Search

Sub-daily extreme precipitation can generate fast hydro-geomorphic hazards such as flash floods and debris flows, which cause fatalities and damages especially in mountainous regions. Reliable projections of extreme future precipitation is fundamental for risk management and adaptation strategies. Convection-permitting climate models (CPMs) esplicitely resolve large convective systems and represent local processes, especially sub-daily extreme precipitation, more realistically than coarser resolution models, thus leading to higher confidence in their projections. Given their high computation cost, however, the available CPM simulations cover relatively short time periods (10–20 years), too short for deriving precipitation frequency analyses with conventional extreme value methods based on annual maxima or threshold exceedances.In this work, we evaluate the potential of a non-asymptotic approach based on “ordinary” events, the so-called Simplified Metastatistical Extreme Value (SMEV), to provide information on the future change of short-duration precipitation extremes. We focus on a complex-orography region in the Eastern Italian Alps, where significant changes in sub-daily annual maxima have been already observed. The study is based on COSMO-crCLIM model simulations at 2.2 km resolution under the RCP8.5 scenario and uses three 10-year time periods: historical 1996-2005 (the control period), near-future 2041-2050 and far future 2090-2099. We estimate extreme precipitation for durations ranging from 1 h to 24 h and assess the projected changes with respect to the control period. Specifically, we analyze annual maxima, return levels up to 50 years, and the parameters of the statistical model. A bootstrap procedure is used to evaluate the uncertainty of the estimates, and a permutation test is applied to assess the statistical significance of the projected changes. We compare our results with a modified Generalized Extreme Value (GEV) approach, recently applied for the study of extremes in CPM future time periods.We found that annual maxima and higher return levels exhibit a general increase in the future especially for the far future and the shorter event durations. On average, the magnitude of the far future change decreases with the precipitation temporal scale. The changes show an interesting spatial organization that can be associated with the orography of the region: significant future increases are mostly located at high elevations, while lowlands and coastal zones show no clear pattern.This work shows that SMEV reduces the uncertainty in the estimates of higher return levels compared to GEV and can thus provide improved estimates of their future changes from short CPM runs. These findings advance our knowledge about the projected changes in extreme precipitation and their spatial distribution at the different time scales. They can thus help improving risk management and adaptation strategies.

Quantifying design rainfall events at varying durations is crucial for assessing flood risk and mitigating losses and damages. Yet, in a changing climate, they are fundamental tool for a reliable design of water related infrastructures, such as flood retention reservoirs, spillways, and urban drainage systems. Usually, design rainfall is quantified where rain gauges are located, and regionalization methods are used to provide estimates in ungauged locations. During the last years, convection-permitting climate models (CPM) are receiving increasing attention because, thanks to their high spatial resolution (~3km) and ability of explicitly resolving atmospheric convection, they allow for better estimating precipitation spatial patterns and extreme rainfall at multiple durations compared to coarser models.In this work, we combine at-site rain gauge measurements with CPM simulations, within a non-asymptotic statistical framework for the analysis of extreme rainfall. We aim at quantifying the added value of the physics-based information provided by CPM simulations for the estimation of high quantiles of rainfall in ungauged locations. The performance of the new regionalization approach is compared with traditional interpolation methods (i.e. interpolation of distribution function parameters) using leave- one-out cross-validation as well as considering different rain gauge densities.Preliminary results show that the proposed methodology based on CPM simulation provides: i) similar performances compared to traditional gauge-based regionalization methods for high station density scenarios and ii) improved performances for low station density scenarios.

*Corresponding author, email: diego.todini@iusspavia.it Stable isotopes are tracers used for the investigation of water flow paths, the quantification of the relative contribution of water sources to stream runoff or root water uptake, as well as to determine the spatial and temporal origin of water exploited by plants for transpiration. Previous studies have shown that uncertainties associated to samplings, soil and plant water extraction methods and the spatial variability in the isotopic composition of the water sources in a catchment can hamper the understanding of water cycling and the interactions between soil and plants.In this work, we used isotopic data collected during four growing seasons in a small forested catchment in the Italian pre-Alps to i) investigate the spatial and temporal variability of the isotopic composition of the sampled water sources, ii) determine the seasonal origin of the water sources, and iii) quantify the contribution of soil water to the root water uptake of beech and chestnut trees. The ecohydrological monitoring took place in the 2.4-ha Ressi catchment. Elevations are comprised between 598 and 721 m a.s.l., and the climate is temperate humid. The catchment is covered by a deciduous forest, with beech, chestnut, hazel and maple as the main tree species.Water samples for isotopic analysis were taken monthly from bulk precipitation, approximately bi-weekly from stream water, shallow groundwater and soil water by two suction lysimeter cups in the riparian zone. Plant water and bulk soil water samples were extracted by cryogenic vacuum distillation bi-weekly during summer. All water samples were analysed by laser spectroscopy, except plant water that was analysed by mass spectrometry. Results show that stream water, groundwater and soil water extracted by suction lysimeters were isotopically similar to precipitation, and on average they had an autumn or spring origin. Bulk soil water obtained by cryogenic vacuum distillation showed an evaporation signature, particularly on the hillslope sites where soil moisture was lower. At greater depths, bulk soil water extracted by cryogenic vacuum distillation was slightly less evaporated and less enriched in heavy isotopes compared to soil water extracted from shallow layers. Plant water was more similar to soil water extracted by cryogenic vacuum distillation, and mainly had a summer origin. Riparian trees tended to take up more water from shallow soil layers, whereas hillslope trees had a slightly larger preference for deep bulk soil water, particularly during the driest months.Our results suggest that, in the study area, trees likely use more bulk soil water than the mobile soil water, groundwater and stream water, and the preference for more shallow or deep soil water is likely due to the different rooting depth of the vegetation in the hillslope and the riparian zone. Keywords: stable isotopes; soil water; plant water; forested catchment; cryogenic vacuum distillation.

Conceptual models of catchment hydrological functioning are crucial to understand and predict runoff and the tracer responses during rainfall events, and thus for sound water resources management and pollution mitigation measures. In this study, we use hydrometric and tracer data (stable isotopes, major ions and electrical conductivity (EC)) collected in the Ressi catchment, a 2-ha watershed in the Italian pre-Alps, to test our existing conceptual model of runoff generation mechanisms. This model was based on previous hydrometric measurements and isotope data for selected events and highlights the importance of precipitation and antecedent conditions for hillslope-riparian zone-stream connectivity. More specifically, we determined if the temporal variability in the concentration-discharge relations can be explained by event characteristics, i.e., rainfall event size, intensity, and antecedent moisture conditions.The Ressi catchment is characterized by high seasonality in runoff response, due to the seasonality in rainfall (high in fall) and evapotranspiration (high in summer). Discharge and rainfall have been measured continuously since August 2012. Stream water, precipitation, shallow groundwater and soil water samples were collected for tracer analyses during 20 rainfall-runoff events between September 2015 and August 2018. All samples were analyzed for EC, isotopic composition (2H and 18O) and major ion concentrations. To investigate the possible controls on the concentration-discharge relations, we determined the main event characteristics (e.g., total event rainfall, rainfall intensity, antecedent soil moisture and depth to water table, runoff coefficient) for each event.Based on previous applications of isotope- and EC-based hydrograph separation in the Ressi catchment, we expected different dynamics of the major ions in stream water, depending on the magnitude of the rainfall-runoff events. For all major ions, we hypothesized a dilution effect, and a more marked response for large, long duration events with wet antecedent conditions. The temporal dynamics of calcium, magnesium, sodium and sulfate concentrations confirmed our hypotheses. On the contrary, nitrate, potassium and chloride concentrations sometimes increased at the onset of the event, before a later dilution. These temporal dynamics led to complex hysteretic relations with discharge that could not be explained by the event characteristics. We attribute the rapid increase in the concentrations of these solutes to a quick flushing from the dry parts of the stream channel and the near surface-soil layers of the riparian zone at the onset of the event. The revised conceptual model for the geochemical response of this catchment should, therefore, include a rapid flow pathway that leads to the mobilization of nitrate, chloride and potassium ions, and describe the rapid establishment of hydrological connectivity along the streambed and the near-channel zones. Keywords: concentration-discharge relation; major ions; electrical conductivity; stable isotopes; hysteresis; forested catchment.

Understanding the impact of orography on the probability distribution of extreme precipitation at short (i.e., sub-daily) temporal scales, as well as on extreme-rainfall causative processes, is critical for managing risk from rainfall-triggered natural hazards in mountainous regions. High-resolution convection-permitting models (CPMs) are crucial for this type of analysis since they better represent convective processes key to short-duration extremes. Here, we assess the ability of multi-model CPM ensemble CORDEX-FPS to represent the upper tail of sub-daily precipitation in a complex-orography region in the Eastern Italian Alps. In this area, different orographic impacts on sub-daily precipitation upper tail were reported at different event durations, and significant temporal trends in precipitation intensity were reported during the last few decades, making it a challenging and interesting test case for CPM simulations. An ensemble of six CPMs with a horizontal grid spacing of ∼2.2 km, driven by ERA-Interim reanalysis, are analysed and evaluated against ∼180 rain gauges. Since CPM simulations are too short (10 years) for analysing extremes using conventional methods, we use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value, SMEV), which was proven to provide reliable results even using short time records. We explore how the model spread vary with elevation and the ability of the multi-model mean to reproduce the distribution parameters and the extreme quantiles up to 100-year return period at different elevations.

Empirical bias adjustment techniques are the backbone of correction procedures for climate model scenarios of precipitation. However, the effect of these adjustments on the spatial patterns of rainfall fields is not properly understood yet. Here, we investigate the quality of empirical bias correction procedures at reproducing the spatial correlation of the rainfall fields by comparing simulated, observed, and adjusted gauge-based rainfall series. We ground our investigation on simulations from a convection-permitting climate model forced with reanalysis data (Era Interim driven COSMO-CLM model) across a 10-year period (2000-2009). Specifically, we aim at understanding if the adjustment procedure can preserve the spatial correlation of both gauge-based and gridded simulated rainfall fields. The area of investigation is an alpine transect in North-Eastern Italy, including (the Veneto region, Trentino and Alto Adige, where a dense network of rain gauges is available). We assess possible changes in the spatial correlation scales of extreme events at different temporal resolutions (e.g. 1, 3, 6, and 12 hours). Our preliminary results suggest a non-negligible effect of bias adjustments in the distortion of spatial patterns of rainfall fields, which would call for the need for proper spatially consistent adjustment procedures.

Over the past decade, the Adige river basin in the Eastern Italian Alps has experienced water scarcity during early spring and late summer, due to a combination of decreased snowmelt, less precipitation, and increasing water demand. This condition has caused tension and disputes between upstream and downstream water users, particularly between hydropower companies in the upstream region (Trentino/South Tyrol) and agricultural users in the downstream areas (Veneto region). The potential for water scarcity impacts to intensify and expand in the future remains a major concern with climate change leading to more frequent warm and snow droughts in the region. Informing the region's administration, institutions, communities, and businesses to manage water scarcity conditions, is essential to prepare and mitigate the potential future impacts. This work aims to explore decision-making options in drought conditions in the Adige river basin, along with the potential impacts of climate change, by exploiting hydrological models for the river basins and for the major reclamation consortium in the area. The study will focus on years with severe drought, such as the 2022 drought period, using simplified decision options and examining how the decisions to meet the water needs of hydropower agencies in the upstream part of the Adige river basin could impact agricultural water use in the downstream part. The analysis will then be repeated in similar conditions, but with the added element of climate change forcing and reduced glacier volumes in the Alps. This study will identify those needs that might not be fulfilled in certain drought scenarios providing valuable insights for decision-makers and supporting the development of effective strategies to prepare and better manage future water scarcity conditions in the region.

This study evaluated the potential of ERA5 reanalysis as a reference dataset for snow water equivalent (SWE) modeling in 16 Alpine basins of varying catchment sizes using the semi-distributed snow model (TOPMELT) in the Upper Adige river basin in the Eastern Italian Alps. The study aimed to identify errors in ERA5 meteorological variables and assess their impact on SWE computation from 1992 to 2019. The findings revealed that ERA5 precipitation overestimated low-intensity rainfall (drizzle problem) and underestimated high-intensity rainfall, while ERA5 temperature underestimated observations. The overestimation of low-intensity rainfall created fictitious low-intensity snowfall events, which, when combined with colder ERA5 temperature, resulted in delayed snowmelt and increased fictitious snow cover days over the study area. The Quantile Mapping (QM) technique was used to remove errors in ERA5 variables. For precipitation, a monthly correction factor accounting the calibration period (1992-2005) was considered to correct the entire period. Temperature errors were corrected individually for calibration and validation periods. The corrected ERA5 SWE simulation showed improved performance, with a decrease in fictitious snow cover days. The study also highlighted the importance of temperature correction over precipitation correction in SWE simulation, particularly for smaller basins, while considering the monthly moving mean to remove seasonality.

A holistic understanding of ecohydrological processes that regulate the variability of tree water use, water flow pathways through the soil and to groundwater and the stream, and the transit time of water at the catchment scale is still challenging. In this work, we rely on an integrated dataset of isotope tracers and hydrometric data to show new evidence on the main ecohydrological processes and their controls that link runoff origin, sources of tree water uptake, and water transit time in a small mountain, forested catchment in central Italy.The Re della Pietra experimental catchment is located in the Tuscan Apennines. The catchment is 2 km2 in size and ranges in elevations between 650 and 1280 m a.s.l.. Forests cover more than 95% of the area, and the main tree species are beech and oak trees, with a much smaller proportion of conifers. Mean annual precipitation is around 950 mm. We collected water samples from April 2019 to January 2023 from precipitation, throughfall, soil, groundwater, springs, stream at different sections, and xylem of beech trees, and determined their isotopic composition. Weather data, streamflow, soil moisture, groundwater level, and throughfall were monitored in a 0.3 km2 headwater sub-catchment. Stream stage and electrical conductivity as additional tracer were measured in three stream sections from the headwater sub-catchment down to the Re della Pietra outlet. Streamflow, groundwater, spring flow, and soil moisture are characterized by a marked seasonality, reflecting the strong seasonal variability in the meteorological forcing, typical of the Mediterranean climate.Based on field observations and preliminary data analysis, we aim to test the following hypotheses:i) Trees growing along a steep hillslope in the headwater sub-catchment use different water sources (soil water vs. shallow groundwater) as a function of their topographic position. ii) Hillslope soil water is an important contributor to streamflow only during wet conditions (i.e., in winter, late fall, and early spring). iii) Spring water flows through shallow pathways and is a negligible contributor to streamflow, especially during dry conditions. iv) Water transit time increases as a function of increasing catchment size (i.e., increasing drainage area). The results will contribute to the conceptualization of the interrelated ecohydrological processes driving water fluxes in Mediterranean forested catchments.

Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This "reverse orographic effect"could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000-2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles., Hydrology and Earth System Sciences, 27 (5), ISSN:1027-5606, ISSN:1607-7938

A well-known approach to seasonal hydrological forecasting involves the use of ensembles as forcing to a hydrological model, based either on historical meteorological data (Ensemble Streamflow Prediction, ESP) or on forecasts produced by one or more dynamic climate models (multi-model approach). This work aims at investigating the role of initial hydrologic conditions (ICs) and seasonal climate forecast skill on the accuracy of seasonal hydrologic predictions in alpine regions, as a function of catchment properties. The Integrated Catchment-scale HYdrological Model (ICHYMOD) is employed, forcing it with historical meteorological data and multi-model ensemble climate predictions produced by the climate forecast systems NCEP CFSv2 and ECMWF SEAS5 (the latter analysis is not included in the present work). The hydro-climatic prediction system is tested on two catchments in the Eastern Italian Alps, that are different in terms of orography and soil/groundwater storage capacity. The diverse catchment properties result in differential parameterization of the subsurface processes in the hydrological model, hence in a different impact of the initial hydrologic conditions on the seasonal runoff predictions.

Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000–2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles.

The RESILIENCE project aims at developing an integrated methodology for assessing the impact of climatic variations and changes on the intense precipitation and wind regimes, and on the consequent triggering of flash floods, debris-flows and wind-related forest damages. A significant increase of short and intense precipitation is expected in the next future due to global warming, with consequent impacts on flash floods and hydro-geomorphic hazards such as shallow landslides and debris flows. Despite their societal importance, only few studies have explored potential climate change effects on these hydrological and hydro-geological processes. In fact, no accepted estimates of such changes to be used in engineering practice or environmental management planning exist so far, nationally or regionally.The RESILIENCE project tries to address this specific knowledge gap. Two recent scientific advances are at the basis of the development of RESILIENCE. The first advance is the advent of high-resolution climate models, also called Convection-Permitting Climate Models (CPM), which improve the representation of both precipitation and wind field at the sub-daily scales compared to the standard coarser resolution Regional Climate Models. However, due to their computational costs, simulations are currently available for only short (typically ten years) time slices and few emission scenarios. These time series are too short to provide reliable statistics of extremes if analyzed using the classical extreme value methods. A second recent advance in the field of extreme value theory, the Metastatistical Extreme Value Distribution (MEVD), allows to overcome this limitation: it provides reliable extreme event probability estimates even from short time series, as in the case of CPM outputs, since it is based on all “ordinary events'' in the series instead of just yearly maxima or a few “peak-over-threshold” values per year as in the traditional methods.Given this background, and focusing on the Veneto region in Italy as a study area, the specific objectives of RESILIENCE are 1) to quantify near (2041-2050) and far (2090-2099) future changes in precipitation and wind extremes probability at sub-daily temporal scales with respect to the baseline (1996-2005) using the MEVD approach and high-resolution COSMO-CLM simulations, 2) to quantify the associated future impacts on flash floods, debris flows and forest damages, 3) to provide data and hazard models to support flood and forest risk management plans in the Italian North-East accounting for future climate changes.RESILIENCE brings together an interdisciplinary group of scientists, from hydrologists, to climate modelers, to statisticians, to forest science experts, and is based on the interaction with three key Project Stakeholders. The project results will be communicated and disseminated to a wide audience of residents in the Veneto region and beyond, through collaborations with Museums, Academies and Local Authorities.

We investigate the spatial and temporal patterns of annual and seasonal biases in extreme precipitation simulated by a convection permitting climate model forced with reanalysis data (Era Interim driven COSMO-CLM model) across a 10-years period (2000-2009). Specifically, we aim at developing an adjustment procedure able to preserve extremes at different temporal and spatial scales, and which can be applied to future scenarios. The biases are here defined as the ratio between simulated and observed rainfall at 130 rain gauges. The quantile-based analysis reveals a general overestimation for gauges located in foothill or mountain areas (elevation > 500 m asl), and a general underestimation over lowland sites. This behavior is recurrent for various investigated quantities, such as annual or seasonal biases, and their counterparts estimated upon Extreme precipitation Values (EV) or wet periods. We also observe a temporal heterogeneity of the biases estimated in different years or seasons. Dry years (e.g. 2003) are characterized by remarkably high biases, while those estimated upon springs and autumns data within the investigation period are generally overestimated and underestimated, respectively. We explore two preliminary adjustment approaches: Quantile Mapping (QM) and Linear Scaling (LS) adjustments. QM corrects simulated rainfall series but can affect the rainfall temporal autocorrelation. Conversely, LS preserves the autocorrelation but fails in the correction of seasonal and annual biases. EV-related biases are not properly corrected, and further statistical methods need to be formulated to correct EV simulated rainfall while both respecting their ACF and taking into account orographic effects.

Quantifying rainfall volumes at varying duration and frequencies (e.g. design rainfall) and their uncertainty is crucial for a reliable design of water related infrastructures, such as flood retention reservoirs, urban drainage systems, spillways, culverts. This is of particular relevance in orographically complex area where extreme rainfall could trigger hydro-geological hazards. Estimate of the design rainfall and its uncertainty is usually done at-site, i.e. at the position where the rain gauge is located and regionalization methods are required to provide estimates in ungauged locations. In this work we exploit the potential of the Simplified Metastatistical Extreme Value (SMEV) statistical framework for the analysis of extreme rainfall based on ordinary events and not only the annual maxima and we evaluated the performances of two different regionalization methods (namely, regionalization of extreme rainfall quantiles and of the distribution function parameters). The performance of the two selected approaches is evaluated by leave one out cross-validation and traditional goodness of fit measures (i.e. percent bias, percent root mean square error, and Kling Gupta efficiency). The study area is the Alto Adige Region located in the Italian Alps, where 57 rain gauges at sub-hourly and hourly time steps are analyzed. Preliminary results show that accounting for elevation in the regionalization (Kriging) methods provides better performances and reduce the design rainfall uncertainty in ungauged locations.

Increasing extreme precipitation intensity at short duration is reported in recent literature and related to the global warming. For improving risk management and adaptation to changing climate, it is important to estimate the changes in hourly extremes, because they cause numerous hydro-geological hazards. High resolution climate models (Convection-permitting models, CPMs) resolve the scales at which convective processes occur, and can provide higher confidence in the future estimates of hourly precipitation than coarser resolution models. However, since actual CPM runs are available for short time slices (10–20 years), estimation of extremes by using classical Extreme Value approaches is difficult. Novel methods based on the concept of ordinary event have shown the capacity of deriving reliable frequency analyses from short data records, and they can be successfully applied to CPMs. Recent literature reported distinct orographic effects on precipitation extremes. In particular, decreasing intensity with elevation for hourly extremes is found (“reverse orographic effect”) as contrasted with the orographic enhancement of precipitation for long durations. The reverse orographic effect was tentatively associated to orography-induced turbulence. These processes could be sub-grid even for CPMs, so it is crucial to understand whether and how CPMs can represent the orographic effect before using the simulations to project future extremes in mountainous areas. We focus our study on an orographically complex area in the Eastern Italian Alps. Precipitation data comes from: i) ~170 5-min resolution rain gauges (our benchmark), ii) CPM simulations from COSMO model, run at 2.2 km spatial resolution and 1h time-resolution. The model is driven with ERA Interim re-analyses for the period 2000-2009. A storm-based statistical method is applied to both observed and simulated time series, and we use a Weibull distribution for modelling the upper tail of ordinary events. We derive the distribution parameters and extreme return levels up to 20-year return period for durations between 1 and 24 h. We look at their dependence on elevation, and we quantify the bias between observations and CPM, the dependence of the biases with elevation. Spatial patterns in the CPM biases on the annual maxima and the modelled return levels emerge for short durations, while a general better agreement between model and observation is found at the daily duration. For the 1h return levels, the bias depends on elevation, with increasing overestimation with elevation, which implies a weak representation of the reverse orographic effect. This work shows that we can have reliable estimates of high return levels from short CPM runs by using proper statistical methods. The results can improve our understanding of the changes in the meteorological processes underlying the changes in the precipitation extremes, and could help us develop adjustment approaches accounting for the role of orography at multiple durations.

Fast hydro-geomorphic hazards such as flash floods and debris flows cause numerous fatalities and large damage, and are triggered by sub-daily extreme precipitation. Projecting future changes in these extremes is thus of great importance for risk management and adaptation strategies. High-resolution climate models, called convection-permitting models (CPMs), represent land-surface characteristics and small-scale processes in the atmosphere, such as convection, more realistically than coarser resolution models. Subdaily extremes could be better represented, and thus CPMs provide higher confidence in the estimate of future changes in extreme precipitation. However, the existing CPM runs are available for relatively short time periods (10–20 years at most) that are too short for deriving precipitation frequency analyses with conventional extreme value methods. Here, we evaluate the potential of a novel statistical approach based on many “ordinary” events rather than just yearly maxima or a few values over a high threshold. This method has the potential to provide reliably estimate of rare return levels from short data record, thus offering the chance to be effectively applied to the analysis of CPM data for reliable frequency analysis on future precipitation. We focus on an Eastern Alpine transect, characterized by a complex orography, where significant changes in sub-daily annual maxima have been already observed. We estimate future changes under the RCP8.5 scenario using COSMO-crCLIM model simulations at 2.2 km resolution. We focus on three 10-year time slices (historical 1996-2005, near-future 2041-2050, and far future 2090-2099). A bias assessment is also performed by comparing the estimated extremes from the historical time-slice to the ones from long records of observed precipitation. We estimate extreme precipitation for duration ranging from 1 h to 24 h and assess the changes between the time periods. Specifically, we analyze: annual maxima, return levels, and parameters of the statistical model. Although the storms' frequency will generally decrease in the region, the mean annual maxima exhibit a general increase in the near and far future, especially at shorter durations. The change in the extreme return levels shows a similar trend, with larger increase in the far future at the shorter duration. Interestingly, the changes show a spatial organization that can be associated to the orographic features of the area: the stronger increasing changes are located in the high elevation zone, while in the lowlands weak decrease and weak increase emerge in the near and far future, respectively. This analysis demonstrates the possibility to have reliable estimates of future extreme precipitation from short CPM runs by using a novel method based on ordinary-events. The relevant findings from this analysis are useful for improving our knowledge about the projected future changes in extreme precipitation and thus for improving the strategies for risk management and adaptation.

Subdaily extreme precipitation may trigger fast hydro-geomorphic responses, such as flash floods and debris flows, which cause numerous fatalities and large damage. High-resolution climate models, called convection-permitting models (CPMs), represent convective processes more realistically than coarser resolution models. These processes are crucial for the correct representation of subdaily extremes, and thus CPMs provide higher confidence in the estimate of future extreme precipitation. However, because of their high computational cost, the existing CPM runs are available for relatively short time periods (10–20 years at most) that are too short for deriving precipitation frequency analyses with conventional extreme value methods. Recent approaches are based on many “ordinary” events rather than on just yearly maxima or a few values over a high threshold. They demonstrate the capability to provide reliably estimate of return levels associated with long return periods from short data record, thus they offer the chance to be effectively applied to the analysis of CPM data.In the present study, we estimate the changes in subdaily precipitation extremes from COSMO-crCLIM model simulations at 2.2 km resolution, for three 10-year time slices (historical 1996-2005, near-future 2041-2050, and far future 2090-2099 – under the RCP8.5 scenario). We focus on the Eastern Alpine transect, characterized by a complex orography, where significant changes in subdaily annual maxima have been already observed. We apply an ordinary-event statistical method for the estimation of the extreme precipitation with duration ranging from 1 h to 24 h. We analyze the changes between the time periods in both the annual maxima, the quantiles, and the distribution parameters. We find that, although the storms' frequency will generally decrease in the region, the mean annual maxima will increase continuously in the near and far future, especially at shorter durations. Investigation of extreme return levels shows a similar trend, with larger changes in the far future at the shorter duration. Interestingly, the emerging spatial patterns in the changes can be associated to the orographic features of the study area: the stronger increasing changes are located in the high elevation zone, while the flat zone shows weak decrease and weak increase in the near and far future, respectively.These analysis and results are useful for improving our knowledge about the projected future changes in extreme precipitation and thus for improving the strategies for risk management and adaptation.

A significant increase in the intensity of extreme precipitation of short duration is expected due to the global warming, and is already reported in recent literature. Estimating the changes in hourly extremes in climate models is of fundamental importance for improving risk management and adaptation to changing climate, because they cause numerous hydrological and geomorphological hazard. Convection-permitting models (CPMs) can resolve the scales at which convective processes occur, and thus provide higher confidence in the future estimates of hourly precipitation than coarser resolution models. However, the existing CPM simulations cover relatively short time periods (10–20 years), and this prevents the use of conventional extreme value methods for the estimation of extremes. Novel methods based on the concept of ordinary event have shown the potential of deriving accurate frequency analyses from short data records, and they can be successfully applied to CPMs.Recent studies reported distinct orographic impacts on precipitation extremes. In particular, a “reverse orographic effect” was reported for hourly durations, meaning that the intensity of hourly extremes tends to decrease with elevation, as opposed to the orographic enhancement of precipitation for long durations. This reverse orographic effect was tentatively associated to orography-induced turbulence. As these processes could be sub-grid even for CPMs, it is crucial to understand whether and how CPMs are able to represent these effects before using these simulations to project future extremes in mountainous areas.Here, we focus on a complex-orography area in North-eastern Italy and we use hourly precipitation records from ~150 5-min resolution rain gauges (our benchmark) and CPM simulations from COSMO model, run at 2.2 km resolution. The model is driven with ERA Interim reanalyses for the period 2000-2009. By applying a storm-based statistical method to both observed and simulated time series, we model the ordinary events tails using a Weibull distribution. We compute the distribution parameters and the extreme quantiles up to 20-year return period for 9 durations between 1 and 24 hours, and we evaluate: their dependence on elevation, the bias between the observation and the CPM, the dependence of the biases with elevation.We find evident spatial patterns in the CPM biases on the annual maxima and the modelled quantiles, especially for short durations. The bias significantly depends on elevation, with increasing overestimation of the 1-hour quantiles with elevation. This seems to confirm our hypothesis that CPMs cannot well represent the “reversed orographic effect”. These findings can improve our understanding of the changes in the meteorological processes underlying the changes in the precipitation extremes, and could help us develop adjustment approaches that can account for the role of orography at multiple durations.

Mountain rivers experience channel widening as a response to high-magnitude hydrological events. Several studies indicate that the unit stream power (ωpk) and the lateral confinement (CI) are among the most important constraints to explain channel modifications induced by a flood. That said, with the same controlling factors, a relatively broad spectrum of width ratios (WR = channel width after/before the flood) is commonly observed in real case studies. Sediment transport in mountain streams occurs via processes classified as debris flows (DFW), debris floods (DFD) and water flows (WF). This study aims to test if different flow-types are one of the drivers of channel response to floods, specifically investigating if there is a relationship between DFD (i.e. a transport condition characterized by extremely high bedload) and intense channel widening.The case study is the Cordevole catchment (Dolomites, Italy; drainage area of 857 km2), which in October 2018 was affected by a severe hydrological event (Vaia storm). Besides the main stem of the Cordevole River, we considered four of its tributaries (Tegnas, Pettorina, Liera and Corpassa torrents). WR was determined at the sub-reach scale through aerial photographs analysis and ωpkwas calculated considering the discharge at the flood peak provided by hydrological modelling. A post-flood survey allowed us to determine the flow-types that occurred at each sub-reach of the Tegnas Torrent during the event. The possible upheaval from WF to DFD along the other streams was determined considering the presence of conditions required for local occurrence of DFD (i.e. ωpk exceeding 5000 Wm-2 and/or DFW tributaries delivering large amount of sediment into a receiving stream).DFD sub-reaches of the Tegnas Torrent experienced widenings that, at the same ωpk, were 2-3 times larger than WR of WF sites. These processes-specific relationships were used to recognize sub-reaches of the other streams were an “anomalous widening” occurred during the Vaia event, i.e. sites where WR was significantly larger-than-expected for a specific ωpkunder conditions of WF. Among 117 sub-reaches, anomalous widening was recognized at 13 and 6 sub-reaches of the Liera (WR up to 16) and Pettorina (WR up to 10) torrents, respectively. All these sub-reaches were characterized by the presence of conditions required for DFD occurrence during a high-magnitude flood, allowing us to infer that the process responsible for sediment transport during the Vaia event was likely DFD. Contrariwise, no sub-reaches of the Cordevole and Corpassa streams experienced anomalous widening, likely because WF occurred along their whole courses due to their morphological characteristics (e.g. wide channel before the flood) and/or lower magnitude of the flooding locally induced by the storm. These results suggest that an extraordinary-widening characterizes DFD channel sites, which, during a severe flood, can be affected by channel changes remarkably more intense than those occurring in response to WF. For this reason, in addition to hydraulic and morphological constraints, the different sediment–water flows possibly occurring at a sub-reach should be considered as a further controlling factor for channel modifications and, consequently, for prediction of geomorphic hazard at local scale.

Recent studies applying stable isotopes of hydrogen and oxygen showed that different methods for extracting water from plant tissues can return different isotopic composition. One of the most used methods to extract plant water is the cryogenic vacuum distillation (CVD), which tends to extract total plant water. Conversely, the Scholander pressure chamber (SPC), which is commonly used by tree physiologists to measure water potential in plant tissues, likely accesses only the mobile plant water (i.e., xylem and inter-cellular water). However, only few studies reported the application of SPC to extract plant water for isotopic analyses, and therefore, an inter-method comparison between SPC and CVD is needed.In this study, we analyzed the variability in the isotopic signature of plant water extracted by SPC and CVD. Furthermore, we considered the potential variability in the isotopic composition of the plant water extracted from various tissues by CVD (i.e., leaves, twig without bark, twig with bark, twig close to the trunk of the tree, and wood core), and from different tree species (i.e., alder, apple, chestnut and beech) located in three different study areas in northern Italy.Results indicate that plant waters extracted by SPC and CVD were significantly different, likely due to the extraction of different plant water domains. The difference in the isotopic composition obtained by the two extraction methods was smaller in the beech samples compared to alder, apple and chestnut samples. The signature of alder, apple and chestnut plant water extracted by SPC was more enriched in heavy isotopes than the samples obtained by CVD (except for the leaf water obtained by CVD, which also had a marked evaporative signature). We conclude that plant water extraction by SPC does not represent an alternative for CVD, as SPC likely extracts mostly the mobile plant water, whereas CVD tends to retrieve all water stored in the sampled tissues. However, studies aiming to quantify the relative contribution of the water sources to transpiration should rely more on the isotopic composition of xylem water transpiring during the sampling day (theoretically sampled by SPC), than the isotopic composition of total plant water (sampled by CVD), which also contains a fraction of water that could be stored in plant tissues for a long time. Keywords: stable isotopes of hydrogen and oxygen; cryogenic vacuum distillation; Scholander pressure chamber; plant water; xylem water.

Subdaily extreme precipitation may trigger fast hydro-geomorphic responses, such as flash floods and debris flows, which cause numerous fatalities and large damage. Compared to coarser resolution models, high-resolution models, called convection-permitting (CPMs), more realistically represent convective processes that are key for the correct representation of subdaily extremes, and thus provide higher confidence in the future extreme estimates. However, due to the high computational demands, the existing CPM simulations are only available for relatively short time periods (10–20 years at most), too short for deriving precipitation frequency analyses with conventional approaches. Recent extreme value analysis methods, based on all “ordinary” observations rather than on just yearly maxima or a few values over a high threshold, offer an opportunity for exploiting these short data records to reliably estimate return levels associated with long return periods. Here, we examine subdaily precipitation extremes from three 10-year time slices (historical 1996-2005, near-future 2041-2050, and far future 2090-2099 – under the RCP8.5 scenario) of COSMO-crCLIM model simulations at 2.2 km resolution. We focus on the Eastern Alpine transect characterised by a complex orography, where significant changes in subdaily annual maxima have been already observed. We find that, although the storms' frequency will decrease in the region, the mean annual maxima will increase continuously in the near and far future, especially at shorter durations. Investigation of extreme return levels shows a similar trend, with larger changes in the far future. A shift in the seasonality is also reported, with extremes moving from late summer-autumn (historical), to autumn (near future), and autumn-winter (far future).

Major natural hazards in mountainous regions, such as flash floods and debris flows are mainly triggered by short-duration extreme precipitation. A better understanding of how these events are affected by orography can significantly improve risk management and adaptation to changing climate. Recently, significant progress has been made in high-resolution (particularly convection-permitting) modelling of precipitation over complex terrain, with the advantages of improved topographical features, physical representation of mountain-precipitation interactions, and avoided errors from convective parameterizations.Here, we examine the mountain-precipitation interactions for subdaily precipitation extremes from three 10-year time slices (historical 1996-2005, near-future 2041-2050, and far future 2090-2099 – under the RCP8.5 scenario) of COSMO-crCLIM model simulations at 2.2 km resolution. We use the Upper Adige river basin in the Eastern Italian Alps, with a good coverage of high-quality precipitation data, as a case study. The ability of the convection-permitting model to represent the orographic impact on precipitation is examined based on a comparison between 2000-2009 simulations from the COSMO model run driven with ERA Interim, and observations from the local rain gauge network.Given the availability of relatively short time-slices of model simulation, which prevent the use of conventional extreme value methods, we use here methods based on the concept of ordinary events, which are all the independent events that share the statistical properties of extremes. This offers now an opportunity for deriving frequency analyses from shorter data records, promising improved applications based on convection precipitation simulations.

Understanding the role of forest on rainfall interception is fundamental for a correct analysis and modelling of runoff generation and catchment hydrological response. Despite many studies were carried out at the stand and hillslope scale, very little is known about the role of hillslope topography and the associated tree population characteristics on throughfall spatio-temporal variability. Therefore, this work aimed at better understanding the dominant factors on throughfall variability and on the temporal persistence of throughfall spatial patterns along a transect on a steep hillslope characterized by trees with different size and density.The experimental activities were carried out in the upper part of the densely-forested Re della Pietra catchment, Tuscany Apennines, Central Italy. The hillslope is roughly 110 m long and 60 m wide, has a mean slope of 30°, and is dominantly covered by beech trees and by sparce individuals of oak trees. A grid of 126 throughfall collectors was installed in July 2020 and divided in three sub-plots: two plots of 144 m2 with 2-m spaced 49 collectors at the bottom and the top of the hillslope, and a transect of 28 1-m spaced collectors from the bottom to the top of the hillslope. A survey was conducted to measure the diameter and basal area of the stand. Throughfall was manually measured from the collectors approximately monthly from June 2020 to November 2021, and compared with gross precipitation measured by a rain gauge placed outside the vegetation cover. Moreover, five automatic gauges connected to 1.5 m-long gutter to increase the collection area were installed in November 2021 along the hillslope to measure throughfall at high temporal resolution.Preliminary results from 25 manual measurements over the experimental grid highlighted a large temporal variability of interception (mean: 17%, standard deviation: ±31%), reflecting the variable seasonal precipitation pattern of Mediterranean areas and the phenological stage of trees (leaves/no leaves). Overall, the spatial variability in throughfall increased with increasing gross precipitation. Particularly, the bottom plot, characterized by lower tree density and larger tree size compared to the top plot, showed a lower spatial variability with respect to the top plot, while the longitudinal transect exhibited an intermediate variability. Analogously, the temporal stability analysis revealed that the most temporally-stable and representative measurement points laid on the transect that, overall, captured the different tree characteristics along the hillslope.Future work will make use of the high-resolution measurements of the five gauges to assess their representativeness compared to the manual grid and to test and validate an interception model at the hillslope scale to be possibly upscaled to the entire catchment.

Extreme precipitation in mountainous regions is the main trigger of hydrological hazards such as flash floods and debris flows, among the most dangerous natural-hazards worldwide, both for social and for economic losses. Mountains significantly influence weather and climate, including altered distribution of precipitation and of its extremes, with different impacts at different durations. Understanding the orographic impact on the statistics of precipitation extremes is therefore crucial for improving hydrological design and risk management strategies. Here, we use a novel statistical approach for the analysis of extremes based on ordinary events, which are defined as the finite independent samples of the analyzed stochastic process (e.g. Marani and Ignaccolo, 2015), to improve our understanding of the orographic impact on extreme precipitation of durations ranging between 5 minutes and 24 hours. We focus on Trentino, a rough orographic region in the eastern Italia Alps, and use data from 78 quality-controlled rain gauges with 5-minute resolution. We validated our statistical framework against statistical properties of the observed annual maxima (Nash-Sutcliffe and Bias) as well as their relation with orography. We then exploit the reduced uncertainty of this approach to quantify the orographic impact on precipitation right-tail statistics and on extreme return levels using a regression analysis. We identify two main modes of orographic relationship: a reverse orographic effect for hourly and sub-hourly durations and an orographic enhancement for durations of ~8 hours or longer. We observe that these two modes result from three main precipitation regimes, which show different proportion between extreme and very-extreme events and which emerge at very short durations mid durations and long durations. These findings are of interest for risk management applications and climate change impact studies.ReferencesMarani, M., & Ignaccolo, M. (2015). A metastatistical approach to rainfall extremes. Advances in Water Resources, 79, 121-126.

Convection‐permitting climate models (CMPs) give a much more realistic representation of sub-daily precipitation statistics compared to coarser resolution climate models, thanks to the explicit representation of convection. Their higher spatial and temporal resolution allows to used them directly to study future changes in the frequency, intensity, and spatiotemporal patterns of heavy rainfall over complex terrain. However, the high computational requirements of CPM runs restricts the existing simulations to relatively short time periods (10–20 years), too short for deriving precipitation frequency analyses with conventional approaches. Alternative methods, based on the so called Metastatistical Extreme Value Distribution, were recently proposed (e.g. Marani and Ignaccolo, 2015) for deriving frequency analyses from shorter data records, promising improved applications based on CPMs. These approaches rely on the concept of ordinary events, which are all the independent events that share the statistical properties of extremes: once the upper tail of the distribution of ordinary events is known, it is possible to derive an extreme value distribution by explicitly considering their yearly occurrence frequency.Here, we investigate the CPM ability to represent the upper tail of sub-daily precipitation in a complex-orography region in the Eastern Italian Alps. In this area, different orographic impacts on sub-daily precipitation upper tail were reported at different durations (Formetta et al., 2021), and significant temporal trends in their intensity were reported during the last few decades (Libertino et al., 2019), making it a challenging and interesting test case for CPM simulations. As CPM we used the COSMO model run at 2.2 km resolution over Europe, driven with ERA Interim for the period 2000-2009. We use 180 rain gauges to benchmark the CPM simulation. CPM time series are extracted for the grid points corresponding to the rain gauges, and hourly time series are created from both stations and CPMs. In each time series, independent storms are separated by 24-hour dry hiatuses, and ordinary events for 9 durations between 1 and 24 hours are defined as the corresponding peak intensity of each storm. Ordinary events upper tails are modeled using a Weibull distribution (two-parameter stretched exponential), which was previously reported to well reproduce the statistics of extremes in the area. The ability of CPMs to reproduce the model parameters and extreme quantiles up to 100-year return period, and their dependence on elevation are evaluated, together with the dependence of the biases with elevation. A general overestimation is found for annual maxima (10-40%), and the estimated quantiles (10-60%), especially for short durations. The bias significantly depends on elevation, with increasing overestimation of the 1-hour quantiles with elevation. It seems that CPMs cannot represented well the “reversed orographic effect” reported by previous studies.

Extreme precipitation of multiple durations is responsible for major natural hazards in mountainous regions, such as flash floods and debris flows. Understanding the orographic impact on the statistics of precipitation extremes is therefore crucial for improving hydrological design and risk management strategies. Here, we use a novel statistical approach for the analysis of extremes based on ordinary events to improve our understanding of the orographic impact on extreme precipitation of durations ranging between 5 min and 24 h. We focus on Trentino, a rough orographic region in the eastern Italia Alps, and use data from 78 quality-controlled rain gauges with 5-minute resolution. We show that our framework well reproduces the statistical properties of the observed annual maxima (Nash-Sutcliffe 0.82–0.95, Bias from -4% to 7%) as well as their relation with orography. We then exploit the reduced uncertainty of this approach to quantify the orographic impact on precipitation right-tail statistics and on extreme return levels using a regression analysis. We identify two main modes of orographic relationship: a reverse orographic effect for hourly and sub-hourly durations (10–20% decrease per 1000 m elevation) and an orographic enhancement for durations of ∼8 h or longer (7.5–10% increase per 1000 m elevation). We observe that these two modes result from three main precipitation regimes, which show different proportion between extreme and very-extreme events and which emerge at very short durations (∼20 min or shorter), mid durations (∼30 min-1 hour) and long durations (∼2 h or longer). These findings are of interest for risk management applications and climate change impact studies.

In mountain environments, the coupling of hillslopes processes with the channel network during extreme events is of great importance for rivers dynamics, as debris flows and landslides are among the most important sources of sediments. The Stolla Creek (40 km drainage area, South Tyrol, Italy) is a confined and partly confined mountain channel that was affected by an extreme flood in August 2017, followed by a smaller event in August 2020. The geomorphic effects of the two floods were investigated both in the main channel and over the entire basin with the aim to assess the impacts of the lateral sediment connectivity to the channel response and to the event-scale sediment export. An integrated approach was applied, including radar rainfall estimation, hydrologic-hydraulic analysis, analysis of morphological changes and sediment delivery to the stream network. Hillslope and channel processes were mapped and characterized by using geomorphological analysis of multitemporal orthophotos and Digital Terrain Models. Debris-flow connectivity to the main channel was derived by combining field evidence and GIS-based analysis. The 2017 flood was caused by rainfall with a short duration (6 h) and a rainfall intensity exceeding 45 mm/h. More than 600 debris flows were triggered in the Stolla basin, and the main channel experienced widening (width ratio between 1.3 and 4.9) through bank erosion and overbank deposition. Widening was accompanied by aggradation in the river corridor up to 1.2 m or incision down to -2.2 m. The 2020 flood was characterized by lower rainfall intensity (max 17 mm/h) and a longer duration (48 h), and debris flows were not triggered. The moderate magnitude of the 2020 flood peak did not lead to channel widening, but marked bed incision (up to -1.4 m) occurred in the reaches where aggradation took place during the 2017 event. In both flood events, limited volumes of sediments were exported from the catchment outlet. Overall, our results highlight how structural connectivity at the basin scale determines the potential sediment cascades linking hillslopes to channels but time-varying functional connectivity - driven by hydrological drivers as rainfall intensities and durations - eventually control the actual sediment transport effectiveness both on hillslopes and along the channel.

Gathering systematic information on the effects of extreme weather events (e.g., floods, landslides and debris flows, windthrows) is a fundamental prerequisite to establishing rapid-response strategies and putting into practice management policies. However, the collection of field data requires significant economic and human efforts by local authorities. Furthermore, events occurring in remote areas are rarely detected and mapped accurately as they have a low chance of intersecting human infrastructures. These missed detections lead to incorrect assumptions in relation to both the extreme events’ spatial distribution and, especially, the real occurrence probability. This work proposes a framework for obtaining the automatic identification of severe weather events that may have caused important erosion processes or vegetation damage, combined with a rapid preliminary change detection mapping over the identified areas. The proposed approach leverages the free availability of both high-resolution global scale radar rainfall products and Sentinel-2 multi-spectral images to identify the areas to be analyzed and to carry out change detection algorithms, respectively. Radar rainfall data are analyzed and the areas where high-intensity rainfall and/or very important cumulative precipitation has occurred, are used as a mask for restricting the subsequent analysis, which, in turn, is based on a multi-spectral change detection algorithm. The whole procedure feeds a geodatabase (storing identified events, retrieved data and computed changes) for proper data management and subsequent analyses. The testing phase of the proposed methodology has provided encouraging results: applications to selected mountain catchments hit by intense events in northeastern Italy were capable of recognizing flooded areas, debris-flow and shallow landslide activations, and windthrows. The described approach can serve as a preliminary step toward detailed post-event surveys, but also as a preliminary “quick and dirty” mapping framework for local authorities especially when resources for ad hoc field surveys are not available, or in the case of an event that triggers changes in remote areas. Such a systematic potential change identification can help for a more homogeneous and systematic detection and census of the events and their effects. The workflow herein presented is intended as a starting point on top of which more modules can be added (e.g., radar climatology, SAR change detection for near real-time, other severe sources such as lightning, earthquakes or wildfires, machine learning algorithms for image classification, land use and morphological filtering of the results). Future improvements of the described procedure could be finally devised for allowing a continuous operational activity and for maintaining an open-source software implementation.

Numerous scholars have unravelled the complexities and underlying uncertainties of coupled human and water systems in various fields and disciplines. These complexities, however, are not always reflected in the way in which the dynamics of human-water systems are modelled. One reason is the lack of social data times series, which may be provided by longitudinal surveys. Here, we show the value of collecting longitudinal survey data to enrich sociohydrological modelling of flood risk. To illustrate, we compare and contrast two different approaches (repeated cross-sectional and panel) for collecting longitudinal data, and explore changes in flood risk awareness and preparedness in a municipality hit by a flash flood in 2018. We found that risk awareness has not changed significantly in the timeframe under study (one year). Perceived preparedness also did not change, but we observed differences related to damage severity. More precisely, preparedness increased only among those respondents who suffered low damages during the flood event. We also found gender differences across both approaches for most of the variables explored. Lastly, we argue that results that are consistent across the two approaches constitute robust data that can be used for the parametrisation of sociohydrological models. We posit that there is a need to improve socio-demographic heterogeneity in modelling human-water systems in order to better support risk management.

Flash flooding is strongly modulated by the spatial and temporal variability in heavy precipitation. Storm motion prompts a continuous change of rainfall space-time variability that interacts with the drainage river system, thus influencing the flood response. The impact of storm motion on hydrological response is assessed for the 28 September 2012 flash flood over the semi-arid and medium-sized Guadalentín catchment in Murcia, southeastern Spain. The influence of storm kinematics on flood response is examined through the concept of ‘catchment-scale storm velocity’. This variable quantifies the interaction between the storm system motion and the river drainage network, assessing its influence on the hydrograph peak. By comparing two hydrological simulations forced by rainfall scenarios of distinct spatial and temporal variability, the role of storm system movement on the flood response is effectively isolated. This case study is the first to: (i) show through the catchment-scale storm velocity how storm motion may strongly affect flood peak and timing; and (ii) assess the influence of storm kinematics on hydrological response at different basin scales. In the end, this extreme flash flooding provides a valuable case study of how the interaction between storm motion and drainage properties modulate hydrological response.

Understanding past changes in precipitation extremes could help us predict their dynamics under future conditions. We present a novel approach for analyzing trends in extremes and attributing them ...