One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent studies (from 2010 to 2022) that have explored drought features in Turkey. To this end, a total of 71 articles were selected from the Web of Science (WoS) and Scopus databases. The selected papers were clustered into two categories: (i) drought monitoring studies and (ii) drought forecasting articles. Then, the representative papers were reviewed in detail regarding the implemented indices, models (techniques), case study area, and source of the indicators used to derive drought indices. The review results showed that most of the studies aimed at meteorological drought monitoring and forecasting. An increasing trend was also observed in the use of machine learning for short-term meteorological and hydrological drought prediction. On the other hand, the emerging RS technology and satellite-driven indicators were rarely used in the country. The review showed that there is room for more research on agricultural and hydrological drought monitoring, forecasting, and pattern detection in Turkey.

The amount of municipal waste produced worldwide has seen a significant increase in recent years. The need to store waste is a significant problem in the context of environmental threats and spatial development. Due to the fact that the easiest way to reduce the amount of waste is to incinerate it, and despite the fact that waste incineration plants have existed since the late 1800s, the amount of illegal waste burning and landfills has also increased. Subsurface landfill fires are particularly dangerous, as they can affect the structure of the landfill. Methane also plays an important role in fires as it is flammable and contributes to the spread of fire. In addition, the leachate from incinerated waste is much more dangerous than the leachate from waste in its natural state. The effluents from incinerated waste include heavy metals and persistent organic pollutants, as well as sulphates, chlorides, and polycyclic aromatic hydrocarbons. Other pollutants also end up in the atmosphere. The aim of this article is to present the problem of landfill fires and their impact on air, soil, and water, based on a review of the selected documented fires as well as indicators of fires in the areas in which the authors of this article conducted their research. The article presents an overview of methods and tests, such as dynamic leaching tests, monitoring tests, and lysimetric tests, all of which can be implemented to prevent fires as well as for research purposes after a fire has occurred, so that this article can be utilitarian not only for researchers, but also for decision makers.

Assessing runoff under changing land use/land cover (LULC) and climatic conditions is crucial for achieving effective and sustainable water resource management on a global scale. In this study, the focus was on runoff predictions across three diverse Indian watersheds—Wunna, Bharathapuzha, and Mahanadi—spanning distinct agro-climatic zones to capture varying climatic and hydrological complexities. The soil and water assessment (SWAT) tool was used to simulate future runoff influenced by LULC and climate change and to explore the related sustainability implications, including related challenges and proposing countermeasures through a sustainable action plan (SAP). The methodology integrated high-resolution satellite imagery, the cellular automata (CA)–Markov model for projecting LULC changes, and downscaled climate data under representative concentration pathways (RCPs) 4.5 and 8.5, representing moderate and extreme climate scenarios, respectively. SWAT model calibration and validation demonstrated reliable predictive accuracy, with the coefficient of determination values (R²) > 0.50 confirming the reliability of the SWAT model in simulating hydrological processes. The results indicated significant increases in surface runoff due to urbanization, reaching >1000 mm, 600 mm, and 400 mm in southern Bharathapuzha, southeastern Wunna, and northwestern Mahanadi, respectively, especially by 2040 under RCP 8.5. These findings indicate that water quality, agricultural productivity, and urban infrastructure may be threatened. The proposed SAP includes nature-based solutions, like wetland restoration, and climate-resilient strategies to mitigate adverse effects and partially achieve sustainable development goals (SDGs) related to clean water and climate action. This research provides a robust framework for sustainable watershed management in similar regions worldwide.

The precise selection of agricultural land is essential for guaranteeing global food security and sustainable development. Additionally, agricultural land suitability (AgLS) analysis is crucial for tackling issues including resource scarcity, environmental degradation, and rising food demands. This research examines the synergies and trade-offs among the sustainable development goals (SDGs) using a hybrid geographic information system (GIS)–fuzzy analytic hierarchy process (FAHP)–geostatistical framework for AgLS analysis in Attur Taluk, India. The area was chosen for its varied agro-climatic conditions, riverine habitats, and agricultural importance. Accordingly, data from ten topographical, climatic, and soil physiochemical variables, such as slope, temperature, and soil texture, were obtained and analyzed to carry out the study. The geostatistical analysis demonstrated the spatial variability of soil parameters, providing essential insights into key factors in the study area. Based on the receiver operating characteristic curve analysis, the results showed that the FAHP method (AUC = 0.71) outperformed the equal-weighting scheme (AUC = 0.602). Moreover, suitability mapping designated 17.31% of the study area as highly suitable (S1), 41.32% as moderately suitable (S2), and 7.82% as permanently unsuitable (N2). The research identified reinforcing and conflicting correlations with SDGs, emphasizing the need for policies to address trade-offs. The findings showed 40% alignment to climate action (SDG 13) via improved resilience, 33% to clean water (SDG 6) by identifying low-salinity zones, and 50% to zero hunger (SDG 2) through sustainable food systems. Conflicts arose with SDG 13 (20%) due to reliance on rain-fed agriculture, SDG 15 (11%) from soil degradation, and SDG 2 (13%) due to inefficiencies in low-productivity zones. A sustainable action plan (SAP) can tackle these issues by promoting drought-resistant crops, nutrient management, and participatory land-use planning. This study can provide a replicable framework for integrating agriculture with global sustainability objectives worldwide.

Flash floods rank among the most devastating natural hazards, causing widespread socio-economic, environmental, and infrastructural damage globally. Hence, innovative management approaches are required to mitigate their increasing frequency and intensity, driven by factors such as climate change and urbanization. Accordingly, this study introduced an integrated flood assessment approach (IFAA) for sustainable management of flood risks by integrating the analytical hierarchy process-weighted linear combination (AHP-WLC) and fuzzy-ordered weighted averaging (FOWA) methods. The IFAA was applied in South Khorasan Province, Iran, an arid and flood-prone region. Fifteen controlling factors, including rainfall (RF), slope (SL), land use/land cover (LU/LC), and distance to rivers (DTR), were processed using the collected data. The AHP-WLC method classified the region into flood susceptibility zones: very low (10.23%), low (23.14%), moderate (29.61%), high (17.54%), and very high (19.48%). The FOWA technique ensured these findings by introducing optimistic and pessimistic fuzzy scenarios of flood risk. The most extreme scenario indicated that 98.79% of the area was highly sensitive to flooding, while less than 5% was deemed low-risk under conservative scenarios. Validation of the IFAA approach demonstrated its reliability, with the AHP-WLC method achieving an area under curve (AUC) of 0.83 and an average accuracy of ~75% across all fuzzy scenarios. Findings revealed elevated flood dangers in densely populated and industrialized areas, particularly in the northern and southern regions, which were influenced by proximity to rivers. Therefore, the study also addressed challenges linked to sustainable development goals (SDGs), particularly SDG 13 (climate action), proposing adaptive strategies to meet 60% of its targets. This research can offer a scalable framework for flood risk management, providing actionable insights for hydrologically vulnerable regions worldwide.

Ecological-environmental quality was evaluated for Tabriz and Rasht cities (in Iran) with different climate conditions using artificial intelligence (AI) and remote sensing (RS) techniques. Sampling sites were surveyed and ecological experts assigned eco-environment background values (EBVs) of sites. Then, eco-environmental attributes were extracted as RS derived, and meteorological attributes were observed. Three AI-based models, artificial neural network (ANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) were then applied to learn the relationship between a target set of known EBVs and eco-environmental attributes as inputs. According to the results of the single models, none of the models could evaluate EBV appropriately for all regions and classes. Thereafter, three combining techniques were applied to the outputs of single models to enhance spatial evaluation of EBV. It was observed that the modeling for Tabriz led to more accurate results. It seems that the better network performance for Tabriz may be due to a more heterogeneous dataset in this kind of climate. Furthermore, results indicated that SVR led to better performance than both ANN and ANFIS models, but the models' combining techniques were shown to be superior. Combining techniques enhanced performance of single AI modeling up to 26% in the verification step.