Ramona Magno

Rainfall is an oscillatory rather than purely stochastic signal, whose variability reflects alternating hydrological regimes rather than long-term trends. Recognizing this regime-based nature marks a conceptual shift in the way climatology interprets rainfall variability. At the monthly to multiannual scale, precipitation evolves through irregular wet, dry, and stationary phases whose duration and intensity vary over time. Although trend analyses, anomaly-based metrics, and spectral methods may at times suggest contrasting interpretations – each being sensitive to different aspects of the signal – they capture only partial views of a shared underlying variability. Framing precipitation as a sequence of irregular regimes offers a unifying perspective that helps reconcile these approaches and clarifies how rainfall fluctuations actually unfold. Using the Po River basin (Northern Italy) as an illustrative case, we show that Fourier and wavelet analyses confirm the intermittent character of rainfall oscillations, with regular periodicities emerging only at limited intervals. The Cumulative Deviation from Normal (CDN), computed as the cumulative sum of standardized monthly precipitation (SPI1), provides a simple yet physically consistent framework to visualize these irregular regimes and to quantify the resulting changes in water availability driven by cumulative surplus or deficit.

Rainfall is an oscillatory rather than purely stochastic signal, whose variability reflects alternating hydrological regimes rather than long-term trends. Recognizing this regime-based nature marks a conceptual shift in the way climatology interprets rainfall variability. At the monthly to multiannual scale, precipitation evolves through irregular wet, dry, and stationary phases whose duration and intensity vary over time. Although trend analyses, anomaly-based metrics, and spectral methods may at times suggest contrasting interpretations – each being sensitive to different aspects of the signal – they capture only partial views of a shared underlying variability. Framing precipitation as a sequence of irregular regimes offers a unifying perspective that helps reconcile these approaches and clarifies how rainfall fluctuations actually unfold. Using the Po River basin (Northern Italy) as an illustrative case, we show that Fourier and wavelet analyses confirm the intermittent character of rainfall oscillations, with regular periodicities emerging only at limited intervals. The Cumulative Deviation from Normal (CDN), computed as the cumulative sum of standardized monthly precipitation (SPI1), provides a simple yet physically consistent framework to visualize these irregular regimes and to quantify the resulting changes in water availability driven by cumulative surplus or deficit.

Rainfall is an oscillatory rather than purely stochastic signal, whose variability reflects alternating hydrological regimes rather than long-term trends. Recognizing this regime-based nature marks a conceptual shift in the way climatology interprets rainfall variability. At the monthly to multiannual scale, precipitation evolves through irregular wet, dry, and stationary phases whose duration and intensity vary over time. Although trend analyses, anomaly-based metrics, and spectral methods may at times suggest contrasting interpretations – each being sensitive to different aspects of the signal – they capture only partial views of a shared underlying variability. Framing precipitation as a sequence of irregular regimes offers a unifying perspective that helps reconcile these approaches and clarifies how rainfall fluctuations actually unfold. Using the Po River basin (Northern Italy) as an illustrative case, we show that Fourier and wavelet analyses confirm the intermittent character of rainfall oscillations, with regular periodicities emerging only at limited intervals. The Cumulative Deviation from Normal (CDN), computed as the cumulative sum of standardized monthly precipitation (SPI1), provides a simple yet physically consistent framework to visualize these irregular regimes and to quantify the resulting changes in water availability driven by cumulative surplus or deficit.

Rainfall is an oscillatory rather than purely stochastic signal, whose variability reflects alternating hydrological regimes rather than long-term trends. Recognizing this regime-based nature marks a conceptual shift in the way climatology interprets rainfall variability. At the monthly to multiannual scale, precipitation evolves through irregular wet, dry, and stationary phases whose duration and intensity vary over time. Although trend analyses, anomaly-based metrics, and spectral methods may at times suggest contrasting interpretations – each being sensitive to different aspects of the signal – they capture only partial views of a shared underlying variability. Framing precipitation as a sequence of irregular regimes offers a unifying perspective that helps reconcile these approaches and clarifies how rainfall fluctuations actually unfold. Using the Po River basin (Northern Italy) as an illustrative case, we show that Fourier and wavelet analyses confirm the intermittent character of rainfall oscillations, with regular periodicities emerging only at limited intervals. The Cumulative Deviation from Normal (CDN), computed as the cumulative sum of standardized monthly precipitation (SPI1), provides a simple yet physically consistent framework to visualize these irregular regimes and to quantify the resulting changes in water availability driven by cumulative surplus or deficit.

Rainfall is an oscillatory rather than purely stochastic signal, whose variability reflects alternating hydrological regimes rather than long-term trends. Recognizing this regime-based nature marks a conceptual shift in the way climatology interprets rainfall variability. At the monthly to multiannual scale, precipitation evolves through irregular wet, dry, and stationary phases whose duration and intensity vary over time. Although trend analyses, anomaly-based metrics, and spectral methods may at times suggest contrasting interpretations – each being sensitive to different aspects of the signal – they capture only partial views of a shared underlying variability. Framing precipitation as a sequence of irregular regimes offers a unifying perspective that helps reconcile these approaches and clarifies how rainfall fluctuations actually unfold. Using the Po River basin (Northern Italy) as an illustrative case, we show that Fourier and wavelet analyses confirm the intermittent character of rainfall oscillations, with regular periodicities emerging only at limited intervals. The Cumulative Deviation from Normal (CDN), computed as the cumulative sum of standardized monthly precipitation (SPI1), provides a simple yet physically consistent framework to visualize these irregular regimes and to quantify the resulting changes in water availability driven by cumulative surplus or deficit.

Rainfall is an oscillatory rather than purely stochastic signal, whose variability reflects alternating hydrological regimes rather than long-term trends. Recognizing this regime-based nature marks a conceptual shift in the way climatology interprets rainfall variability. At the monthly to multiannual scale, precipitation evolves through irregular wet, dry, and stationary phases whose duration and intensity vary over time. Although trend analyses, anomaly-based metrics, and spectral methods may at times suggest contrasting interpretations – each being sensitive to different aspects of the signal – they capture only partial views of a shared underlying variability. Framing precipitation as a sequence of irregular regimes offers a unifying perspective that helps reconcile these approaches and clarifies how rainfall fluctuations actually unfold. Using the Po River basin (Northern Italy) as an illustrative case, we show that Fourier and wavelet analyses confirm the intermittent character of rainfall oscillations, with regular periodicities emerging only at limited intervals. The Cumulative Deviation from Normal (CDN), computed as the cumulative sum of standardized monthly precipitation (SPI1), provides a simple yet physically consistent framework to visualize these irregular regimes and to quantify the resulting changes in water availability driven by cumulative surplus or deficit.

Drought monitoring systems often rely on multiple standardized indices computed at fixed time scales, leaving end users with fragmented information and weak links to actual impacts. Here we present Drought Scan (DS), an operational drought monitoring and forecasting system designed to provide a synoptic, impact-oriented view of drought at the river-basin scale.DS is entirely driven by basin-aggregated monthly precipitation and builds on a continuous multi-scale representation of standardized precipitation anomalies (SPI from 1 to 36 months). The core of the system is a synthetic indicator, D(SPI), obtained through a weighted aggregation of multi-scale SPI values. The weighting scheme is optimized against observed river discharge, maximizing the correlation between D(SPI) and standardized monthly streamflow (SQI1). As a result, unlike conventional indices, D(SPI) acts as a proxy of hydrological stress, despite being derived solely from precipitation. This makes the indicator explicitly impact-oriented and directly interpretable in terms of water availability.The system integrates three complementary components: (i) a multi-scale SPI heatmap that reveals drought triggers, persistence, and propagation across temporal scales; (ii) the D(SPI) indicator, which condenses this information into a single, basin-specific drought signal calibrated on hydrological response; and (iii) the cumulative deviation from normal (CDN), which captures the long-term memory of wet and dry phases and contextualizes drought severity within multi-year precipitation regimes.By construction, DS bridges the meteorological–hydrological continuum without relying on hydrological modeling or extensive ancillary data. Once an impact-oriented indicator is defined from precipitation alone, the system naturally lends itself to be applied into forecast estimates at sub-seasonal and seasonal scales: projected precipitation can be propagated through the same framework to obtain forecasts of D(SPI), i.e. forecasts of drought conditions expressed in terms of expected hydrological stress. Different forecasting approaches can be adopted (numerical such as those provided by Copernicus Climate Change Service or those estimated by machine learning algorithms), but the emphasis remains on the indicator and its interpretability rather than on the predictive technique itself. To facilitate this interpretation, forecasts are coupled with probabilistic scenarios that also can allow the quantification of rainfall needed to recover from drought phases.DS is conceived as a climate service tool developed within the Drought Central framework (www.droughtcentral.it), suitable for monitoring, early warning, and scenario exploration, and designed to translate complex drought dynamics into information that is robust, transparent, and operationally meaningful for water management and decision-making.

Rainfall is an oscillatory rather than purely stochastic signal, whose variability reflects alternating hydrological regimes rather than long-term trends. Recognizing this regime-based nature marks a conceptual shift in the way climatology interprets rainfall variability. At the monthly to multiannual scale, precipitation evolves through irregular wet, dry, and stationary phases whose duration and intensity vary over time. Although trend analyses, anomaly-based metrics, and spectral methods may at times suggest contrasting interpretations – each being sensitive to different aspects of the signal – they capture only partial views of a shared underlying variability. Framing precipitation as a sequence of irregular regimes offers a unifying perspective that helps reconcile these approaches and clarifies how rainfall fluctuations actually unfold. Using the Po River basin (Northern Italy) as an illustrative case, we show that Fourier and wavelet analyses confirm the intermittent character of rainfall oscillations, with regular periodicities emerging only at limited intervals. The Cumulative Deviation from Normal (CDN), computed as the cumulative sum of standardized monthly precipitation (SPI1), provides a simple yet physically consistent framework to visualize these irregular regimes and to quantify the resulting changes in water availability driven by cumulative surplus or deficit.