Analysis of drought events in Italy evaluated by means of rainfall remote products

Accurate rainfall estimation is a fundamental prerequisite for effective hydrological drought monitoring. In fact, precipitation represents the primary input to most drought indicators, and even small systematic biases can significantly affect the identification, timing, and severity of drought events. This is particularly relevant for the indices, such as the Standardized Precipitation Index (SPI), which rely exclusively on precipitation time series and are widely used for operational drought monitoring at multiple temporal scales. In regions such as Italy, characterized by complex topography, coastal–mountain interactions, and an uneven distribution of rain-gauge stations, uncertainties in rainfall estimation can therefore propagate directly into drought assessments, potentially limiting the reliability of decision-support systems. To address these limitations, satellite-based precipitation products have become an essential complement to ground observations, providing spatially continuous coverage and near–real-time data. However, their performance varies considerably depending on retrieval methodology, spatial resolution, and prevailing meteorological conditions, making a comprehensive evaluation necessary before their application to drought monitoring.

The objective of this study is to assess how different satellite precipitation products affect SPI-based drought characterization over Italy. Five widely used satellite precipitation products (CHIRPS, GPM, HSAF, PDIRNOW, and SM2RAIN) were selected to represent a broad range of retrieval approaches, including infrared–station hybrid techniques, passive microwave integration, geostationary multi-sensor blending, neural-network–based infrared methods, and soil-moisture inversion algorithms. Their diverse temporal and spatial resolutions make them suitable for both scientific analyses and operational monitoring frameworks.

The SPI data derived from each satellite product were compared. The analysis highlights substantial differences in SPI magnitude, frequency, and duration depending on the input precipitation dataset, emphasizing the sensitivity of drought assessment to rainfall estimation errors. Results indicate that no single satellite product consistently outperforms the others across all metrics and temporal aggregations and suggest that integrating multiple satellite products or adopting hybrid approaches can improve the reliability of SPI-based drought monitoring over complex Mediterranean environments, enhancing early warning capabilities and supporting more informed water-resources management.

This work was funded by the Next Generation EU—Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277)—project Tech4You—Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions; neither the Ministry for University and Research nor the European Commission can be considered responsible for them.