Abstract
Common approaches to large flood management are Natural Water Retention Measures and detention basins. In this study, a Partial Least Squares-Path Model (PLS-PM) was defined to set up a relationship between dam wall heights and biophysical parameters, in critical flood risk zones of continental Portugal. The purpose was to verify if the heights responded to changes in the biophysical variables, and in those cases to forecast landscape changes capable to reduce the heights towards sustainable values (e.g., <8 m). The biophysical parameters comprised a diversity of watershed characteristics, such as land use and geology, surface runoff, climate indicators and the dam heights. The results have shown that terrain slope (w > 0.5), rainfall (w > 0.4) and sedimentary rocks (w > 0.5) are among the most important variables in the model. Changes in these parameters would trigger visible changes in the dam wall height, but they are not easily or rapidly modified by human activity. On the other hand, the parameters forest occupation and runoff coefficient seem to play a less prominent role in the model (w < 0.1), even though they can be significantly modified by human intervention. Consequently, in a scenario of land cover change where forest occupation is increased by 30% and impermeable surfaces are decreased by 30%, interferences in the dam heights were small. These results open a discussion about the feasibility to mitigate large floods using non-structural measures such as reforestation.
Highlights
One of the consequences of climate change is the increase of spatial and temporal water variability as well as extreme events, in frequency and intensity [1,2]
The Partial Least Squares-Path Model (PLS-PM) model for the eight Critical Zones, the 75 sub-basins with dam height >8 m is provided as Supplementary Material (Table S3) and was compiled from the previous work by Reference [16]. This model relates the measured variables (MV) to their latent variables (LV) through the MIMIC approach, i.e., through a mixture of reflective and formative models [56]
The formative model requires an assessment of multi collinearity among measured variables through analysis of variance inflation factors (VIF), which must be lower than 5 for predictive purposes [58]
Summary
One of the consequences of climate change is the increase of spatial and temporal water variability as well as extreme events, in frequency and intensity [1,2]. Floods are among the most destructive water-related hazards and are the greatest economic natural disaster that occur in Europe via damage property and infrastructure, as well as physical injury and loss of human lives [3]. Technical means for controlling extreme floods remain limited, fosters a need for an ongoing paradigm shift in how to deal with floods [4]. These difficulties powered the necessity for effective action programmes driven by policy in Europe. According to these limitations, European Commission and the Council of the European Union prompted to put forward the Directive 60/2007/EC, referred to as the Floods.
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