Hydrological Web Services for Operational Flood Risk Monitoring and Forecasting at Local Scale in Niger

Emerging hydrological services provide stakeholders and political authorities with useful and reliable information to support the decision-making process and develop flood risk management strategies. Most of these services adopt the paradigm of open data and standard web services, paving the way to increase distributed hydrometeorological services’ interoperability. Moreover,sharing of data, models, information, and the use of open-source software, greatly contributes to expanding the knowledge on flood risk and to increasing flood preparedness. Nevertheless, services’ interoperability and open data are not common in local systems implemented in developing countries. This paper presents the web platform and related services developed for the Local Flood Early Warning System of the Sirba River in Niger (SLAPIS) to tailor hydroclimatic information to the user’s needs, both in content and format. Building upon open-source software components and interoperable web services, we created a software framework covering data capture and storage, data flow management procedures from several data providers, real-time web publication, and service-based information dissemination. The geospatial infrastructure and web services respond to the actual and local decision-making context to improve the usability and usefulness of information derived from hydrometeorological forecasts, hydraulic models, and real-time observations. This paper presents also the results of the three years of operational campaigns for flood early warning on the Sirba River in Niger. Semiautomatic flood warnings tailored and provided to end users bridge the gap between available technology and local users’ needs for adaptation, mitigation, and flood risk management, and make progress toward the sustainable development goals

AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture

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

An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories

Abstract An integrated monitoring platform was developed for real-time monitoring of air pollution and traffic flows in urban areas. The air quality monitoring unit, integrating the "Arduino" open-source technology with low-cost and high-resolution sensors, collects concentrations of CO, NO 2 and CO 2 . The traffic monitoring device, equipped with a camera sensor and a video analysis software, collects vehicles' counts, speed and size. Air pollution and traffic readings are archived on a spatial data infrastructure composed of a central GeoDatabase, a GIS engine, and a web interface. A platform's description and the results of its installation in Florence (Italy) are presented

Recent Changes in Hydroclimatic Patterns over Medium Niger River Basins at the Origin of the 2020 Flood in Niamey (Niger)

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 postdrought 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

Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant

Open Data ed agricoltura di precisione – AgroSat

<p>Presentazione di AgroSat 2.0.</p> <p>Ungeoportale, dinamicoedaggiornato, per garantirela mappaturadelleareein produzione, per ilsupportoallagestionedellerisorse, per implementareun primo sistemadi geotracciabilità(prodottisingolio di filiera).</p

Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

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

Hydrological Web Services for Operational Flood Risk Monitoring and Forecasting at Local Scale in Niger

Emerging hydrological services provide stakeholders and political authorities with useful and reliable information to support the decision-making process and develop flood risk management strategies. Most of these services adopt the paradigm of open data and standard web services, paving the way to increase distributed hydrometeorological services’ interoperability. Moreover, sharing of data, models, information, and the use of open-source software, greatly contributes to expanding the knowledge on flood risk and to increasing flood preparedness. Nevertheless, services’ interoperability and open data are not common in local systems implemented in developing countries. This paper presents the web platform and related services developed for the Local Flood Early Warning System of the Sirba River in Niger (SLAPIS) to tailor hydroclimatic information to the user’s needs, both in content and format. Building upon open-source software components and interoperable web services, we created a software framework covering data capture and storage, data flow management procedures from several data providers, real-time web publication, and service-based information dissemination. The geospatial infrastructure and web services respond to the actual and local decision-making context to improve the usability and usefulness of information derived from hydrometeorological forecasts, hydraulic models, and real-time observations. This paper presents also the results of the three years of operational campaigns for flood early warning on the Sirba River in Niger. Semiautomatic flood warnings tailored and provided to end users bridge the gap between available technology and local users’ needs for adaptation, mitigation, and flood risk management, and make progress toward the sustainable development goals