Land use changes

Overall the region witnessed a statistically significant trend in the increase of built-up area (p-value 0.0275). The imperviousness in the 70s (Fig. 2a) was related to the larger cities in the region (e.g. Padova and Venezia). In 1990 and 2009, the whole region appears to be highly impervious. The highest increase in imperviousness is the one from 1970 to 1990 (Fig. 2d), and it is mostly related to the floodplain area of the region.

Figure 2: Kernel Density Estimate (KDE) of the degree of imperviousness during the years. (a) 1970, (b) 1990, (c) 2009, and KDE of imperviousness changes (Imp ch ) between (d) 1970–1990 and (e) 1990–2009. The map was created with ArcGIS version 10.4 (https://www.arcgis.com/). Full size image

The significant changes in imperviousness can be explained by the economic trend of Veneto during the years. In the 1970s, the Gross Domestic Product (GDP) increased more than 30% per year, resulting in Veneto being the second Italian region for GDP30. During the 80s, Italy became one of the main economies of Western Europe, thanks to the northern Italy regions. Amongst them, the Veneto economy became an international example. In the 80s the region of Veneto experienced an economic transformation in parallel with a profound change in politics as well31. Since 1982, the region’s GDP has kept up a trend of constant growth achieving, from 1993 to 1999, one of the most relevant levels in the whole Europe30. In 2000–2007, the GDP grew by 1.3%, showing the greater growing patterns throughout all period analysed except during the crises that penalised the region from 2007 until 201130. The economic growth caused major changes in families’ way of life and local sociability networks. Veneto is characterised by industrial facilities relocation and the diffusion of a manufacturing system based on small firms, whereas small dispersed rural settlements characterise the local social and cultural structure. These interactions resulted, especially in the 1970–1990 timeframe, in dispersed settlements developing within the network of large urban centres28,32, mostly resulting in loss of agricultural landscape. This high level of urbanisation along with agricultural mechanisation and the regulation of watercourses determined a certain tendency to simplification and unification of the landscape32. Differently, the changes over the 1990–2010 period mostly followed transport infrastructures (Fig. 3), and resulted in a low-density suburban development in the periphery of cities, as also witnessed in other countries of Europe33.

Figure 3: Veneto floodplain. Built-up area in 1970, 1990 and 2012. The map was created with ArcGIS version 10.4 (https://www.arcgis.com/). Full size image

The analysed urban sprawl and the development of urban land also transformed the properties of soil, reducing its capacity to perform its essential functions. A fully functioning soil for the analysed landscape34 can store water for more than 300 m3/ha. Covering land with impermeable layers reduces the amount of rain that can be absorbed by the soil. The average changes in imperviousness between 1970 and 1990 (~6%) and between 1990 and 2009 (~2%) can roughly imply in a loss of ~30 M m3 and ~10 M m3 of water storage over the whole region. In cities with a high proportion of sealed surfaces this loss of storage, especially during heavy rains, can quickly overwhelm drains, causing sewage systems to overflow35.

Climatic trends

The climatic trends (Fig. 4) are in line with those already published36.

Figure 4: Rainfall concentration (Cl a ) over the region for the different timeframes and average value derived from these maps. The map was created with ArcGIS version 10.4 (https://www.arcgis.com/). Full size image

Overall, there is a general statistically significant (p-value 0.086) trend of increase of the concentration of the rainfall. The average Climatic aggressiveness (Cl a ) moves from 0.59 (in 1910–1930) to a value of 0.64. The high value of the last timeframe might be in line with the changes in the decadal climate of the years 1991–2000 and 2001–201037. While Cl a seems to increase constantly during the five timeframes, the timeframe 1970–1990 shows a sensible decrease in the index compared the previous time spans, especially in the central part of the region. Climatically, the region registered a statistically negative trend in the yearly amount of rainfall, more marked in the winter season, with a drastic variation of the winter events in the 80s36. These changes are in line with those registered at a wider scale38,39 and might be correlated with the variability of the global atmospheric circulation40.

The coastal area (east/south-east) of the region appears to be the one distinctly different from the rest of the area regarding precipitation concentration, with the higher values of Cl a in each timeframe. This particular climate is due to the proximity to the sea, which causes convective rainfalls to pair with synoptic durations combined to produce exceptionally high rainfall accumulations in this area39.

Flood analysis

The overall trend between the percentage of flooding days in each year and the number of flooded locations for the whole 1900–2010 timeframe shows the presence of drastic changes in the curve steepness (knick points, labelled with Ks in Fig. 5), implying that during the years fewer days of flood contribute to a notable increase in the percentage of the flooded locations.

Figure 5: Flood analysis and major past floods in Veneto. Accumulated percentages of flooded sites contributed by the cumulative percentage of flooding days in each year for the whole 1900–2010 timeframe. Knickpoints of the curve are labelled with Ks. Full size image

The first knickpoint around 1917 (K 0 in Fig. 5) is probably due to the nature of the considered database that covers only non-systematically the periods 1900 to 191641. The other knickpoints (K 1 to K 5 ) are related to major flood events that affected the region in 1928 (K 1 in Fig. 6)41, 1951 (K 2 in Fig. 5)42, 1966 (K 3 in Fig. 5)41, 1992 (K 4 in Fig. 5)41, 1998 (K 5 in Fig. 5)43.

Figure 6: Flood concentration (Fl a ) over the region and trends during the year, evaluated considering or excluding the major flood events. The map was created with ArcGIS version 10.4 (https://www.arcgis.com/). Full size image

It is interesting to notice that 1) the analysis of the days of floods VS flooded locations allows to identify clearly major events; 2) between these major events, the trends between days of flood and the percentage of the flooded locations appear to be constant. Analysing the graph considering the timeframes proposed for the research [1910–1930, 1930–1950, 1950–1970, 1970–1990, 1990–2010], it is possible to define statistically significant relationships (p-value always < 0.01). In the timeframe 1950–1970, the high slope of the trend is mostly due to the two major events (1951 and 1966). Differently, in the recent decade (1990–2010), the trend seems to be related to a larger number of flood events with a relatively shorter duration (fewer days of floods), which hit a greater number of locations, in addition to the two major floods (1992 and 1998). Thus, suggesting a larger coupling of the land-use and climatic influence for the more recent timeframe.

The average flood concentration (Fig. 6) shows an increasing trend over the considered timeframes, from a value of 0.7 in the period 1910–1930 to a value of 0.9 for the timeframe 1990–2010. Clearly, the major flood events that hit the region have an influence on the index. However the trend appears to be similar, and it becomes more regular when removing such events (1928, 1966, 1992, 1998) from the computation.

The 1910 to 2010 trend is non-significant, both including and excluding the major flood events. The data in the 1910–1930 timeframe are, however, hindered due to the nature of the considered database, that covers only non-systematically the periods 1900 to 191641. Removing the 1910–1930 timeframe makes the trend in Fl a significant (p-value 0.089).

Land-use, Climate and Flood interaction

In both 1970–1990 and 1990–2010, the areas having the larger changes in imperviousness (Fig. 7a and d) do not necessarily correspond to regions with the higher climatic concentration (Fig. 7b and e). However, in these areas, the concentration of the floods is high (Fig. 7c and f).

Figure 7 Changes in imperviousness (Imp ch in a,d), climatic concentration (Cl a in b,e), and flood concentration (Fl a in c,f) for the timeframe 1970–1990 (a,b,c) 1990–2010 (d,e,f). The values are classified into Low, Medium-Low, Medium-High and High based on a Natural Breaks approach. A map showing the overall elevation of the region is also shown (g). The map was created with ArcGIS version 10.4 (https://www.arcgis.com/). Full size image

In both timeframes, there is a significant relationship between the flood concentration and the rainfall concentration (Table 1). Individually, Imp ch and Cl a have a significant effect on Fl a .

Table 1 ANOVA analysis showing the significance of effects on Fl a of rainfall concentration (Cl a ), changes in imperviousness (Imp ch ), rainfall concentration upstream (Cl up ), changes in imperviousness upstream (Imp up ), and their interaction (indicated by the column). Full size table

The statistical significance and the changes in order of magnitude (O m changes in Table 1) highlight that despite the overall decrease in the yearly rainfall registered during the years36, the increased concentration of the rainfall events (more rain in fewer days) might have resulted in increased concentration of floods, whereas more localities are flooded in fewer days of floods. As well, despite being lower in amount, the changes in imperviousness are still significantly impacting the flood concentration index. In 1990–2010, in addition to the large-scale event of 1998 (K 5 in Fig. 5), the higher significance was also connected to the influence of climate and urbanisation on local flooding attached to the failure of the urban or peri-urban drainage system10,11,15,32.

Furthermore, one should consider that the eastern part of the Region, along the coastline where the climate is more aggressive (e.g. Fig. 7b and e), is characterised by lands lying below sea level (Fig. 7g) that require continuous management of the reclamation networks to stay viable34. These areas often witness flooding due to lack of volumes of storage for water within the channels, and the intensity of rainfall events has a significant effect on this15,18.

To better exemplify and analyse the interaction among changes in imperviousness and climatic concentration, Figure 8 shows the estimated effects on Fl a of keeping one predictor fixed (Climatic concentration, Cl a and changes in imperviousness, Imp ch ) while varying the other. The average effect is shown as a circle, while the horizontal bars are showing the confidence interval for the estimated effect.

Figure 8 Estimated effects on Fl a of keeping one predictor fixed (Climatic concentration, Cl a in the top half, and changes in imperviousness, Imp ch in the bottom one) while varying the other for the (a) 1970–1990 and (b) 1990–2010 timeframe. The average effect is shown as a circle, while the horizontal bars are showing the confidence interval for the estimated effect. The blue symbols represent the overall average effect obtained by changing one predictor independently from the other, while the red ones represent the average effect achieved by changing one predictor over different values of the other one. Full size image

For both timeframes, the interaction plots show that the increase in rainfall concentration has a direct (positive) effect on the flood concentration (blue symbol in the top half in Fig. 8a and b). The changes in land use have a greater effect in 1970–1990 respect 1990–2010 (confirming the ANOVA analysis) (blue symbol in the bottom half in Fig. 8a and b): this mostly because the amount of changes in that timeframe is higher (Fig. 7a and b). Overall, the higher the changes in imperviousness, the higher the effect on Fl a at the increase in rainfall concentration (red symbol in the top half in Fig. 8a and b). Changes in imperviousness instead have different implications for the flood concentration, depending on the climatic concentration (red symbols in the bottom half in Fig. 8a and b). For the higher climatic concentration (Cl a = High in Fig. 8a and b), an increase in imperviousness is correlated directly to an increase in the flood concentration (Fl a has a positive variation). However, for Medium-Low values of Cl a , changes in imperviousness does not seem to have a great impact on the flood concentration, but still, they imply a slightly positive variation in Fl a , at least in the 1970–1990 timeframe. It is interesting to notice, however, that for the least aggressive climate (Cl a = Low in Fig. 8a and b), the increase of imperviousness seems to have a lowering effect on the flood concentration. Different trends in climate and urbanisation depending on the topographic location might explain this latter point (Fig. 9). In both timeframes, part of the region is characterised by an inverse relationship between changes in imperviousness and elevation: urbanisation increases largely in the floodplains, while changes in imperviousness are low (but still positive) for mountain areas. In this same zone, however, the climatic trend is opposite: the rainfall concentration increases with increasing elevation due to the complex role of topography influencing the characteristics of the daily rainfall frequency44. The Alpine together with the higher zone of the Pre-alpine territory has an overall low (but increasing at the growth in elevation) level of Cl a that could explain the adverse effects of the increase in imperviousness on the flood concentration in Fig. 8a and b.

Figure 9 Flood concentration (Fl a ) for 1970–1990 (a) and 1990–2010 (b) as related to the landscape topography (elevation m a.s.l.), climatic concentration -Cl a -, and changes in imperviousness -Imp ch -. The percentage changes in imperviousness are shown as multiples of 102. Elevation was computed considering a Digital Elevation Model with a 50 m cell size. Thus they are an approximation of the actual elevation. Full size image

A second part of the region, instead, differs in the two timeframes. This area comprises among the rest, the coastal part of the area, where the proximity to the sea produce exceptionally high rainfall accumulations39, and urbanisation is highly prominent. In 1970–1990 this part was characterised by an increase in rainfall concentration and a simultaneous growth in imperviousness (Fig. 9a). Especially in the time frame 1970–1990 (Fig. 9a) these contemporary trends result in the highest level of flood concentration. The 1990–2010 period partially differs (Fig. 9b). While the trend for the areas having the lower rainfall concentration is similar to that of 1970–1990 (changes in imperviousness decreases with elevation, while climatic concentration increases), the area with the higher rainfall concentration has a different land-use dynamic. In the 1990–2010 timeframe, there are few areas where urbanisation happens in a relatively larger area of the lower Pre-Alps in addition to the floodplain.

For the rainfall concentration (Cl up ) and changes in imperviousness (Imp up ) upstream it is possible to partially draw the same conclusions as described for the local Cl a and Imp ch (Table 1): both parameters, when taken independently, have a significant effect on the flood concentration. However, the significance diminishes in the recent timeframe. The changes in the order of magnitude (O m changes in Table 1) highlight how the changes of imperviousness upstream, despite being still significant, have a lower effect on flood aggressiveness respect to the past (p-value increases of 12 orders of magnitude).

The results highlight that in the 1990–2010 timeframe, local changes in imperviousness seem to couple significantly with climate concentration, both considering either the upstream climate alone (Imp ch :Cl up in Table 1) or combined with local climate concentration (Cl a :Imp ch :Cl up in Table 1). For this timeframe, the imperviousness changes -either upstream or local- cannot explain the increase of flood concentration independently from the climate input (Imp ch :Imp up in Table 1), while they interacted in the 1970–1990 timeframe with a direct effect on Fl a . Despite being significant when taken separately, local climate input and upstream climate do not have a significant interaction in either timeframe. In the 1970–1990 timeframe, upstream climate aggressiveness was more significant when coupling with the upstream land use changes with or without coupling with the local climate changes (Cl up :Imp up and Cl a :Cl up :Imp up in Table 1).

In 1990–2010, when local climate concentration is low (Fig. 10a), the increase of climate concentration upstream (Cl up from Low to Medium-High) has an opposite effect on the local flood concentration. However, when the local imperviousness changes are high (Imp ch = High), the changes in climate upstream lose their effect on Fl a (top half Fig. 10a). An increase of the local imperviousness changes (Imp ch from Medium-Low to High in the bottom half Fig. 10a) has a slightly negative effect on Fl a : this effect, however, gets closer to positive when climate concentration upstream is Medium-High.

Figure 10: Timeframe 1990–2010. Estimated effects on Fl a of keeping one predictor fixed (Climatic concentration upstream, Cl up, in the top half, and local changes in imperviousness, Imp ch, in the bottom one) while varying the other for (a) Low local climate concentration (Cl a ) (b) Average local climate concentration and (c) High local climate concentration. The average effect is shown as a circle, while the horizontal bars are showing the confidence interval for the estimated effect. The blue symbols represent the overall average effect obtained by changing one predictor independently from the other, while the red ones represent the average effect achieved by changing one predictor over different values of the other one. Full size image