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This paper discusses the radiation budget and its temporal variability over Poland. The data analysis is based on the MERRA-2 reanalysis for the years 1980–2020. During the last four decades, the enhancement of climate warming has been observed, which coincides with the changes in the radiation budget. Positive and statistically significant trends at the top of the atmosphere (TOA; 0.7 ± 0.2 W/m2/10 year) and on the Earth's surface (1.5 ± 0.2 W/m2/10 year) radiation budget (net downward flux) are mainly a consequence of changes in the amount of aerosol and greenhouse gases (GHG). According to MERRA-2, the AOD during this period decreased by − 0.19 (at 550 nm), which is 87% of the long-term (1980–2021) mean value (0.22). The reduction of AOD is due mainly to the decline of non-absorbing sulfate particles, which leads to a reduction of single-scattering albedo (SSA) by − 0.008 per decade and Angstrom exponent (AE) by − 0.06 per decade (both trends statistically significant). On the other hand, the GHG concentration increased by 4.9%/10 year and 3%/10 year, respectively, for CO2 and CH4. The total column of water vapor increased (1.3%/10 year), while ozone decreased (− 1%/10 year). Despite the fact that cloud cover and cloud optical depth (COD) decreased (− 1.8%/10 year and − 1.0%/10 year), the impact of cloud on temporal variability radiation budget is small. It can be explained by nearly compensated shortwave (SW) cooling and longwave (LW) heating effects. During the analysis period, near-surface air temperature increased by 2.0 °C. The estimated increase in SW net surface radiation (7.9 W/m2) leads to climate warming by 0.8 °C, which is a consequence mainly of the reduction of aerosol (0.4 °C) and cloud cover (0.2 °C). The impact of the change of SW radiation on air temperature is more pronounced during the warm season, while during the cold, air temperature change is controlled mainly by the variability of air mass advection. [ABSTRACT FROM AUTHOR]

The present paper analyzes long term (1960–2021) of the minimum, maximum, and average temperatures in Central Anatolia Region, corresponding middle part of Turkey, aiming to reveal how strongly the temperatures increase, which is a precondition for sustainable development in this region. For this purpose, temporal trends, variability, and anomalies in temperatures of 27 meteorological stations were detected using Mann–Kendall test (M-K), coefficient of variations, and Gaussian filter, respectively. Results show a statistically significant increasing trend in annual average temperatures at approximately 92% of all stations. This shows that the increasing trend in spring and summer temperatures plays an important role in amplifying the warming trend of annual temperatures. The coefficients of variation in annual average, maximum, and seasonal temperatures increase from west to east. Significant strong increasing trend (at 0.001 level) in winter temperatures was detected only at Seydişehir. While positive anomalies have been observed in the northwestern part of the region since 2007, they have been observed in other parts since 1995–1996. Since the strong increase in summer temperatures in Aksaray, Cihanbeyli, and Seydişehir is also observed in autumn, warm conditions continue until the end of autumn in these settlements. For this reason, four seasons do not prominently occur for all three settlements. Generally, annual maximum and minimum temperatures illustrate statistically significant increasing trends for all stations and 74% of all stations, respectively. According to the M-K test results, climate of the region has warmed on average by 1.44 °C in last 31 years. [ABSTRACT FROM AUTHOR]

Climate model, a complex numerical representation of the global climate system, has been developed to simulate current climate and used to project future climatic conditions. Simulated climatic variables from climate models often exhibit significant deviations from observations. In climate projections, different approaches were introduced to deal with systematic deviations and random model errors. This paper demonstrates the intercomparison of four bias correction approaches (linear scaling, delta change correction, distribution mapping, and variance scaling) underlying the assumptions of stationary output from climate models. Mean monthly temperatures derived from five global climate models were corrected by four bias correction approaches for five states of southern India. The suitability of correction approaches depends upon the climate models and regional framework. The applied approaches improve the mean values and other statistical properties. The results show that all four bias techniques significantly improved the simulated data, but distribution mapping and variance scaling were more effective in removing systematic model biases. [ABSTRACT FROM AUTHOR]

In the next 20 years, there will be an extensive investment in transport infrastructure. Although the cut and embankment slopes seem to have the same appearance, they have different responses to climate variations. Understanding their characteristics and performance is necessary to design a safer and more sustainable slope infrastructure. This paper provides a thorough examination of the seasonal performance of cut slopes and embankments. Furthermore, this study suggests an introduction to the impacts of climate change, amplifying seasonal shrinkage–swelling and progressive failure of slope construction under extreme drought and precipitation. Volumetric water content and pore water pressure fluctuations due to seasonal variation were analysed and compared from both the cut slope and the embankment. Moreover, stress path and slope deformation were also investigated in this study to understand the behaviour of the cut slope and the embankment. The results suggest that the cut slope retains more pore water pressure during the wet season due to its lower permeability than an embankment with respect to the construction history. However, pore water pressure and displacement in the cut slope tend to be increased due to the consolidation process after excavation, which requires more time to reach equilibrium. In addition, greater displacement in the cut slope can increase the possibility of delayed failure in the future. [ABSTRACT FROM AUTHOR]

The aim of the study was to determine the direction and rate of the projected changes of the start, end, and duration of the growing season in Poland in two-time horizons: 2021–2050 and 2071–2100. The main part of the paper was preceded by an analysis of changes in the start and end dates and the duration of the growing season in Poland in the period 1966–2020. The growing season in Poland is projected to be the shortest in mountain areas and in the north-eastern regions of Poland, where the date of growing season start is the latest and the date of the growing season end is the earliest. Whereas the longest growing season due to the projected earliest start and latest end dates is expected in the southwestern Poland. In the case of the coast, its late end will be of the greatest importance for its duration as a result of the warming effect of the sea in the autumn–winter period. The most intensive changes are forecasted in the long-term perspective in the case of the scenario regarding a high level of greenhouse gas emissions. The forecasts show that outside mountain areas, the growing season duration will vary from less than 255 days in the northeastern regions to more than 290 days in southwest and western Poland. In the duration of the mountains, the growing season will vary from 180 days on Kasprowy Wierch to 188 days on Śnieżka. This suggests significant changes in agroclimatic conditions in Poland. [ABSTRACT FROM AUTHOR]

Air temperature is one of the most important parameters that contribute to weather variability over time, being influenced by the flow of solar radiation, the general circulation of currents in the atmosphere relief. The present paper analyzes the minimum, maximum, and mean temperatures in Dobrogea, on the Romanian Black Sea coastal area, aiming to illustrate their evolution, which is a precondition for sustainable development in this region, from the perspective of regional and global climate changes. The weather stations included in this study are Constanta, Mangalia, Gura Portitei, Sfantu Gheorghe, and Sulina. The Pettit Test and the Standard Normal Homogeneity Test were used to determine changes in the evolution of the air temperature. For the period 1990–2020, the analysis of the change points, with a 95% confidence level, shows a particularly interesting situation supporting the general evolution of air temperature at global level. Nonparametric tests including linear regression, Mann–Kendall, and Sen's slope tests were used to analyze trends for monthly, seasonal, and annual series. Results showed an increasing trend in the annual minimum, maximum, and mean temperatures in all five weather stations. [ABSTRACT FROM AUTHOR]

This study investigates possible rainfall and drought trends using data from 38 rainfall stations in the Medjerda basin (northeast of Algeria) over 54 years (1965–2018). Drought-related data were calculated with the Standardized precipitation index (SPI). The Mann–Kendall test was used to find positive or negative precipitation trends. The magnitude of these trends was calculated using Sen's slope method. According to the analysis, a decrease during the spring precipitation season was observed. Furthermore, the authors found the maximum increasing (decreasing) precipitation magnitude to be 2.14 mm/season (− 4.41 mm/season) in winter (spring). In addition, the magnitude of the precipitation trend per year ranged from − 6.26 to 2.54 mm/year, with an average reduction of 39% for the entire basin. From the outcomes of drought trend analysis, it can be inferred that for a short-time scale, the innovative trend analysis method exhibited a negative trend for the minimum and maximum SPI values. Drought severity was found to have increased during severe and extreme wet episodes, directly affecting Algeria's frequently drought-affected agricultural regions, such as the Merdja plain and the irrigated perimeters of Sedrata and Zouabi. Considering the long-time scales, an increase was detected in drought severity and a decline during severe and extreme wet episodes. These findings show that the southeastern and central parts of the Medjerda basin's long-term water resources have been severely affected, which negatively impacts the newly-constructed Ouldjet Mellegue dam in Tébessa province. [ABSTRACT FROM AUTHOR]

Climate change is a global phenomenon impacting all countries. In order to mitigate against the impacts of climate change, it is necessary to take measures to minimise the impacts that will affect the human habitat especially the built environment in the future. In the light of the inequality of development over the world, including varying rates of economic growth, it will be difficult for developing countries to combat the effects of climate change due to the limited capital investment, varying levels of leadership and political commitment and technology levels. All the countries have to share responsibility for the historical and current global emissions of greenhouse gases originating from both developed and undeveloped countries. This studies the built environment and adapts the impacts to be increased in the climate change and this analysis the constraints the opportunities for managing impacts for human sustainability. [ABSTRACT FROM AUTHOR]

Significant research has been done on estimating reference evapotranspiration ( E T 0) from limited climatic measurements using machine learning (ML) to facilitate the acquirement of E T 0 values in areas with limited access to weather stations. However, the spatial generalizability of E T 0 estimating ML models is still questionable, especially in regions with significant climatic variation like Turkey. Aiming at exploring this generalizability, this study compares two E T 0 modeling approaches: (1) one general model covering all of Turkey, (2) seven regional models, one model for each of Turkey's seven regions. In both approaches, E T 0 was predicted using 16 input combinations and 3 ML methods: support vector regression (SVR), Gaussian process regression (GPR), and random forest (RF). A cross-station evaluation was used to evaluate the models. Results showed that the use of regional models created using SVR and GPR methods resulted in a reduction in root mean squared error (RMSE) in comparison with the general model approach. Models created using the RF method suffered from overfitting in the regional models' approach. Furthermore, a randomization test showed that the reduction in RMSE when using these regional models was statistically significant. These results emphasize the importance of defining the spatial extent of E T 0 estimating models to maintain their generalizability. [ABSTRACT FROM AUTHOR]

India economy depends on agriculture with severe climatic changes and a heavy infestation of diseases depleting food crop yield substantially. Rapid identification and real-time infestation feedback that affects plants are accomplished through computer vision and IoT, thereby providing a reliable system for farmers to increase the season's growth yield. With LSTM, CNN provides an efficient way of identifying diseases specific leaf in plants through image recognition techniques. An extensive collection of plant leaf images is trained to recognize season-specific diseases like early blight and late blight, leaf mold, and yellow leaf curl. The proposed CNN model identifies the infestation with high accuracy and precision with significantly fewer training epochs. The proposed model provides an efficient way of identifying leaf borne infestation pertained to a particular agricultural region. Furthermore, there is a need to increase and improve different region-specific infestations that arise due to climatic and seasonal changes. [ABSTRACT FROM AUTHOR]

Population growth and increasing demand for water have posed a significant challenge to access to safe water resources. Climate change and land use in the not-too-distant future add to the complexity of this challenge. Therefore, it is essential to achieve reliable methods for predicting changes in aquifer storage to plan for the sustainable use of groundwater resources. This study aimed to investigate the management, protection, and sustainable use of groundwater resources under climate change and land use change conditions. In this regard, groundwater supply and demand in one of the important plains in Iran (Hashtgerd plain) for 2020 as the base year was simulated to forecast the trends until 2050 by considering climate change and land use to develop management scenarios to adapt to these conditions using the WEAP model. First, climate change prediction was performed using the HadGEM2-ES model under two emission scenarios, RCP2.6, and RCP8.5, of the IPCC Fifth Assessment Report. The LARS-WG model was used to downscale the climatic data, while land use mapping was performed using Landsat satellite images of 1990, 2005, and 2020 in ENVI 5.3 software. Then, the Markov chain method implemented in TerrSet software was used to model land use change for 2050. The effect of climate change and land use on the decrease of groundwater level was then simulated using the MODFLOW model for the period 2020–2050. In order to manage the water allocation in the area, the information obtained from MODFLOW was transferred to the WEAP model using Link Kitchen interface software. The effects of various management scenarios such as increasing irrigation efficiency, reducing the loss of drinking water distribution networks, and allocating water from the transmission line were evaluated on the adaptation to climate change and land use for a 30-year period. The results showed that with the simultaneous consideration of climate change and land use in the most critical state, the average drop in groundwater level would reach 58 m during the study period, and aquifer reserves will be reduced by more than 50%. The evaluation of management scenarios showed that their implementation not only will protect aquifer reserves but, in addition to meeting 100% of the water needs, will result in sustainable exploitation of groundwater resources. [ABSTRACT FROM AUTHOR]

This study analyzes the spatial and temporal distribution of trends on monthly, seasonal, and annual mean temperatures (1967–2017) at 22 stations in the Euphrates Basin of Turkey. The recently proposed innovative trend analysis (ITA) and the nonparametric Mann–Kendall (MK) and Spearman's rho (SR) test at 5% and 1% significance levels were applied to examine the temporal trends of temperatures. Before using the nonparametric trend analysis, the serial correlation of the temperature data was removed with the pre-whitening (PW) method. Then, MK-Z values were mapped in ArcGIS environment using the Empirical Bayesian Kriging (EBK) method to reveal the spatial variation of temperature trends. As a result, according to the ITA method, increasing temperature trends at 1% significance level dominate almost all periods. Based on the MK and SR tests, it was identified that increase trends were dominant at 1% and 5% significance levels in February, March, April, July, August, Spring, Summer, and Annual periods. According to the spatial temperature trend maps, significant increase trends occupy an important place in most of the basin in February, March, April, June, July, August, Spring, Summer, Winter, and Annual periods. The study results provide important information to water resources managers and decision-makers about the regions at significant risk in climate change in the Euphrates Basin. [ABSTRACT FROM AUTHOR]

Decisions that are based on the future climate data, and its consequences are significantly important for many sectors such as water, agriculture, built environment, however, the performance of model outputs have direct influence on the accuracy of these decisions. This study has focused on the performance of three bias correction methods, Delta, Quantile Mapping (QM) and Empirical Quantile Mapping (EQM) with two reference data sets (ERA and station-based observations) of precipitation for 5 single CMIP6 GCM models (ACCESS-CM2, CNRM-CM6-1-HR, GFDL-ESM4, MIROC6, MRI-ESM2-0) and ensemble mean approach over Turkey. Performance of model-bias correction method-reference data set combinations was assessed on monthly basis for every single station and regionally. It was shown that performance of GCM models mostly affected by the region and the reference data set. Bias correction methods were not detected as effective as the reference data set over the performance. Moreover, Delta method outperformed among the other bias correction techniques for the computation that used observation as reference data while the difference between bias correction methods was not significant for the ERA-based computations. Besides ensemble approach, MIROC6 and MRI-ESM2-0 models were selected as the best performing models over the region. In addition, selection of the reference data sets also found to be a dominant factor for the prediction accuracy, 65% of the consistent performance at the stations achieved by the ERA reference used bias correction approaches. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to investigate the effects of land cover changes and vegetation loss under conditions of change in climatic parameters (temperature and precipitation) and determining their impacts on different ecological functions in the eastern watershed of Lake Urmia and its four sub-watersheds in northwestern Iran. The method used has been a quantitative assessment of selected ecosystem functions using a self-parameterizing, physically based model called Water World Policy Support System as well as applying satellite images to detect land-use/cover changes. Modeling of important ecological and hydrological parameters including vegetation density, water balance, surface runoff, and soil erosion was performed and for each of them, the quantity of changes was calculated and mapped. Then, by standardizing the maps for each parameter and overlapping them, cumulative impact levels across the watershed were classified. The result showed that with decrease in rainfall and increase in temperature, the average vegetation density decreased by 32% in the watershed and from 79 to 47%. Decreased vegetation, followed by increased runoff by about 2.5% and equivalent to 19,656.95 cubic meters per year. Consequently, the average net soil erosion has been changed from − 0.012 to 0.20 mm per year per square meter. Hence, the average soil erosion in the watershed has increased by more than 3 tons per hectare. Based on the final results, more than 40% of the watershed area is under severe and very severe ecological impact. [ABSTRACT FROM AUTHOR]

Understanding the role of ecosystem services (ESs) within environmental management has become a critical issue of the twenty-first century. This is because scientific study of ES interactions can aid effective planning and management of ESs, thus curtailing degradation and enhancing restoration. In this study, ES interactions of the climate-sensitive West African Sokoto-Rima basin were quantified using land cover and a series of GIS-derived data as inputs into the InVEST model. Crop production (CP), seasonal water yield (SWY), habitat quality (HQ), and nutrient retention ratio (NRR) between 1992 and 2015 were assessed. Climate change assessment was based Mann–Kendall trend of precipitation and temperature for both past (1951–2017) and future (2018–2050) climates. The climate dynamics present a drying-warming trend with localised cooling–warming spells in some locations and a general future wetting–warming trend. Areas dominated by cropland and CP exert significant influence on the spatiotemporal dynamics of ES interactions contributing to the manifestation of substantial trade-offs and synergies across the past (2015) and the future (2050) climates. This also regulates the overbearing pattern of multiple ES interactions such that the relationship CP > SWY > HQ > EVI > NRR was observed over the study area. The persistence of these diverse relationships will stimulate the possible degradation of natural regulating ESs. Improvement of existing crop cultivation clusters, cultivation of flood-resistant crop varieties, and agroforestry expansion were proposed as climate and ESs interaction-based restoration measures. When adopted, these measures will douse the increasing ES pressures within this semi-arid zone. [ABSTRACT FROM AUTHOR]

Climate change is the meteorological alterations that occurred in the past by natural causes, which is occurring today and being intensified by the human behavior. Some of the main consequences of these alterations are: increase in the air temperature, changes in the hydrological cycle and occurrence of extreme weather events with greater intensity and frequency. This work was performed in Alto Camaquã in the Pampa Biome, which is considered as a region of stability and of great importance for biodiversity conservation. The main economical and sociocultural activity in the region is livestock run by different families, which is also considered fundamental for the maintenance of this biome. The objective of this work was to infer information on hydric deficit in Alto Camaquã watershed related to the increase in the air temperature and its impacts on family livestock. For this purpose, we estimated the climatic water balance (CWB) derived from pluviometric and meteorological data. We also estimated the maximum capacity of available water for the period between 1990 and 2016 and also for the predictive scenarios when an increase as well as no change in the air temperature occur. It has been observed that there will be hydric deficit during the summer, except for Rainy Year, independent on whether a change in the air temperature occurs or not. The results demonstrated that the effects of climate change are already occurring and that, even if the ongoing climate change stops before the predictions come true, the hydric deficit has already been experienced by the society. [ABSTRACT FROM AUTHOR]