The influence of land management and seasonal changes in surface vegetation on flood mitigation in twoUKupland catchments

Abstract

As the frequency and magnitude of storm events increase with climate change, understanding how season and management influence flood peaks is essential. The influence of season and management of grasslands on flood peak timing and magnitude was modelled for Swindale and Calderdale, two catchments in northern England. Spatially‐Distributed TOPMODEL was used to investigate two scenarios across four storm events using empirically‐based soil and vegetation data. The first scenario applied seasonal changes in vegetative roughness, quantifying the effect on flood peaks at catchment scale. The second scenario modelled the influence of grassland management from historical high‐intensity grazing to a series of natural succession stages between grassland and woodland, and a conservation‐based management. Model outputs were analysed by flow type, measuring total, overland and base flow peaks at the catchment outlet. Seasonal changes to vegetation were found to increase overland flow peaks by up to +2.2% in winter and reduce them by −5.5% in summer compared to the annual average. Percentage changes in flood peak due to hillslope grassland management scenarios were more substantial; overland flow peaks were reduced by up to 41% in Calderdale where extensive woodland development was the most effective mitigation strategy, and up to 35% in Swindale, where a rank grassland dominated catchment was the most effective. Conservation‐based farming practices were also useful, reducing overland flow peak by up to 42% compared to the high intensity grazing scenario. Neither management nor seasonality changed the timing of runoff peaks by >45 min. Where overland flow dominates, especially in catchments with shallow soils, surface roughness was found to be more influential than soil permeability for flood mitigation. We recommend that seasonal changes to roughness are considered alongside the spatial distribution of Natural Flood Management in mosaiced upland catchments.

Investigating the impact of woodland placement and percentage cover on flood peaks in an upland catchment using spatially distributed TOPMODEL

“…We used 48 simulations of SD‐TOPMODEL to identify the best performing set of parameters ( m = 6 mm, K = 6.9 m·h −1 , k v = 40, see Supplementary Material 2). There was good correspondence between this best fit model and observed flow in the calibration period (NSE was 0.85) (Figure 2) with similar NSE to previous work (Bond et al, 2022; Gao et al, 2017).…”

“…It is important to note, that catchment characteristics, such as slope, also have a role to play in determining the hydrological response to land use change. For example, land cover change on steeper slopes may not ‘slow the flow’ of overland flow to the same extent as shallower slopes (Bond et al, 2022; Maske & Jain, 2014).…”

“…This allows a model setup that explores more of the catchment heterogeneity than is possible with a lumped or semi‐distributed model setup, making it a useful tool to examine the impact of land cover on flow (Gao et al, 2015). SD‐TOPMODEL has been used to simulate the impact of land‐cover change in peatlands (Gao et al, 2016), agricultural practices (Bond et al, 2022; Gao et al, 2017) and landscape features, such as stone walls and hedges (Willis & Klaar, 2021) on flood peaks in upland catchments.…”

“…As in previous work (Bond et al, 2022; Gao et al, 2017) the Nash–Sutcliffe efficiency coefficient (NSE) was used to evaluate SD‐TOPMODEL and to select the best performing model. The NSE is a widely used indicator of model performance (Ritter & Munoz‐Carpena, 2013) and was chosen as it assesses the shape of the hydrograph and to be consistent with previous work.…”

Evaluating the effectiveness of land use management as a natural flood management intervention in reducing the impact of flooding for an upland catchment

“…SD-TOPMODEL has been applied previously to an upland peatland catchment to model the effects of typical upland management activities such as grazing, vegetation restoration and rotational burning (Bond et al, 2022;Gao et al, 2016Gao et al, , 2017. Previous SD-TOPMODEL applications have derived all parameter values primarily from literature values (Boisgontier, 2018).…”