Indicators Of Climate Change Research Articles

Revisiting the role of climate change on crop production: evidence from Mediterranean countries

AbstractThe Mediterranean region is an important agricultural center and is expected to be most affected by climate change due to its location. In this study, the role of climate change on agriculture is examined for eight South European countries on the Mediterranean coast for the period of 1996–2016. Carbon dioxide emissions, temperature and precipitation values are used as the indicators of climate change while cereal production is taken as a proxy for agricultural production. Results of the panel vector autoregression approach reveal that an increase in the carbon dioxide emissions and temperature have a negative impact on the cereal production. However, this effect is statistically significant only in the initial periods. On the other hand, an increase in rainfall has a statistically significant positive effect on crop production only in one period. However, eventually this effect turns to negative as expected, since excessive rainfall has a negative effect on agriculture as much as drought. In conclusion, the importance of bringing the policies and strategies to the forefront in ensuring the sustainability of agriculture and minimizing the negative effects of climate change in the region are discussed.

Impact of Climate Change on the Development of Viticulture in Central Poland: Autoregression Modeling SAT Indicator

Ongoing climate change is having a profound impact on agriculture, which is attracting attention from the scientific community. One of its effects is an increase in average temperature, which is a key factor in grape cultivation. This may increase the popularity of viticulture in central Europe. The aim of this study was to assess the potential for the development of viticulture in central Poland based on SAT changes from 1975 to 2021, in addition to changes in evapotranspiration, occurrence of late spring and early autumn frosts and frosty days in selected years from this period as an important factors relating to climate change. The research utilized data obtained from the Institute of Meteorology and Water Management—National Research Institute. The Bai–Perron test was used to determine the direction of temperature changes. An AR(1) autoregression model was used to predict SAT changes in central Poland for the years 2022–2026, based on the results of the Bai–Perron test. As part of the in-depth research on the SAT index, reference evapotranspiration calculations were also made as a second factor that is considered an important indicator of climate change. The Sum of Active Temperatures from 1975 to 2021 in the provinces of central Poland showed an increasing trend of 0.07% per year. The average SAT in central Poland in 2022–2026 is expected to range from 2700 °C to 2760 °C. Considering the current thermal conditions in central Poland and the forecasts for the coming years, it can be expected that vineyard cultivation will develop in this region. However, the research shows that the observed increasing trend in evapotranspiration, both in total in individual years and in the period of the greatest vegetation, i.e., in the months from May to the end of August, will result in an increasing need in central Poland to ensure adequate irrigation in developing vineyards.

Significant contribution of the Tianshan lakes to their water storage and water resources

Study regionThe Tianshan region Study focusThe Tianshan region possesses a complex landscape with lakes, and changes in the water storage of these lakes serve as a crucial indicator of climate change. They also represent a significant water resource for the local area, profoundly impacting water resource allocation and playing a pivotal role in understanding the mass balance in the Tianshan region. Nevertheless, there is still a lack of systematic research on the Tianshan lake group. New hydrological insights for the regionThe results reveal that the total lake water storage is exhibiting an increasing trend (0.73±0.10 Gt/a). The Tianshan region experienced two periods of mass anomalies in 2010 and 2016, which were reflected not only in the changes of lake water storage but also in soil moisture, snow water, and even Global Navigation Satellite System (GNSS) displacement fields. These two mass anomalies were closely related to abnormal precipitation resulting from El Niño events (r=0.55, p<0.01). During these two mass anomaly periods, the regional total mass increased by 65.73 Gt and 67.35 Gt. Compared with their contributions to the mass (22% and 14%), the variations in lake area were remarkably slight (0.01% and 0.014%). This study contributes to understanding the sensitivity of lakes to climate responses and underscores the necessity of lake research for water resource management in complex surface environments.

Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

Enviro‐economic and feasibility analysis of industrial hemp value chain: A systematic literature review

AbstractA recent renaissance of industrial hemp has been driven by a plethora of ecologically amicable products and their profitability. To identify its environment and economic fate across the value chain (VC), this study conducts a systematic review of 98 studies published in ScienceDirect, Web of Science, and Scopus‐indexed journals. The thematic content of the articles is categorized using three deductively derived classification categories: lifecycle analysis (n = 40), VC analysis (n = 30), and feasibility analysis (n = 28). Bibliometric analysis indicates that the majority (&gt;90%) of the studies were conducted in selected regions of Europe or North America, with further findings around regionally prioritized industrial hemp products, such as hempcrete in Southwest Europe, solid biofuel in North European states, and textile fiber and bio‐composites in East Europe and North America. Lifecycle analysis studies highlight nitrogenous fertilizer use during industrial hemp cultivation as a major ecological hotspot, which is taking a toll on the climate change index. However, hemp‐based products are generally climate‐friendly solutions when contrasted against their fossil fuel counterparts, with hempcrete in particular a highly touted carbon‐negative (−4.28 to −36.08 kg CO2 eq/m2) product. The review also identifies key issues within the hemp VC and presents innovative solutions alongside the recognition of value‐adding opportunities. Furthermore, feasibility analysis indicates unprofitability in using hemp for bioenergy production and there is a relative cost worthiness of hemp bio‐composites and hempcrete at the upstream level. Positive returns are observed under co‐production schemes. In contemplating the literature findings, we discussed and identified gap in existing literature for future exploration, including more studies to provide insights from the Global South, and the production of industrial hemp under a biophysically constrained landscape.

Spatiotemporal Patterns and Environmental Drivers of Particulate Inorganic Carbon (PIC) Variability in the Southern Waters of Sumatra‐Java Islands

AbstractThe southern waters of the Sumatra‐Java Islands, within the Indo‐Pacific Warm Pool, are highly susceptible to global climate change impacts. Understanding particulate inorganic carbon (PIC) dynamics and environmental responses in this region is critical for assessing climate change effects on marine ecosystems. Utilizing two decades of remote sensing PIC data (2003–2022), we identified five sub‐regions via K‐means clustering: the Sunda Strait, southern coastal/offshore waters of Sumatra Island, and southern coastal/offshore waters of Java Island. Self‐Organizing Map explored interannual PIC variations, while Generalized Additive Models delineated driving factors. In the Sunda Strait, PIC dynamics are influenced by water exchanges between the Indonesian Seas (ISs) and eastern Indian Ocean (EIO). Seasonally, PIC concentrations peak during the east monsoon (boreal summer); decreasing during positive Indian Ocean Dipole (+IOD) and El Niño events. Along the southern Sumatra, PIC variations positively correlate with rainfall and photosynthetically active radiation (PAR). Along the southern Sumatra, upwelling drives PIC variations, with higher concentrations during mature summer monsoon induced upwelling periods. PIC increases during +IOD and decreases during El Niño. Our study highlights the nexus between nutrients, physical factors (e.g., rainfall), and climatic events (e.g., El Niño) on PIC concentrations. Climate‐induced changes in oceanic physical processes modulate nutrient concentrations, thereby governing PIC variations. We propose a mechanistic model to elucidate PIC variations in upwelling areas. Findings suggest that prospective climate variability, encompassing alterations in climate events, rainfall, and temperature, may escalate PIC concentrations within the study area, advocating PIC as a forward‐looking climate change indicator.

Monitoring of spatio-temporal glaciers dynamics in Bhagirathi Basin, Gharhwal Himalayas using remote sensing data

Glacier retreat represents a highly sensitive indicator of climate change and global warming. Therefore, timely mapping and monitoring of glacier dynamics is strategic for water budget forecasting and sustainable management of water resources. In this study, Landsat satellite images of 2000 and 2015 have been used to estimate area extent variations in 29 glaciers of the Bhagirathi basin, Garhwali Himalayas. ASTER DEM has been used for extraction of glacier terrain features, such as elevation, slope, area, etc. It is observed from the analysis that Bhagirathi sub-basin has a maximum glaciated area of ~ 35% and Pilang has the least with ~ 3.2%, whereas Kaldi sub-basin has no glacier. In this region, out of 29 glaciers, 25 glaciers have shown retreat, while only 4 glaciers have shown advancement resulting in a total glacier area loss of ~ 0.5%, while the retreat rate varies from ~ 0.06 m/yr to ~ 19.4 m/yr. Dokarni glacier has maximum retreat rate (~ 19.4 m/yr), whereas Dehigad has maximum advancing rate (~ 10.1 m/yr). Glaciers retreat and advance have also been analyzed based on terrain parameters and observed that northern and southern orientations have shown retreat, whereas the area change is highly correlated with glacier length. The study covers more than 65% of the total glaciated area and based on the existing literature represents one of the most exhaustive studies to cover the highest number of glaciers in all sub-basins of the Bhagirathi basin.

Use of Soil Moisture as an Indicator of Climate Change in the SUPer System

Soil moisture can be an important indicator of climate change in humid and semi-arid areas. This indicator can more efficiently propose different public policies related to climate change than just using precipitation and temperature data. Given the above, the objective of this study is to evaluate changes in soil moisture in the state of Pernambuco during the period 1961–2021, using the System of Hydrological Response Units for Pernambuco. In this study, two river basins in the state of Pernambuco that represent the different climatic conditions of the state were chosen. The results show that in the coastal region there is a tendency towards more saturated soils, and in the semi-arid region there is a tendency towards drier soils. With these results, it is possible to conclude that public policy decisions for the economy, environment, and society must consider this vital water balance variable. Leveraging soil moisture and precipitation data makes it possible to differentiate between flood risks and landslide vulnerabilities, particularly in regions characterized by higher levels of rainfall. Monitoring soil water content in humid and semi-arid areas can significantly enhance early warning systems, thereby preventing loss of life and minimizing the socio-economic impacts of such natural events. As such, this study provides a holistic understanding of the relationship between climatic patterns, soil moisture dynamics, and the occurrence of droughts and floods, ultimately contributing to more effective disaster preparedness and response measures in Pernambuco and similar regions.

Permafrost temperature dynamics and its climate relations in various Tibetan alpine grasslands

Permafrost temperature is a vital indicator of climate and permafrost changes, benefiting ecosystem development and informing local climate strategies. Alpine grasslands impact moisture and heat exchange between the surface and atmosphere, thereby affecting the thermal state of underlying permafrost. This study analyzed permafrost temperatures (2004–2019) from various alpine grasslands (including alpine meadow, alpine steppe, alpine desert grassland, and barren land) in the Beiluhe region of the Tibetan Plateau and revealed their connections to climate change and controlling factors, using time–frequency analysis. The findings revealed that in the time–frequency domain, permafrost temperatures exhibited multiple time scales characteristics, driven by climate fluctuations. Changes in the active layer closely followed monthly climate variations, while permafrost dynamics responded to annual climate changes. Significant oscillations with periods of 10–11, 8–9, and 14 years were observed in the surface, permafrost table, and deep permafrost layers, respectively. Among the different types of alpine grasslands, alpine meadows proved to be the most sensitive to climate change, with the intensity of periodic fluctuations initially decreasing and then increasing with depth in alpine meadows, while it consistently decreased with depth in the other three alpine grasslands. The impact of air temperature, precipitation, and wind speed on permafrost dynamics exhibited depth-dependent variations in the time–frequency domain, contrasting with the time domain where permafrost temperature changes were predominantly associated with air temperature across all depths.

Role of Climate Change in Afghanistan’s Geopolitical Power

Purpose: This study examines the role of climate change in Afghanistan’s geopolitical power. Global warming and climate change are the most significant environmental threats today. The effects of climate change include water and food scarcity, disease, unemployment, migration, poverty, resource conflicts, and global instability.Methodology:This study examines the role of climate change in Afghanistan's geopolitical power. This study employs a descriptive-analytical approach and a survey method. The statistical research community includes experts and specialists in geopolitics, political science, climate science, and university professors. The questionnaire was distributed among them based on climate change, development, and geopolitics indicators. 80 questionnaires were collected.Findings: Based on these findings, a significant and inverse relationship exists between climate change's impacts and development. Furthermore, reducing the development process significantly and negatively impacts Afghanistan's geopolitical power. Finally, reducing development affects change. Climate plays a mediating role in geopolitics. Then, the priority of parameters influencing climate change in geopolitics was discussed using Friedman's comparison test.Implication:These parameters include 1) poverty, social anomalies, drought, and migration. In the suggestions section, effective methods, such as irrigation and the use of clean energy, must be implemented to reduce greenhouse gas emissions and adapt to the impacts of climate change.

Multi-Source Dataset Assessment and Variation Characteristics of Snow Depth in Eurasia from 1980 to 2018

Snow is an indicator of climate change. Its variation can affect surface energy, water balance, and atmospheric circulation, providing important feedback on climate change. There is a lack of assessment of the spatial characteristics of multi-source snow data in Eurasia, and these data exhibit high spatial variability and other differences. Therefore, using data obtained from the Global Historical Climatology Network Daily (GHCND) from 1980 to 2018, snow depth information from ERA5, MERRA2, and GlobSnow is assessed in this study. The spatiotemporal variation characteristics and the primary spatial modes of seasonal variations in snow depth are analyzed. The results show that the snow depth, according to GlobSnow data, is closer to that of the measured site data, while the ERA5_Land and MERRA2 data are overestimated. The annual variations in snow depth are consistent with seasonal variations in winter and spring, with an increasing trend in the mountains of Central Asia and Siberia and a decreasing trend in most of the rest of Eurasia. The dominant patterns of snow depth in late autumn, winter, and spring are all north–south dipole patterns, and there is overall consistency in summer.

Long-term species-level observations indicate the critical role of soil moisture in regulating China's grassland productivity relative to phenological and climatic factors

As a sensitive indicator of climate change and a key variable in ecosystem surface-atmosphere interaction, vegetation phenology, and the growing season length, as well as climatic factors (i.e., temperature, precipitation, and sunshine duration) are widely recognized as key factors influencing vegetation productivity. Recent studies have highlighted the importance of soil moisture in regulating grassland productivity. However, the relative importance of phenology, climatic factors, and soil moisture to plant species-level productivity across China's grasslands remains poorly understood. Here, we use nearly four decades (1981 to 2018) of in situ species-level observations from 17 stations distributed across grasslands in China to examine the key mechanisms that control grassland productivity. The results reveal that soil moisture is the strongest determinant of the interannual variability in grassland productivity. In contrast, the spring/autumn phenology, the length of vegetation growing season, and climate factors have relatively minor impacts. Generally, annual aboveground biomass increases by 3.9 to 25.3 g∙m2 (dry weight) with a 1 % increase in growing season mean soil moisture across the stations. Specifically, the sensitivity of productivity to moisture in wetter and colder environments (e.g., alpine meadows) is significantly higher than that in drier and warmer environments (e.g., temperate desert steppes). In contrast, the sensitivity to the precipitation of the latter is greater than the former. The effect of soil moisture is the most pronounced during summer. Dominant herb productivity is more sensitive to soil moisture than the others. Moreover, multivariate regression analyses show that the primary climatic factors and their attributions to variations in soil moisture differ among the stations, indicating the interaction between climate and soil moisture is very complex. Our study highlights the interspecific difference in the soil moisture dependence of grassland productivity and provides guidance to climate change impact assessments in grassland ecosystems.

ҚЫЗЫЛОРДА ОБЛЫСЫНДАҒЫ АУА ТЕМПЕРАТУРАСЫНЫҢ 1961...2020 ЖЫЛДАР АРАЛЫҒЫНДА ӨЗГЕРУІ

The purpose of the article is to analyze the dynamics of air temperature, one of the most obvious indicators of climate change, and to determine the long-term trends of air temperature change in Kyzylorda region. Kyzylorda region is located in the south-west of Kazakhstan, is characterized by arid climate and belongs to the zone of ecological disaster. In this paper, the authors studied the changes in air temperature in the period from 1961 to 2020 based on the nonparametric statistical method of Mann–Kendall. The Mann-Kendall method is used to determine whether there is a monotonic trend in the studied data. The Mann–Kendall test is aimed at increasing the accuracy of the result of time series estimation and allows to determine the presence of a trend. The obtained results are characterized by a significant change in temperature in time series of Kyzylorda region. It is noted that for the selected period, to assess changes in the criterion Sen, the average annual temperature changes increased by 0.02...0.05°C. Seasonal trends showed an increase in average temperatures during the year, and the summer months significantly contributed to the warming observed in Kyzylorda region. The obtained data can serve as a basis for assessing both regional climate change and for assessing the ecological state, as meteorological parameters, namely air temperature is one of the important indicators in the study of atmospheric air pollution in Kyzylorda region.

Environmental performance of an autonomous laser weeding robot—a case study

PurposeChallenges in sustainable development envisioned in the European Union for the agricultural sector require innovation to raise the efficiency of production and safety of farming processes for farmers and ensure food safety for consumers. One of the key productivity factors in plant production is effective weeding. The WeLASER project aimed to develop a high-power autonomous vehicle with lasers to control weeds. To be sustainable, the invention should have a high environmental performance in the whole life cycle perspective, including its production, use in agriculture, and end-of-life phase. In the publication, a life cycle assessment (LCA) of the WeLASER weeding robot is presented. The aim was to identify weak and strong aspects of the invention in environmental terms and provide suggestions for its improvement.MethodsThe machinery was characterized based on technical data provided by the developers, relevant literature, Ecoinvent 3.8 database, and own calculations. The quantitative assessment of environmental impacts was performed using the Simapro tool. For interpretation Recipe 2016 method (egalitarian perspective) was applied.ResultsThe results show that the energy issue related to autonomous laser-based weeding machine operations is the most challenging. It is related to impacts on climate change indicators and fossil fuel depletion. Production phase is characterized with impacts on human toxicity and is related to extensive application of electronic and electric components in the robot.ConclusionIn comparison with other weeding techniques, the high-power autonomous vehicle with lasers to control weeds shows potential for environmentally efficient use of the machinery in practice. Achieving high performance in the life cycle perspective requires improvements in the design, operational features, and smart application in agricultural practice enhanced through expertise, guidance, and advice.

Analysis of Probability Distributions for Modelling Extreme Rainfall Events and Detecting Climate Change: Insights from Mathematical and Statistical Methods

Exploring the realm of extreme weather events is indispensable for various engineering disciplines and plays a pivotal role in understanding climate change phenomena. In this study, we examine the ability of 10 probability distribution functions—including exponential, normal, two- and three-parameter log-normal, gamma, Gumbel, log-Gumbel, Pearson type III, log-Pearson type III, and SQRT-ET max distributions—to assess annual maximum 24 h rainfall series obtained over a long period (1972–2022) from three nearby meteorological stations. Goodness-of-fit analyses including Kolmogorov–Smirnov and chi-square tests reveal the inadequacy of exponential and normal distributions in capturing the complexity of the data sets. Subsequent frequency analysis and multi-criteria assessment enable us to discern optimal functions for various scenarios, including hydraulic engineering and sediment yield estimation. Notably, the log-Gumbel and three-parameter log-normal distributions exhibit superior performance for high return periods, while the Gumbel and three-parameter log-normal distributions excel for lower return periods. However, caution is advised regarding the overuse of log-Gumbel, due to its high sensitivity. Moreover, as our study considers the application of mathematical and statistical methods for the detection of extreme events, it also provides insights into climate change indicators, highlighting trends in the probability distribution of annual maximum 24 h rainfall. As a novelty in the field of functional analysis, the log-Gumbel distribution with a finite sample size is utilised for the assessment of extreme events, for which no previous work seems to have been conducted. These findings offer critical perspectives on extreme rainfall modelling and the impacts of climate change, enabling informed decision making for sustainable development and resilience.

Characteristics of Soil Temperature Change in Lhasa in the Face of Climate Change

Soil temperature is an important index of climate change, and the analysis of soil temperature change is of great significance for understanding climate change and ecohydrological processes. This study was based on the measured meteorological data of a meteorological station, combined with the soil temperature data of 0–10, 10–40, 40–100 and 100–200 cm from the Global Land Data Assimilation System (GLDAS-NOAH). The Mann–Kendall test, wavelet analysis, linear tendency estimation and other methods were used to analyze the variability, periodicity and trend of soil temperature in Lhasa from 2006 to 2022. The results showed that the soil temperature of different soil layers had abrupt changes in annual and seasonal time series, and all showed a warming phenomenon after abrupt changes. In terms of periodicity, the average annual soil temperature of different soil layers has similar periodic changes, and the periodic oscillation is strong around 10a, which is the main cycle of soil temperature change. The soil temperature in Lhasa showed a significant rising trend in the interannual and seasonal time series, and the average annual rising trend of soil temperature was greater than that of air temperature. The correlation between soil temperature and mean air temperature (MAT), maximum air temperature (Tmax), minimum air temperature (Tmin) and snow depth (SD) was investigated by Pearson correlation analysis. Soil temperature in spring, autumn and winter had a strong correlation with MAT, Tmax and Tmin, showing a significant positive correlation. The negative correlation between soil temperature and SD in 0–40 cm soil in spring and winter was more severe. The research results show that Lhasa has experienced a rise in air temperature and soil temperature in the past 17 years, and reveal the specific changes in soil temperature in Lhasa against the background of climate change. These findings have reference significance for understanding the impact of climate change on the natural environment.

Understanding Farmers Perception of Climate Change to Prepare Adaptation of Farmers

The impact of climate change has resulted in a decline in agricultural productivity. This study estimated farmers’ perceptions of climate change by comparing Imogiri Sub-district represent highland area and Pundong Sub-district deputize lowland area farmers in Bantul Regency. This study collected data using questionnaires to 79 respondents then analysis by likerts scale and descriptive statistical. The results showed that there six indicators of climate change became farmers’ perceptions. The farmers perception on highland regarding climate change is that 54% of farmers feel a decrease in rainfall, 54% feel drought, 55.2% feel a longer dry season, 60.8% feel reduced water sources, 74.4% feel a decrease in crop quality and 73.2% feel a decrease in crop quantity. At the same time, the perception of farmers in the lowland area regarding climate change is 66.9% of farmers feel a decrease in rainfall, 68.28% feel drought, 71.03% feel a longer dry season, 68.28% feel reduced water sources, 77.93% feel a decrease in crop quality and 77.93% feel a decrease in crop quantity. The importance of farmers’ perceptions of climate change may be used to adaptation from climate change. Understanding farmers’ perceptions can aid the impact of climate change on agricultural productivity.

Hierarchical and Non- Hierarchical Cluster Classification of Precipitation Time Series Data in Nigeria

Precipitation is highly unpredictable over space and time, and is an indicator for climate change. The present work classified average yearly precipitation over the 36 states and the Federal Capital Territory (FCT) in Nigeria into various groups. The hierarchical and non- hierarchical (k-means) cluster method was used in the classification of 37- years precipitation data (1984-2020). The results showed that six major clusters were formed. The Precipitation across the 36 states and the FCT in Nigeria can be classified as Light precipitation for Sokoto, Borno, Kastina, Yobe and Kano States; Moderate precipitation for Kebbi, Zamfara, Dutse, Yola, Gombe and Bauchi States; Heavy precipitation for Kaduna, Plateau, Kwara, Taraba, Nassarawa, Benue and Kogi States; Very Heavy precipitation for Osun, Ogun, FCT, Ekiti, Niger and Lagos States; Shower for Edo, Abia, Delta, Anambra, Ebonyi, Enugu, Ondo and Oyo States and lastly, Heavy Shower for Akwaibom, Imo, Rivers, Bayelsa and Cross River States.. The rangs of precipitation based on the six classification are 1.5-1.8mm (Light precipitation), 1.9-2.7mm (Moderate precipitation), 2.8-3.6mm (Heavy precipitation), 3.7-4.3mm (Very Heavy precipitation), 4.4-5.4mm (Shower) and greater than 5.5mm (Heavy Shower). The hierarchical cluster method performed better than the non- hierarchical (k-means) cluster method in structuring the precipitation data and shaping it into groups (clusters) such that observations within the same group are more similar than those in different groups. The results of this study can contribute to a better understanding of the climate condition of the36 States and the FCT in Nigeria.

Lake Ice From Historical Records to Contemporary Science

AbstractLake ice phenology is a critical component of the cryosphere and a sensitive indicator of climate change that has some of the longest records related to climate science. Records commenced for numerous reasons including navigation, hydropower development, and individual curiosity, demonstrating the value of lake ice as a seasonal event of significant importance to a broad swath of peoples and countries. At the same time, lake ice loss has been rapid and widespread with lakes losing ice at an average rate of 17 days per century. In this Perspective, we examine the earliest known records of ice cover and the scientific studies that developed from that practice of record keeping. Studies in lake ice began in the nineteenth Century and have included relationships between climate, biology, and ice cover. Early studies developed some of the foundational principles that limnologists and climate scientists are still exploring, such as the relationship between ice phenology and climate variables, large‐scale climate oscillations, and morphological characteristics, with implications for lake ice physical structure and under‐ice ecosystems in a warming climate. We conclude with an examination of the state of the field and how these centuries‐long lake ice records can continue to inform cutting edge science by validating satellite remote sensing techniques, in addition to modeling approaches and collaborations across disciplines, that can improve our understanding of the loss of lake ice in a warmer world.

Penerapan ARIMA Pada Data Curah Hujan di Stasiun Meteorologi Kelas II Minangkabau Padang Pariaman

Rainfall is one of the indicators of climate change that has an impact. Class II Minangkabau Meteorological Station Padang Pariaman is one of the locations that has an impact on rainfall. This location is Minangkabau International Airport so that rainfall information is very important. The rainfall data used in this study is monthly data with the period January 2017 to June 2022. The ARIMA model is applied to perform forecasting or prediction of rainfall data for the next period. The results of the study based on the analysis of the Akaike Information Criterion (AIC) value obtained the best model ARIMA (3,1,0).