Land use change in the Veneto floodplain and consequences on minor network drainage system
AbstractAnthropic pressure has been proven to be one of the most evident forces able to alter landscapes. Its impact on the surroundings can be easily detectable especially in a high-density populated country such as Italy. Among the most evident anthropic alterations, the most important are the urbanization processes but also changes in cultural techniques that have been occurring in rural areas. These modifications influence the hydrologic regimes in two ways: by modifying the direct runoff production and by having a strong impact on the drainage system itself. The main objectives of this work are to evaluate the impact of land cover changes in the Veneto region (north-east Italy) on the minor drainage network system, and to analyze changes in the direct runoff in the last 50 years. The study area is a typical agrarian landscape and it has been chosen considering its involvement in the major flood of 2010 and considering also the availability of data, including historical aerial photographs, historical information, and a high resolution LiDAR DTM. The results underline how land cover variations over the last 50 years have strongly increased the propension of the soil to produce direct runoff (increase of the Curve Number value) and they have also reduced the extent of the minor network system to the detriment of urbanized areas and changes of plots of land boundaries. As a consequence, the capacity of the minor network to attenuate and eventually laminate a flood event is decreased as well. These analysis can be considered useful tools for a suitable land use planning in flood prone areas.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2013 Massimo Prosdocimi, Giulia Sofia, Giancarlo Dalla Fontana, Paolo Tarolli
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.