Month: June 2024
Performance ranking of global precipitation estimates over data scarce Western Himalayan Region of India
Abstract
With the advent of numerous global precipitation estimates (GPEs) in the recent decades, dependability of hydrologists has lessened on the station data as the GPEs can be readily availed and utilized. Since the skills of GPEs may differ from region-to-region, it is vital to analyse their ability in resolving the regional precipitation climatology using appropriate statistical methods. In this study, a total of five GPEs, viz., APHRODITE, PERSIANN-CDR, CHIRPS, CMORPH, and IMERG were evaluated for their abilities in resolving regional precipitation climatology of WHR with respect to gridded precipitation product of India Meteorological Department (IMD). Different performance indicators i.e., Probability of Detection (POD), False Alarm Ratio (FAR), Normalised Root Mean Square Deviation (NRMSD), Pearson Correlation Coefficient (CC) and Skill Score (SS) were used for evaluating the GPEs. Multicriterion Decision Making (MCDM)approaches i.e., Compromise Programming (CP), Cooperative Game Theory (CGT), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Average Technique (WAT), and Fuzzy TOPSIS were used for ranking the GPEs across different grids in WHR. Entropy based weight assignment to NRMSD, CC, and SS were performed while applying them in MCDM methods. Group Decision Making (GDM) approach utilizing spearman correlation coefficient and additive ranking rule was employed to obtain the final ranking of GPEs from multiple rankings assigned through different MCDM methods. Across 115 grids, APHRODITE exhibits superior performance compared to other GPEs in 89 grids. Conversely, CHIRPS and CMORPH emerge as the least favorable products among the five GPEs across more than 70 grids, being consistently ranked either 4th or 5th. Notably, IMERG was identified as the best-performing product in 14 grids and as the second-best product in 63 grids, positioning it as the second most suitable option after APHRODITE for monthly rainfall time series analysis. Similar results, as detailed in the paper, were also obtained for month-wise rainfall time series analysis.
Comparative analysis of bias correction techniques for future climate assessment using CMIP6 hydrological variables for the Indian subcontinent
Abstract
The study focuses on the bias correction of Coupled Model Intercomparison Project Phase 6 (CMIP6) hydrologic variables for the Indian region. The performance of two widely accepted bias correction methodologies, namely Quantile Mapping (QM) and Bias Correction Spatial Disaggregation (BCSD), is compared. The study undertakes to evaluate the application of these popular bias correction methodologies on four important hydrologic variables viz. precipitation, temperature, and surface wind. The QM methodology is employed and compared with BCSD based bias corrected variables obtained from NEX-GDDP-CMIP6 dataset. The selected GCM historical bias corrected climate variables using QM are compared with the NCEP reanalysis variables. The objective is to improve the reliability and accuracy of climate projections by minimizing biases present in the GCM outputs. Through a comprehensive comparative analysis, it is determined that QM exhibits superior performance in reducing biases when compared to BCSD. Thus, use of QM demonstrates higher efficacy by effectively capturing the statistical distribution characteristics of observed data and transferring them to the GCM outputs. The future climate change over the Indian region is observed for both QM and BCSD algorithms for SSP5-8.5, SSP2-4.5, and SSP1-2.6. The result emphasizes the importance of selecting an appropriate bias correction methodology to enhance the reliability of climate projections in the Indian region. Ultimately, the findings of this study contribute to the broader field of climate modeling and impact assessment, providing valuable insights into the selection and application of bias correction techniques for CMIP6 datasets in the Indian subcontinent region.
Graphical abstract
Disinformation on the COVID-19 pandemic and the Russia-Ukraine War: Two sides of the same coin?
Abstract
Recently, Europe has been embroiled in serious disinformation crises. In 2020, the WHO declared the term “infodemic” due to the massive increase in hoaxes about the COVID-19 pandemic. In 2022, Russia’s invasion of Ukraine was dubbed the first digital world war. In this context, this research aims to establish similarities and differences between disinformation disseminated in Europe related to the pandemic during 2020–2021 and the war between Russia and Ukraine throughout 2022–2023. The methodology is based on triangulation, combining quantitative content analysis of fact-checking publications (N = 812) from eight fact-checkers representing four European countries (Spain, Germany, the United Kingdom, and Poland) and qualitative interviews with specialized agents (N = 8). Regarding the main findings, the frequency and nature of verified hoaxes during critical events, such as the COVID-19 pandemic and the Ukraine conflict, are closely intertwined with peaks in current events. Initially, both crises saw a surge in hoax verifications, but this trend gradually declined, particularly in the case of the Ukraine conflict. Hoaxes related to the pandemic were primarily disseminated in written form, whereas the complexities of the Russian-Ukraine war hindered widespread hoax dissemination in an audiovisual way. The typology of hoaxes correlated with their format, with fabricated content predominant in pandemic hoaxes and out-of-context images prevalent in those related to the conflict. Social media, notably Facebook and X (Twitter), emerged as the primary platforms for hoax dissemination, overshadowing traditional media channels. In both events, disinformation aimed to provoke emotional responses and polarize audiences, emphasizing the role of social networks in amplifying disinformation during crises.
Evolution and implications of SARS-CoV-2 variants in the post-pandemic era
Abstract
SARS-CoV-2, the cause of the COVID-19 pandemic, has introduced a challenging era characterized by the persistent emergence of subvariants. Even after the World Health Organization announced the end of the pandemic, the virus continues to evolve, posing significant challenges to public health responses. This comprehensive review examines the multifaceted impacts of these subvariants, emphasizing their significance across diverse dimensions. SARS-CoV-2 has genetic variability, especially at the spike protein region, which has given rise to Variants of Concern, including Beta, Delta, Gamma, Alpha, and the highly mutable Omicron, which differently exhibit varying levels of immune evasion, disease severity, and transmissibility. Subvariants within the Omicron lineage, including BA.1, BA.2, BA.3, and others, further complicate the landscape with distinct genetic signatures and varying infectivity levels. The impacts extend to diagnostic techniques, treatment strategies, and vaccine effectiveness, underscoring the need for a comprehensive public health response emphasizing preventive measures, genomic surveillance, and vaccination campaigns. Sustaining these interventions is critical, necessitating long-term strategies considering socio-political factors, community involvement, continuous adaptation of healthcare approaches, robust monitoring, and sustainable public health interventions to effectively combat the virus's ever-changing landscape.
Mapping risk of heat stress for dairy cattle in Tigray Regional State, Northern Ethiopia
Abstract
This study aimed to assess the risk of heat stress conditions for dairy cattle in the Tigray regional state of Ethiopia under historical and future climatic conditions. The daily thermal heat index (THI) was computed for each of the 14 weather stations after quality control of the maximum and minimum temperature datasets. The calculations were performed for the historical period (1980–2023) and two future climate periods (mid-term: 2040–2069 and end-term: 2070–2099) using an ensemble of 20 global circulation models under two representative concentration pathways (RCP 4.5 and 8.5). During the historical period, the frequency of severe heat stress was 3.4% (13 days/year), predominantly occurring in the western corner of the region (39.5% of days/year). The frequency of projected severe heat stress days across the region is expected to increase to 5.4% (mid-term) and 6% (end-term) under the RCP 4.5 emission scenario. Under the RCP 8.5 scenario, the frequency is expected to rise to 6.2% (mid-term) and 9.4% (end-term). On average, there were 6–9 consecutive severe heat stress days in both the historical and future climate periods. It is crucial to emphasize that the mapping of heat stress risk in dairy cattle was carried out using THI thresholds developed elsewhere. However, it is imperative to underscore the significance of conducting local experiments to determine context-specific thresholds.
Assessing potential impacts of climate change on China’s ski season length: a data-constrained approach
Abstract
Faced with the challenges presented by climate change, the necessity to navigate the sustainable development of China’s skiing industry emerges as a pivotal and pressing concern, especially considering the region’s vulnerability to climate variations and its burgeoning status as an emerging skiing destination. This study develops a methodology to assess the impact of climate change on ski resorts that is especially applicable in situations with limited climate station data and can be employed by ski industry stakeholders. A multiple linear regression (MLR) based on climate parameters from 1981 to 2010 is coupled with climate change projections under RCP4.5 and RCP8.5 scenarios for the 2020s, 2050s, and 2080s. To validate the precision of the MLR model assessment, the study compares the results with those of the SkiSim 2.0 model — a model widely applied in various countries and regions for evaluating the impact of climate change on the ski industry. Results from the MLR model reveal that there are comparatively modest decreases in skiing days in the northeast and northwest regions, contrasting with significant declines in the eastern, central, and southwestern areas. The findings of the MLR model are largely consistent with SkiSim 2.0, thereby broadly validating this approach. A series of implications and recommendations for further studies and industry applications are provided.
A CMIP6-ensemble-based evaluation of precipitation and temperature projections
Abstract
Understanding climate change’s effects on dam basins is very important for water resource management because of their important role in providing essential functions such as water storage, irrigation, and energy production. This study aims to investigate the impact of climate change on temperature and precipitation variables in the Altınkaya Dam Basin, which holds significant potential for hydroelectric power generation in Türkiye. These potential impacts were investigated by using ERA5 reanalysis data, six GCMs from the current CMIP6 archive, and two Shared Socioeconomic Pathways (SSP2 − 4.5 and SSP5 − 8.5) scenario data. Four Multi-Model Ensemble (MME) models were developed by using an Artificial Neural Network (ANN) approach (ENS1), simple averaging (ENS2), weighted correlation coefficients (ENS3), and the MARS algorithm (ENS4), and the results were compared to each other. Moreover, quantile delta mapping (QDM) bias correction was used. The 35-year period (1980–2014) was chosen as the reference period, and further evaluations were conducted by dividing it into three future periods (near (2025–2054), mid-far (2055–2084), and far (2085–2100)). Considering the results achieved from the MMEs, variations are expected in the monthly, seasonal, and annual assessments. Projections until the year 2100 indicate that under optimistic and pessimistic scenarios, temperature increases could reach up to 3.11 °C and 5.64 °C, respectively, while precipitation could decrease by as much as 19% and 43%, respectively. These results suggest that the potential changes in temperature and precipitation within the dam basin could significantly impact critical elements such as future water flow and energy production.