion (15\%) & 0.5\({}^{\circ}\) spatial resolution; \\ \cline{2-3}  & & Monthly mean value of 2010 \\ \hline \multirow{2}{*}{Accessibility to water services (20\%)} & Access to sanitation (40\%) & Country scale; 2014 \\ \cline{2-3}  & Access to drinking water (60\%) & Country scale; 2014 \\ \hline \multirow{2}{*}{Safety and quality (20\%)} & Water quality index (50\%) & Country scale; 2012 \\ \cline{2-3}  & Global flood frequency (50\%) & Country scale; 1985–2003 \\ \hline \multirow{3}{*}{Management (15\%)} & World governance index (70\%) & Country scale; 2010 \\ \cline{2-3}  & Transboundary legal framework (15\%) & River Basin scale; 2015 \\ \cline{1-1} \cline{2-3}  & Transboundary political tension (15\%) & River Basin scale; 2015 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Composition of the water security index (adapted from [5])could be tested as well. Since the 1960s, changes in experts influence significantly impacted Dutch and American flood governance in order to limit exposure to floods during [92]. Linking these changes with accurate measures about floods, through EODC, allows for a systematic analysis of the reorganization of SESs. These avenues highlight that the versatility of the EODC enables research designs with treatment effect, and therefore causal inference. Indeed, natural experiments are rare in the social world and ecological information are not reliable or accessible worldwide and for every time. Consequently, elaborating robust experimental designs for SES analysis is extremely difficult. With EODC, if we observe a change in relevant social variables (e.g., new policies, new information initiating or diffusing learning), we could design robust causal tests that would advance empirical and theoretical knowledge about SES dynamic.