Abstract
One potential Natural Flood Management (NFM) option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterized for hydraulic models. Remote sensing offers high-resolution datasets capable of characterizing vegetation structure from a variety of platforms, but they contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood, and leaf area indices (20, 10, 30, 20%, respectively) resulted in a combined Manning’s n uncertainty from 11–13% to 11–17% at 2 m to 8 m flow depths. This equates to 7–8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, especially for flow though Pine plantations. For deeper flows, leaf and woody areas become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4–6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterization and flood modeling for NFM.
Highlights
River flooding between 1987–2017 has killed an estimated 665,000 and displaced 628 million people worldwide, while extreme events (>100 year recurrence interval) account for 290,000 deaths and 265 million displaced people [1]
We explore the effects of propagating various levels of uncertainty in predicting roughness in a series of test forest types: young and mature poplar plantations, young and mature pine plantations, and an unmanaged riparian forest, using literature forest structure uncertainty from Section 2
The size class distribution of each of the five forest types are presented in Figure 1, along with the vertical distribution leaf area index (LAI) and wood area index (WAI) for each forest type (Figure 1, middle row)
Summary
River flooding between 1987–2017 has killed an estimated 665,000 and displaced 628 million people worldwide, while extreme events (>100 year recurrence interval) account for 290,000 deaths and 265 million displaced people [1]. The science and message to policy concerning forest effects on flooding have been conflicted [17], especially in relation to the magnitude of flow and the complexity of the system at large spatial scales, such as studies stating that forests cannot effectively delay large-scale floods in larger river systems [18,19]. Most of these considerations have not largely focused on riparian nor floodplain forests, nor accurately describing their structural frontal area to flow
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