Nature-based solutions (NBS) are solutions that can protect, sustainably manage, and restore natural or modified ecosystems in urban and rural areas, while providing many benefits and co-benefits including stormwater mitigation, biodiversity enhancement, and human well-being. As such, NBS have the potential to alleviate many of the environmental, social, and economic issues that we face today. Grey infrastructure, such as lined trenches and catch basins, pipes, and concrete dikes are frequently used for stormwater management and flood protection, but they do not provide many of the co-benefits that are common with NBS. Grey infrastructure is designed to quickly collect and remove rainwater, whereas NBS keep rainwater where it falls, and where it can be used by the environment. Many stakeholders lack knowledge of the capabilities and benefits of NBS, and as a result, they continue to rely on grey infrastructure in their projects. When information is made available on the benefits and how they can be quantitatively measured, it is hoped that NBS will be promoted to a mainstream infrastructure choice. A valuable way to quantify and highlight the benefits of NBS is by using an evaluation framework. There are several evaluation frameworks that qualitatively assess the potential benefits of possible NBS, however there is a need for quantitative frameworks that can assess the actual benefits (or performance) of implemented (or existing) NBS. This article presents an evaluation framework that aims to quantify the benefits and co-benefits of implemented NBS. The framework involves five main steps: (1) selection of NBS benefit categories, (2) selection of NBS indicators, (3) calculation of indicator values, (4) calculation of NBS grade, and (5) recommendations. The outcome of the framework is a single numerical grade that reflects the benefit functioning for an NBS site and values for each performance indicator. This information may be used by decision makers to determine their budget allocations to expand or construct a new NBS site, to update maintenance plans that will improve the benefits of that site, to set up programs to monitor the NBS benefits and co-benefits over time, and to schedule labour and resources for other NBS projects. The framework was tested and validated on a case study of NBS in Thailand. Through conversations with stakeholders and knowledge of the case study area, relevant categories and indicators were chosen. Using data and information obtained through various means, values for each indicator and the overall NBS grade were calculated. The values revealed which benefits were pronounced, those that were weak, and where improvements were required.

/pdf/a-framework-for-assessing-benefits-of-implemented-nature-1ydqe6li8f.pdf

A Framework for Assessing Benefits of Implemented Nature-Based Solutions

Mainstreaming nature-based solutions for climate resilient infrastructure in peri-urban sub-Saharan Africa

Green infrastructure (GI) is a fundamental building block of our cities. It contributes to the sustainability and vitality of cities by offering various benefits such as greening, cooling, water, air quality, and managing carbon emissions. GI plays an essential role in enhancing overall well-being. The utilisation of artificial intelligence (AI) technologies for GI optimisation is perceived as a powerful approach for cities. A knowledge gap, nevertheless, remains in research on AI-driven GI optimisation for tackling climate change. This study aims to consolidate the comprehension of AI-driven GI optimisation, particularly for tackling climate change. The study methodology adopts the PRISMA protocol to perform a systematic literature review. The review results are analysed from six aspects—i.e., optimisation objectives, objectives categories, indicators, models, GI types, and scales. The findings revealed: (a) GI optimisation was mainly undertaken in the areas of air quality, biodiversity and ecosystem security, energy efficiency, public health, heat islands, and water management; (b) Indicator categories were mainly concentrated on indicators related to GI, indicators related to the objective, and other general/supporting indicators. Based on these findings, a framework was developed to enhance the understanding of the AI-driven GI optimisation process within the realm of climate change. 

Algorithmic Green Infrastructure Optimisation: Review of Artificial Intelligence Driven Approaches for Tackling Climate Change

In recent years, there has been an increase in the frequency of urban floods as a result of three determinant factors: the reduction in systems’ capacity due to aging, a changing environment that has resulted in alterations in the hydrological cycle, and the reduction of the permeability of watersheds due to urban growth. Due to this, a question that every urban area must answer is: Are we ready to face these new challenges? The renovation of all the pipes that compose the drainage system is not a feasible solution, and, therefore, the use of new solutions is an increasing trend, leading to a new operational paradigm where water is stored in the system and released at a controlled rate. Hence, technologies, such as stormwater tanks, are being implemented in different cities. This research sought to understand how Climate Change would affect future precipitation, and based on the results, applied two different approaches to determine the optimal location and sizing of storage units, through the application of the Simulated Annealing and Pseudo-Genetic Algorithms. In this process, a strong component of computational modeling was applied in order to allow the optimization algorithms to efficiently reach near-optimal solutions. These approaches were tested in two stormwater networks at Bogota, Colombia, considering three different rainfall scenarios.

Reducing Flood Risk in Changing Environments: Optimal Location and Sizing of Stormwater Tanks Considering Climate Change

As environmental change is happening at an unprecedented pace, a reliable and proper urban drainage design is required to alleviate the negative effects of unexpected extreme rainfall events occurring due to the natural and anthropogenic variations such as climate change and urbanization. Since structure/configuration of a stormwater network plays an imperative role in the design and hydraulic behavior of the system, the goal of this paper is to elaborate upon the significance of possessing redundancy (e.g., alternative flow paths as in loops) under simultaneous hydraulic design in stormwater pipe networks. In this work, an innovative approach based on complex network properties is introduced to systematically and successively reduce the number of loops and, therefore, the level of redundancy, from a given grid-like (street) network. A methodology based on hydrodynamic modelling is utilized to find the optimal design costs for all created structures while satisfying a number of hydraulic design constraints. As a general implication, when structures are subject to extreme precipitation events, the overall capability of looped configurations for discharging runoff more efficiently is higher compared to more branched ones. The reason is due to prevailing (additional) storage volume in the system and existing more alternative water flow paths in looped structures, as opposed to the branched ones in which only unique pathways for discharging peak runoff exist. However, the question arises where to best introduce extra paths in the network? By systematically addressing this question with complex network analysis, the influence of downstream loops was identified to be more significant than that of upstream loops. Findings, additionally, indicated that possessing loop and introducing extra capacity without determining appropriate additional pipes positions in the system (flow direction) can even exacerbate the efficiency of water discharge. Considering a reasonable and cost-effective budget, it would, therefore, be worthwhile to install loop-tree-integrated stormwater collection systems with additional pipes at specific locations, especially downstream, to boost the hydraulic reliability and minimize the damage imposed by the surface flooding upon the metropolitan area.

https://www.mdpi.com/2073-4441/12/4/1003/pdf

Assessing Redundancy in Stormwater Structures Under Hydraulic Design

Green infrastructure (GI) has been regarded as an effective intervention for urban runoff reduction. Despite the growing interest in GI, the technical knowledge that is needed to demonstrate their advantages, cost, and performance in reducing runoff and pollutants is still under research. The present paper describes a framework that aims to obtain the optimal configuration of GI (i.e., the optimal number of units distributed within the catchment) for urban runoff reduction. The research includes an assessment of the performance of GI measures dealing with pollution load, peak runoff, and flood volume reduction. The methodological framework developed includes: (1) data input, (2) GI selection and placement, (3) hydraulic and water quality modelling, and (4) assessing optimal GI measures. The framework was applied in a highly urbanized catchment in Cali, Colombia. The results suggest that if the type of GI measure and its number of units are taken into account within the optimisation process, it is possible to achieve optimal solutions to reduce the proposed reduction objectives with a lower investment cost. In addition, the results also indicate a pollution load, peak runoff, and flood volume reduction for different return periods of at least 33%, 28%, and 60%, respectively. This approach could assist water managers and their stakeholders to assess the trade-offs between different GI.

Configuring Green Infrastructure for Urban Runoff and Pollutant Reduction Using an Optimal Number of Units

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Roberto Galindo

Author Tools

Chat about Author

Papers

Configuring Green Infrastructure for Urban Runoff and Pollutant Reduction Using an Optimal Number of Units