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Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

A data management framework for strategic urban planning using blue-green infrastructure.

J Sörensen¹, A S Persson², J Alkan Olsson²

¹Water Resources Engineering, Lund University, Lund, Sweden.

Journal of Environmental Management

|September 15, 2021

Summary

Effective Blue-Green Infrastructure (BGI) planning requires better data management. This study developed a framework to bridge data gaps and improve BGI implementation in urban planning.

Keywords:

Blue-green infrastructure Climate change adaptation Data management Spatial planning Stormwater management Strategic planning Urban green spaces

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Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

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Design and Construction of an Urban Runoff Research Facility

Design and Construction of an Urban Runoff Research Facility

Published on: August 8, 2014

Area of Science:

Urban Planning
Environmental Management
Geographic Information Systems (GIS)

Background:

Spatial planning of Blue-Green Infrastructure (BGI) necessitates context-specific solutions.
Adequate data provisioning is crucial for effective BGI governance, including planning, design, construction, and maintenance.
A significant obstacle is the gap between data availability and BGI implementation in urban planning.

Purpose of the Study:

To explore the data availability gap for BGI implementation in Swedish urban planning authorities.
To develop a framework for structured and user-friendly data collection and use for BGI.
To identify challenges and propose solutions for data management in BGI planning.

Main Methods:

Multi-method approach: brainstorming, semi-structured interviews with urban planners and BGI/GIS experts, and validating workshops.
Exploration of data availability and BGI implementation challenges.
Development of a data management framework for BGI.

Main Results:

Key challenges identified: data availability, data management, and GIS knowledge.
Need for improved data management organization and trans-disciplinary cooperation skills.
Strategic political goals must align with data collection for efficient BGI planning.

Conclusions:

A data management framework comprising ideal structure, data needs, and access mechanisms was developed.
Pan-municipal, cross-sectoral data management systems are essential for efficient urban environmental management.
The framework supports citizen involvement in data collection and access, aiding BGI development.