Floods have recently become a major hazard in West Africa (WA) in terms of both their magnitude and frequency. They affect livelihoods, infrastructure and production systems, hence impacting on Sustainable Development (SD). Early Warning Systems (EWS) for floods that properly address all four EWS components, while also being community and impact-based, do not yet exist in WA. Existing systems address only the main rivers, are conceived in a top-down manner and are hazard-centered. This study on the Sirba river in Niger aims to demonstrate that an operational community and impact-based EWS for floods can be set up by leveraging the existing tools, local stakeholders and knowledge. The main finding of the study is that bridging the gap between top-down and bottom-up approaches is possible by directly connecting the available technical capabilities with the local level through a participatory approach. This allows the beneficiaries to define the rules that will develop the whole system, strengthening their ability to understand the information and take action. Moreover, the integration of hydrological forecasts and observations with the community monitoring and preparedness system provides a lead time suitable for operational decision-making at national and local levels. The study points out the need for the commitment of governments to the transboundary sharing of flood information for EWS and SD.

Niamey, the capital of Niger, is particularly prone to floods, since it is on the banks of the Niger River, which in its middle basin has two flood peaks: one in summer (the red flood) and one in winter (the black flood). In 2020, the Niger River in Niamey reached its all-time highest levels following an abundant rainy season. On the other hand, the floods in Niamey have been particularly frequent in the last decade, a symptom of a change in hydroclimatic behaviour already observed since the end of the great droughts of the 1970s and 1980s and which is identified with the name of Sahelian Paradox. This study, starting from the analysis of the 2020 flood and from the update of the rating curve of the Niamey hydrometric station, analyses the rainfall–runoff relationship on the Sahelian basins of the Medium Niger River Basin (MNRB) that are at the origin of the local flood. The comparative analysis of runoffs, annual maximum flows (AMAX) and runoff coefficients with various rainfall indices calculated on gridded datasets allowed to hydroclimatically characterise the last decade as a different period from the wet one before the drought, the dry one and the post-drought one. Compared to the last one, the current period is characterised by a sustained increase in hydrological indicators (AMAX +27%) consistent with the increase in both the accumulation of precipitation (+11%) and the number (+51%) and magnitude (+54%) of extreme events in the MNRB. Furthermore, a greater concentration of rainfall and extremes (+78%) in August contributes to reinforcing the red flood’s positive anomalies (+2.23 st.dev in 2020). The study indicates that under these conditions the frequency of extreme hydrological events in Niamey will tend to increase further also because of the concurrence of drivers such as river-bed silting and levee effects. Consequently, the study concludes with the need for a comprehensive flood-risk assessment on the Niamey city that considers both recent hydroclimatic trends and urbanisation dynamics in flood zones hence defining the most appropriate risk-reduction strategies.

A drought-monitoring and forecasting system developed for the Tuscany region was improved in order to provide a semi-automatic, more detailed, timely and comprehensive operational service for decision making, water authorities, researchers and general stakeholders. Ground-based and satellite data from different sources (regional meteorological stations network, MODIS Terra satellite and CHIRPS/CRU precipitation datasets) are integrated through an open-source, interoperable SDI (spatial data infrastructure) based on PostgreSQL/PostGIS to produce vegetation and precipitation indices that allow following of the occurrence and evolution of a drought event. The SDI allows the dissemination of comprehensive, up-to-date and customizable information suitable for different end-users through different channels, from a web page and monthly bulletins, to interoperable web services, and a comprehensive climate service. The web services allow geospatial elaborations on the fly, and the geo-database can be increased with new input/output data to respond to specific requests or to increase the spatial resolution.

Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as the freely and open access policy adopted by the European Space Agency (ESA), software and tools are also available for data processing and deeper analysis. Nowadays, a bottleneck in this valuable chain is represented by the difficulty in shadow identification of Sentinel-2 data that, for precision agriculture applications, results in a tedious problem. To overcome the issue, we present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d’Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA.