Climate Indices Research Articles

Evaluating and enhancing the applicability of thermal comfort indices in diverse outdoor environments using Bayesian and random forest regression

This research aims to evaluate and improve the applicability of three commonly utilized thermal comfort indices—standard effective temperature (SET), physiological equivalent temperature (PET), and universal thermal climate index (UTCI)—in outdoor environments. The study was conducted in four typical spaces of a university in Xi’an, China, covering different environmental settings: East Lake (Place 1), tree-lined path (Place 2), campus playground (Place 3), and campus square (Place 4). A total of 322 volunteers participated by completing a subjective sensory questionnaire in randomized environments, and on-place meteorological data were collected. The study yielded three primary findings. First, the tree-lined path (Place 2) was identified as the most comfortable outdoor space on the university campus during summer, whereas East Lake (Place 1) was the least comfortable. Second, substantial residuals were observed for SET, PET, and UTCI across different places when assessing the thermal comfort variations experienced by the subjects. Third, Bayesian regression and random forest regression were employed to analyze and adjust the thermal comfort indices. The comparative study found that after correction, the model explanatory power of PET increased by up to 20%, with the standard deviation of residuals decreasing by up to 0.314 and MSE decreasing by up to 0.1912. The model explanatory power of SET increased by up to 31.2%, with the standard deviation of residuals decreasing by up to 0.3867 and MSE decreasing by up to 0.3012. For UTCI, the model explanatory power increased by up to 10.5%, the standard deviation of residuals decreased by up to 0.2215, and the MSE decreased by up to 0.1002.

A 1940-2020 spatiotemporal analysis of thermal discomfort days in Southeast Asian countries

Abstract The high temperature and humidity make Southeast Asia (SEA) one of the regions most susceptible to the occurrence of thermal discomfort days (TDDs) in the world. In the context of global warming, SEA’s rapid population growth and urban expansion further exacerbated the region’s exposure to TDD, posing greater risks in public health. However, there exists a significant knowledge gap in the understanding of the long-term spatiotemporal evolution of TDD, as well as its projection in the future. By utilizing the newly released ERA5 datasets of Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT), this study presented the 81-year analysis of TDDs in SEA countries, spanning from 1940 to 2020, with projections extending to 2100. While the mean increase in UTCI was relatively modest, the corresponding rise in TDD likelihood was disproportionately larger, indicating a heightened risk of exposure. A distinct contrast was observed between continental and maritime regions, with maritime countries showing smaller absolute rises in both indices but larger trends in TDD frequency due to oceanic moderating effects. Seasonal analyses highlighted the dominant influence of the East Asia monsoon over SEA, and spatial analyses revealed a negative correlation between TDD occurrence and elevation, with low-lying areas being hotspots. Projections for 2050 suggest continued warming. These findings underscore the urgency of proactive measures to address climate change impacts, particularly in vulnerable maritime and low-lying areas, providing valuable insights for enhancing climate resilience and adaptation in SEA. Our findings reveal critical insights into the trends and future scenarios of thermal discomfort in the region, underscoring the urgent need for effective climate adaptation strategies.

A WRF-UCM-SOLWEIG framework for mapping thermal comfort and quantifying urban climate drivers: Advancing spatial and temporal resolutions at city scale

The capability to evaluate city-scale outdoor thermal comfort is crucial for understanding the public's experience of heat stress, especially during heat waves. Nevertheless, there remains a methodological gap in accurately mapping thermal comfort with high spatial and temporal resolutions in complex urban environment and in quantifying the contributions of various urban climate drivers, such as sky view factors and tree canopy fraction. To bridge this gap, we propose an innovative WRF-UCM-SOLWEIG framework that couples a mesoscale model with an urban microclimate model. Specifically, it integrates the Weather Research and Forecasting model coupled with the urban canopy model (WRF-UCM) and the Solar and Longwave Environmental Irradiance Geometry model (SOLWEIG). This framework is then applied to a densely populated tropical city in China, to quantify the impact of urban climate drivers on the Universal Thermal Climate Index (UTCI). The results reveal a significant diurnal variation in the impact of urban morphology. Notably, the most significant drivers affecting daytime UTCI are the impervious surface fraction and the tree canopy fraction, with maximum Pearson's correlation coefficients of 0.80 and -0.70, respectively. These findings suggest that urban heat mitigation strategies should prioritize on reducing impervious surfaces and enhancing shade management, alongside expanding urban forestry measures.

Prediction of heatwave related mortality magnitude, duration and frequency with climate variability and climate change information

Given the link between climatic factors on one hand, such as climate change and low frequency climate oscillation indices, and the occurrence and magnitude of heat waves on the other hand, and given the impact of heat waves on mortality, these climatic factors could provide some predictive skill for mortality. We propose a new model, the Mortality-Duration-Frequency (MDF) relationship, to relate the intensity of an extreme summer mortality event to its duration and frequency. The MDF model takes into account the non-stationarities observed in the mortality data through covariates by integrating information concerning climate change through the time trend and climate variability through climate oscillation indices. The proposed approach was applied to all-cause mortality data from 1983 to 2018 in the metropolitan regions of Quebec and Montreal in eastern Canada. In all cases, models introducing covariates lead to a substantial improvement in the goodness-of-fit in comparison to stationary models without covariates. Climate change signal is more important than climate variability signal in explaining maximum summer mortality. However, climate indices successfully explain a part of the interannual variability in the maximum summer mortality. Overall, the best models are obtained with the time trend and the North Atlantic Oscillation (NAO) used as covariates. No country has yet integrated teleconnection information in their heat-health watch and warning systems or adaptation plans. MDF modeling has the potential to be useful to public health managers for the planning and management of health services. It allows predicting future MDF curves for adaptive management using the values of the covariates.

Local Weather and Global Climate Data-Driven Long-Term Runoff Forecasting Based on Local–Global–Temporal Attention Mechanisms and Graph Attention Networks

The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff.

The state of the ocean in the northeastern Atlantic and adjacent seas

Abstract. In this paper, the Copernicus Ocean State Report offers detailed scientific analysis of the ocean under climate change, ocean variability, and ocean extremes in the northeastern Atlantic and adjacent seas. Major results show that the northeastern Atlantic Ocean and adjacent seas have experienced consistent warming, with sea surface temperatures increasing at a rate of 0.25 ± 0.03 °C per decade since 1982, doubling the global average trend. This warming is most pronounced in the Black Sea, Mediterranean Sea, and Baltic Sea. Sea levels have risen significantly over the past 30 years, particularly in the Baltic and Mediterranean seas. Ocean acidification has also increased, with pH decreasing at a rate of −0.017 ± 0.001 units per decade. Marine heatwaves have intensified and expanded, affecting over 60 % of the region in 2022 and 2023. Over the past 16 years, most extreme wind speeds exceeding 22 m s−1 prevailed in the central and subpolar North Atlantic and northern Mediterranean Sea. The region has also seen significant variability in ocean climate indicators and circulation patterns, including increased Atlantic Water transport to the Arctic Ocean through the Fram Strait and notable variations in the Mediterranean Sea's meridional overturning circulation. No major Baltic inflow occurred in winter 2022/23.

Arctic and Barents Sea ice extent variability and trends in 1979–2022

The aim of the paper is to assess interannual and decadal variability of the Barents Sea and Arctic ice extent in various seasons for the period from 1979 to 2022.The material for the study was satellite data on the Barents Sea and Arctic ice extent, climate indices, oceanographic data obtained during surveys in the Barents Sea by PINRO and other available information on hydrometeorological conditions of the sea in 1979–2022.Methods of descriptive statistics as well as comparative, correlation, regression, harmonic and cluster analyses were applied.Results: The year-to-year changes in the Barents Sea and Arctic ice extent in various seasons are described for 1979–2022. The modern climate changes, manifested in substantial warming of air and water masses in the Arctic, resulted in a considerable ice extent reduction in the studied areas in recent decades. Well-marked negative trends took place in all months and seasons. The annual mean ice extent was decreasing at a rate of 532 thousand km2 per decade in the Arctic, and at a rate of 105 thousand km2 per decade in the Barents Sea. Over the past 40 years, the ice extent in the Arctic has decreased by 14%, and in the Barents Sea — by half (by 51%), while the ice-free period duration has increased in the Barents Sea from one to three months. Hydrometeorological parameters were identified that are most closely related to the ice extent in the Arctic (|r| ≥ 0.40) and Barents Sea (|r| ≥ 0.60). Reliable regression models were developed that explain 45.2 and 88.0% of its variability, respectively. The contribution of individual factors to the ice extent variability explained by regression equations was quantitatively assessed, and the leading role of air temperature was noted.Practical significance: The obtained results are useful for a better understanding of the climate processes occurring in the Arctic and Barents Sea in recent decades and can be used to assess the impact of climate changes on the main objects of Russian fishery in these areas.

Characterizing the seasonal influenza disease burden attributable to climate variability: A nationwide time-series modelling study in Japan, 2000−2019

BackgroundAmbient temperature and humidity are established environmental stressors with regard to influenza infections; however, mapping disease burden is difficult owing to the complexities of the underlying associations and differences in vulnerable population distributions. In this study, we aimed to quantify the burden of influenza attributable to non-optimal ambient temperature and absolute humidity in Japan considering geographical differences in vulnerability. MethodsThe exposure−lag−response relationships between influenza incidence, ambient temperature, and absolute humidity in all 47 Japanese prefectures for 2000−2019 were quantified using a distributed lag non-linear model for each prefecture; the estimates from all the prefectures were then pooled using a multivariate mixed-effects meta-regression model to derive nationwide average associations. Association between prefecture-specific indicators and the risk were also examined. Attributable risks were estimated for non-optimal ambient temperature and absolute humidity according to the exposure−lag−response relationships obtained before. ResultsA total of 25,596,525 influenza cases were reported during the study period. Cold and dry conditions significantly increased influenza incidence risk. Compared with the minimum incidence weekly mean ambient temperature (29.8 °C) and the minimum incidence weekly mean absolute humidity (20.2 g/m3), the cumulative relative risks (RRs) of influenza in cold (2.5 °C) and dry (3.6 g/m3) conditions were 2.79 (95% confidence interval [CI]: 1.78−4.37) and 3.20 (95% CI: 2.37−4.31), respectively. The higher RRs for cold and dry conditions were associated with geographical and climatic indicators corresponding to the central and northern prefectures; demographic, socioeconomic, and health resources indicators showed no clear trends. Finally, 27.25% (95% empirical CI [eCI]: 5.54−36.35) and 32.35% (95% eCI: 22.39−37.87) of all cases were attributable to non-optimal ambient temperature and absolute humidity (6,976,300 [95% eCI: 1,420,068−9,306,128] and 8,280,981 [95% eCI: 8,280,981−9,693,532] cases), respectively. ConclusionsThese findings could help identify the most vulnerable populations in Japan and design adaptation policies to reduce the attributable burden of influenza due to climate variability.

Uncovering Interdecadal Pacific Oscillation’s Dominance in Shaping Low-Frequency Sea Level Variability in the South China Sea

The low-frequency sea level variability in the South China Sea (SCS) is examined using high-resolution regional ocean model simulations that span the last six decades. The analysis reveals interdecadal oscillations with a periodicity of 12–13 years as the dominant mode of sea level variability in the SCS. The fluctuations in the Luzon Strait transport (LST) are identified as primary drivers of interannual to interdecadal sea level variability, rather than atmospheric forcing within the SCS. Fourier spectrum analysis is employed to investigate the association between SCS sea level variability and the Interdecadal Pacific Oscillation (IPO), using principal components of SCS sea surface height anomalies, wind stress curl, wind stress components, net short wave flux, as well as the LST and various climate indices. The variations in the SCS sea level are driven by the IPO, which modifies the LST and ocean heat content, impacting the steric sea level.

Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices

This paper aims to propose a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) with a bi-directional long-short term memory (BiLSTM) for week-ahead prediction of daily flood index (IF) for Bangladesh. The neighbourhood component analysis (NCA) is assigned for significant feature selection with synoptic-scale climatic indicators. The results successfully reveal that the hybrid CNN-BiLSTM model outperforms the respective benchmark models based on forecasting capability, as supported by a minimal mean absolute error and high-efficiency metrics. With respect to IF prediction, the hybrid CNN-BiLSTM model shows over 98% of the prediction errors were less than 0.015, resulting in a low relative error and superiority performance against the benchmark models in this study. The adaptability and potential utility of the suggested model may be helpful in subsequent flood monitoring and may also be beneficial to policymakers at the federal and state levels.

District-wise evaluation of meteorological factors and outdoor thermal comfort in India using UTCI – Insight into future climatic scenario

The study aims to comprehensively analyze outdoor meteorological conditions and thermal comfort across 592 districts in India, addressing the critical issues of thermal comfort which significantly impacts health, well-being and productivity. Given the anticipated global warming, understanding these factors becomes crucial, particularly for low-income populations who spend considerable time outdoors. While most previous studies in India have focused on indoor environments or on broad climatic regions, our research provides a granular analysis at the district level, incorporating future climate scenarios from 2050 to 2080. Using the Universal Thermal Climate Index (UTCI), our findings reveal that the northwestern part of India experiences ‘Strong’ to ‘Extreme’ heat stress, with temperatures exceeding 50 °C, while the Himalayan regions face ‘Strong’ cold stress, with temperatures dropping below -20 °C. The geographical distribution of UTCI classes shows that the western and central regions suffer from high thermal stress during summer afternoons, whereas coastal areas, benefiting from higher relative humidity and wind speeds, exhibit moderate UTCI values. The Himalayan regions consistently present lower UTCI values, indicating colder conditions. We have developed a district-wise climate atlas of India, mapping key environmental parameters and the outdoor thermal stress of UTCI values. The main objective of this study is to provide localized insights into how climate change will affect outdoor thermal comfort, facilitating informed decision-making for public health planning, energy infrastructure, and climate adaptation strategies across India.

Climate variability through the lens of applied weather index insurance in Senegal-a novel perspective on the implications of decadal variation

IntroductionWeather-based index insurance is a financial instrument which allows smallholder farmers to protect themselves against climate shocks such as droughts and floods. In many cases, insurance indices are based on one or more earth observation datasets (e.g., rainfall, soil moisture, vegetative health) which are partly covering periods of more than 40 years. While remote sensing products and their associated data have improved over this time, understanding the historical climate variability and trends remains an essential piece in ensuring the development of indexes that best represent farmers’ risks. From a practical perspective, shortening time series to limit the risk of understudied climate variability, such as the Atlantic Multidecadal Variability, sometimes seems to be a quick solution. However, shorter time series jeopardize the overall robustness of the index. Therefore, understanding the links between climate variability, index design, and implications for farmers is key. Weather-based index insurance products in Sahelian West Africa usually face a challenge in robustly quantify underlying climatic decadal variation in seasonal rainfall.MethodsThis study analyzes the influence of decadal shifts in rainfall patterns in Sahelian West Africa, in particular Senegal, on index insurance calibration and design, concluding with practical recommendations for the next generation of drought risk finance instruments in the region.ResultsOur findings indicate that decadal variability has not led to a clear decrease in payouts in recent years compared to earlier years, despite an overall increase in seasonal rainfall. Rather, we find that interannual variability has increased which may be a more critical factor for assessing farmers’ agricultural risk than the increase in total rainfall.DiscussionFocusing on key moments of the cropping calendar in the design of an index shows that an increase in the total average rainfall per season does not result in fewer payouts.

Evaluating the climatic suitability for camping in South Africa: an application of the Camping Climate Index (CCI)

ABSTRACT Nature-based tourism is a prominent component of the tourism sector in South Africa, attracting both domestic and international travellers. Camping plays a crucial role, providing both affordable lodging and an opportunity to engage more directly and continuously with the natural environment. Climate is a key determinant of the feasibility of camping activities and has been empirically established as a significant influence in tourists’ decision-making process when selecting a destination. The climate in South Africa, while highly heterogenous, has been generally classified as ‘ideal’ for tourism. The Camping Climate Index (CCI) has recently been developed to quantify the climatic suitability of diverse camping locations, and the suitability of the index confirmed for South Africa. This study applies the CCI to 33 stations across South Africa for the period 1990–2022 or for the longest time period for which data are available. The average annual CCI scores range from 3.5 (‘unfavourable’) for Sutherland to 5.3 (‘acceptable’) for King Shaka International Airport. Mean monthly scores for the common period 2017–2021 range from 2.7 (‘unfavourable’) for Sutherland in August to 6.2 (‘good’) for Springbok in January and December. This facilitates the advancement of the tourism sector in South Africa by providing a quantitative assessment of the climatic conditions suitable for camping activities.

Data-driven nexus between malaria incidence and World Bank indicators in the Mekong River during 2000-2022.

The increase in hydro dams in the Mekong River amidst the prevalence of multidrug-resistant malaria in Cambodia has raised concerns about global public health. Political conflicts during Covid-19 pandemic led cross-border movements of malaria cases from Myanmar and caused health care burden in Thailand. While previous publications used climatic indicators for predicting mosquito-borne diseases, this research used globally recognizable World Bank indicators to find the most impactful indicators related with malaria and shed light on the predictability of mosquito-borne diseases. The World Bank datasets of the World Development Indicators and Climate Change Knowledge Portal contain 1494 time series indicators. They were stepwise screened by Pearson and Distance correlation. The sets of five and four contain respectively 19 and 149 indicators highly correlated with malaria incidence which were found similarly among five and four GMS countries. Living areas, ages, career, income, technology accessibility, infrastructural facilities, unclean fuel use, tobacco smoking, and health care deficiency have affected malaria incidence. Tonle Sap Lake, the largest freshwater lake in Southeast Asia, could contribute to the larval habitat. Seven groups of indicator topics containing 92 indicators with not-null datapoints were analyzed by regression models, including Multiple Linear, Ridge, Lasso, and Elastic Net models to choose 7 crucial features for malaria prediction via Long Short Time Memory network. The indicator of people using at least basic sanitation services and people practicing open defecation were health factors had most impacts on regression models. Malaria incidence could be predicted by one indicator to reach the optimal mean absolute error which was lower than 10 malaria cases (per 1,000 population at risk) in the Long Short Time Memory model. However, public health crises caused by political problems should be analyzed by political indexes for more precise predictions.

Deep hybridnet for drought prediction based on large-scale climate indices and local meteorological conditions

Deep hybridnet for drought prediction based on large-scale climate indices and local meteorological conditions

Statistical analysis of climatic drought indices as a decision-making tool in irrigation matters—case of citrus orchards in Middle and Upper Cheliff (Algeria)

Statistical analysis of climatic drought indices as a decision-making tool in irrigation matters—case of citrus orchards in Middle and Upper Cheliff (Algeria)

Climatic variability of wave heights in the shelf zone of the North-Eastern part of the Black Sea according field data

Wave parameters and their variability under climate change are important for forecasting in many areas, such as coastal zone management. This study analyzed the variability of key climate indices from August to October over the period 1996–2020. The relationship between these changes and changes in average monthly significant wave heights was assessed based on field measurements taken at a consistent location on the northeastern Black Sea shelf during various years within this period.The analysis revealed that negative values of NAO, AO, and EA, along with positive values (greater than 1) of EA/WR, are associated with increases in wave heights. The study identified the key periods when joint changes in these climate indices are likely to have the greatest impact on average monthly wave heights: 4 and 7 years for August, 17 and 20 years for September, and 4, 13, and 10 years for October. The discussion emphasizes that identifying the primary regional climate indices and studying their relationship with wave parameters in detail could form the foundation for zoning the Black Sea into quasi-homogeneous zones based on climate variability. This approach could also support the development of simple prognostic models for different parts of the Black Sea, taking into account the non-stationary nature of climate change.

Climatic temperature indices for the coastal resilience observatory in Tabasco (Dos Bocas)‒LANRESC

This study focused on calculating climate change indices derived from maximum and minimum temperature variables for the Coastal Resilience Observatory in Tabasco, Dos Bocas, located in Paraíso, Tabasco, Mexico. The indices were computed using CLIMPACT2, 2007 version, and a total of 26 indices were obtained for the period from 1940 to 2022. Subsequently, significant trends were calculated using statistical tests such as Mann‒Kendall, trend-free prewhitening, bias correction applied to prewhitening, and variance correction (by two methods). Sen's slope method was employed to determine the extent of alteration in extreme climate indices. Increasing trends were found in the indices tn90p (%), tx90p (%), tr (days), txgt50p (%), wdsi (days), su (days), tmge10 (days), tmge5 (days), gddgrow (days), and gsl (days), indicating the presence of local warming and providing evidence of climate change detection. These results contribute to updating climate information in the area and serve as a proposal for replication in the other six Coastal Observatories of the National Coastal Resilience Laboratory in Mexico, as part of measures related to climate stressors in coastal areas where these observatories are located. This information is valuable for decision-makers and the general population, as it will support socio-environmental adaptation and mitigation measures in the face of climate change as part of efforts to enhance the resilience of socioecosystems in coastal areas.

The Spatiotemporal Evolution of Extreme Climate Indices in the Songnen Plain and Its Impact on Maize Yield

Global climate change is intensifying and extreme weather events are occurring frequently, with far-reaching impacts on agricultural production. The Songnen Plain, as an important maize production region in China, faces challenges posed by climate change. This study aims to explore the effects of climate extremes on maize yield and provide a scientific basis for the adaptation of agriculture to climate change in this region. The study focuses on the spatial and temporal evolution characteristics of climate extremes during the maize reproductive period from 1988 to 2020 in the Songnen Plain and their impacts on maize yield. Daily temperature and precipitation data from 11 meteorological stations were selected and combined with maize yield information to assess the spatial and temporal trends of extreme climate indices using statistical methods such as the moving average and Mann–Kendall (M-K) mutation test. Pearson correlation analysis and a random forest algorithm were also used to quantify the degree of influence of extreme climate on maize yield. The results showed that (1) the extreme heat and humidity indices (TN90p, TX90p, CWD, R95p, R10, and SDII) tended to increase, while the cold indices (TN10p, TX10p) and the drought indices (CDD) showed a decreasing trend, suggesting that the climate of the Songnen Plain region tends to be warmer and more humid. (2) The cold indices in the extreme temperature indices showed a spatial pattern of being higher in the north and lower in the south and lower in the west and higher in the east, while the warm indices were the opposite, and the extreme precipitation indices showed a spatial pattern of being higher in the east and lower in the west. (3). Both maize yield and trend yield showed a significant upward trend. Maize meteorological yield showed a fluctuating downward trend within the range of −1.64~0.79 t/hm2. During the 33 years, there were three climatic abundance years, two climatic failure years, and the rest of the years were normal years. (4) The cold index TN10p and warm indices TN90p and CWD were significantly correlated with maize yield, in which TN90p had the highest degree of positive correlation with yield, and in the comprehensive analysis, the importance of extreme climatic events on maize yield was in the order of TN90p, TN10p, and CWD. This study demonstrates the impact of extreme climate indices on maize yield in the Songnen Plain, providing a scientific basis for local agricultural management and decision-making, helping to formulate response strategies to mitigate the negative effects of extreme climate, ensure food security, and promote sustainable agricultural development.

Climate variability impact on groundwater quality in Small Island Developing States: Mauritius Island as a case study

This study investigates the impact of climate variability on groundwater quality in Mauritius. This is achieved by analyzing the physical and chemical water quality of the five main aquifers over eleven years. Temporal variations in water quality properties were compared to the Standardised Precipitation Index (SPI), Dry Spells, the Standardised Evapotranspiration Index (SPEI), and other climate variables to gain insights into how precipitation controls groundwater quality. The results reveal that the SPI and the SPEI correlate minimally with water quality indicators. Sulphate is the only water quality indicator that showed correlations above 0.4 in aquifers 2 and 3 against a 12-month SPI. Sulphate, alongside chloride, showed what is termed ”notable correlation,” a concept defined in this paper to accommodate correlations that fall above 0.3 when assessed against global climate modes ENSO and AAO, respectively. These results signify that sulphate is the most sensitive water quality indicator to water quantity changes, notwithstanding the modesty of the correlations. Heavy storms occurring during cyclones impact groundwater quality with respect to conductivity, TDS, salinity, and nitrate, although this could not be statistically tested given the lack of water quality indicators collected on the days surrounding the storm. Therefore, the conclusion is made based on one storm event. The study revealed that individual correlations between climate indices and water quality variables are present but weak. However, the long-term trend in water quality is visible.