Open AccessIs planting trees the solution to reducing flood risks?
Author(s) -
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 gando,
Golicha David Duba,
Gupta Garima,
Grainger Matthew,
Hasanaliyeva Gultakin,
Hodgson David John,
LopezCapel 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
Publication year - 2019
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12484
Subject(s) - flood myth , flooding (psychology) , confounding , environmental science , propensity score matching , meta analysis , channel (broadcasting) , business , econometrics , hydrology (agriculture) , economics , geography , computer science , statistics , engineering , mathematics , psychology , geotechnical engineering , archaeology , medicine , computer network , psychotherapist
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.