Climate analyses at a local scale are an essential tool in the field of sustainable development. The evolution of reanalysis datasets and their greater reliability contribute to overcoming the scarcity of observed data in the southern areas of the world. The purpose of this study is to compute the reference monthly values and ranges of maximum and minimum temperatures for the eight main inhabited villages of North Horr Sub-County, in northern Kenya. The official ten-day dataset derived from the Kenyan Meteorological Department (KMD), the monthly datasets derived from the ERA-Interim reanalysis (ERA), the Observational-Reanalysis Hybrid (ORH) and the Climate Limited Area Mode driven by HadG-EM2-ES (HAD) are assessed on a local scale using the most common statistical indices to determine which is more reliable in representing monthly maximum and minimum temperatures. Overall, ORH datasets showed lower biases and errors in representing local temperatures. Through an innovative methodology, a new set of monthly mean temperature values and ranges derived from ORH datasets are calculated for each location in the study area, in order to guarantee to locals an historical benchmark to compare present observations. The findings of this research provide insights for environmental risk management, supporting local populations in reducing their vulnerability.

This paper presents a methodology aimed at enabling local government personnel and decision makers to easily process satellite-derived precipitation data for the assessment of extreme precipitation hazard and to integrate them with geospatial reference datasets for the production of timely and meaningful flood risk information, considering also the assessment of exposed infrastructure, population or assets. The methodology relies on the use of the Malawi Spatial Data Platform (MASDAP), a GeoNode web platform for the management and publication of geospatial data, developed in the framework of the Shire River Basin Management Program (SRBMP). The proposed work-flow has been illustrated during a capacity building training held in Blantyre (Malawi) in December 2015 and constitutes a standardizable decision support approach, particularly useful for countries where meteo-hydrological observations are scarce or have a too coarse resolution.

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Since the last century, an unprecedented settlement expansion, mainly generated by an extraordinary world population growth, has made urban communities always more exposed to disasters. Casualties and economic impacts due to hydro-meteorological hazards are dramatically increasing, especially in developing countries. Although the scientific community is currently able to provide innovative technologies to accurately forecast severe weather events, scientific products are often not easily comprehensible for local stakeholders and more generally for decision makers. On the other hand the integration of different layers, such as hazard, exposed assets or vulnerability through GIS, facilitates the risk assessment and the comprehension of risk analysis. This work presents a methodology to enhance urban resilience through the integration into a GIS of satellite-derived precipitation data and geospatial reference datasets. Through timely and meaningful hydro-meteorological risk information, this methodology enables local government personnel and decision makers to quickly respond and monitor natural phenomena that could impact on the local community. This methodology is applied to the January 2015 Malawi Flood case study and result will be discussed along with further recommended developments.

The purpose of this study was to complete the existing ITHACA drought EWS, developed for the African continent and based on the monitoring of vegetation phenological parameters, introducing proper procedures able to monitor rainfall conditions in near real-time and detect anomalies using the SPI. The studied relationships between rainfall and vegetation dynamics allowed to determine the areas where the spatial and the temporal variability in vegetation conditions are closely related to the climate, that is, the areas where the monitoring of vegetation could be correctly integrated by the rainfall anomaly one, and, finally, the best rainfall cumulating interval to be used for SPI calculation purposes.