Changes In Minimum Temperatures Research Articles

Temporal variation in river ice phenology of the Heilongjiang River in response to climate change

Study regionUpstream of the Heilongjiang River, located in Northeast Asia, is the transboundary river between China and Russia. Study focusUsing long-term hydrological records provided by the Mohe station in combination with multiple statistical approaches, we investigate how river ice phenology of the Heilongjiang River has varied during the period 1957–2016, and quantitatively assess the individual contribution of changes in different climatic factors to such variation in river ice phenology. New insights for the regionWe show the later occurrence for newly formed ice date, ice-on date, and freeze-up date and the earlier occurrence for beak-up date and ice-off date, ultimately leading to a decrease in persistence of river ice in the Heilongjiang River. River ice phenology exhibits a general lag response to climate change, with ranging from 1 to 5 months. Further attribution analysis that explicitly considers time-lag effect suggests that climate change plays a dominant role in regulating river ice phenology. More specifically, compared to other climatic factors, air pressure and maximum and minimum temperature changes have larger contributions to variation in river ice phenology, particularly for the delay of formation time. These findings provide technical basis and beneficial reference to the development of regional river management strategies involved in climate change adaptation framework.

Climatic Challenges in the Growth Cycle of Winter Wheat in the Huang-Huai-Hai Plain: New Perspectives on High-Temperature–Drought and Low-Temperature–Drought Compound Events

Global climate change increasingly impacts agroecosystems, particularly through high-temperature–drought and low-temperature–drought compound events. This study uses ground meteorological and remote sensing data and employs geostatistics, random forest models, and copula methods to analyze the spatial and temporal distribution of these events and their impact on winter wheat in the Huang-Huai-Hai Plain from 1982 to 2020. High-temperature–drought events increased in frequency and expanded from north to south, with about 40% of observation stations recording such events from 2001 to 2020. In contrast, low-temperature–drought events decreased in frequency, affecting up to 80% of stations, but with lower frequency than high-temperature–drought events. Sensitivity analyses show winter wheat is most responsive to maximum and minimum temperature changes, with significant correlations to drought and temperature extremes. Copula analysis indicates temperature extremes and drought severity are crucial in determining compound event probability and return periods. High-temperature–drought events are likely under high temperatures and mild drought, while low-temperature–drought events are more common under low temperatures and mild drought. These findings highlight the need for effective agricultural adaptation strategies to mitigate future climate change impacts.

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Prince Edward Island (PEI) is an agricultural province heavily relying on rainfed agriculture. The island has already experienced significant impacts from climate change. Accurate projections of PEI temperature extreme indices are required to mitigate and adapt to the changing climate conditions. This study aims to develop ensemble projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) global circulation models (GCMs) to analyze temperature extremes on PEI. In this study, the ECMWF ERA5 reanalysis dataset was chosen for stepwise cluster analysis (SCA) due to its high accuracy. Three CMIP6 (NorESM2-MM, MPI-ESM1.2-HR, and CanESM5) GCMs, along with their ensemble average, were utilized in the SCA model to project future changes in daily maximum temperature (Tmax) and minimum temperature (Tmin) at four meteorological stations on PEI (East Point, Charlottetown, Summerside, and North Cape) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). These GCMs were selected based on their low, medium, and high Equilibrium Climate Sensitivity. The bias-corrected results for the future period of Tmax and Tmin showed that the GCM-specific changes in the ECS also impact the regional scale. Additionally, several temperature extreme indices, including the daily temperature range (DTR), summer days (SU), growing degree days (GDD), growing season length (GSL), ice days (ID), and frost days (FD), were analyzed for two future periods: FP1(202–2050) and FP2 (2051–2075). The results indicate that DTR, SU, GDD, and GSL are expected to increase, while ID and FD are projected to decrease during FP1 and FP2 under both scenarios. The future projected mean monthly changes in Tmax, Tmin, and the selected temperature extreme indices highlight warmer future periods and an increase in agriculture-related indices such as GDD and GSL. Specifically, July, August, and September are expected to experience even higher temperatures in the future. As the climate becomes warmer, cold extreme events are projected to be shorter in duration but more intense in terms of their impact. The largest increments/decrements for Tmax, Tmin, and their relevant indices were observed during FP2 under SSP5-8.5. The outcomes of this study provide valuable insights for agricultural development, water resource management, and the formulation of effective mitigation strategies to address the impacts of climate change on PEI.

Anomalous Warm Temperatures Recorded using Tree Rings in the Headwater of the Jinsha River during the Little Ice Age

As a feature of global warming, climate change has been a severe issue in the 21st century. A more comprehensive reconstruction is necessary in the climate assessment process, considering the heterogeneity of climate change scenarios across various meteorological elements and seasons. To better comprehend the change in minimum temperature in winter in the Jinsha River Basin (China), we built a standard tree-ring chronology from Picea likiangensis var. balfouri and reconstructed the regional mean minimum temperature of the winter half-years from 1606 to 2016. This reconstruction provides a comprehensive overview of the changes in winter temperature over multiple centuries. During the last 411 years, the regional climate has undergone seven warm periods and six cold periods. The reconstructed temperature sensitively captures the climate warming that emerged at the end of the 20th century. Surprisingly, during 1650–1750, the lowest winter temperature within the research area was about 0.44 °C higher than that in the 20th century, which differs significantly from the concept of the “cooler” Little Ice Age during this period. This result is validated by the temperature results reconstructed from other tree-ring data from nearby areas, confirming the credibility of the reconstruction. The Ensemble Empirical Mode Decomposition method (EEMD) was adopted to decompose the reconstructed sequence into oscillations of different frequency domains. The decomposition results indicate that the temperature variations in this region exhibit significant periodic changes with quasi-3a, quasi-7a, 15.5-16.8a, 29.4-32.9a, and quasi-82a cycles. Factors like El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and solar activity, along with Atlantic Multidecadal Oscillation (AMO), may be important driving forces. To reconstruct this climate, this study integrates the results of three machine learning algorithms and traditional linear regression methods. This novel reconstruction method can provide valuable insights for related research endeavors. Furthermore, other global climate change scenarios can be explored through additional proxy reconstructions.

Spatio-Temporal Trends in Precipitation, Temperature, and Extremes: A Study of Malawi and Zambia (1981–2021)

Analyzing long-term climate changes is a prerequisite for identifying hotspot areas and developing site-specific adaptation measures. The current study focuses on assessing changes in precipitation, maximum and minimum temperatures, and potential evapotranspiration in Zambia and Malawi from 1981 to 2021. High-resolution precipitation and temperature datasets are used, namely, Climate Hazards Group InfraRed Precipitation with Station data (0.05°) and Multi-Source Weather (0.1°). The Mann–Kendall trend test and Sen’s Slope methods are employed to assess the changes. The trend analysis shows a non-significant increase in annual precipitation in many parts of Zambia and Central Malawi. In Zambia and Malawi, the average annual and seasonal maximum and minimum temperatures show a statistically significant increasing trend (up to 0.6 °C/decade). The change in precipitation during the major rainy seasons (December–April) shows a non-significant increasing trend (up to 3 mm/year) in a large part of Zambia and Central Malawi. However, Malawi and Northern Zambia show a non-significant decreasing trend (up to −5 mm/year). The change in December–April precipitation significantly correlates with El Niño–Southern Oscillation (Indian Ocean Dipole) in Southern (Northern) Zambia and Malawi. To minimize the impact of the observed changes, it is imperative to develop adaptation measures to foster sustainability in the region.

Variations in Maximum and Minimum Temperature in Mount Qomolangma during 1971–2020

Based on the daily maximum and minimum temperature observational data during 1971–2020, the variabilities of the maximum and minimum temperature of Mount Qomolangma are analyzed. The daily maximum temperature is 25.8 °C and the daily minimum temperature is −31.4 °C during the study period in Mount Qomolangma. Overall, there has been an upward trend with decadal laps for both maximum and minimum temperature. On monthly, seasonal, and annual scales, neither maximum temperature nor minimum temperature time series exhibit an increasing trend from 1971 to 2020. The increasing trends in monthly minimum temperature are even more pronounced than those in maximum temperature. Abrupt changes are noted in both monthly, seasonal, and annual maximum and minimum temperature time series. Specifically, an abrupt change in annual maximum temperature occurred in the 1980s, while an abrupt change in annual minimum temperature occurred in the 1990s. Differences between the north and south slope of Mount Qomolangma are evident, with temperature fluctuations of the north slope being more extreme than those of south slope. The seasonal and annual maximum temperature of the north slope is higher than that of the south slope, except for winter, and the seasonal and annual minimum temperatures of the north slope are all lower than those of the south slope. The tendences of maximum and minimum temperatures in the north slope are more dominant than those in the south slope. The findings are beneficial for understanding the characteristics of local climate change on the Tibetan plateau and to underscore the significant role of Mount Qomolangma in the context of global warming.

Climate change and hydrological response of Megech catchment, Upper Blue Nile River Basin, Ethiopia

Climate change scenarios of precipitation and temperature were divided into three time windows of 30 years each from 2011 to 2099. The new version of Soil and Water Assessment Tool (QSWAT v2.6.1) was used to simulate the hydrological response and it was first calibrated and validated using observed data as an input for assessing the hydrological responses. The results showed that the QSWAT calibration and validation reveals a good agreement (R2=0.77) during calibration and validation. Whereas nash and Sutcliffe simulation efficiency (NSE) was found to be 0.76 during calibration and 0.73 during validation. Based on changes of precipitation, maximum and minimum temperature, the monthly flow volume did not show systematic trends i.e. increases non-rainy months and decreases in the wet-months. Thus, the hydrology of Megech River is highly vulnerable to climate change which causes water stress for domestic use and environmental flow.

The evolutionary characteristics of temperature change in China during 1961–2020

AbstractThe changes of annual average, minimum and maximum temperature (Tavg, Tmin and Tmax) in China over 1961–2020 were examined based on the Chinese gridded observation dataset (CN05.1), and the relative contributions of Tmin and Tmax to the tendency of Tavg were addressed. The results show that the nationwide mean warming rate reached 0.29°C·decade−1 for Tavg, 0.39°C·decade−1 for Tmin and 0.24°C·decade−1 for Tmax, respectively, from 1961 to 2020, which depended on temporal windows. The relative contributions of Tmin and Tmax to the trends of Tavg over China during 1961–2020 are 56% and 44%, respectively. On the spatial scale, the majority of China exhibits a greater relative contribution of Tmin than Tmax, except for some areas along the Hu Huanyong Line. As in the case of the warming rate, the temporal patterns of the magnitude of the relative contribution of Tmin and Tmax are also identified. For the first 30 years (1961–1990), Tmin is the primary contributor to changes in the trend of Tavg over a broad area excluding the Qinling Mountain‐Huai River to the Nanling Mountain. The national average magnitudes of the relative contribution of Tmax is slightly larger than Tmin in the latter 30 years (1991–2020). The areas with larger contributions of Tmax are mainly scattered along Hu Huanyong Line, the southern part of the Qinghai‐Tibet Plateau and the patchy parts of Northwest China. Changes in downward longwave radiation, solar radiation, cloud cover, land use, soil moisture, atmospheric circulation and urbanization likely accounted for much of the trend difference of Tmin and Tmax, thereby leading to various relative contributions across different regions during different periods. The warming mechanisms in China have been subjected to novel changes. The study could provide information for decision making on scientific response to warming.

An Assessment of the Impact of Climate Change on Asphalt Binder Selection in East China Based on the ARIMA Model

Temperature is a key factor considered in the selection of asphalt binders for asphalt pavement construction. Currently, the asphalt binders used in some regions’ asphalt pavements are no longer suitable for anticipated climate conditions. The reasonable selection of asphalt binder is an important measure for asphalt pavement to adapt to climate change. This paper focuses on the potential impact of climate change on asphalt binder selection in East China in the future. This study is based on the performance grade (PG) system with SUPERPAVE specifications. It involved collecting meteorological data from 109 meteorological stations in East China from 1960 to 2019 and used the ARIMA prediction model to calculate the maximum and minimum design temperatures for road surfaces over the next 20 years. Based on the forecasted road surface temperature data, the impact of climate change on the choice of asphalt binder in East China was discussed. The research findings indicate that, validated by historical data, using the ARIMA model for future temperature prediction has proven reliability. There are some differences in different regions regarding the change in maximum and minimum pavement design temperatures. In 2019 and 2039, there are three and four high temperature grades in East China; these are PG52, PG58, and PG64 and PG52, PG58, and PG64, PG70 respectively. The dominant high temperature grade in East China will remain PG64, and a total of 23.80% of the regions in East China will experience a one-grade upward shift in high temperature grades. PG-28, PG-22, PG-16, and PG-10 are the four low temperature grades distributed in East China in both 2019 and 2039. Compared with 2019, the proportion of areas with grade PG-16 will increase from 33.86% to 34.89%, and the dominant low temperature grade in East China will remain PG-10 in 2039. In the next 20 years, low-temperature cracking issues related to asphalt pavement in some areas of East China will intensify, but the primary challenge will still be problems caused by high temperatures.

Low-cost, 3D printed irradiation system for invitro photodynamic therapy experiments.

The development of a suitable irradiation setup is essential for invitro experiments in photodynamic therapy (PDT). While various irradiation systems have been developed for PDT, only a few offer practical and high-quality setups for precise and reproducible results in cell culture experiments. This report introduces a cost-effective illumination setup designed for invitro photodynamic treatments. The setup consists of a commercially available light-emitting diode (LED) lamp, a cooling unit, and a specially designed 3D-printed enclosure to accommodate a multiwell plate insert. The LED lamp is versatile, supporting various irradiation wavelengths and adjustable illumination fields, ensuring consistent and reliable performance. The study evaluates the setup through various parameters, including photon flux density, illumination uniformity, photon distribution across the multiwell plate, and temperature changes during irradiation. In addition, the effectiveness of the LED-based illumination system is tested by treating mouse mammary breast carcinoma cells (4T1) with Rose Bengal and LED irradiation at around 525 nm. The resulting IC50 of 5.2 ± 0.9 μM and a minimum media temperature change of ca. 1.2°C indicate a highly promising LED-based setup that offers a cost-effective and technically feasible solution for achieving consistent, reproducible, and uniform irradiation, enhancing research capabilities and potential applications.

Weathergami

Abstract Adapted from the sports concept of scorigami, the weathergami chart is introduced. Weathergami charts depict the frequency of occurrence of the full range of daily maximum and minimum temperature combinations observed at a location. These charts highlight essential features of climate not evident in traditional representations. A variation of the weathergami chart displays transition frequencies, which describe the likelihood of particular day-to-day changes in maximum and minimum temperatures. Likewise, weathergami anomaly charts reveal characteristics of changing climate not evident in standard time series representations. Several examples are provided, with comparisons to climate descriptions found in popular textbooks.

How Do Extreme Lake Water Temperatures in Poland Respond to Climate Change?

Lakes are vital components of the Earth’s hydrological cycle and are susceptible to the impacts of climate change. Understanding the changes in terms of minimum and maximum lake surface temperatures is crucial for assessing the effects of climate change on freshwater ecosystems. This study focuses on ten lakes in Poland to investigate the impacts of climate change on lake temperatures in different geographical regions. The Mann–Kendall (MK) and Sen tests were employed to analyze trends and changes in minimum and maximum water temperatures, respectively. The results reveal significant increases in the minimum and maximum temperatures, particularly in May and June. Different lakes exhibit varying trends and variability in temperature changes over time, indicating the vulnerability of these ecosystems. The current study also examines the magnitude of annual temperature changes and classifies them into different levels. This analysis highlights the complex relationship between air temperature, seasonal cycles, and lake morphometric characteristics in shaping variations in lake surface water temperature. These findings contribute to understanding the impacts of climate change on Poland’s lakes and provide valuable insights for developing conservation strategies and adaptive measures to protect freshwater resources.

A New Method to Calibrate Cardinal Temperatures for Eucalyptus Plantation

Developing a good understanding of the interactions between forest plantation growth and climate is essential for predicting the impact of climate change on terrestrial ecosystems and for assessing the adaptation and vulnerability of tree species. One such interaction, the response in growth rate of a forest stand to changes in temperature, may be described mathematically. Some models that run on monthly time steps assume a yearly optimum, minimum, and maximum temperature for simplicity, which may not represent well to actual forest growth. Here, we developed a finer-resolution methodology that encompasses monthly growth rates and temperature limits to calibrate the parameters for an envelope curve in Eucalyptus plantations in South America. Several polynomial curves were tested to determine temperature patterns, and their yearly tree growth patterns demonstrated that responses to temperature differed by as much as 10 °C among seasons. The best curve was a second-degree polynomial curve, whose extreme values indicated the optimum temperature and whose real roots limited the minimum and maximum temperatures for growth. This polynomial was fitted every month to describe yearly changes in optimum, maximum, and minimum temperatures. When fitted to annual data, it determined 7 °C, 19 °C, and 31 °C as the minimum, optimum, and maximum temperatures for tree growth, respectively. The monthly model predictions indicated that the minimum, optimum, and maximum temperatures lay between 8 °C and 16 °C, 18 °C and 22 °C, and 27 °C and 30 °C, respectively. These monthly temperature ranges can improve the estimation of productivity in process-based models. Our results contribute to the understanding of tree growth dynamics and its relationship to changes in temperature. Accurate ranges of temperature can be used to improve productivity predictions in new expanding planting regions with no previous information or to suggest a regionalization for potential species.

Insights from the bias-corrected simulations of CMIP6 in India's future climate

In this study, we have used thirteen statistically downscaled bias-corrected General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under the Shared Socioeconomic Pathways (SSP) scenarios. Based on these models, the projected changes in mean rainfall and daily extremes (number of rainy days and simple daily intensity) during the southwest (SW) and northeast (NE) monsoon seasons in the near (2021–2050) and far future (2071–2100) under the SSP2-4.5 & SSP5-8.5 scenarios relative to the baseline period (1985–2014) are studied. In addition to the rainfall, the maximum, minimum, and mean temperature changes and their daily extremes are also examined. Our results show that the Multi-Model Mean (MMM) slightly underestimated (overestimated) the SW (NE) monsoon rainfall compared to the observed rainfall in the baseline period. A considerable increase in future rainfall during the SW monsoon season is noticed in central India, the Himalayan region, and over the northwestern parts of India. In the NE monsoon season, south peninsular India experiences more rainfall under SSP5-8.5 than SSP2-4.5. In general, both SSPs scenarios shows increase in monsoon rainfall from the mid-century onwards. Interestingly, by the end of the 21st century the number of rainy days are projected to reduce, whereas the intensity of rainfall projected to be drastically increase over many areas of India. Analysis of temperature revealed that there is increase in the projected warming with maximum temperature is around 4.5°C during summertime, with minimum temperatures of about 5°C in the wintertime in northern parts of India by the end of the 21st century. Under the SSP5-8.5 scenario, increase in the highest maximum temperatures is seen in the Himalayan region and elevated minimum temperature pattern over the Indo-Gangetic Basin (IGB) is noted. This increased warming may affect the agriculture, water, health, and power sectors severely.

The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

AbstractThe popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms.

Demodulation of temperature with high spatial resolution based on RDTS system

Measurement of temperature along the fiber based on Raman distributed temperature sensing (RDTS) system has been studied and applied broadly. However, the minimum fiber length and temperature change required to correctly obtain the actual temperature variation in periodic regions (PRs) are limited by the systematic spatial resolution of the order of meters. To improve the spatial resolution of temperature measurement, we propose a novel signal processing scheme that firstly associates the position of each region with the relevant sampling points in the temperature rise curve, then combines the theoretically and experimentally derived relation that different intensities can be obtained by sensing temperature with the luminous flux at different locations within the pulsed light, and finally separates the Raman intensity generated by each region from the temperature rise curve of PRs. The experimental results show that the temperature demodulation of five PRs with a length of 0.5 m can be achieved with an average accuracy of ±2.3 °C. The proposed scheme improves the spatial resolution of the conventional RDTS system from 4 m to 0.5 m and provides a new solution for the temperature monitoring of PRs.

Phenological phase affects carrot seed production sensitivity to climate change – A panel data analysis

New Zealand is a major producer of carrot seeds globally. Carrots are an important nutritional crop for human consumption. Since the growth and development of carrot seed crops mainly depend on climatic factors, seed yield is extremely susceptible to climate change. This modeling study was undertaken using a panel data approach to determine the impact of the atmospheric conditions (proxied by maximum and minimum temperature) and precipitation during the critical growth stages for seed production in carrot, viz., juvenile phase, vernalization phase, floral development phase, and flowering and seed development phase on carrot seed yield. The panel dataset was created using cross-sections from 28 locations within the Canterbury and Hawke's Bay regions of New Zealand that cultivate carrot seed crops and time series from 2005 to 2022. Pre-diagnostic tests were performed to test the model assumptions, and a fixed effect model was selected subsequently. There was significant (p < 0.01) variability in temperature and rainfall throughout different growing phases, except for precipitation at the vernalization phase. The highest rate of changes in maximum temperature, minimum temperature, and precipitation were recorded during the vernalization phase (+0.254 °C per year), floral development phase (+0.18 °C per year), and juvenile phase (−6.508 mm per year), respectively. Based on marginal effect analysis, the highest significant influence of minimum (187.724 kg/ha of seed yield decrease for each 1 °C increment) and maximum temperature (1 °C rise increases seed yield by 132.728 kg/ha), and precipitation (1 mm increment of rainfall decreases the seed yield by 1.745 kg/ha) on carrot seed yield were reported at vernalization, and flowering and seed development, respectively. The minimum and maximum temperatures have a higher marginal effect on carrot seed production. Analysis of the panel data demonstrates that the production of carrot seeds will be vulnerable to climatic change.

Crop varieties, phenological phases and the yield-weather relationship: evidence from the Italian durum wheat production

The impacts of extreme weather events on crop productions are largely heterogeneous along the timing dimension of the shocks, and the varieties being affected. We investigate the relationships yield-weather for three durum wheat varieties (early, middle, and late). We disentangle the time dimension distinguishing five phenological stages, as identified by Growing Degree Days approach. Our panel regression models show that the starting and development stages are sensitive to changes in minimum temperatures, regardless of wheat variety. Raises in maximum temperatures during the starting stage are associated with increases in yields; the opposite is true for increases in high temperatures from seed fill to dough stage (for late varieties), and at maturity complete stage (for early varieties). The analysis suggests that advancing sowing dates may help preventing yield losses due to heat stress, whereas the cold stress should be managed through other on-farm risk management strategies or via crop insurance.&#x0D;

Assessing farmers’ perception based composite drought vulnerability in Godavari Middle Sub-basin, India

Climate change has increased the frequency and intensity of drought in various parts of the world. Godavari Middle Sub-basin of India has been experiencing significant change in rainfall, maximum and minimum temperature. Farmers of Sub-basin depend on mercy of rainfall for their cultivation. Occurrence of drought lead to farmers suicide due to crop failure and losses of agricultural production. Therefore, composite drought vulnerability assessment is essential in the Sub-basin. This paper makes an initial attempt to quantify composite vulnerability to drought using socio-economic and environmental indicators at household level. Stratified random sampling was utilized to collect primary data. A total number of 450 households were surveyed. The composite vulnerability index calculation was based on 5 domains namely economic, social, environmental, ecological, adaption and mitigation strategies. Results revealed that highest drought vulnerability was found in marginal farmers among all the types of farmers due to high climate change vulnerability and low adaptation strategies. Low level of education, low annual income and high dependency on local money lenders were the main causes of high drought vulnerability for marginal farmers. High adaptation strategies helped large farmer to mitigate the impact of drought. The adaptations and mitigation strategies encompassed change in crops, diversification of crops, keeping land unsown after the possibility of drought, change in traditional irrigation practices to sprinkler and drip irrigation, change in crop calendar and sowing of drought-tolerant crops. Community-based preparedness planning could be effective to mitigate the impact of climate change induced drought vulnerability in Godavari Middle Sub-basin.

Spatial and temporal distributions of temperature and rainfall on tropical islands of Vietnam

Abstract This study presents the spatial-temporal distribution of temperature and rainfall on the tropical islands of Vietnam using observed data, and historical and future climate data extracted from the ACCESS-ESM1.5. Regarding spatial distribution, the average temperature increased from North to South while rainfall did not vary significantly. Regarding temporal distribution, this research employed the Mann–Kendall test at a 5% significance level to analyze the trend of climate change. According to the calculation result, only annual temperature tended to increase in all stations but at different rates. Other trends of climate change in maximum, temperature, minimum temperature, annual rainfall, and maximum 1-day rainfall are inconsistent and only found significant in islands with a long-observed period. Using 1970–2000 as the reference period with all R2 above 0.94, this research extracted and predicted climate trends in the period of 2021–2100 for areas above 1 km2 based on four scenarios which are SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, and SSP 5-8.5. The projected maximum and minimum temperatures have shown increasing trends whereas the change in annual rainfall is not clear. The results of this study provide important implications for climate change adaptation scenarios and territorial planning of tropical islands.