Nature-based solutions (NBS) are being promoted as adaptive measures against predicted increasing hydrometeorological hazards (HMHs), such as heatwaves and floods which have already caused significant loss of life and economic damage across the globe. However, the underpinning factors such as policy framework, end-users' interests and participation for NBS design and operationalisation are yet to be established. We discuss the operationalisation and implementation processes of NBS by means of a novel concept of Open-Air Laboratories (OAL) for its wider acceptance. The design and implementation of environmentally, economically, technically and socio-culturally sustainable NBS require inter- and transdisciplinary approaches which could be achieved by fostering co-creation processes by engaging stakeholders across various sectors and levels, inspiring more effective use of skills, diverse knowledge, manpower and resources, and connecting and harmonising the adaptation aims. The OAL serves as a benchmark for NBS upscaling, replication and exploitation in policy-making process through monitoring by field measurement, evaluation by key performance indicators and building solid evidence on their short- and long-term multiple benefits in different climatic, environmental and socio-economic conditions, thereby alleviating the challenges of political resistance, financial barriers and lack of knowledge. We conclude that holistic management of HMHs by effective use of NBS can be achieved with standard compliant data for replicating and monitoring NBS in OALs, knowledge about policy silos and interaction between research communities and end-users. Further research is needed for multi-risk analysis of HMHs and inclusion of NBS into policy frameworks, adaptable at local, regional and national scales leading to modification in the prevalent guidelines related to HMHs. The findings of this work can be used for developing synergies between current policy frameworks, scientific research and practical implementation of NBS in Europe and beyond for its wider acceptance.

Most of the Earth's Ecosystem Services (ESs) have experienced a decreasing trend in the last few decades, primarily due to increasing human dominance in the natural environment. Identification and categorization of factors that affect the provision of ESs from global to local scales are challenging. This study makes an effort to identify the key driving factors and examine their effects on different ESs in the Sundarbans region, India. We carry out the analysis following five successive steps: (1) quantifying biophysical and economic values of ESs using three valuation approaches; (2) identifying six major driving forces on ESs; (3) categorizing principal data components with dimensionality reduction; (4) constructing multivariate regression models with variance partitioning; (5) implementing six spatial regression models to examine the causal effects of natural and anthropogenic forcings on ESs. Results show that climatic factors, biophysical factors, and environmental stressors significantly affect the ESs. Among the six driving factors, climate factors are highly associated with the ESs variation and explain the maximum model variances (R2 = 0.75-0.81). Socioeconomic (R2 = 0.44-0.66) and development (R2 = 27-0.44) factors have weak to moderate effects on the ESs. Furthermore, the joint effects of the driving factors are much higher than their individual effects. Among the six spatial regression models, Geographical Weighted Regression (GWR) performs the most accurately and explains the maximum model variances. The proposed hybrid valuation method aggregates biophysical and economic estimates of ESs and addresses methodological biases existing in the valuation process. The presented framework can be generalized and applied to other ecosystems at different scales. The outcome of this study could be a reference for decision-makers, planners, land administrators in formulating a suitable action plan and adopting relevant management practices to improve the overall socio-ecological status of the region.

Remote sensing techniques are effectively used for measuring the overall loss of terrestrial ecosystem productivity and biodiversity due to forest fires. The current research focuses on assessing the impacts of forest fires on terrestrial ecosystem productivity in India during 2003-2017. Spatiotemporal changes of satellite remote sensing derived burn indices were estimated for both fire and normal years to analyze the association between forest fires and ecosystem productivity. Two Light Use Efficiency (LUE) models were used to quantify the terrestrial Net Primary Productivity (NPP) of the forest ecosystem using the open-source and freely available remotely sensed data. A novel approach (delta NPP/delta burn indices) is developed to quantify the effects of forest fires on terrestrial carbon emission and ecosystem production. During 2003-2017, the forest fire intensity was found to be very high (>2000) across the eastern Himalayan hilly region, which is mostly covered by dense forest and thereby highly susceptible to wildfires. Scattered patches of intense forest fires were also detected in the lower Himalayan and central Indian states. The spatial correlation between the burn indices and NPP were mainly negative (-0.01 to -0.89) for the fire-prone states as compared to the other neighbouring regions. Additionally, the linear approximation between the burn indices and NPP showed a positive relation (0.01 to 0.63), suggesting a moderate to high impact of the forest fires on the ecosystem production and terrestrial carbon emission. The present approach has the potential to quantify the loss of ecosystem productivity due to forest fires.

Plastic pollution poses a significant environmental threat to the existence and health of biodiversity and the marine ecosystem. The intrusion of plastic to the food chain is a massive concern for human health. Urbanisation, population growth, and tourism have been identified as major contributors to the growing rate of plastic debris, particularly in waterbodies such as rivers, lakes, seas, and oceans. Over the past decade, many studies have focused on identifying the waterbodies near the coastal regions where a high level of accumulated plastics have been found. This research focused on using high-resolution Sentinel-2 satellite remote sensing images to detect floating plastic debris in coastal waterbodies. Accurate detection of plastic debris can help in deploying appropriate measures to reduce plastics in oceans. Two unsupervised (K-means and fuzzy c-means (FCM)) and two supervised (support vector regression (SVR) and semi-supervised fuzzy c-means (SFCM)) classification algorithms were developed to identify floating plastics. The unsupervised classification algorithms consider the remote sensing data as the sole input to develop the models, while the supervised classifications require in situ information on the presence/absence of floating plastics in selected Sentinel-2 grids for modelling. Data from Cyprus and Greece were considered to calibrate the supervised models and to estimate model efficiency. Out of available multiple bands of Sentinel-2 data, a combination of 6 bands of reflectance data (blue, green, red, red edge 2, near infrared, and short wave infrared 1) and two indices (NDVI and FDI) were selected to develop the models, as they were found to be most efficient for detecting floating plastics. The SVR-based supervised classification has an accuracy in the range of 96.9–98.4%, while that for SFCM and FCM clustering are between 35.7 and 64.3% and 69.8 and 82.2%, respectively, and for K-means, the range varies from 69.8 to 81.4%. It needs to be noted that the total number of grids with floating plastics in real-world data considered in this study is 59, which needs to be increased considerably to improve model performance. Training data from other parts of the world needs to be collected to investigate the performance of the classification algorithms at a global scale.