Review and assessment of the potential restoration of ecosystem services through the implementation of the biodiversity management plans for SDG-15 localization.

Effective restoration strategies play a crucial role in mitigating the environmental impact of mining and colliery activities while promoting ecological resilience and rejuvenating ecosystem services. However, many organizations find it challenging to understand and balance their efforts in restoring degraded lands. For example, their restoration plans lack clarity and overlook relevant ecosystem services. This study reviews and focuses on the potential restoration of ecosystem services at TATA Steel's Noamundi Iron Ore Mine and West Bokaro Colliery to contribute to Sustainable Development Goals (SDGs), particularly SDG-15, for localization. The approach involved assessing the number of preventive measures being implemented to restore a particular ecosystem service. Moreover, the potential of each preventive measure is to restore that ecosystem service. The findings underscore the significance of preventive measures and comprehensive restoration plans in enhancing carbon sequestration, soil fertility, habitat creation, and genetic diversity conservation. Our results showed that the impact scores and ranks of various ecosystem services demonstrate the positive effects of restoration efforts, emphasizing the importance of reestablishing forests, restoring water bodies and wetlands, and allocating land for agriculture and public use. The research provides valuable insights for decision-makers in developing sustainable land management strategies, ensuring biodiversity conservation and local communities' well-being. By prioritizing ecosystem services in restoration initiatives, stakeholders can contribute to the sustainable management of natural resources and foster a harmonious coexistence between human activities and the environment.

Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards.

The evaluation of the vulnerability of coupled socio-ecological systems is critical for addressing and preventing the adverse impacts of various environmental hazards and devising strategies for climate change adaptation. The initial step in vulnerability assessment involves exposure assessment, which entails quantifying and mapping the risks posed by multiple environmental hazards, thereby offering valuable insights for the implementation of vulnerability assessment methodologies. Consequently, this study sought to model the exposure of coupled social-ecological systems in mountainous regions to various environmental hazards. By a set of socio-economic, climatic, geospatial, hydrological, and demographic data, as well as satellite imagery, and examining 11 hazards, including droughts, pests, dust storms, winds, extreme temperatures, evapotranspiration, landslides, floods, wildfires, and social vulnerability, this research employed machine learning (ML) techniques and the fuzzy analytical hierarchy process (FAHP). Expert opinions were utilized to guide hazard weighting and calculate the exposure index (EI). Through the precise spatial mapping of EI variations across the socio-ecological systems in mountainous areas, this investigation provides insights into vulnerability to multiple environmental hazards, thereby laying the groundwork for future endeavors in supporting national-level vulnerability assessments aimed at fostering sustainable environments. The findings reveal that social vulnerability and pests receive the highest weighting, while floods and landslides are ranked lower. All hazards demonstrate significant correlations with the EI, with droughts exhibiting the strongest correlation (r > 0.81). Spatial analysis indicates a north-south gradient in forest exposure, with southern regions showing higher exposure hotspots (EI 29.08) compared to northern areas (EI 10.60). Validation based on Area Under Curve (AUC) and Consistency Rate (CR) in FAHP demonstrates robustness, with AUC values exceeding 0.78 and CR values below 0.1. Considering the anticipated intensification of hazards, management strategies should prioritize reducing social vulnerability, restore degraded areas using drought-resistant species, combat pests, and mitigate desertification. By integrating multidisciplinary data and expert opinions, this research contributes to informed decision-making regarding sustainable forest management and climate resilience in mountain ecosystems.

Understanding land degradation induced by gully erosion from the perspective of different geoenvironmental factors.

Complex interrelationships between landscape-level geoenvironmental factors and natural phenomena have rendered land degradation control measures ineffective. For control to be effective, this study argues that the interactions between different geoenvironmental factors and gully erosion (as an indicator of land degradation) should be more fully investigated and spatially mapped. To do so, gully locations of the Konduran watershed, Iran, were detected in the field and modeled in response to seventeen geoenvironmental factors using three machine learning methods, i.e., multivariate adaptive regression splines (MARS), random forest (RF), regularized random forest (RRF), and Bayesian generalized linear model (Bayesian GLM). The models' performance was validated, the relationship of gully occurrence with each factor was quantified, the probability of gully erosion (i.e., land degradation) was retrospectively estimated, and the spatially explicit maps of land degradation susceptibility were produced. Based on the area under the receiver operating characteristic curve (AUC), the RRF and MARS models with AUC = 0.98 achieved the greatest goodness-of-fit with the training dataset, whereas the RF model with AUC = 0.83 showed the greatest ability in predicting future gully occurrences. Further scrutinization using the sensitivity and specificity metrics demonstrated the efficiency of the RF model for correctly classifying the gully (sensitivity-training = 92%; sensitivity-validation = 90%) and non-gully (specificity-training = 95%; specificity-validation = 68%) pixels. Nearly 13% of the study area ended up being the hardest hit region due to their general characteristics of distance from roads and rives, altitude, and normalized difference vegetation index (NDVI) that were identified as the most influential factors in gully erosion occurrence. Given the resolution quality and reliable predictive accuracy, our spatially explicit maps of land susceptibility to gully erosion can be used by authorities and urban planners for identifying the target areas for rehabilitation and making more informed decisions for infrastructure development. Although our study was strictly focused on a certain region, our recommendations and implications are of global significance.

Fuzzy-based vulnerability assessment of coupled social-ecological systems to multiple environmental hazards and climate change.

Climate change and combining related parameters of environmental hazards have left a considerable challenge in assessing social-ecological vulnerability. Here we integrated a fuzzy-based approach in the vulnerability assessment of mangrove social-ecological systems combining environmental parameters, socio-economic, and vegetative components from exposure dimensions, sensitivity and adaptive capacity along the northern coasts of the Persian Gulf and the Gulf of Oman for the first time. This study aims to provide critical information for habitat-scale management strategies and adaptation plans by assessing the vulnerability of mangrove social-ecological systems. This study provides a methodology framework that consists of five steps. Step 1: We combined the fuzzy weighted maps of seven environmental hazards, including tidal range, maximum wind speeds, drought magnitude, maximum temperatures, extreme storm surge, sea-level rise, significant wave height, and social vulnerability. This map combination determined that the computed exposure index is from 1.07 to 4.32 across the study areas, with an increasing trend from the coasts of the Persian Gulf to the Gulf of Oman. Step 2: We integrated the fuzzy weighted maps of four sensitivity variables, including area change, health change, seaward edge retreat, and production potential change. The findings show that the sensitivity index is from 1.40 to 2.64 across the study areas, increasing the trend from the Persian Gulf coast to the Gulf of Oman. Step 3: Besides, we combined the fuzzy weighted maps of three adaptive capacity variables, including the availability of migration areas, recruitment, and local communities' participation in restoration projects and education programs. The result showed that the index value across the study areas varies between 0.087 and 2.38, decreasing the trend from the Persian Gulf coast to the Gulf of Oman. Step 4: Implementing fuzzy hierarchical analysis process to determine the relative weight of variables corresponding to exposure, sensitivity and adaptive capacity. Step 5: The integration of exposure, sensitivity and adaptive capacity and the vulnerability index maps in the study areas showed variation from 0.25 to 5.92, with the vulnerability of mangroves from the west coast of the Persian Gulf (Nayband) decreasing towards Khamir, then increasing to the eastern coasts of the Gulf of Oman (Jask and Gwadar). Overall, the results indicate the importance of the proposed approach to the vulnerability of mangroves at the habitat scale along a coastal area and across environmental gradients of climatic, maritime and socio-economic variables. This study validated the findings based on the ground truth measurements, and high-resolution satellite data incorporated the Consistency Rate (CR) in the Fuzzy Analytic Hierarchy Process (FAHP). The overall accuracy of all classified remote sensing images and maps consistently exceeded 90%, and the CR of the 25 completed questionnaires was <0.1. Finally, this study indicates differences in vulnerability of various habitats, leading to focus conservation completion and rehabilitation and climate change adaptation planning to support the Sustainable Development Goal (SDG)-13 implementation.

Vulnerability of coastal communities to climate change: Thirty-year trend analysis and prospective prediction for the coastal regions of the Persian Gulf and Gulf of Oman.

This study relates changes in social vulnerability of 20 counties on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) over a 30-year period (1988-2017) to changing socio-economic conditions and environmental (climate) hazard. Social vulnerability in 2030, 2040 and 2050 is predicted based on the RCP8.5 climate change scenario that projects drought intensities and rising sea levels. Social vulnerability was based on the three dimensions of sensitivity, exposure, and adaptive capacity using 18 socio-economic and five climate indicators identified by experts. All but one indicator related very strongly to the dimension it sought to represent. Despite improvements in adaptive capacity over time, social vulnerability increased between 1988 and 2017 and rates of change accelerated after change point years that occurred between 1998 and 2002 in most counties. Extrapolating past changes of each indicator over time enabled forecasts of social vulnerability in the future. While social variability decreased between 2017 and 2030, it increased again between 2030 and 2050. The lowest future social vulnerability is expected along the eastern PG coast, the greatest along the western PG and the GO. The worsening of socio-economic indicators contributed to increased sensitivity, and increased drought intensities plus the expected rise in sea levels will lead to social vulnerabilities in 2050 comparable to present levels. Between 1.4 and 1.7 M people will live in areas that are likely submerged by water in the future. About 80% of these people live in six counties with variable social vulnerabilities. While counties with lower social variabilities might be better able to cope with the challenges posed by climate change, adaptation programs to enhance the resilience of the residents in these and the remaining counties along the PG and the GO need to be implemented soon to avoid uncontrolled mass migration of millions of people from the region.

Spatial modeling of exposure of mangrove ecosystems to multiple environmental hazards.

Determining the level of ecosystems exposure to multiple environmental hazards or risk factors is of paramount importance for developing, adopting, and planning management strategies to minimize the harmful effects of these hazards. We quantified the level of exposure of mangroves on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) between 1986 and 2019 to eight environmental hazards, i.e., drought, maximum temperatures, rising sea levels, change of freshwater inflows to coasts, extreme storm surges, significant wave height (SWH), seaward edge retreat in the mangroves, and fishery intensity. Based on expert opinion, fuzzy weights were used to integrate these exposures into a single index (EI) for the region. Experts gave the greatest weight/importance to the risks posed by sea-level rise and seaward retreat of mangroves and the lowest risk to significant wave height and fishery intensity in coastal waters. The overall EI and six of eight individual variables (except fishery intensity and maximum temperatures) pointed to exposure levels of mangroves that increased from the coasts of the PG (EI 0.69) to the GO (EI 6.69). Since these hazards are expected to continue in the future, local/regional management responses should focus on minimizing regional anthropogenic threats and halt conversion of natural areas to agricultural and open areas to maintain freshwater inputs to coastal areas, particularly on the GO. Further, uplands that may serve as future refugia into which mangroves may expand over time as sea levels continue to rise should be protected from development. This was the first study that used an analytic framework to compute a mangrove exposure index to a suite of physical and socio-economic hazards across a region. This framework may provide insights into cost-effective resilience-based design and management of socio-ecologically coupled ecosystems in an era of increasing types and intensities of environmental hazards.

Multi-hazards vulnerability assessment of southern coasts of Iran.

Coastal vulnerability assessment has become one of the most important tools for decision making and providing effective managerial solutions to reduce adverse socio-economic impacts of multiple environmental hazards on coupled social-ecological systems of coastal areas. The aim of this study was to assess the vulnerability of the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) in the Hormozgan province of Iran. Nine variables of vulnerability that included the rate of coastline change, relative sea level rise, coastal slope, mean tidal range, coastal geomorphology, significant wave height (SWH), extreme storm surge, population density, and fishing intensity were weighted, mapped, and combined into the Coastal vulnerability index (CVI). Experts viewed sea level rise, shoreline change and extreme storm surge as most important for imparting vulnerabilities on the northern coasts of PG and GO. Socio-economic variables (i.e., population density and fishery intensity) were considered least important. Of the total length of the provincial shoreline, 27% were classified into the very low vulnerability class, 31% into the low, 17.4% into the moderate, 15.4% into the high, and 9.2% into the very high vulnerability class. About 1295 km (58%) of shorelines were classified into the low and very low vulnerability classes (CVI value ≤ 8.32) and mainly consisted of shorelines on the western coast along the PG. In contrast, 553 km (24.6%) of shorelines were classified into the high and very high vulnerability classes (CVI values > 13.39) and were located along the central coasts (especially in the Qeshm Island and Strait of Hormuz) and on the east coasts of the GO. At least a quarter of all shorelines in the province have high and very high vulnerability to environmental hazards that are the harbingers of climate change.

Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran.

Leaf Area Index (LAI; as an indicator of the health) of the mangrove ecosystems on the northern coasts of the Persian Gulf and the Gulf of Oman was measured in the field and modeled in response to observed (1986-2017) and predicted (2018-2100) drought occurrences (quantified using the Standardized Precipitation Index [SPI]). The relationship of LAI with the normalized difference vegetation index (NDVI) obtained from satellite images was quantified, the LAI between 1986 and 2017 retrospectively estimated, and a relationship between LAI and SPI developed for the same period. Long-term climate data were used as input in the RCP8.5 climate change scenario to reconstruct recent and forecast future drought intensities. Both the NDVI and the SPI were strongly related with the LAI, indicating that realistic LAI values were derived from historic satellite data to portray annual changes of LAI in response to changes in SPI. Our findings show that projected future drought intensities modeled by the RCP8.5 scenario increase more and future LAIs decreased more on the coasts of the Gulf of Oman than the coasts of the Persian Gulf in the coming decades. The year 1998 was the most significant change-point for mean annual rainfall amounts and drought occurrences as well as for LAIs and at no time between 1998 and 2017 or between 2018 and 2100 are SPI and LAI values expected to return to pre-1998 values. LAI and SPI are projected to decline sharply around 2030, reach their lowest levels between 2040 and 2070, and increase and stabilize during the late decades of the 21st century at values similar to the present time. Overall, this study provides a comprehensive picture of the responses of mangroves to fluctuating future drought conditions, facilitating the development of management plans for these vulnerable habitats in the face of future climate change.