Urbanisation and the consequent loss of permeable surfaces, is influencing the likelihood and severity of flood events. This, coupled with recent climate change predictions which suggest that sea levels will rise and that extreme weather events will become more frequent, has pushed the issue of flooding up the public agenda, encouraging the relevant authorities to more robustly target the challenges of increased flood risk. Tree planting is gaining momentum as a potential method of natural flood management (NFM) due to its capacity to break up soil and increase infiltration and water storage. Consequently, the aims of this study were to simulate the influences of woodland planting on infiltration and area hydrology through use of empirical observations and hydrological modelling, and extrapolate data to predict the likely hydrological changes across the area in the future, considering climate change. The infiltration characteristics throughout defined Heart of England (HofE) Forest owned areas of Warwickshire (England), planted with woodland between 2006 and 2020, were sampled using a Mini Disk infiltrometer (MDI). In total, 1686 measurements were taken at both proximities between November 2019 and August 2021. Two individual hydrological models were built, calibrated and validated using the US Hydrologic Engineering Centre’s Hydrological Modelling System (HEC-HMS). In total 448 HEC-HMS simulations were undertaken. Infiltration was found to be 75.87% higher at 10 cm proximity compared with the 200 cm proximity in winter, and 25.19% higher in summer. The mean 10 cm infiltration was 192% higher in summer compared with winter, and mean 200 cm infiltration is 310% higher in summer compared with winter. Regarding the hydrological simulations, woodland planting reduced peak flow intensity compared to impermeable land cover by an average of 6%, 2%, and 1% for 6-, 24-, and 96-hour winter storms respectively, and 48%, 18%, and 3% for 6-, 24- , and 96-hour summer storms respectively. Grassland simulations show the greatest reduction in peak flows, being 32%, 21%, and 10%, lower than woodland for 6-, 24-, and 96-hour winter storms respectively, and 6%, 3%, and 0.5% lower than woodland for 6-, 24-, and 96-hour summer storms respectively. Model projections show that woodland planting is unlikely to mitigate future projected peak flow and total discharge from the HofE site throughout the winter, however it is more likely to have an impact throughout summer. This is primarily due to soil texture characteristics across the site, the influence of hydrological model parameters, and the inclusion of interception throughout the summer. ii Overall, tree planting does influence infiltration on a case-by-case basis, however ‘present-day’ hydrological simulations show grassland to reduce peak and total discharge from the site to a greater extent than woodland. Projections show that this will likely change as woodland matures, however further research will have to be conducted to further solidify this finding. It is concluded that woodland is beneficial as a method of NFM, however should not be relied upon to mitigate against larger storm events. The outcomes of this study are contextualised, and recommendations posed, with regards to the construction and forestry industry, and current and future water policy; namely, the Department of Food and Rural Affairs (DEFRA) policies, the Agricultural Act, the 25-year plan, the England trees action plan, and additional international policy. The overarching conclusions are that tree planting is featuring more prominently in recent and upcoming policy, however the main motivation being the ability of woodland to sequester carbon. It is recommended that more emphasis be targeted towards the hydrological benefits of trees, to further justify the planting of large-scale woodland, and encourage researchers to investigate the hydrological benefits of woodland planting, as has been done throughout this study. This, in turn, would aid in the current knowledge gap regarding woodland planting as a method of NFM.
| Date of Award | Jul 2022 |
|---|
| Original language | English |
|---|
| Awarding Institution | |
|---|
| Supervisor | Craig Lashford (Supervisor), Matthew Blackett (Supervisor) & Matteo Rubinato (Supervisor) |
|---|