Current Radiology Reports

ABSTRACT In order to recognize the effect of the drying up of Lake Urmia on the climate, using the Woś classification method, the types of weather of the two cities of Tabriz and Urmia, were determined during 1980–2018. Then, eight weather types in the range of hot weather types without rain to cool and very cold weather types with low cloudiness and no precipitation showed an occurrence frequency of more than 61%. The highest number of weather types with 45 types in the cold season and 26 types in the hot season was determined in Tabriz and Urmia, respectively. Then, modified Mann–Kendall (MMK) and Sen's slope estimator tests were used to determine the changing trend of the weather types. Investigations showed that, the hot weather type without precipitation and the slightly cool weather type without precipitation and of course without clouds had an increasing trend respectively, and the cold types also showed a decreasing trend in favor of cool types. The intensity of these changes is much greater in Tabriz. In addition, using the Mann–Kendall test, it was also determined that the increasing trend of hot weather in Tabriz started in 2000 at the same time as the lake began to dry up.

ABSTRACT Hydrological prediction is crucial for managing water resources, and innovations like machine learning (ML) present an opportunity to enhance predictive modeling capabilities. The aim of this study is to compare the usage of ML algorithms, such as CatBoost, with traditional techniques such as ridge regression, support vector machines (SVMs), and gene expression programming (GEP) in climate projection. In order to assess the accuracy of the best models, statistical measures such as root mean squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE) were used. The investigation found that CatBoost was superior to conventional models in the testing period, with RMSE 3.78 m3/s, MAE 2.613 m3/s, Kling–Gupta efficiency (KGE) 0.650, root mean square error to standard deviation ratio (RSR) 0.611 and NSE 0.626. After it was proven that the best-performing model is CatBoost, future projections according to the NorESM2-MM scenarios were calculated using this model. Climate projections are based on simulations from the Coupled Model Intercomparison Project Phase 6 model, utilizing shared socioeconomic pathway (SSP) scenarios. The results show that SSP3-7.0 and SSP5-8.5 scenarios indicate an increasing trend between 2015 and 2100, while SSP1-2.6 and SSP2-4.5 expect a balancing tendency. This suggests that climate change has little effect on the measuring station and its basin and that the flow is increasing positively.

ABSTRACT Due to ongoing climate change, accurately predicting rainfall has become increasingly critical. This paper explores an approach utilizing two different machine learning algorithms, including multilayer perceptron neural networks (MPNN) and random forest regressors (RFR), to enhance rainfall forecast accuracy. Historical daily weather data spanning 100 years (1913–2023) from the Agro Climate Research Centre at Tamil Nadu Agricultural University were used. The study focused on global climate drivers like the Southwest Monsoon (SWM) and Northeast Monsoon (NEM) over the Coimbatore region; this region receives more rainfall during NEM. Normalization and scaling techniques addressed missing values, preserving 70–85% of the original data for the training set. Results demonstrated that MPNN outperformed RFR, achieving an accuracy of 85.55% for SWM and NEM, while RFR outperformed MPNN, producing an accuracy of 86.50%. The coefficient of determination (R2) for predicted versus observed values was 0.8 for daily rainfall from 2020 to 2023.

ABSTRACT Liaoning Province, a major grain production base in China, has faced increasingly frequent extreme drought events under global climate change, impacting local economic and social sustainability. Effective prevention requires comprehensive risk assessments. However, existing risk assessment studies often suffer from low spatial resolution and limited integration of geographic big data. This study integrates multi-source geographic big data, using 10 indicators across risk, vulnerability, and exposure dimensions. A comprehensive drought disaster risk assessment model was established by combining the analytic hierarchy process (AHP) and the entropy weight method. Theil–Sen median analysis evaluated drought risks from 2001 to 2021 and predicted future trends. Results revealed spatial heterogeneity in drought risks, with ‘higher in the west and north, lower in the east and south’ distribution. Chaoyang City, in the western hilly region, had the highest risk, with a vulnerability index above 0.65, while Panjin City in the east showed lower risk and a vulnerability index below 0.45. Over 20 years, the overall risk declined across the province. This method aligns with actual drought losses, validating its effectiveness and enhancing understanding of drought risk patterns to mitigate impacts.

ABSTRACT Climate change (CC) and global warming are widely acknowledged as the most important environmental problems faced by the world now. The rise in global and local mean temperature (Tmean), along with increased human interventions, has significantly impacted the hydrology of the Cauvery River Basin (CRB). Thus, this study carried out a comprehensive trend analysis for the Tmean data over the CRB from 1970 to 2022. Four aspects of the trend, viz. magnitude, significance, nature, start, and end are assessed using various statistical tests. Also, the regional significance of the trends is evaluated utilizing the false discovery rate (FDR) test. Discrete wavelet transform (DWT) is used in combination with the Mann–Kendall (MK) test/MK test with Block Boot Strapping (MKBBS test) and Sequential Mann–Kendall (SQMK) test to determine the time scale that dominated the trends in Tmean data over the basin. The results showed that consistent warming trends are observed in Tmean for all temporal scales throughout the basin with positive Sen's slope (SS) values. From the decomposition, it is observed that the trends are driven by periodic patterns lasting under 10 years, or, generally, 2 and 4 years for annual and seasonal scales and 4 and 8 months for the monthly time scale.

ABSTRACT The study develops a 2D hydrodynamic model using HEC-RAS to assess flood hazards and generate inundation maps for the lower Narmada basin in Gujarat, India. The model effectively simulates flood dynamics by integrating data, i.e. digital elevation models, stream gauge data, and land use and land cover information. Results indicate strong model performance, with R² and RMSE values of 0.92 and 0.12 for calibration and 0.77 and 0.08 for validation. Approximately 326.45 km² of the basin is severely inundated during major flood events, with agriculture being most affected. The model's outputs, including depth maps, velocity maps, and water arrival time predictions, provide critical insights for flood management. Floodwaters take 110 h to reach nearby areas and 361 h to reach validation points from upstream dam releases, offering an 8-h window to alert residents of potential overflow conditions. This information is vital for early warning systems and evacuation planning.

ABSTRACT Mapping flood-prone areas’ sensitivity to extreme rainfall is crucial for disaster risk reduction. Using the analytical hierarchy process (AHP) and fuzzy AHP methods with Geographic Information Systems (GIS) data, including factors like slope, elevation, precipitation, land use, and vegetation indices, enhances disaster preparedness. This approach enables creating maps that support informed decision-making for community resilience. In this study, AHP and fuzzy AHP models were applied across different return periods (2, 5, 10, 100, and 1,000 years) and average annual rainfall, revealing that high-risk areas vary by model: the AHP model showed high susceptibility in 2.19–25.55% of the area, while the fuzzy AHP model identified 1.53–24.51% as very sensitive. These risks are concentrated in the low-slope zones of the Boussellam and K'sob watersheds, posing a threat to three out of four nearby cities for 100-year or longer events.

ABSTRACT The interannual variability in Indian summer monsoon rainfall (ISMR) results from numerous multi-scale interrelated phenomena. Using reconstructed rainfall data from 1813 to 2020 for 20 river basins in India, the research reveals a substantial decline in the Ganga, Brahmaputra, Cauvery, Brahmani, Pallar, andPonniyar ranging from 6.6 to 19.7%, and a notable increase of 7.9% in Surma in past two decades compared to the last century. Despite significant regional disparities, a modest increase of 1.3% in ISMR is observed over 101 years, indicating long-term stability. Spectral analysis highlights the dominance of short-term fluctuations (75%) in interannual variability. The evolving relationships between rainfall and global climatic indices are underscored. The Southern Oscillation Index (SOI), Arctic Oscillation (AO), Niño3.4, and Pacific Decadal Oscillation (PDO) are the most influential indices across basins, mostly showing a stronger relationship in June and September compared to July and August. Over time, AO and SOI maintained a significant positive relationship and Niño3.4 inverse relationship with ISMR, while the AO-ISMR link weakened post-1980s indicating a shift in traditional teleconnections with climate change. Northern and eastern basins exhibit strong correlations with the warming over eastern and central Afro-Asian highlands, while southern basins are influenced by equatorial climate dynamics. The findings emphasize the need for region-specific model predictions and localized adaptive water management strategies.

ABSTRACT Solar radiation modification (SRM) has been discussed as a potential strategy to rapidly mitigate global warming by reflecting more sunlight into space. However, its impact on tropical hydrological cycles remains underexplored. This study investigates the potential impacts of SRM on streamflow of the Kelantan River Basin (KRB) by incorporating climate projections from the Geoengineering Model Intercomparison Project Phase 6 (GeoMIP6) into the Soil and Water Assessment Tool plus (SWAT+) model. The findings reveal that UKESM1-0-LL and MPI-ESM1-2-LR exhibit greater uncertainty in representing the climate of the KRB compared to CNRM-ESM2-1 and IPSL-CM6A-LR. Maximum and minimum temperatures under SSP5-8.5 are projected to increase by up to 3.52 °C by the end of the 21st century, while these increases could be limited to between 1.72 and 2.33 °C under SRM scenarios, corresponding to 1.96 to 2.22 °C under SSP2-4.5. The multi-model ensemble mean projected an inverse V-shaped trend in annual precipitation, with a peak in the mid-21st century before declining, except for G6sulfur, which exhibits a steady decrease. Increases in monthly precipitation during the 2045–2064 period may intensify flooding in the KRB. Meanwhile, decreases in streamflow during dry months are projected for the periods 2045–2064 and 2065–2085 under G6sulfur, particularly in the middle and upper basins.

ABSTRACT Floods are devastating and costly, making accurate flood frequency and magnitude estimates essential for engineering and planning. This study examines flood characterization within a hydroclimate context. At-site analysis links flood events to atmospheric moisture pathways and source origins to understand the floods regionality and develop new modalities for flood frequency estimation in a changing climate. A novel principal curves approach is developed and applied to 623 stream gauges across the conterminous United States (US) over the 1956–2015 period, analyzing 37,380 annual floods, with Atmospheric Rivers (ARs) contributing ∼73%. The Northwest, West, Northeast, and Southeast experienced 70–100% AR-driven floods, while the East North Central, Central, South, and Southwest saw <70%, and the West North Central had <15%. Attributing floods to their moisture source significantly improves flood frequency curves, enhancing upper-tail fit and reducing uncertainties. Pacific Ocean ARs increase the 100-year flood magnitude by up to 345% in the West, Atlantic Ocean ARs amplify it by 400% in the East North Central, Caribbean Sea and Gulf of Mexico ARs raise it by 350% in the South, and Local Moisture increases it by 600% in the Southeast. These findings enhance flood risk management and climate adaptation strategies.

ABSTRACT Crop water requirements depend on climate, soil, and plant characteristics, necessitating responsive and adaptive irrigation systems for efficient water use. The objectives of this study include assessing the implementation of irrigation technology and its impact on water use efficiency, reviewing smart irrigation systems employed as irrigation management systems, and introducing evapotranspirative irrigation technology as a straightforward smart irrigation approach. Globally, research on irrigation technologies highlights significant potential for water conservation. Smart irrigation system, as a facet of irrigation system management, is considered strategic approach for effective irrigation implementation. The adoption of micro-irrigation systems in cultivated crops has shown promising results in enhancing water productivity and significantly increasing yield rates, but smallholder farmers resist due to high costs. This study introduces innovative approaches using simple automatic technology based on the principle of evapotranspiration, aiming to mitigate high costs. This technology is designed to distribute water optimally at the highest evapotranspiration rate during prolonged dry periods. The key success indicators focus on water productivity, encompassing crop water, irrigation water, and total water. The evapotranspirative irrigation system is pivotal in regulating evapotranspiration rates, resulting in reduced water evaporation and increased land and water productivities, making it adaptive to the impacts of climate change.

ABSTRACT This study aims to investigate the impacts of climate change and reservoir operation on drought in the Upper Part of Dong Nai (UPDN) River Basin. Climate change scenarios for the UPDN River Basin were portrayed according to the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on an ensemble of five general circulation models (GCMs), namely, EC-Earth3-Veg, CanESM5, EC-Earth3, HadGEM3-GC31-LL, and CNRM-CM6-1-HR for two Shared Socioeconomic Pathway scenarios, SSP2-4.5 and SSP5-8.5. The historical climate monitoring data at five weather stations and three hydrological gauges related to upstream reservoirs in the period 1990–2020 were collected. The streamflow for the period 2021–2099 was simulated by applying the Soil and Water Assessment Tool (SWAT) model. Two meteorological and hydrological drought indices of the 6-month time scale, namely, standardized precipitation index (SPI6) and streamflow drought index (SDI6), were calculated through designed modules integrated in the Drought Index Calculator (DrinC) software. The results show that climate change coupled with reservoir operation has seasonally changed the runoff, which has changed the drought situation in the entire basin. The lag time variations of hydrological drought in response to meteorological drought were significant in the main basin of the UPDN River Basin. These findings provide useful information for managers and policymakers in sustainable water resources management and development adapting to climate change.

ABSTRACT The rainfall series exhibits uncertainty and non-stationarity. Improving the accuracy of rainfall prediction is of significant importance for flood prevention and mitigation. This study proposes a hybrid model and applies it to rainfall forecasting in the eastern region of Hubei Province. The proposed method first uses variational mode decomposition and improved complete ensemble empirical mode decomposition with adaptive noise to reduce high-frequency noise components. Then, particle swarm optimization and support vector machine are used for training and forecasting. Compared with other models, the prediction model after noise reduction shows better performance than the model without secondary decomposition, with results that are closer to the actual values. The proposed hybrid model outperforms other models, with the predicted trend more closely aligning with the actual data, and the value of R² of predictions for individual cities reaches 0.96. This study not only provides an efficient method for rainfall forecasting but also holds significant importance for understanding and addressing climate change.

ABSTRACT Analysis of atmospheric pollutants in rainwater provides valuable information on the environmental impacts of both natural and anthropogenic pollution sources. Air pollution studies often show that particulate matter (PM) collection on filters is used for subsequent analysis. However, this approach can result in significant particle accumulation on filters and complicate their characterization by semi-quantitative analytical techniques. In this study, insoluble PM in sequentially collected rainwater samples was analyzed for particle size, morphology, and chemical composition. Scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectrometry (EDS) and a particle size analyzer were used for these analyses. By combining SEM–EDS data with particle size distribution analyses, chemical composition findings, and upper atmosphere back-orbit modeling, specific sources of insoluble particles in rainwater were identified. In addition, rainwater samples were analyzed for pH, electrical conductivity, and major anions and cations. The pH values varied from 6.19 to 7.04, while the electrical conductivity values varied from 5.35 to 83.53 μS/cm. Among the major ions, relatively high concentrations of Ca2+, SO42−, and NO3− were detected, while F−, Mg2+, Na+, and Cl− were observed in lower concentrations. The contributions of sea salt were evidenced by the presence of Cl− and Na+ ions.

ABSTRACT The present research examines the impact of climate change on meteorological parameters using global climate models (GCMs) and artificial intelligence, with a case study in Fars Province, Iran. In this study, the meteorological parameters of minimum temperature, maximum temperature, precipitation, and solar radiation, as well as 12 GCMs from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), are utilized. A statistical downscaling model (multilayer perceptron neural network) is employed to extract climate change predictions under two scenarios, representative concentration pathway (RCP)4.5 and RCP8.5, for four synoptic stations (Shiraz, Abade, Fasa, and Lar), each representing different climatic regions. Correlation analysis is used to identify the most influential predictor variables for each meteorological parameter. The results indicate a projected increase in maximum temperature by up to 2.67 °C, which could significantly impact agricultural productivity in Fars Province. This finding is accompanied by minimum temperature ranges from 0.23 to 2.71 °C, solar radiation increasing by up to 1.91 MJ/m², and precipitation fluctuation between a decrease of 7% and an increase of 36.5%. These findings suggest that the region may face increased agricultural stress due to higher temperatures and variable precipitation patterns, necessitating adaptive strategies for sustainable water resource management.