A new framework for river restoration planning at catchment scale in the UK
Date published
Free to read from
Supervisor/s
Journal Title
Journal ISSN
Volume Title
Publisher
Department
Type
ISSN
Format
Citation
Abstract
The main aim of catchment planning is to prioritise measures that will reverse the decline of biological communities. In recent decades, there has been an increase in methods, tools and the availability of data to aid this process. However, how we use data to make decisions is the crucial and often neglected part of catchment planning, and there is sometimes a tendency to revert to reach‐scale opportunism rather than planning at the catchment scale. Planning approaches in the UK have ranged from public sector–led plans in the 1990s to the present‐day partnership approach led by the third sector (non‐governmental charitable or not‐for‐profit organisations). We have reviewed 237 catchment plans from the UK to understand the approaches that have been taken. Our findings indicate that many plans do not clearly link evidence and data to decision‐making; problems are poorly defined using broad terms such as ‘issues’ instead of characterising pressures and impacts; catchment objectives tend to be broad and not specific; measures are often prioritised based on opportunity; and it is not always clear how measures are expected to contribute to the achievement of catchment targets. Altogether, we noted the absence of agreed, standardised frameworks for producing plans, describing how data should be analysed, problems identified and actions prioritised. We propose a new catchment planning framework that encourages evidence‐based decisions through the assessment of pressures and impacts, and ultimately the prioritisation of river restoration options (encompassing rehabilitation, renaturalisation, enhancement, re‐creation and mitigation of the hydrology, water quality and geomorphology of the river, floodplain and wider catchment) based on their contribution to the alleviation of catchment‐scale impacts, and which can be applied by nonspecialists using citizen science data.