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Understanding how plants adapt to shifting water availability is imperative for predicting ecosystem vulnerability to drought. However, the spatial–temporal dynamics of the plant–water relationship remain uncertain. In this study, we employed the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI4g), an updated version succeeding GIMMS NDVI3g spanning from 1982 to 2022. We integrated this dataset with the multiple scale Standardized Precipitation Evapotranspiration Index (SPEI 1 to 24) to investigate the spatial–temporal variability of sensitivity and lag in vegetation growth in response to water variability across China. Our findings indicate that over 83% of China's vegetation demonstrates positive sensitivity to water availability, with approximately 66% exhibiting a shorter response lag (lag < 1 month). This relationship varies across aridity gradients and diverges among plant functional types. Over 66% of China's vegetation displays increased sensitivity to water variability and 63% manifests a short response lag to water changes over the past 41 years. These outcomes significantly contribute to understanding vegetation dynamics in response to changing water conditions, implying a heightened susceptibility of vegetation to drought in a future warming world. [ABSTRACT FROM AUTHOR]

This study focused on assessing and analyzing meteorological characteristics based on rainfall and temperature data from eight stations in Australia over the past three decades (1991–2000, 2001–2010, and 2011–2020). A Mann–Kendall test, followed by Sen's slope analysis, was conducted to evaluate the spatial and temporal patterns of the most common indicators of climate change. Several drought indices were used to monitor and detect drought occurrences in the study area. It was found that from 2011 to 2020, most stations recorded negative rainfall trends, with no significant trends overall. Northern Australia saw slight rainfall increases, the northeast had notable increases, and the southwest and southeast showed drying trends. Minimum temperatures increased in northern and northeastern Australia, with significant positive trends at some stations. Maximum temperatures showed slight increases, with several stations having significant positive trends. PET values were higher at stations with low rainfall and high temperatures. The results of the drought indices show a normal drought cycle that recurs every ten years. Additionally, the driest conditions were recorded in the most recent decade, with extreme dryness noted in 2011–2020 compared to previous decades, highlighting the complex and changing climate patterns in Australia with varying regional impacts. [ABSTRACT FROM AUTHOR]

The mid-Piacenzian Warm Period (MPWP, ~ 3.264–3.025 Ma) is the most recent example of a persistently warmer climate in equilibrium with atmospheric CO2 concentrations similar to today. Towards studying patterns and dynamics of a warming climate the MPWP is often compared to today. Following the Pliocene Model Intercomparison Project, Phase 2 (PlioMIP2) protocol we prepare a water isotope-enabled Community Earth System Model (iCESM1.2) simulation that is warmer and wetter than the PlioMIP2 multi-model ensemble (MME). While our simulation resembles PlioMIP2 MME in many aspects we find added insights. (1) Considerable warmth at high latitudes exceeds previous simulations. Polar amplification (PA) is comparable to proxies, enabled by iCESM1.2's high climate sensitivity and a distinct method of ocean initialization. (2) Major driver of warmth is the downward component of clear-sky surface long-wave radiation ( Δ T rlds _ clearsky ). (3) In iCESM1.2 modulated dominance of dynamic (δDY) processes causes different low-latitude (~ 30 S°–10°N) precipitation response than the PlioMIP2 MME, where thermodynamic processes (δTH) dominate. (4) Modulated local condensation leads to lower δ18Op across tropical Indian Ocean and surrounding Asian-African-Australian monsoon regions. (5) We find contrasting changes in tropical atmospheric circulations (Hadley and Walker cells). Anomalous regional meridional (zonal) circulation, forced by changes in tropical-subtropical (tropical) diabatic processes, presents a more comprehensive perspective than explaining weakened and expanded Hadley circulation (strengthened and westward-shifted Walker circulation) via static stability. (6) Enhanced Atlantic meridional overturning circulation owes to a closed Bering Strait. [ABSTRACT FROM AUTHOR]

Monitoring areas susceptible to desertification contributes to the strategic development of regions located in environments of extreme hydric and social vulnerability. Therefore, the objective of this study is to evaluate the process of soil degradation in the Desertification Nucleus of Cabrobó (DNC) over the past three decades using remote sensing techniques. This study used primary climatic data from TerraClimate, geospatial data of land use and land cover (LULC), and vegetation indices (SAVI and LAI) via Google Earth Engine (GEE) from Landsat 5/TM and 8/OLI satellites, and established the aridity index (AI) from 1992 to 2022. The results indicated 10 predominant LULC classes with native vegetation suppression, particularly in agriculture and urbanization. SAVI ranged from −0.84 to 0.90, with high values influenced by La Niña episodes and increased rainfall; conversely, El Niño episodes worsened the rainfall regime in the DNC region. Based on the Standardized Precipitation Index (SPI), it was possible to correlate normal and severe drought events in the DNC with years under the influence of El Niño and La Niña phases. In summary, the AI images indicated that the DNC remained semi-arid and that the transition to an arid region is a cyclical and low-frequency phenomenon, occurring in specific periods and directly influenced by El Niño and La Niña phenomena. The Mann–Kendall analysis showed no increasing trend in AI, with a Tau of −0.01 and a p-value of 0.97. During the analyzed period, there was an increase in Non-Vegetated Areas, which showed a growing trend with a Tau of 0.42 in the Mann–Kendall analysis, representing exposed soil areas. Annual meteorological conditions remained within the climatic pattern of the region, with annual averages of precipitation and actual evapotranspiration (ETa) close to 450 mm and an average temperature of 24 °C, showing changes only during El Niño and La Niña events, and did not show significant increasing or decreasing trends in the Mann–Kendall analysis. [ABSTRACT FROM AUTHOR]

Wheat and barley yields in Japan are considerably lower than those in the UK, even where similar Climate Zones (CZs) of relatively cold and humid nature are shared. In order to understand this difference, it is first necessary to find out if any common climate–yield relationship exists between the two countries. The Climate Zonation Scheme (CZS) developed in the Global Yield Gap Atlas (GYGA) was used to analyse actual yield (Ya) with three climatic factors of the GYGA-CZS, i.e., growing degree days (GDD), aridity index (AI) and temperature seasonality (TS). A significant relationship was found between AI scores and Ya values across the two countries. Ya values decreased with an increase in AI scores; in other words, lower yields are associated with higher AI scores. In addition, the degree of yield reduction with the rise in AI scores was greater in Japan than in the UK. The present study also proposed a novel method to link CZs of the GYGA-CZS to regional classification units, especially for countries where statistical crop yield data are available only at a coarse scale. [ABSTRACT FROM AUTHOR]

Understanding drought occurrence and evolution is important in minimizing the impacts associated with it. This work assesses the performance of 10 commonly used meteorological indices to measure drought in Morocco. The studied indices are Deciles Index (DI), Percent of Normal Index (PNI), Z‐Score Index (ZSI), China‐Z Index (CZI), Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), its self‐calibrated variant (scPDSI) and Palmer Z Index (PZI). Rainfall and temperature gridded data is sourced from PERSIANN‐CDR and ERA5, respectively, for the period 1983–2021. The study area exhibits three main climatic regimes; subhumid, semi‐arid and arid, with a drying and warming climate, as depicted by the rainfall and temperature trends analysis. Results show that most rainfall‐based indices perform relatively poorly in drought monitoring in the study area. DI and PNI appear to be inconsistent and abnormally responsive to rainfall. RAI reports droughts 56.5% more frequently and slightly underestimate drought intensity compared to other indices. Similarly, ZSI and CZI largely underestimate drought intensity. PDSI and scPDSI are computationally demanding, often underestimate drought intensity and overestimate drought duration by at least 115% compared to SPI and SPEI. Conversely, PZI can be used for drought onset detection as it reported droughts early compared to the other indices. SPI and SPEI perform overall better regarding their consistent drought identification and severity assessment. However, SPEI is found to be more suitable than SPI in the arid and semi‐arid regions and performed better considering the warming climate of the country. [ABSTRACT FROM AUTHOR]

Questions: How does the grass layer affect seedlings across large environmental gradients in savannas? Location: Savanna sites in Argentina, Tanzania, and South Africa. Methods: We carried out a joint analysis of three grass removal experiments in which seedlings of various Fabaceae species were transplanted into plots with native grass and companion plots where grass had been removed. First, we estimated the effect of grasses on tree seedling mortality and seedling growth rate at each site. Then, we used the resulting coefficient estimates from site‐level models to examine the impact of two climate (monthly precipitation and aridity index) and two soil (soil organic carbon content and clay content) variables on the direction and magnitude of the grass effects. Results: Grasses increased the risk of mortality, but there was no evidence for a global effect of grasses on tree seedling rate of height growth. The best model fit indicated a high mortality risk of tree seedlings in response to grasses at intermediate aridity index values. No other climate or soil variable influenced tree seedling survival or growth (monthly precipitation, soil organic carbon content and clay content). Conclusions: Our results support the notion that the grass layer consistently creates a bottleneck to tree seedling establishment in African and South American savannas beyond climate and soil conditions, mainly by affecting tree seedling survival. The negative effect of grasses on seedling survival was lower in dry conditions compared to intermediate aridity levels. These results suggest that grass–seedling interaction is less intense in drier conditions, possibly due to reduced total grass biomass, which leads to decreased site evapotranspiration and improved soil water retention capacity. [ABSTRACT FROM AUTHOR]

In this study, the aridity index (AI) based on gridded climate data was validated for defining aridity and classifying aridity zones in Iraq through comparison with the results obtained by the station-based aridity index. Gauge-based gridded climate data taken from Climatic Research Unit Timeseries (CRU TS) were used to determine the annual value of four aridity indices (Lang, De Martonne, Ernic and UNEP AI) over the period 1998-2011. The results showed that the aridity distribution maps derived using grid-based aridity indices were reasonably close to those found using station-based ones. The four aridity indices properly identified similar aridity (dryness) classifications in both the station-based and grid-based aridity maps. The area percentage of each aridity class predicted by grid-based AIs was also compared with that obtained by the station-based AIs. The results showed that the variances between the area percentages predicted by grid-based AIs and those estimated using station-based AIs are fairly slight. The Lang AI exhibited the least variance (0.4%) while the De Martonne AI had the biggest variance (-4.8%). Despite these minor variances, it is however possible to conclude that the grid-based aridity index classified the aridity zones of Iraq as properly as the station-based aridity index did. [ABSTRACT FROM AUTHOR]

This research evaluates the influence of climate change on precipitation, temperature, and evapotranspiration in Iran, a region with diverse climatic conditions, using UNEP and Demartonne climate classification systems to explore changes in Iran's climatic zones from the reference period (1961-1990) to the most recent normal period (1993-2022). The results show a decrease in the number of humid climate stations and an increase in semi-arid, dry, and desert stations under both classification methods. MannKendall and Sen's slope analyses reveal significant decreases in precipitation, and increases in temperature and evapotranspiration in over 75% of the stations, with the highest annual precipitation decrease observed in Gorgan, and the highest temperature and evapotranspiration increases in Mashhad and Ahvaz, respectively. The findings suggest that Iran's climate is rapidly shifting towards drier conditions due to global warming, with approximately 13 stations having shifted to drier conditions and 29 stations experiencing increased aridity when comparing the two normal periods. These results align with previous studies, highlighting the urgent need to address the impacts of climate change in Iran and providing valuable insights for policymakers and stakeholders to develop effective strategies for climate change adaptation and mitigation in the region. [ABSTRACT FROM AUTHOR]

Assessment of the impact of climate variability on evapotranspiration and moisture availability is crucial for planning of water resources and achieving food security in north-west India. Therefore, spatio-temporal variability in annual and seasonal climatic variables [maximum temperature (Tmax), minimum temperature (Tmin) and rainfall), reference evapotranspiration (ETo), moisture index (MI) and aridity index (AI)] were assessed for three regions (north-east, central and south-west) of Indian Punjab during baseline period (1971–2018), mid-century (2040–2060) and end century (2080–2095). The long-term data of Tmax, Tmin and rainfall during baseline period was collected from difference sources, and the MarkSim data of Representative Concentration Pathway 4.5 during mid-century (2040–2060) and end century (2080–2095) was used. The ETo, AI and MI were calculated from Tmax, Tmin and rainfall during different time periods, and the temporal variations in these parameters during baseline period, mid-century and end century were studied using Mann–Kendall test. In addition, spatial variability in these parameters was studied using inverse distance weighted method. The maximum and minimum temperatures are likely to increase by 1.1 to 4.8 °C in different regions of Punjab, and the variable pattern of rainfall is expected by mid-century and end century. Annual and Kharif (June–October) season rainfall is likely to increase by 18.2 to 42.9%, but it is expected to decrease by − 7.6 to − 62.3% (− 27.0 to − 7.6% for north-east, − 23.5 to − 17.6% for central and − 42.8 to − 62.3% for south-west region) during Rabi season (October–April). As a result of increase in temperature and erratic rainfall pattern, ETo is likely to increase by 7.4 to 13.2% in the three regions of the state. The analysis of MI and AI indicated better moisture availability conditions in the north-east followed by central and lowest in the south-west region. Analysis for mid-century (2040–2060) and end century (2080–2095) indicated that the annual MI is predicted to be slightly dry in north-east (− 32.2 and − 26.7) and central (− 54.4 and − 53.7) regions, but semi-dry in the south-west region (− 68.4 and − 68.9). Similarly, annual aridity index is predicted to be humid in north-east and semi-arid in central and south-west region. During the baseline period, wheat and rice yields were observed to be positively correlated with ETo in all the regions. However, aridity index, moisture index and rainfall were observed to be negatively correlated with wheat yield and positive with rice yield. The results of this study suggested that climatic variations by mid-century and end century can have significant implications on crop and water productivity along with severe pressure on water resources in the region. As the climatic conditions are likely to become more uncertain in future with large fluctuations, hence research is required to precisely explain the moisture extraction patterns of crops and their productivity under future climatic scenarios. [ABSTRACT FROM AUTHOR]

As an important factor that directly affects agricultural production, the social economy, and policy implementation, observed changes in dry/wet conditions have become a matter of widespread concern. However, previous research has mainly focused on the long-term linear changes of dry/wet conditions, while the detection and evolution of the non-linear trends related to dry/wet changes have received less attention. The non-linear trends of the annual aridity index, obtained by the Ensemble Empirical Mode Decomposition (EEMD) method, reveal that changes in dry/wet conditions in China are asymmetric and can be characterized by contrasting features in both time and space in China. Spatially, most areas in western China have experienced transitions from drying to wetting, while opposite changes have occurred in most areas of eastern China. Temporally, the transitions occurred earlier in western China compared to eastern China. Research into the asymmetric spatial characteristics of dry/wet conditions compensates for the inadequacies of previous studies, which focused solely on temporal evolution; at the same time, it remedies the inadequacies of traditional research on linear trends over centennial timescales. Analyzing the non-linear trend also provides for a more comprehensive understanding of the drying/wetting changes in China. [ABSTRACT FROM AUTHOR]

Flash droughts feature rapid onsets of soil moisture drought events and result in severe impacts and damages, especially on agricultural and ecological systems. How the flash drought regime across China varies on multitemporal scales with climate change is not fully clear yet. In this study, we extended the flash drought definition to apply to arid regions by adding an absolute soil moisture variation criterion. Then, we detected flash drought events across China during 1981–2021 and characterized their frequency, duration, and affected area changes on seasonal, annual, and decadal scales, using soil moisture data from the European Center for Medium-Range Weather Forecasts climate reanalysis-Land. Results show that flash drought hotspots appeared in North China and the Yangtze River Basin. During 1981–2021, the hotspots, even nationwide, underwent significant increases in frequencies, durations, and affected areas of flash droughts. The increases held in the extremely high values of the frequencies and durations in the decadal comparisons. Especially, North China saw the most extensive and rapid increases. Seasonally, flash drought frequencies and durations intensified more during spring and autumn, and seasonal hotspots in eastern China shifted in phase with spatial patterns of soil moisture loss balanced by precipitation and evapotranspiration. Thus, flash droughts tended to amplify atmospheric aridity. These findings on the hotspot regions and the spatiotemporal evolutions of flash droughts across China would pinpoint soil moisture responses to climate change and prepare for climate change impacts on local ecosystems. [ABSTRACT FROM AUTHOR]

This study investigates the evolving dry-wet climate patterns in southern India during 2020–2023, focusing on the impact of climate change. Spanning all 30 districts of Tamil Nadu, our analysis employs the HadGEM3-GC31-LL climate model, projecting a significant increase in humidity levels from 2021 to 2100. Key findings reveal consistently higher post-monsoon aridity indices compared to the monsoon season, exceeding 0.65 and raising concerns about potential flash floods. Regions most affected include Kanniyakumari, Nilgiris, Chennai and others. To address these challenges, the study recommends urgent policy interventions, emphasizing water conservation through initiatives like farm pond construction. Tailored policies are crucial to shield farmers and dairy producers from economic fallout, with an emphasis on integrating indigenous knowledge for effective climate change adaptation. In summary, this research highlights the need for immediate action, advocating for comprehensive strategies such as water conservation and tailored policies to enhance resilience and mitigate the impact of climate change in the studied regions.

Formulation of the problem. Remote sensing data might be used for indirect assessment of croplands conditions and drought stress through the calculation of specific vegetation indices, such as vegetation health index (VHI), agriculture stress index (ASI), and drought intensity or weighted mean vegetation health index (WMVHI). However, the accuracy of these indices is not clear for some territories. For example, the South of Ukraine is a zone of risky agriculture, because of low natural moisture supply and high evapotranspiration. Moisture supply is the main limiting factor for sustainable crop production in this region. The goals of this study were: 1) to assess the reliability of the mentioned vegetation indices in drought assessment through the direct comparison with the UNEP aridity index; 2) to find out whether remote sensing drought indicators could be used for the yield prediction of major crops on the regional scale. Methods. The study was conducted for Kherson region of Ukraine, as it is one of the most arid regions of the country with very high drought risks. The data on average weighted annual VHI, ASI, and WMVHI for the period 1984-2022 (Season 1) were collected and generalized from the FAO Earth Observation services. UNEP aridity index was calculated using the data from Kherson regional hydrometeorological center. Correlation and linear regression analysis were performed using common statistical methodology. Results. As a result, it was found that 1) all the studied remote sensing drought indicators demonstrate poor correlation with the aridity index, therefore, they should not be used to determine meteorological drought in the region; 2) all the studied remote sensing indices, especially VHI, demonstrate moderate-to-strong correlation with the yields of certain crops, cultivated in Kherson region (R=0.54-0.86), and could be used for the yield prediction; 3) the aridity index have poor relation to the yields of major crops, cultivated in the studied area; 4) VHI-based linear regression models for the crops’ yields prediction are reliable and reasonable for scientific and practical use just for cereal crops, and are much less accurate for grain corn and sunflower; 5) based on the study findings, it could be concluded that aridity index provides pure climatological characteristics of the region, while the studied vegetation indices are mainly focused on the level of drought stress that impacts crops during the growing season. Scientific novelty and practical significance. The article provides novel insights on the implementation of remote sensing data in drought risks assessment in crop production, and their utilization for the purpose of croplands productivity prediction. The study has theoretical and practical importance for current agriculture, and the findings could be used both in scientific, educational, and practical purposes.

This investigation explores the spatio-temporal variations of aridity across the Sabarmati River basin (SRB), India, using rainfall and temperature datasets for the baseline (1951–2019) and future (2020–2100) periods. The projected changes are analysed using a multi-model ensemble of five general circulation models under two representative concentration pathways (RCPs). The long-term variation and dependency between the aridity index (AI) and other climate indices are explored. Further, the sensitivity of Kharif and Rabi crops to atmospheric warming is investigated using anthesis heat stress (AHS) at the district level. The results project increased rainfall and temperature at the end of the 21st century. The projected rise in AI denotes a transition from semi-arid to sub-humid conditions in parts of the SRB, particularly the southern SRB. However, AI shows a stronger positive association with rainfall compared to temperature, which drives the basin towards moisture-sufficient conditions. High to very high AHS levels are noted for the Kharif and Rabi crops in all the districts. The escalating severe temperature episodes during RCP8.5 may significantly impact crop stress and food security in the SRB. Thus, there is a need to adopt resilient agricultural practices to overcome the negative impact of increasing temperatures in the future. HIGHLIGHTS Baseline and projected changes in aridity and its dependency on climate indices are evaluated.; Anthesis heat stress is calculated for Kharif and Rabi crops at the district level.; Transition of SRB towards moisture-sufficient conditions is noted.; Higher heat stress levels would cause food security issues, especially in RCP8.5 scenario.; Need to adopt resilient agricultural practices.;

Persistent drought events frequently intensify the aridity of ecosystems and cause catchment runoff depletion. Here, using large and long-term data sets of meteorological and hydrologic variables (precipitation, runoff, temperature, and potential evapotranspiration) investigated the major causes that modulated catchment runoff depletion between the years 1980 and 2020 in southern-central Chile. We identified the hydrological years where aridity index intensified, analyzed its relationship with annual runoff, and evaluated the effect of the annual evaporation index and annual aridity index on water balance of 44 catchments with different precipitation regimes located between 35° and 40°S. Our results showed that observed precipitation and runoff significantly decreased between 1980 and 2020 in 64% of the catchments in the study area. Potential evapotranspiration increased significantly in 39% of the catchments. Hydrological years in which precipitation decreased showed a decreased runoff trend. This result suggests that meteorological droughts tend to significantly decrease observed runoff. The runoff value decreased as the aridity index increased from 0.3 to 6.7, and the Budyko curve captured 98.5% of the annual variability of all catchments. HIGHLIGHTS Significant contrast in aridity in response to decreased precipitation. Consecutive droughts lead to a moderate runoff deficit and a higher aridity index.; Extreme aridity index over 6.5, PET far exceeds mean annual Q and P.;

Understanding how plants adapt to shifting water availability is imperative for predicting ecosystem vulnerability to drought. However, the spatial–temporal dynamics of the plant–water relationship remain uncertain. In this study, we employed the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI4g), an updated version succeeding GIMMS NDVI3g spanning from 1982 to 2022. We integrated this dataset with the multiple scale Standardized Precipitation Evapotranspiration Index (SPEI 1 to 24) to investigate the spatial–temporal variability of sensitivity and lag in vegetation growth in response to water variability across China. Our findings indicate that over 83% of China’s vegetation demonstrates positive sensitivity to water availability, with approximately 66% exhibiting a shorter response lag (lag < 1 month). This relationship varies across aridity gradients and diverges among plant functional types. Over 66% of China’s vegetation displays increased sensitivity to water variability and 63% manifests a short response lag to water changes over the past 41 years. These outcomes significantly contribute to understanding vegetation dynamics in response to changing water conditions, implying a heightened susceptibility of vegetation to drought in a future warming world.

Wheat and barley yields in Japan are considerably lower than those in the UK, even where similar Climate Zones (CZs) of relatively cold and humid nature are shared. In order to understand this difference, it is first necessary to find out if any common climate–yield relationship exists between the two countries. The Climate Zonation Scheme (CZS) developed in the Global Yield Gap Atlas (GYGA) was used to analyse actual yield (Ya) with three climatic factors of the GYGA-CZS, i.e., growing degree days (GDD), aridity index (AI) and temperature seasonality (TS). A significant relationship was found between AI scores and Ya values across the two countries. Ya values decreased with an increase in AI scores; in other words, lower yields are associated with higher AI scores. In addition, the degree of yield reduction with the rise in AI scores was greater in Japan than in the UK. The present study also proposed a novel method to link CZs of the GYGA-CZS to regional classification units, especially for countries where statistical crop yield data are available only at a coarse scale.

Monitoring areas susceptible to desertification contributes to the strategic development of regions located in environments of extreme hydric and social vulnerability. Therefore, the objective of this study is to evaluate the process of soil degradation in the Desertification Nucleus of Cabrobó (DNC) over the past three decades using remote sensing techniques. This study used primary climatic data from TerraClimate, geospatial data of land use and land cover (LULC), and vegetation indices (SAVI and LAI) via Google Earth Engine (GEE) from Landsat 5/TM and 8/OLI satellites, and established the aridity index (AI) from 1992 to 2022. The results indicated 10 predominant LULC classes with native vegetation suppression, particularly in agriculture and urbanization. SAVI ranged from −0.84 to 0.90, with high values influenced by La Niña episodes and increased rainfall; conversely, El Niño episodes worsened the rainfall regime in the DNC region. Based on the Standardized Precipitation Index (SPI), it was possible to correlate normal and severe drought events in the DNC with years under the influence of El Niño and La Niña phases. In summary, the AI images indicated that the DNC remained semi-arid and that the transition to an arid region is a cyclical and low-frequency phenomenon, occurring in specific periods and directly influenced by El Niño and La Niña phenomena. The Mann–Kendall analysis showed no increasing trend in AI, with a Tau of −0.01 and a p-value of 0.97. During the analyzed period, there was an increase in Non-Vegetated Areas, which showed a growing trend with a Tau of 0.42 in the Mann–Kendall analysis, representing exposed soil areas. Annual meteorological conditions remained within the climatic pattern of the region, with annual averages of precipitation and actual evapotranspiration (ETa) close to 450 mm and an average temperature of 24 °C, showing changes only during El Niño and La Niña events, and did not show significant increasing or decreasing trends in the Mann–Kendall analysis.

The availability of water is a crucial factor influencing the stability of an ecosystem, the vitality of its flora and fauna, and the economic growth of a society. The changes in the aridity of a climate have an important effect not only on ecosystems, but also on economies, agriculture, and the available water supply. The aridity index (AI) is one of the most frequently used indices for assessing the degree of dryness of a climate. The AI is based on long-term water deficits and therefore expresses over long periods changes. Fluctuations in aridity can also be used to quantify the impact of climate change on regional climatic conditions. Our research is focused on quantifying changes in climate conditions for 76 climatological stations in Slovakia; these changes are based on an analysis of trends in the aridity index. We also examined changes in the seasonal and long-term patterns of related variables and climate drought indexes (SPI10, SPEI10) to identify the drivers of changes in the aridity of the climate of the region. Our findings indicate rising trends in climate aridity, primarily driven by increases in air temperatures, especially the minimum air temperature, even though the trend in precipitation totals is still constant (but also increasing in some areas). Furthermore, our analysis reveals that drought has become a year-round concern over recent decades, which reinforces our assertion that certain regions of Slovakia are at risk of desertification. [ABSTRACT FROM AUTHOR]

The process of climate warming significantly affects agroclimatic resources and agricultural production. We study the agroclimatic resources and their variability on the territory of the Republic of Bashkortostan (Russia). The Bashkortostan has a high agricultural potential and holds a leading position in the country in the production of grain crops, potatoes, milk, and honey. Currently, no detailed studies have been conducted for this area to assess the effects of global climate change on agro-climatic resources. World experience shows such research becomes strategically important for regions with powerful agricultural production. We used the sums of average daily air temperatures above 0 and 10 °C, the G.T. Selyaninov hydrothermal coefficient, and the Ped aridity (humidification) index as agroclimatic indicators. We used data of long-term meteorological observations of 30 meteorological stations for the period of 1961–2020. We revealed the long-term dynamics of the agroclimatic indicators and the spatial and temporal regularities in their distribution on the territory of Bashkortostan. There is a steady increase in the sums of average daily air temperatures above 0 and 10 °C. Against this background, aridity increases, which is especially manifested in the southern parts of the Republic of Bashkortostan. We assessed the impact of agroclimatic indicators on the main types of agricultural crops in the republic. We revealed that the greatest positive impact on the yield of oilseeds, cereals, and industrial crops is made by precipitation at the beginning (r = 0.50, r = 0.44, and r = 0.52, respectively) and in the middle of the growing season (r = 0.55, r = 0.76, and r = 0.51, respectively). Temperature and precipitation during the growing season have a complex effect on cereals. This is proven by correlations with HCS and the Ped index (r = 0.45 and r = −0.56, respectively). Aridity at the beginning of the growing season affects the yield of oilseeds and potatoes. This is confirmed by correlations with the Ped index (r = −0.49 and r = −0.52, respectively). In general, the aridity of the growing season has a significant impact on the yield of cereals (r = −0.57). Negative relationships have been found between the air temperature growing season and the yield of potatoes (r = −0.50) and cereals (r = −0.53). The results of the study were compared with data from the Copernicus Climate Change Service database. We identified climate trends under RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios. These scenarios should be taken into account when developing plans for the adaptation of agriculture in the Republic of Bashkortostan to changes in the regional climate. Maximum decrease in precipitation is established for the RCP 6.0 scenario. This can have an extremely negative impact on crop yields. This problem is especially relevant for the southern part of the Republic of Bashkortostan. The information presented in the study will allow for a more effective adaptation of the agricultural sector to current and future climate changes. [ABSTRACT FROM AUTHOR]

Persistent drought events frequently intensify the aridity of ecosystems and cause catchment runoff depletion. Here, using large and long-term data sets of meteorological and hydrologic variables (precipitation, runoff, temperature, and potential evapotranspiration) investigated the major causes that modulated catchment runoff depletion between the years 1980 and 2020 in southern-central Chile. We identified the hydrological years where aridity index intensified, analyzed its relationship with annual runoff, and evaluated the effect of the annual evaporation index and annual aridity index on water balance of 44 catchments with different precipitation regimes located between 35° and 40°S. Our results showed that observed precipitation and runoff significantly decreased between 1980 and 2020 in 64% of the catchments in the study area. Potential evapotranspiration increased significantly in 39% of the catchments. Hydrological years in which precipitation decreased showed a decreased runoff trend. This result suggests that meteorological droughts tend to significantly decrease observed runoff. The runoff value decreased as the aridity index increased from 0.3 to 6.7, and the Budyko curve captured 98.5% of the annual variability of all catchments. [ABSTRACT FROM AUTHOR]

Climate change and intensified human activities are expected to alter the natural hydrological cycle would cause changes in the distribution of water resources and their availability across space and time. Therefore, the present study focused on understanding the spatiotemporal variations of hydroclimatic variables namely rainfall, surface runoff, water yield, and the Aridity Index (AI) of the Krishna River Basin (KRB) under combined impact of climate and Land Use/Land Cover (LULC) change scenarios. From trend analysis, the best model (CNRM-CM5 driven RCM) projected an increasing trend in rainfall under both RCP 4.5 and 8.5 scenarios for the entire century. Results indicated that, except for the central part, most of KRB experiences high runoff and water yield conditions under both RCP 4.5 and 8.5 scenarios in the mid and end centuries, conversely it may face low runoff and water yield conditions in the early century. Most of the central part is in arid and semi-arid conditions and, the eastern part of the basin is in dry sub-humid conditions, while the Western Ghats, Palleru, and Munneru regions of the basin fall under humid and hyper humid regions under RCP 4.5 scenario. Whereas the majority of the basin falls under humid and hyper humid regions under RCP 8.5 scenario except for the central and few other parts of the basin. In light of this, it was essential to review the current water management strategies and plan future projects to provide efficient and effective ways to mitigate the adverse impacts resulting from climate variability. [ABSTRACT FROM AUTHOR]

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Climate plays a critical role in altering soil carbon (C) turnover and long‐term soil C storage by regulating water availability and temperature, and in turn biological activity. However, a systematic analysis of how key climatic factors shape the global patterns of soil C turnover is still lacking. Using global observation‐based data sets and a transit time theory, here we show that—excluding croplands and cold regions—soil C turnover time (τTO) and its variability are strongly related to ecosystem aridity through a power law scaling. According to such a relation, soil C turnover is faster but also more variable in wetter regions, suggesting more complex C cycling processes. The observed scaling of τTO and its coefficient of variation with aridity underlines the fundamental controls of climate on soil C turnover and may help reconcile soil C models with empirical observations for improved projection of soil C dynamics under climate change. Plain Language Summary: Climate regulates the metabolic activity of both plants and soil microorganisms by regulating temperature and water availability of ecosystems and, in turn, influences how fast C cycles through soils. Recent observation‐based data sets provide global estimates of both soil C age and turnover time, paving the way for novel analyses exploring how they simultaneously change across climates. Here, by analyzing these data we show that, excluding croplands and cold regions, soil C turnover time and its variability are related to ecosystem aridity through simple power laws across a broad range of aridity. Such relations reveal that, especially in wet regions, C cycling through soils is mostly young C and can consist of C with a broad range of turnover times. Our findings may help us improve the structure of soil C models and hence projections of soil C cycling under climate change. Key Points: The ratio of soil C turnover time to age shows a strong sensitivity to aridity in warm regionsSoil C turnover in warm regions is more variable under wetter conditionsThe scaling of soil C turnover time and its variability with aridity follows a self‐similar power law [ABSTRACT FROM AUTHOR]

Climate change can alter the spatial and temporal distribution of aridity around the world through a combination of factors such as reduced precipitation, rising temperatures and decreased evapotranspiration. Especially the Mediterranean region has been identified as vulnerable to anthropogenic climate change due to a significant reduction in precipitation compared to other land regions in all climate models operated under different scenarios. Despite numerous studies on aridity trends, few have focused specifically on Türkiye and considered a comprehensive range of aridity indices and scenarios. This study aims to fill this research gap by providing a more detailed understanding of future aridity trends in Türkiye under various climate change scenarios and using multiple aridity indices. A novelty of this study lies in the simultaneous examination of three aridity indices (PCI, EAI and UNEP AI) for Türkiye, allowing for a more comprehensive assessment of future aridity trends. Furthermore, this study considers three future time periods (2011–2040, 2041–2070 and 2071–2100) and three shared socioeconomic pathways (SSPs) to evaluate the potential range of climate change impacts on aridity in Türkiye. Therefore, in this study, we aimed to determine the changes in aridity conditions in Türkiye until the end of the 21st century. We used three aridity indices: the Pinna combinative index, the Erinç aridity index and the UNEP aridity index. These indices were calculated for the baseline period of 1981–2010 using gridded data (CHELSA) and for the periods of 2011–2040, 2041–2070 and 2071–2100 using three climate models (GFDL‐ESM4, MRI‐ESM2‐0 and IPSL‐CM6A‐LR) and multi‐model means with three SSPs to represent different future scenarios. The results showed that all three indices indicate an increase in dry climate conditions in Türkiye after 2041, with particularly notable increases expected in Central Anatolia, Southeastern Anatolia and parts of the Eastern Mediterranean, as well as eastern parts of Eastern Anatolia and the inner Aegean region. Under the SSP3‐7.0 scenario, the expansion of semi‐arid and arid areas is predicted to cover more than 30% of the country by the end of the century (2071–2100). This increase in aridity could increase the region's vulnerability to climate change and the risk of desertification, which should be taken into consideration in national water management and planning. [ABSTRACT FROM AUTHOR]

Study region: Ghana, West Africa. Study focus: NASA's Prediction of Worldwide Energy Resource (NASA POWER) satellite-based reanalysis products are used for estimating the aridity index (AI) in Ghana, West Africa. The NASA estimates are compared and bias-corrected with temperature-based potential evapotranspiration estimates and rainfall data from 22 synoptic climate stations. The cumulative distribution function (CDF) matching technique was used for bias correction New Hydrological Insights for the region: The results indicated a previous 36% over-estimation of arid conditions in dryland climates and an under-estimation of wetland climate regions by the NASA POWER data compared with the station-based estimation. Post bias-correction, the satellite-based estimates showed substantial improvements, as evidenced by a correlation coefficient of R2 = 0.87. The rectified data suggests that with accurate interpretations and calibrations, satellite-based metrics can play a pivotal role in advancing hydrological studies and water resource management in West Africa Sub-region. This insight underscores the potential of satellite data in augmenting regional hydrological research, establishing a foundation for similar studies in analogous global environments.

One of the most celebrated textbook examples of physiological adaptations to desert environments is the unique ability that desert mammals have to produce hyperosmotic urine. Commonly perceived as an adaptation mainly observed in small rodents, the extent to which urine-concentrating ability has evolved independently in distinct mammalian lineages has not previously been assessed using modern phylogenetic approaches. We review urine-concentrating ability data from the literature in 121 mammalian species with geographic ranges encompassing varying climatic conditions. We explicitly test the general hypothesis that desert-dwelling mammals have evolved greater ability to concentrate urine than non-desert species, controlling for body mass, phylogenetic affinity and other covariates. Ancestral state reconstruction across our dataset’s phylogeny shows that the ability to produce hyperosmotic urine, measured as maximum urine osmolality, has evolved convergently in mammalian species with geographic ranges characterised by low mean annual aridity index. Phylogenetic generalised least-squares (PGLS) models show that the mean annual aridity index of a species’ geographic range largely predicts its urine-concentrating ability, even when accounting for body mass differences, phylogenetic correlations, the specific condition under which urine osmolality was measured, the method used to measure urine osmolality, and the species’ diet. In contrast, we find much weaker correlations between mass-adjusted basal metabolic rate and environmental variables when analysing 84 of the species included in the urine osmolality analysis. Taken together, our results not only show that desert mammals effectively concentrate more urine than non-desert mammals, but further suggest that aridity is likely to have been one of the main selective pressures leading to increasing maximum urine-concentrating ability and driving its repeated evolution in different desert mammalian lineages.

Renewable bioenergy has the potential to contribute sustainably to the energy sector. Forestry is the main source of biomass for energy in Europe, and poplar (genus Populus) is widely used for short rotation coppice (SRC). Many studies have assessed poplar clones’ productivity but there is a lack of regional studies and links with the climate. We investigated the biomass productivity of twenty hybrid poplar clones for SRC. Clones were planted in sixteen locations across nine countries in Europe, although not all clones were replicated in all locations. In each location, clones were planted in three replicated plots. All plots were harvested after four years, and the aboveground dry biomass estimated. We fitted clone-specific linear mixed models of total aboveground dry biomass production at plot level as function of climatic variables. For many clones (eight) only annual heat moisture deficit negatively affected productivity, in few cases (3) together with a quadratic term for a smoother relationship. In some other clones (five) only the mean summer precipitation positively and linearly affected productivity. On average, the variance explained by the fixed effects in those models was 56%. For the remaining clones (seven), no climate variables resulted significant. Our study explicitly investigated the quantitative link between water availability and poplar SRC productivity, one of the most important known factors but not often studied with a modelling approach. Further, we show the most productive clones in dried conditions. We also highlight the need to larger scale regional experiments to produce models that can be used in climate change scenarios.

Abstract Climate plays a critical role in altering soil carbon (C) turnover and long‐term soil C storage by regulating water availability and temperature, and in turn biological activity. However, a systematic analysis of how key climatic factors shape the global patterns of soil C turnover is still lacking. Using global observation‐based data sets and a transit time theory, here we show that—excluding croplands and cold regions—soil C turnover time (τTO) and its variability are strongly related to ecosystem aridity through a power law scaling. According to such a relation, soil C turnover is faster but also more variable in wetter regions, suggesting more complex C cycling processes. The observed scaling of τTO and its coefficient of variation with aridity underlines the fundamental controls of climate on soil C turnover and may help reconcile soil C models with empirical observations for improved projection of soil C dynamics under climate change.

The Aridity Index (AI), calculated from 99 homogeneous meteorological stations from 1961 to 2020, was used to analyze the variation of dryness/wetness climate change in Xinjiang in the past 60 years. The results show that the annual AI in Xinjiang has demonstrated a significant decreasing trend over the past 60 years; that is, the climate in Xinjiang, especially southern Xinjiang, has shown a relatively wetting trend. The interdecadal variations from the 1960s to 2010s explained that the total station ratio of arid and extremely arid areas showed a decreasing trend. In contrast, the semi-arid, humid, and semi-humid areas showed increasing trends, especially since the beginning of the twenty-first century. The interdecadal spatial evolution characteristics show that Xinjiang's dryness/wetness climate reversed in the 1990s. An abrupt change in the annual AI occurred in 1986, after which the study region was continuously wetting. The first empirical orthogonal function (EOF) decomposition mode is consistent in Xinjiang; that is, the climate in Xinjiang is generally dry or wet, and the intensity of this change varies among different regions. The second mode reflects the opposite spatial distribution characteristics of the dryness/wetness climate in southern and northern Xinjiang with the Tianshan Mountains. Dryness/wetness climate changes in Xinjiang mainly exhibit 2.5-year and 6-year oscillation periods, between which the 6-year period is even more significant. [ABSTRACT FROM AUTHOR]

Numerous drylands worldwide have experienced degradation of both soil and vegetation in proximity to watering areas. Degradation can be observed in satellite imagery as fading radial brightness belts extending away from the water sources. The main objective of this study was to examine the spatio-temporal patterns of land degradation and rehabilitation in the drylands of the southeast Iberian Peninsula. The brightness index of tasseled cap was discovered to be the best form of spectral transformation for enhancing the contrast between the bright-degraded areas near the points and the darker surrounding areas far from and in between these areas. To comprehend the spatial structure present in spaceborne imagery of two desert sites and three key time periods, semi-variograms were created (mid-late 2000s, around 2015 and 2020). To assess spatio-temporal land-cover patterns, a kriging was used to smooth the brightness index values extracted from 30 m spatial resolution images. To assess the direction and intensity of changes between study periods, a change detection analysis based on kriging prediction maps was performed. These findings were linked to the socioeconomic situation prior to and following the EU economic crisis. The study discovered that degradation occurred in some areas as a result of the region's agricultural activities being exploited. [ABSTRACT FROM AUTHOR]

A simplified differential equation for the dynamics of soil organic carbon (SOC) that describes the rate of SOC change (dSOC/dt) was constructed using the LASSO regression—a regularized linear regression machine learning method. This method selects the best predefined explanatory variables and empirically evaluates the relevant parameters of the equation. The result, converted into a formula for the long-term equilibrium level of soil carbon, indicates the existence of carbon sequestration potential in the studied regions of Poland. In particular, the model predicts high SOC content in regions with a high Topographic Wetness Index (TWI), such as river valleys or areas with high cattle density, as expected. [ABSTRACT FROM AUTHOR]

Introduction and Objective: The problem of drought and water crisis in the in the central Iran watershed has become serious in recent years due to consecutive droughts and increased water consumption in drinking, agriculture and industry sectors. Since rain is the only source of water supply in this catchment area, a decrease in the amount of rainfall with this intensity can cause a widespread drought and eventually large migrations from these areas. Therefore, the aim of this research is to analyze the changes in the aridity index in the vast area of the central Iran watershed in future. Material and Methods: In order to achieve the study's objective, the minimum and maximum air temperature, sunshine hours, and precipitation of 40 synoptic stations in the Central Iran watershed for a period of 20 years (1991-2010) were received from the Meteorological Organization.Then the values of potential evapotranspiration, aridity index, and Angot precipitation index were calculated for each station in the current conditions. In order to project changes in the aridity index in the future, the output of three MIROC5, HadGEM2-ES, and GFDL-CM3 models for the historical period (1991–2010) and the medium future (2041–2060) under the RCP4.5 scenario and based on the LARS- WG6 scaled down. After evaluating the accuracy of the models against the observational data, the index values were calculated in future conditions, and zoning maps were drawn for the study area. Results: Projection of changes in evapotranspiration values under the RCP4.5 scenario (2041– 2060) showed that the most changes were in January, March, December, and November. Shahrekord showed the highest percentage of incremental changes, with 36.8% change in January compared to the base period. In terms of rainfall regime, the Central Iran watershed has a winter rainfall regime based on the Angot precipitation index in the current conditions. The HadGEM2-ES model is consistent with the base period, but based on the MIROC5 and GFDLCM3 models, the winter precipitation in many stations will change to the all-season precipitation regime in the future. Climatic zoning of the UNEP aridity index in the studied area indicated that in current conditions, more than half of the stations have a dry climate. Only Kohrang has a humid climate with an aridity index value of 0.78. Shemiranat and Oligoders have semi-dry conditions with values of 0.21 and 0.20, respectively. The evaluation of the future climate conditions according to the GFDL-CM3 model shows that the humid climate in central Iran will gradually disappear, while the semi-arid climate will expand in this region. The output of the MIROC5 model also shows the expansion of the ultra-arid climate zone in different parts of the catchment area of Central Iran. Conclusion: The results showed that the aridity index values based on selected GCM models have decreased in the studied area compared to the observation period. Therefore, a large number of stations that have dry conditions in the base period will experience ultra-dry conditions in the future climate.The evapotranspiration potential in the Central Iran watershed is increasing during all months of the year, especially in wet stations in the west. This issue shows the dominance of the aridity index over this region in future conditions. [ABSTRACT FROM AUTHOR]

In this study, drought climatology and drought variability in the continental semi-arid and dry sub-humid Central Anatolia Region were analyzed using the annual and seasonal precipitation totals, Aridity Index (AI) and Standardized Precipitation Index (SPI), along with the long-term trends in their time-series. Trends in precipitation totals, 12-month SPI and annual AI series were statistically determined by applying the Mann–Kendall rank correlation test (M–K test) and its sequential analysis. The climatological probability of drought and wet events was evaluated according to different classes in the region. Annual AI values indicate that dry sub-humid climate was dominant from the mid-1990s to mid-2000s, and then semi-arid conditions were dominant from the mid-2000s to recent years. The climatological probability of monthly SPI values being severely dry is dominant mostly in the semi-arid and dry sub-humid central, south and east districts of the region. According to the M–K test, statistically significant decreasing trends were determined for annual total precipitation and the 12-month SPI series in Keskin, Pınarbaşı, Sarız, Ürgüp and Akşehir. Nonsignificant and statistically significant decreasing trends in spring precipitation were determined at approximately 67% of the stations and at Ilgaz, Kulu, Eskişehir, Keskin, Zara, Ürgüp and Sarız stations, respectively. With respect to the desertification in the Central Anatolia Region, the most vulnerable subregions for desertification processes are the Konya subregion and the Upper Kızılırmak subregion, mainly characterized by semi-arid and dry sub-humid environmental conditions. [ABSTRACT FROM AUTHOR]

Evaluation of changes in dry-wet climate is crucial in the context of global climate change to ensure regional water resources, ecosystem stability, and socio-economic development. Long-term daily meteorological data, including temperature, precipitation, relative humidity, wind speed, sunshine duration, and air pressure data from 1680 stations across mainland China from 1971 to 2019, were collected to investigate the temporal and spatial variations in aridity index (AI), precipitation (P), reference evapotranspiration (ET0), and the underlying driving climatic factors. Results indicated that the Northwest, Northeast, and Huang-Huai regions were undergoing significant wetting processes, while the Southwest and Southeast China were undergoing significant drying processes. The changing AI was mainly decided by the changing trends of ET0. For most regions, ET0 has undergone significant increases. The average increasing rate over mainland China was 3.76 mm/10a. Stations with decreasing trends were mainly located in the Tibet Plateau, Huang-Huai, and northern Northeast China. Trends in ET0 were negatively affected by the increasing changes in relative humidity and positively affected by the decreasing changes in wind speed and sunshine duration and the increasing changes in air temperature. Wind speed and relative humidity were found to be the main dominant factors driving the changes in ET0, and their contribution varied with regions. Huang-Huai and northern Northeast China showed a significant downward trend in ET0, mainly driven by the decrease in wind speed, while the increase in relative humidity was the primary contributor to the significant upward trends in ET0 across all other regions in China. [ABSTRACT FROM AUTHOR]

Abstract How much terrestrial precipitation is used by vegetation and how much runs off, represents central issues in hydrologic science, ecology, climate change, and even geopolitics. We present a theory for the water balance to predict the fractional change in streamflow due to given fractional changes in temperature and precipitation. The theory involves a single parameter whose value is derived under the conditions of neither energy‐ nor water‐limitations and, therefore, is not an adjustable parameter. By comparison with extensive data for precipitation elasticity ϵp at global scale, we find that the theory captures the key trends of the variations of the median value of ϵp with the aridity index AI. In contrast to a shortcoming of the classical Budyko phenomenology, namely, convergence to ϵp = 4 for large AI, our theory yields a value of 2 for the median value of ϵp for all AI > 1, in accord with the data for major river basins, as well as with the median value of summaries of global and continental data sets. Incorporating in the theory the effects of annual changes in water storage leads to the ability to predict the range of observed values of the elasticity as a function of the aridity index, or its inverse, the humidity index, as well as the run‐off ratio. When changes in storage are neglected, the theory yields more accurate predictions for major river drainages than for small watersheds, particularly if the large basin spans various climate regimes and, as such, an integration over climates tends to reduce relative changes in the storage.

Formulation of the problem. Remote sensing data might be used for indirect assessment of croplands conditions and drought stress through the calculation of specific vegetation indices, such as vegetation health index (VHI), agriculture stress index (ASI), and drought intensity or weighted mean vegetation health index (WMVHI). However, the accuracy of these indices is not clear for some territories. For example, the South of Ukraine is a zone of risky agriculture, because of low natural moisture supply and high evapotranspiration. Moisture supply is the main limiting factor for sustainable crop production in this region. The goals of this study were: 1) to assess the reliability of the mentioned vegetation indices in drought assessment through the direct comparison with the UNEP aridity index; 2) to find out whether remote sensing drought indicators could be used for the yield prediction of major crops on the regional scale. Methods. The study was conducted for Kherson region of Ukraine, as it is one of the most arid regions of the country with very high drought risks. The data on average weighted annual VHI, ASI, and WMVHI for the period 1984-2022 (Season 1) were collected and generalized from the FAO Earth Observation services. UNEP aridity index was calculated using the data from Kherson regional hydrometeorological center. Correlation and linear regression analysis were performed using common statistical methodology. Results. As a result, it was found that 1) all the studied remote sensing drought indicators demonstrate poor correlation with the aridity index, therefore, they should not be used to determine meteorological drought in the region; 2) all the studied remote sensing indices, especially VHI, demonstrate moderate-to-strong correlation with the yields of certain crops, cultivated in Kherson region (R=0.54-0.86), and could be used for the yield prediction; 3) the aridity index have poor relation to the yields of major crops, cultivated in the studied area; 4) VHI-based linear regression models for the crops’ yields prediction are reliable and reasonable for scientific and practical use just for cereal crops, and are much less accurate for grain corn and sunflower; 5) based on the study findings, it could be concluded that aridity index provides pure climatological characteristics of the region, while the studied vegetation indices are mainly focused on the level of drought stress that impacts crops during the growing season. Scientific novelty and practical significance. The article provides novel insights on the implementation of remote sensing data in drought risks assessment in crop production, and their utilization for the purpose of croplands productivity prediction. The study has theoretical and practical importance for current agriculture, and the findings could be used both in scientific, educational, and practical purposes. [ABSTRACT FROM AUTHOR]

Water scarcity is a growing concern globally, with climate change and increasing population exacerbating the issue. Here, we introduce a new framework for assessing water availability in 708 Brazilian catchments that considers the effect of CO2 concentrations on potential evapotranspiration, uses CMIP6 bias‐corrected climate change simulations, and presumes an open water balance assumption, while considering the human‐aspect by incorporating water demand projections. We note an average reduction of water security in 81% of the analyzed catchments by 2100. Among these catchments, 37% presented a reduction of future water availability, while 63% undergo a worse scenario due to an increase in human water use, which highlights the role of the human aspect in water security assessment. Our study shows important aspects for both advancing future water availability studies and for drawing a picture of the impacts of changes in climate and water use on Brazilian future water security that may be useful for water resources management practices and advancing hydrologic studies. Key Points: We introduce a new framework for assessing climate change and water use impacts on water availability in 708 Brazilian catchmentsWe consider an open water balance, the effect of CO2 concentrations on potential evapotranspiration, and water demand projections81% of the catchments may experience reduced water security by 2100 [ABSTRACT FROM AUTHOR]

Understanding future dry and wet changes under global climate change is of great regional significance in China. Based on six Global Climate Models (GCM) from the Coupled Model Intercomparison Project phase 6 (CMIP6), we project the changes of dry‐wet climate regions in China in the 21st century under the three SSP‐RCP (shared socioeconomic pathway‐representative concentration pathway) scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), and further identify the key climatic factors that lead to the transition of dry‐wet climate regions. The results show the area of humid and subhumid regions in China will show an upward trend in the future, while the area of semiarid and arid regions will show a downward trend. Especially under the SSP5‐8.5 scenario, the area of the subhumid region has the largest increase trend (0.042% per year), and the area of the arid region has the largest decline trend (−0.048% per year). The three main transfer directions of dry‐wet climate regions are from arid to semiarid regions, from semiarid to subhumid regions, and from subhumid to humid regions in the future. Due to the future extensive wetting trend in the north, the future dry‐wet climate regions will shift to wetter types in Tibet, North China, the Qinling‐Huaihe regions and the northeast. In addition, the key climatic factors for the transition of dry‐wet climate regions are mainly precipitation, maximum temperature and solar radiation. The increase of precipitation plays a dominant role for areas projected to change to wetter types, and the increase of potential evapotranspiration (PET) plays a dominant role for areas projected to change to drier types. The PET changes in changed areas of dry‐wet climate regions are mainly dominated by maximum temperature and solar radiation. Our research has important implications for future water resource management and reconsideration of climate mitigation and adaptation policies. [ABSTRACT FROM AUTHOR]

• AFRICLIM's bioclimatic data can be associated with plots to assess their influence on the abundance and distribution of species. • Ricinodendron heudelotii is distributed in the most watered regions of Benin. • The aridity index significantly influences the abundance of R. heudelotii. • The Yoruba-Nagot community of Benin could have considered the presence of R. heudelotii as one of the criteria for selecting a site to settle. Climate change and human activities pose a major threat to forest resources and the livelihoods of vulnerable communities. Understanding the factors that influence the distribution and abundance of priority species for these communities is crucial for conservation and sustainable management purposes. We identified the pedoclimatic and anthropogenic factors that influence the geographical distribution and abundance of Ricinodendron heudelotii , a woody species with high socio-cultural value in Benin, and assessed the demographic structure of its populations. Through a survey carried out in all phytodistricts of the Guineo-Congolian zone and the Sudano-Guinean transition zone of Benin, the species' presence was recorded with the help of the hunters and Fulani communities living in the area. We set thirty-eight plots of 1 ha (100 m× 100 m) in forests where the largest populations of the species were found. Generalized linear mixed models were used to compare the mean abundance of the species between phytodistricts and to identify underlying factors shaping observed differences. Slope analysis of the ordinary least squares regression of tree density against diameter size class distribution midpoint and the skewness coefficient were used to assess the populations' structure of the species. The species is distributed in the phytodistrict within latitude 6° 50′ - 9° 50′. These phytodistricts are the most watered phytodistricts of Benin with rainfall between 900 mm and 1300 mm per year. The abundance of the species varied from one phytodistrict to another (from 9.25 ± 2.39 to 19.5 ± 4.5 trees.ha-1) and is significantly influenced by aridity index, moisture index of the arid quarter, harvest intensity, and wildfires. An assessment of the population structure revealed a predominance of small-diameter individuals, suggesting some population stability. Long-term conservation of the species requires controlling anthropogenic pressures on its populations. [ABSTRACT FROM AUTHOR]

Events of climate change have led to increased aridification, which alters local vegetation patterns and results in the invasion of opportunistic species. Though many studies assess the impact of invasive weeds and aridification at the agronomic level, studies investigating changes in local vegetation are severely lacking. We investigated the impact of the invasive plant Verbesina encelioides (Asteraceae) on the local vegetation composition across different dryland ecosystems in Punjab, northwestern India. Based on the aridity index for the period of 1991–2016, three major dryland ecosystems, i.e., arid, semi-arid, and sub-humid, were found in Punjab. The impact of V. encelioides on local biodiversity was measured in terms of species diversity (using Shannon's diversity index, Simpson's dominance index, Hill's evenness index, and Margalef's richness index), species composition (using non-metric multidimensional scaling based on Bray-Curtis's dissimilarity index), and species proportion in the two invasion classes (uninvaded and invaded) and across the three aridity zones (arid, semi-arid, and sub-humid). The vegetation survey depicted the presence of 53 flowering species belonging to 22 families, including 30 exotics and 23 natives. Verbesina encelioides decreased species diversity and proportion, with a more pronounced impact in arid and semi-arid ecosystems. In contrast, species composition varied between uninvaded and invaded classes only in arid ecosystems. Ecological parameters derived from population statistics (number of individuals) were more drastically affected than those from species abundance data. Since the ecological impacts of V. encelioides were manifested with increased aridification, it is a matter of apprehension under the potential climate change scenario. [ABSTRACT FROM AUTHOR]

Mediterranean regions are hot spots of climate change, where the expected decrease in water resources threatens the sustainability of shrublands at their arid margins. Studying spectral vegetation indices' relationships with rainfall and potential evapotranspiration (PET) changes across Mediterranean to arid transition zones is instrumental for developing methods for mapping and monitoring the effects of climate change on desert fringe shrublands. Here we examined relationships between 17 spectral vegetation indices (VIs) and four climate and aridity measures, i.e., rainfall, PET, aridity index (AI), and water deficit (WD), calculated at accumulation lags between 1 and 6 months. For this purpose, VIs for 38 sites (100 × 100 m each) representing less disturbed areas were extracted from Sentinel 2A images for 3 years with high (2016), low (2017), and average (2018) annual rainfall. Most of the VIs had shown the highest correlation with the four climate and aridity measures at 2-month accumulation interval. While NDVI relationships with climate measures gained the widest use, our data suggest that indices combining NIR and SWIR bands better correlate with climate parameters. AI is one of the leading annual measures of dryness worldwide; when calculating it monthly, WD was found to represent better the balance between precipitation and PET across the climate transition zone and to be better correlated with VIs. Relationships between NIR and SWIR VIs and water deficit may thus facilitate improvements in monitoring and mapping desert fringe shrublands' responses to climate change if supported by similar results from other areas. [ABSTRACT FROM AUTHOR]

Due to climatological changes, a study was conducted in the Gediz Basin, Turkey, where agricultural production holds an important place. In the study prepared, 12 general circulation models (GCMs) were utilized under representative concentration pathway (RCP)4.5, RCP6.0, and RCP8.5 scenarios of the fifth assessment report (AR5) of IPCC for the period 2015–2050. The statistical downscaling methods were employed and the projections were derived right after applying the weighted-averaged ensemble mean by the Bayesian Model Averaging (BMA) method and bias correction by equidistant quantile mapping (EDQM). The temperature-based potential evapotranspiration (PET) formulas were modified in accordance with the Penman–Monteith method and the aridity indexes were calculated by UNEP's formula. According to the projections, the mean annual temperature increases between 1.5 and 2.2 °C and the mean total annual PET increases between 5 and 8% are foreseen in the Gediz Basin for the near future. It is foreseen that a semi-arid climate regime may predominate over the region for all of the RCP scenarios under the increasing dryness in basin climate. In addition, it was obtained in the study that sub-humid climate state occurrence for all of the regions included by the basin may be unexpected in the future for the RCP8.5 scenario. The presence of semi-arid climate conditions may be more potent with the increasing trend of radiative forcing over time. HIGHLIGHTS Artificial intelligence methods were performed for downscaling.; A multi-model ensemble strategy was carried out for climate projection.; A robust bias-correction method was utilized.; Climate change forecasts were integrated with aridity concepts.; Spatio-temporal aridity changes were presented for the future term.;