Understanding the total water flows and pollutant loads to the Baltic Sea is important for effective coastal-marine ecosystem management. Current assessments often overlook the unmonitored flows and submarine groundwater discharge (SGD). This study proposes and outlines a conceptual modelling framework for overcoming this common neglect by integrated quantification of (1) the monitored surface water flows, and the unmonitored (2) surface water flows and (3) SGD from land to the Baltic Sea. The study outlines how unmonitored runoff and SGD can be estimated by various quantification approaches based on commonly available hydro-climatic, hydrogeological, and other characteristic catchment data. It also describes how modules for the different monitored and unmonitored discharge components are linked and should be integrated in modelling to total annual, seasonal, or finer-resolved water flows to the Baltic Sea, and analogously also in other coastal regions around the world. Though quantitative modelling remains ongoing, the conceptualization opens pathways to improve assessments and management of freshwater flows and associated pollutant loads to the Baltic Sea.

Landslides are a formidable natural geological hazard that impose significant disruptions on societal and economic functions. The magnitude of impact of landslides on human life makes early detection imperative in landslide-prone areas. The aims of this study were to detect landslide locations triggered by the Taiwan Morakot typhoon in 2009 using Sentinel-2 imagery. The fully convolutional network; image cascade network; red, green, blue plus depth (RGB-D) fusion network (RDFNet); and segmented neural network algorithms were developed to more accurately detected historical landslide locations and afterward generate a landslide susceptibility map using standard and optimized deep learning models (convolutional neural network [CNN], CNN-whale optimization algorithm, and CNN-Harris hawk optimization [HHO]). Analysis using frequency ratio to evaluate the relationship between classes of different causative factors and landslide occurrence revealed that slope length and land cover were most strongly correlated with landslide occurrence. Among the algorithms tested, RDFNet demonstrated superior accuracy in detecting historical landslide locations, achieving the highest F1-score (0.67), precision (0.55), recall (0.84), and accuracy (0.92). The landslide inventory dataset derived from RDFNet was used in landslide susceptibility modeling, and was divided into 70 % and 30 %, respectively, for training and testing the CNN-based models. Mean square error (MSE), root mean square error (RMSE), standard deviation (StD), and area under the receiver operating characteristic curve (AUROC) were used to evaluate goodness-of-fit and predictive ability of the CNN-based models. The most reliable outcome was obtained using CNN-HHO, with AUROC = 0.85, MSE = 0.017, RMSE = 0.132, and StD = 0.129 during the testing step. By leveraging insights provided by modeling into areas with high landslide susceptibility, authorities can proactively implement measures to mitigate the potential consequences of landslides and promote sustainable stewardship of natural resources.

Landslides pose a significant global threat, causing extensive loss of life, economic damage and environmental degradation. Despite advancements in landslide susceptibility mapping, existing methods often lack global-scale applicability and fail to incorporate robust optimization strategies for improved predictive accuracy. This study addresses these gaps by developing an optimized framework using support vector regression (SVR) enhanced with meta-heuristic algorithms (grey wolf optimizer [GWO] and bat algorithm) to refine model hyper-parameters. It integrates a globally representative data set of 37,984 landslide and non-landslide locations, ensuring broader applicability and generalizability. The information gain ratio method assessed the relative importance of 12 geo-environmental factors influencing landslide. The results indicated that all models achieved good predictive performance during the testing phase, as evidenced by an area under the receiver operating characteristic curve (AUC) value exceeding 0.8, but the SVR-GWO model exhibited the highest prediction accuracy (AUC = 0.92), making it suitable for large-scale hazard assessment. Plan curvature emerged as the most influential factor, surpassing slope, land use, and rainfall that are dominant at regional or local scales. The five countries with the highest landslide-prone areas were Russia, Canada, USA, China, and Brazil. The results support policymakers and urban planners in developing efficient strategies to minimize landslide risks.

Drought is a global phenomenon with significant negative impacts on water availability, agricultural production, livelihoods, and socioeconomic conditions. Despite its destructive effects, spatially predicting drought hazards remains a challenging task. This study developed an innovative framework by leveraging two state-of-the-art deep learning models: convolutional neural networks (CNNs) and the long short-term memory (LSTM) model. Key predictive factors, including the topographic wetness index, soil depth, mean annual precipitation, elevation, slope, sand content, clay content, and plant-available water-holding capacity (PAWC), were carefully selected for analysis. An agricultural drought inventory map was generated based on the relative departure of soil moisture. The performance of the CNN and LSTM models was evaluated using root mean square error (RMSE), standard deviation (StD), and the area under the receiver operating characteristic curve (AUC). The results indicated that certain parts of the research area were highly susceptible to drought. Both models performed well, achieving AUC values of 81.9% (CNN) and 81.7% (LSTM). The RMSE and StD further confirmed the predictive capabilities of these models. Sensitivity analyses highlighted the importance of PAWC, mean annual precipitation, and clay fraction in detecting drought-prone areas. The drought susceptibility map provides valuable insights into the vulnerability and likelihood of an area experiencing drought conditions, offering essential information for decision-makers to effectively prioritize resources and mitigate drought impacts.

A GIS-based multicriteria decision analysis (MCDA) is presented to evaluate the suitability of land for the implementation of nature-based solutions (NbS) to enhance carbon sequestration in Emilia-Romagna, Italy. Excessive carbon emissions into the atmosphere have caused rapid and profound climate change that needs to be mitigated. The use of NbS has emerged as an effective strategy to sequester atmospheric carbon and improve environmental resilience. This study focuses on identifying the best NbS to maximise carbon sequestration for three environmental zones: urban, peri-urban and agricultural. The analysis identifies optimal locations for three area-specific NbS: street trees, green spaces and buffer strips. The region was divided into 30 × 30 m grid pixels, with each grid cell assigned a value from 1 (least suitable) to 5 (most suitable). The results show that most of the high-quality pixels are located near the main urban centres and along the coastline. These results provide useful information for policy makers and urban planners who can be guided in the strategic implementation of NbS to achieve maximum environmental benefits. The work also includes an individual sensitivity analysis to validate the robustness of the proposed model and a quantitative estimate of the carbon that can be sequestered by these NbS.

Today, cities are the main hubs of human activity, and this puts pressure on local natural resources and ecosystems, leading to challenges such as pollution and overuse. Employing comprehensive assessment and policymaking methods is effective in such areas due to the various components at play. For this purpose, the concept of the water-food-energy-ecosystem services (WFEES) nexus is used in this study. The components of this nexus are simulated through a newly developed tool that integrates urban water metabolism, water resources, and ecosystem services. This simulator evaluates management strategies and considers population growth and climate change scenarios to inform future policymaking. A novel composite index is developed to evaluate these strategies, considering economic, environmental, and social aspects. Additionally, this study utilizes the regional safe operating space (RSOS) framework to measure the state of the system compared with resilience thresholds. This study focuses on the western part of Tehran province and considers a 25-year simulation period (2016-2040). According to the findings, with the implementation of superior management packages, the average values of the composite index and the distance-to-threshold index will be 13.04 % and 38.03 % greater, respectively, than those in the business-as-usual (BAU) scenario. In conclusion, this study presents a set of indicators that urban policymakers can use to evaluate the state of the WFEES nexus under various climate and demographic scenarios. By examining how these indicators interact and evolve, the study provides valuable insights to guide effective and sustainable urban planning strategies for the future.

This study investigates how the seven core resilience principles are integrated into assessments of forest system resilience to natural or human-induced disturbances across engineering, ecological, and social-ecological resilience concepts. Following PRISMA guidelines, a literature search in the Web of Science database using the keywords “resilience”, “forest” and “ecosystem services” yielded 1828 studies, of which 330 met the selection criteria. The most commonly used criterion was diversity, a sub-criterion of “diversity and redundancy”, appearing in 50% of studies. The results indicate that social and governance-related principles, learning and experimentation (7%), participation (11%), and polycentric governance (9%) have not been frequently addressed. Although numerous studies have employed various principles for assessing forest resilience, none have considered all seven principles jointly. This highlights a significant research gap, emphasising the need to quantify these principles in forest systems. Understanding forest-community dynamics is essential for enhancing the long-term resilience and sustainability of both systems.

Heatwaves can greatly impact societies, underscoring the need to extend current heatwave prediction lead times. This study investigates multiple machine learning (ML) model approaches for heatwave occurrence prediction with long lead times of one to five months. Five ML classifiers, built using Google Earth Engine remote sensing datasets, are developed and tested for heatwave prediction for the national scale (case example of Sweden) over time period 1989–2019. The ML modelling is based on 13 final explanatory atmospheric and landscape features. The balanced random forest model exhibits the consistently best performance among the tested ML models, stable across all investigated lead times (from one to five months) with balanced accuracy of around 0.77, even though not overall identifying actual heatwave occurrence (decreased recall for heatwave occurrence from 0.87 to 0.81). Application of SHapley Additive exPlanations technique for model interpretation shows increasing importance of model output with increasing lead time for landscape features such as runoff and soil water. Overall, more frequent heatwave occurrence emerges for places characterized by lower values of geopotential height, evaporation, precipitation, and topographical slope, and higher values of temperature, runoff, and sea level pressure. The study also exemplifies how the developed ML modelling approach could be used to identify and warn for early signs of forthcoming heatwave occurrence, and further step-wise improve the identification and warning toward less uncertainty for shorter lead times. This can facilitate earlier warning in support of better planning of measures to mitigate adverse heatwave impacts, up to several months ahead of their possible occurrence.

Tropical forest systems (TFSs), play a crucial role in maintaining the planet's ecological balance, supporting life on Earth, and providing different ecosystem services, which are vulnerable to environmental (e.g., severe droughts) and human-induced disturbances (e.g., deforestation).The resilience concept is usually considered in evaluating a forest system under these severe disturbances. However, while resilience evaluations have mainly focused on engineering and ecological perspectives, the integration of social core resilience principles (3SRPs)- learning and experimentation (P5), participation (P6), and polycentric governance (P7)- remains limited. This study performs a systematic review of papers published between 2000 and 2024, focusing on social resilience in tropical forest systems to assess the application of the 3SRPs, following the (PRISMA) framework for systematic reviews, and identify the research gaps in social-based resilience studies. The keywords “resilience”, “forest”, and “ecosystem services” were searched in the “Web of Science” and “Scopus” databases from 2000 to 2024. The 24-year timeframe captures the evolution of resilience theory from early ecological foundations to contemporary social-ecological applications. The results show that despite the recognition of social aspects in selected studies (49), 55% of studies have considered one social principle, 12% studies taken two principles into account (i.e., P6 and P7), and only 8% of reviewed studies have incorporated all three social principles together in their assessments. Social aspects such as stakeholders' participation and governance are often overlooked, with the majority of evaluations focusing on ecological criteria. There is a crucial need for an integrated approach that considers social and ecological criteria to assess forest resilience, with an emphasis on the effective application of 3SRPs.

With the acceleration of global urbanization, the ecosystem services (ES) and ecological balance of nature reserves have been significantly impacted. However, quantitative assessments of the multiple contributions of nature reserves to urban ecological sustainability are still lacking. This study selects Panjin, a wetland city in China (3788 km2), as the study area, utilizing the InVEST model to quantify ES (water yield, carbon storage, soil retention, and habitat quality), and employing redundancy analysis to explore the influencing factors. Ecological source areas were identified, and the Sustainable Development Goals (SDGs) score was calculated to systematically evaluate the contribution of nature reserves. The results indicate that from 1990 to 2010, the built-up area of Panjin increased by approximately 159%, leading to a reduction in carbon storage, soil retention, and habitat quality by 20%, 4%, and 14%, respectively. From 2010 to 2020, ecological restoration policies resulted in a 63% increase in ES compared to 2010. Nature reserves played a crucial role in maintaining ecological stability, providing over 40% of the ecological source areas while occupying only 24% of the city’s area and contributing more than 30% to the overall urban ecological sustainability. This study is the first to systematically assess the multiple contributions of nature reserves to urban ecological sustainability, providing ecological management recommendations for policymakers based on innovative environmental indicators and methods to support sustainable urban development.