LiDAR‐based fluvial remote sensing to assess 50–100‐year human‐driven channel changes at a regional level: The case of the Piedmont Region, Italy

Abstract

AbstractRemote Sensing (RS) technology has recently offered new and promising opportunities to analyze river systems. In this paper, we present a calibration of characteristic Hydraulic Scaling Law (HSL) using a regional database of river geomorphic features. We consistently linked discharge with channel geometry features for estimated Bankfull Channel Depth (eBCD), Active Channel Width (ACW), and Low Flow water Channel Width (LFCW), which are continuously available from RS data along the river course. We then used historical information and external sources of information on channel reaches that were relatively unaffected by human pressure over periods ranging from a few decades to a century (measured in comparable geographical areas) to infer relatively Unaltered HSLs (rUHSLs). Adopting rUHSL validated with available local historical evidence on channel geometry, we were able to assess historical changes in channel geometry consistently over the entire region and within the studied temporal window.The case study was conducted for the Po basin in the Piedmont Region, north‐west Italy. From our analysis, it emerges that regionally 74% of the river network has riverbed incisions exceeding 1 m, while 66% of channels have halved their historical widths with a total of 617 ha of land subtracted from the active channel. LFCW is, on average, wider in Alpine rivers compared with those located in the North Apennines.Although it is currently not possible to measure the accuracy of these estimates, the evidence generated is coherent with available historical information, characteristic hydraulic scaling laws, evidence from relatively unaltered reaches and the available literature on local fluvial systems. This methodology provides robust, novel and quantitative information regarding decadal to secular channel changes that have occurred on a regional scale. This new layer of information enriches our ability to rationally address assessments of large‐scale past and future channel trajectories. © 2018 John Wiley & Sons, Ltd.