| 31 Jul 2026
Trends in geo-hydrological risk to the population of Italy
Abstract. Geo-hydrological hazards, including floods and landslides, are among the most widespread hazards and a major cause of human loss worldwide. Their impacts are shaped by meteorological forcing and by long-term changes in exposure, vulnerability, and settlement patterns. Yet robust assessments of long-term fatality trends are rare because sufficiently long, accurate, and homogeneous records of damaging events are lacking. A key unresolved question is whether the human toll of geo-hydrological events has changed structurally through time, and which climatic, socio-economic, or policy factors best explain that change. Here we show, using a unique 1950–2024 catalogue of fatal events in Italy, that geo-hydrological risk underwent a marked transition, with fatalities and fatal days declining sharply until about 1970 and then stabilising at much lower levels. This decline was driven mainly by landslides, whereas flood-related losses showed weaker long-term change. The decline was not explained by specific risk policies or management programmes, as most of it preceded their implementation. Instead, precipitation was the main climatic driver of the remaining interannual variability, particularly for floods and days with fatal events. By contrast, population and gross domestic product did not account for the long-term trends once shared temporal changes were considered. Fatal days were less overdispersed and more predictable than fatalities, suggesting that meteorological factors control more directly the occurrence of dangerous conditions than the final death toll. Fatalities, instead, depend strongly on exposure, vulnerability, timing, location, and other event-specific circumstances. Together, these findings indicate that broad socio-economic dynamics can reduce geo-hydrological mortality even as hazardous processes persist, whereas meteo-climatic conditions continue to shape year-to-year risk.
1. The use of fatal days is conceptually flawed. A fatal day is still defined by the occurrence of a death. It therefore depends on exposure, vulnerability, warnings, and chance. It does not represent all days with hazardous conditions. Days with serious floods or landslides but no fatalities are completely excluded. Thus, comparing fatalities with fatal days cannot separate hazard occurrence from human consequences. This claim is repeated throughout the manuscript but is not supported by the data. Independent records of both fatal and non-fatal events are needed.
2. The predictor analysis is too coarse to identify drivers. National annual precipitation cannot represent the local and short-duration rainfall extremes that trigger floods and landslides. National GDP and population are also poor measures of local exposure and vulnerability. Moreover, these variables share strong trends with calendar year. Adding a smooth time term creates serious concurvity. Removing the shared trend and then concluding that GDP or population did not explain the decline is circular. The policy analysis is equally weak. It uses 378 models with arbitrary and overlapping windows. There is no adequate correction for multiple testing or credible causal counterfactual. The conclusions about climate, socioeconomic development, and policies go far beyond what these models can show.
3. The predictability comparison is not valid. Fatal-day counts have a much narrower distribution than fatality counts. They will naturally have lower negative log predictive scores, even without greater temporal predictability. The authors must compare each model against an appropriate climatological or negative-binomial baseline and report a normalized skill score. The entropy analysis is also unreliable with only 75 annual observations, especially after dividing the data into quantile states and using two lags. Important details are missing, including the number of states, bias correction, and bootstrap procedure. Finally, four evaluation years are not enough to validate the forecasts. Showing that four observations fall within very wide prediction intervals is not evidence of forecasting skill. The projections to 2030 are therefore not convincing.