Is planting trees the solution to reducing flood risks?. (2nd August 2018)

• Record Type: Journal Article
• Authors: Carrick, Jayne; Abdul Rahim, Mohd Shaiful Azman Bin; Adjei, Cosmos; Ashraa Kalee, Hassan Habib Hassan; Banks, Steven James; Bolam, Friederike Charlotte; Campos Luna, Ivone Maritza; Clark, Beth; Cowton, Jake; Domingos, Israel Freitas Nongando; Golicha, David Duba; Gupta, Garima; Grainger, Matthew; Hasanaliyeva, Gultakin; Hodgson, David John; Lopez‐Capel, Elisa; Magistrali, Amelia Jo; Merrell, Ian George; Oikeh, Idiegberanoise; Othman, Mwanajuma Salim; Ranathunga Mudiyanselage, Thilanka Kumari Ranathunga; Samuel, Carl Warren Charles; Sufar, Enas KH; Watson, Philip Alexander; Zakaria, Nik Nur Azwanida Binti; Stewart, Gavin
• Is Part Of: Journal of flood risk management. Volume 12(2019)Supplement 2
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-02
• Subjects: Bayesian network -- flood risk -- meta‐analysis -- systematic review -- tree cover; Flood control -- Periodicals; Floods -- Risk assessment -- Periodicals; Flood damage prevention -- Periodicals; Flood warning systems -- Periodicals; 627.405
• DOI: 10.1111/jfr3.12484
• Languages: English
• ISSNs: 1753-318X

Abstract

Abstract : Flood risk and associated impacts are major societal and policy concerns following widespread flooding in December 2015, which cost the UK economy an estimated £5 billion. Increasing advocacy for alternatives to conventional hard engineering solutions is accompanied by demands for evidence. This study provides a systematic review and meta‐analysis of direct evidence for the effect of tree cover on channel discharge. The results highlighted a deficiency in direct evidence. From 7 eligible studies of 156 papers reviewed, the results show that increasing tree cover has a small statistically significant effect on reducing channel discharge. Meta‐analysis reveals that tree cover reduces channel discharge (standardised mean difference −0.35, 95%CI, −0.71 to 0.00), but the effect was variable ( I 2 = 81.91%), the potential for confounding was high, and publication bias is strongly suspected (Egger Test z = 3.0568, p = .002). Due to the lack of direct evidence the overall strength of evidence is low, indicating high uncertainty. Further primary research is required to understand reasons for heterogeneity and reduce uncertainty. A Bayesian network parameterised with data from the meta‐analysis supports investment in integrated catchment management, particularly on infrastructure density and water storage (reservoirs), for effective responses to flood risk.