<p>In view of recent dramatic floods and drought events, the detection of trends in the frequency and magnitude of long time series of flood data is of scientific interest and practical importance. It is essential in many fields, from climate change impact assessment to water resources management, from flood forecasting to drought monitoring, for the planning of future water resources and flood protection systems.&#160;<br>To detect long-term changes in river discharge a dense, in space and time, network of monitoring stations is required. However, ground hydro-meteorological monitoring networks are often missing or inadequate in many parts of the world and the global supply of the available river discharge data is often restricted, preventing to identify trends over large areas. &#160;<br>The most direct method of deriving such information on a global scale involves satellite earth observation. Over the last two decades, the growing availability of satellite sensors, and the results so far obtained in the estimation of river discharge from the monitoring of the water level through satellite radar altimetry has fostered the interest on this subject. &#160;<br>Therefore, in the attempt to overcome the lack of long continuous observed time series, in this study satellite altimetry water level data are used to set-up a consistent, continuous and up-to-date daily discharge dataset for different sites across the world. Satellite-derived water levels provided by publicly available datasets (Podaac, Dahiti, River& Lake, Hydroweb and Theia) are used along with available ground observed river discharges to estimate rating curves. Once validated, the rating curves are used to fill and extrapolate discharge data over the whole period of altimetry water level observations. The advantage of using water level observations provided by the various datasets allowed to obtain discharge time series with improved spatio-temporal coverages and resolutions, enabling to extend the study on a global scale and to efficiently perform the analysis even for small to medium-sized basins. &#160;<br>Long continuous discharge time series so obtained are used to perform a global trend analysis on extreme flood and drought events. Specifically, annual maximum discharge and peak-over threshold values are extracted from the simulated daily discharge time series, as proxy variables of independent flood events. For flood and drought events, a trend analysis is carried out to identify changes in the frequency and magnitude of extreme events through the Mann-Kendall (M-K) test and a linear regression model between time and the flood magnitude. &#160;<br>The analysis has permitted to identify areas of the world prone to floods and drought, so that appropriate actions for disaster risk mitigation and continuous improvement in disaster preparedness, response, and recovery practices can be adopted.&#160;</p>
The African Database of Hydrometric Indices (ADHI)
