Aridity Index Research Articles

Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany

Abstract. Climatic variability can considerably affect catchment-scale root zone storage capacity (Sumax), which is a critical factor regulating latent heat fluxes and thus the moisture exchange between land and atmosphere as well as the hydrological response and biogeochemical processes in terrestrial hydrological systems. However, direct quantification of changes in Sumax over long time periods and the mechanistic drivers thereof at the catchment scale are missing so far. As a consequence, it remains unclear how climatic variability, such as precipitation regime or canopy water demand, affects Sumax and how fluctuations in Sumax may influence the partitioning of water fluxes and therefore also affect the hydrological response at the catchment scale. Based on long-term daily hydrological records (1953–2022) in the upper Neckar River basin in Germany, we found that variability in hydro-climatic conditions, with an aridity index IA (i.e. EP/P) ranging between ∼ 0.9 and 1.1 over multiple consecutive 20-year periods, was accompanied by deviations ΔIE between −0.02 and 0.01 from the expected IE inferred from the long-term parametric Budyko curve. Similarly, fluctuations in Sumax, ranging between ∼ 95 and 115 mm or ∼ 20 %, were observed over the same time period. While uncorrelated with long-term mean precipitation and potential evaporation, it was shown that the magnitude of Sumax is controlled by the ratio of winter precipitation to summer precipitation (p < 0.05). In other words, Sumax in the study region does not depend on the overall wetness condition as for example expressed by IA, but rather on how water supply by precipitation is distributed over the year. However, fluctuations in Sumax were found to be uncorrelated with observed changes in ΔIE. Consequently, replacing a long-term average, time-invariant estimate of Sumax with a time-variable, dynamically changing formulation of that parameter in a hydrological model did not result in an improved representation of the long-term partitioning of water fluxes, as expressed by IE (and fluctuations ΔIE thereof), or in an improved representation of the shorter-term response dynamics. Overall, this study provides quantitative mechanistic evidence that Sumax changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water (and thus latent heat) fluxes as expressed by deviations ΔIE from the parametric Budyko curve over multiple time periods with different climatic conditions. Similarly, it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment. This further suggests that accounting for the temporal evolution of Sumax with a time-variable formulation of that parameter in a hydrological model does not improve its ability to reproduce the hydrological response and may therefore be of minor importance for predicting the effects of a changing climate on the hydrological response in the study region over the next decades to come.

Microbial resistance and resilience to drought across a European climate gradient

Drought and rainfall events will become more frequent and intense with climate change. At the same time, soil moisture is one of the major factors controlling soil microbial processes such as carbon cycling. When challenged with drought there are two main growth responses microorganisms can use: (1) they can maintain growth rates during drought (i.e., resistance) and (2) they can recover growth rates faster when the drought ends (i.e., resilience). Microbial communities are shaped by multiple other factors in the soil environment, however how those impact drought responses remain unclear. Here we investigate how climate (estimated as aridity index) and soil properties determine microbial growth resistance and resilience to drought across a climate gradient in Europe. To test this, we exposed the different soils to a standardised drought cycle in controlled conditions. We assessed bacterial growth, fungal growth and respiration during soil drying to determine resistance and in high resolution during three days after rewetting to estimate resilience to drought. We found that alpha diversity was the strongest driver of both bacterial drought resistance and resilience, which occurred via changes in soil pH. This shows the importance of diversity for sustaining bacterial functions during drought stress. A secondary driver of bacterial drought resistance and resilience was the aridity index was also an important driver, where bacterial communities from more arid climates had higher resistance and resilience to drought. Fungal communities were both more resistant and resilient compared to bacteria, but this was independent of other measured environmental factors. Bacterial resilience was partly linked with differences in community composition. Our results suggest that if sites are exposed to increased aridity due to climate change or are managed to promote bacterial diversity, they will have higher bacterial growth rates during drought perturbations, which could potentially promote soil carbon storage.

Spatiotemporal Characteristics and Triggers of Flash Droughts across All the River Basins in India

Abstract Flash droughts (FDs) have attracted widespread attention in recent years due to their sudden onset and rapid intensification with significant impacts on ecosystems, water resources, and agriculture. These features of FDs pose unique challenges for their forecast, monitoring, and mitigation. The impact of FDs on society can vary depending on several factors, such as the frequency of their occurrence, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study developed a novel approach to quantitatively define FD based on the aridity index. This new approach was used to examine spatiotemporal characteristics (including trends) and triggers of FDs over 25 river basins across India from 1981 to 2021. The hydrometeorological conditions, including soil moisture percentiles, anomalies of precipitation, temperature, and vapor pressure deficit were investigated at different stages of FD. Results suggest that FDs with high intensification rates are more common in humid areas compared to subhumid and semiarid areas. Both precipitation and temperature are primary triggers of FDs over a major part of the study area. The individual effects of soil moisture and precipitation also act as a trigger across some regions (like northeast India and the Western Ghats). Additionally, atmospheric aridity can create conditions conducive to FDs, and when combined with depleted soil moisture, it can accelerate their rapid onset. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India. Significance Statement Flash droughts have attracted widespread attention due to their sudden onset and rapid intensification with significant impacts on multiple vectors. The impact of flash drought on society depends on their frequency, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study develops a novel approach to quantitatively define flash drought based on the aridity index. This new approach is used to examine spatiotemporal characteristics and triggers of flash drought over 25 river basins across India from 1981 to 2021. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.

Characteristics and drivers of the soil multifunctionality under different land use and land cover types in the drylands of China

The drylands of China cover approximately 6.6×106 km2 and are home to approximately 5.8×108 people, providing important ecosystem services for human survival and development. However, dryland ecosystems are extremely fragile and sensitive to external environmental changes. Land use and land cover (LULC) changes significantly impact soil structure and function, thus affecting the soil multifunctionality (SMF). However, the effect of LULC changes on the SMF in the drylands of China has rarely been reported. In this study, we investigated the characteristics of the SMF changes based on soil data in the 1980s from the National Tibetan Plateau Data Center. We explored the drivers of the SMF changes under different LULC types (including forest, grassland, shrubland, and desert) and used structural equation modeling to explore the main driver of the SMF changes. The results showed that the SMF under the four LULC types decreased in the following descending order: forest, grassland, shrubland, and desert. The main driver of the SMF changes under different LULC types was mean annual temperature (MAT). In addition to MAT, pH in forest, soil moisture (SM) and soil biodiversity index in grassland, SM in shrubland, and aridity index in desert are crucial factors for the SMF changes. Therefore, the SMF in the drylands of China is regulated mainly by MAT and pH, and comprehensive assessments of the SMF in drylands need to be performed regarding LULC changes. The results are beneficial for evaluating the SMF among different LULC types and predicting the SMF under global climate change.

Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina

Abstract The research subject is the application of geographic information systems (GIS) in assessing land suitability for agriculture in the Sana River Basin in Bosnia and Herzegovina. The aim of the research is to apply the analytic hierarchy process (AHP) in identifying suitable areas for agricultural production. Within the AHP framework, the following factors were considered: pedology, land use, elevation, slope, aridity index, and distance from rivers. The results of the suitability assessment underwent reclassification (RP) and cluster analysis processes (CAPs). It was found that very unsuitable land (1) covers an area of 0.15% (RP) or 5.83% (CAP), unsuitable land (2) covers 3.44% (RP) or 17.52% (CAP), conditionally suitable land (3) covers 32.11% or 28.47% (CAP), suitable land (4) covers 56.29% or 28.57% (CAP), and very suitable land (5) covers 7.98% (RP) or 19.59% (CAP). At the study area level, a supervised classification process was conducted to identify land use classes: meadows/pastures, water, forest, agricultural, and built-up areas. RP and CAP results were overlaid with supervised classification results to determine the amount of land used for agricultural purposes within each suitability class. It was determined that currently, for agricultural purposes, 0.04 km2 (RP) or 0.88 km2 (CAP) of very unsuitable land (1) is used, 0.41 km2 (RP) or 7.28 km2 (CAP) of unsuitable land (2), 15.75 km2 (RP) or 27.52 km2 (CAP) of conditionally suitable land (3), 185.15 km2 (RP) or 107.06 km2 (CAP) of suitable land (4), and 42.99 km2 (RP) or 101.65 km2 (CAP) of very suitable land (5). The research findings hold substantial importance in elucidating both the potential and constraints of land use practices as a vital natural resource within agriculture. They also have practical importance for relevant institutions in terms of agricultural sector development and making timely land use planning decisions for sustainable development.

Dynamic Stochastic Model of Atmospheric Drought Forecast in Uzbekistan

The article considers a predictive model of atmospheric dryness with a one-month lead time, developed at the Scientific Research Hydrometeorological Institute. The model is based on a dynamic stochastic approach to constructing a regression predictive equation. From the set of existing methods for constructing regression equations, a method based on the characteristic roots (eigenvalues) of a correlation matrix, including a column of the predicted variable and predictor columns (extended matrix) is applied. The predicted variable is the standardized drought index SPI, and the predictors are the average monthly precipitation for the 3 months preceding the forecast month, the average monthly value of variations in solar activity (Wolf numbers) and the average monthly value of the Southern Oscillation index for the month preceding the forecast. The predictors were selected on the basis of mutual correlation and applied time series analyses between the aridity index SPI and the indicated heliogeophysical values. The performed estimates of the studied dependence of the aridity index SPI on the state of solar activity, the influence of El Niño (La Niña) and precipitation preceding the forecast date showed their high correlation. Estimates of the accuracy of the SPI forecast with a monthly advance lead time for the territory of Uzbekistan, performed on an independent sample, were quite high, which was the basis for the introduction of this model into the operational work of the hydrometeorological service of Uzbekistan (Uzhydromet).

A Common Climate–Yield Relationship for Wheat and Barley in Japan and the United Kingdom

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.

The patterns of potential evapotranspiration and seasonal aridity under the change in climate in the upper Blue Nile basin, Ethiopia

Evapotranspiration is one of the determinant components of the hydrological process, highly influenced by climate change due to the increase in atmospheric temperature at global and regional scales. This study was designed to evaluate the extent to which climate change affects the Potential Evapotranspiration (PET) and the consequent Aridity Index (AI) in the high-emission scenario of Representative Concentration Pathways (RCPs) in the Gilgel Abay, Ribb, Gumara, and Megech watersheds using six Global Climate Models in the 2011–2040, 2041–2070, and 2071–2100 relative to the 1971–2000 (baseline period). The average PET is simulated using the Soil Water Assessment Tool (SWAT) model. Penman-Monteith and Hargreaves methods were used in the computation of PET using the water balance technique, and the Hargreaves method was found more efficient in calibration and validation processes. The Aridity Index (AI) of watersheds is calculated using the ratio of precipitation and potential evapotranspiration. The study revealed that the change in annual average PET is showing an increasing pattern in the three time periods, and the highest rate of changes in Megech, Gilgel Abay, Ribb, and Gumara, watersheds are 16.66%, 15.53%, 14.68%, and 13.46%, respectively in the 2071–2100 time period. Seasonally, the highest rate of change in PET is 20.37% (September), 19.29% (April), 17.46% (March), and 17.02% (March) in the Megech, Gilgel Abay, Ribb, and Gumara, respectively. Similarly, the seasonal highest change in Aridity Index (AI) is also likely to be observed in the 2071–2100 in which in the dry season, it accounts –0.303 (March), –0.299 (March), –0.285 (April), and –0.276 (April) in the Ribb, Gumara, Gilgel Abay, and Megech, respectively, whereas in the rainy season, the change is 0.263, 0.258, 0.238, and 0.211 in the Gilgel Abay, Gumara, Ribb, and Megech, respectively. In general, due to the rising atmospheric temperature, the amount of moisture during dry seasons in the headwater catchments of the upper Blue Nile basin is expected to deplete in the 21st century. Therefore, it is highly recommended to use different climate change adaptation mechanisms including adopting suitable physical and biological water conservation techniques to enhance the amount of water stored in the subsurface and joining the groundwater during the rainy season.

Tibetan lake change linked to large-scale atmospheric oscillations via hydroclimatic trajectory

Lakes are known as sentinels of climate change, but their responses may differ from one to another leading to different strategies in lake protection. It is particularly the case in the Tibetan Plateau (TP) of multiple hydrological processes. We employed the Budyko framework to study Tibetan lakes from two lake-basins of contrasting climates for the period between 1980 and 2022: Taro Co Basin (TCB) in a sub-arid climate, and Ranwu Lake Basin (RLB) in a sub-humid climate. Our results showed that total lake area, surface air temperature, evapotranspiration, and potential evapotranspiration increased in both lake-basins, while precipitation and soil moisture increased in the TCB but decreased in the RLB. In the Budyko space, two basins had contrast hydroclimatic trajectories in terms of aridity and evaporative index. The TCB shifted from wetting to drying trend, while the RLB from drying to wetting in early 2000s. Notably, lake change was generally consistent with the drying/wetting phases in the TCB, but in contrast with that in the RLB, which can be attributed to warming-induced glacier melting. Despite of significant correlation with the large-scale atmospheric oscillations, it turned to be more plausible if lake area changes were substituted with basin's hydroclimatic trajectories. Among the large-scale oscillations, El Niño-Southern Oscillation (ENSO) is the most dominant control of lake trends and their drying/wetting shifts. Our findings offer a valuable insight into lake responses to climate change in the TP and other regions.

Soil erosion risk in the Mocache Canton applying multicriteria analysis and geographic information systems

Soil erosion is one of the main and most widespread types of soil degradation, determined by environmental and anthropogenic factors, which significantly impact the physical, chemical and biological properties of the soil, favoring its degradation. Consequently, through this research we sought to evaluate the risk of soil erosion in the canton of Mocache through the application of multicriteria analysis techniques and the use of geographic information systems (GIS). The CORINE model included several criteria for the zoning of potential and actual erosion risk, including erosivity, defined by the Modified Fournier Index (MFI) and the Bagnouls-Gaussen Aridity Index (BGI); erodibility, composed of soil texture, stoniness and depth; soil occupation; slope-orientation; and vegetation cover, through the estimation of the Normalized Difference Vegetation Index (NDVI). An expert consultation supported by Analytic Hierarchy Process (AHP) was applied to define the contribution of each criterion to erosion risk. The resulting layers were combined using a weighted relationship (map algebra) within the GIS environment. It was obtained that slope-orientation and erosivity explain a large part of the erosion risk, with a weighting of 42.46% and 27.48% in order, according to the expert evaluation and the AHP. The high erosivity was contributed by the predominant climatic factors: intense precipitation and high temperatures. In addition, it was found that the potentialand current erosion risk represent 13.1% (7,244 ha) and 10.7% (5,905 ha) of the territorial extension. It is concluded that physical-natural, meteorological factors and anthropogenic activities contribute to the risk of potential and current soil erosion in Mocache canton

Shallow soils in dryland ecosystems: Drivers of C accumulation and land management implications

Shallow soils (i.e., Lithic Entisols) cover about 20% of Brazilian drylands. Inherent soil characteristics (i.e., shallow depths) and water scarcity restrict plant growth and carbon (C) inputs in these soils. In such a sensitive ecosystem, sustainable land management options are key to promoting socio-economic development and ensuring food security. Here, we use a dataset of 50 Lithic Entisol profiles distributed within the Brazilian drylands (northeastern region) to: (i) investigate the effects of land-use (cropland, grassland, and native forest), climate (semi-arid and dry sub-humid), and slope classes (0–3%, 3–8%, 8–20% and 20–45%) on soil C accumulation; and (ii) evaluate how understanding multiple drivers C accumulation can support the identification of sustainable land management options. The results suggested that land use does not affect C stock and C/N ratio, nevertheless, they were affected by climate. Under dry sub-humid climate conditions, C stocks were 41.7 Mg ha−1, 44% higher than under semi-arid climate (28.9 Mg ha−1), a result that reflects the effect of higher biomass production in wetter environments. Under slopes of 20–45%, C stocks were 54.3 Mg ha−1, 172% higher than under slopes of 0–3% (19.9 Mg ha−1), because of the higher altitudes and wetter conditions under steeper slopes. Our results showed that areas under lower slopes have lower C stocks and lower aridity index. These drier conditions reduce the productive potential of annual crops and grasslands but enable the cultivation of high-yielding Crassulacean Acid Metabolism (CAM) crops, such as Agave spp, which have a high potential for biofuel production. Furthermore, the possibility of reintroducing organic residues from ethanol production can promote an increase in C stocks, contributing to climate change mitigation. Ultimately, our study provides insights from a holistic view of SOC accumulation drivers, supporting land use planning of highly sensitive environments in tropical drylands around the world.

Temporal variability of aridity in Argentina during the period 1961–2020

This paper examines the temporal variability of aridity in Argentina during the period 1961–2020. Monthly data from the Climatic Research Unit (CRU) were used to define climate types according to the Aridity Index (AI) from the United Nations Environment Programme (UNEP). Argentina presents a variety of climates ranging from arid and semiarid to subhumid and humid, suggesting simultaneous water deficit and excess conditions. However, an aridity increase in the past six decades has been observed in much of the country, contrasting with reduced areas that have become wetter. Since these changes were not uniform across the study area, a regional-level analysis was conducted to adequately capture the aridity temporal patterns, including trends, jumps, seasonality, and cycles. In general, there was a decrease in the annual AI in most regions, with non-linear patterns such as long-term oscillations, shorter-duration cycles, and abrupt variations in the average. Seasonal aridity changes showed variabilities according to the season, with higher humidity during summer and autumn, and greater aridity during winter and spring. Simultaneously, seasonal AI experienced periodic behavior with significant arid and humid cycles alternating every 10–15 years. These findings highlight the complexity of climate changes, which vary by region and season.

Semi-Arid to Arid Scenario Shift: Is the Cabrobó Desertification Nucleus Becoming Arid?

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.

Response of leaf anatomical structure of Larix gmelinii to climate warming and provenance variation.

Exploring the response of leaf anatomical structure to climate warming is helpful for understanding the adaptive mechanisms of trees to climate change. We conducted a warming experiment by transplanting seedlings of Larix gmelinii from 11 provenances to two common gardens, and examined the response of leaf anatomical structure to climate warming. The results showed that warming significantly increased leaf thickness (TL), upper epidermal mesophyll thickness (TUEM), lower epidermal mesophyll thickness (TLEM), endodermal thickness (TE), vascular bundle diameter (DVB), transfer tissue thickness (TTT), and the percentage of mesophyll thickness to TL(PMT), and significantly decreased the upper epidermal thickness (TUE) and the percentage of epidermal thickness to TL (PE). The mesophyll thickness was positively associated with chlorophyll concentration and maximum net photosynthetic rate. The responses of TL, TUEM, TLEM, TE, DVB, TTT, TUE, PMT and PE to warming differed among all the provenances.As the aridity index of the original site increased, the magnitude of the warming treatment's effect decreased for TL, TUEM, TLEM, TTT and PMT, and increased for TUE and PE. Warming increased the thickness and proportion of profit tissue (e.g., mesophyll) and decreased the thickness and proportion of defensive tissue (e.g., epidermis), and those changes varied among provenances. L. gmelinii could adapt to climate warming by adjusting leaf anatomical structure, and this ability was weak for trees from provenance with high aridity index.

CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060

The “Future Global Aridity Index and PET Database” provides high-resolution (30 arc-seconds) average annual and monthly global estimates of potential evapotranspiration (PET) and aridity index (AI) for 22 CMIP6 Earth System Models for two future (2021-2041; 2041-2060) and two historical (1960-1990; 1970–2000) time periods, for each of four shared socio-economic pathways (SSP). Three multimodel ensemble averages are also provided (All; Majority Consensus, High Risk) with different level of risks linked to climate model uncertainty. An overview of the methodological approach, geospatial implementation and a technical evaluation of the results is provided. Historical results were compared for technical validation with weather station data (PET: r 2 = 0.72; AI: r 2 = 0.91) and the CRU_TS v 4.04 dataset (PET: r 2 = 0.67; AI: r 2 = 0.80). Within the context of projected significant change in the near- and medium-term, the “Future_Global_AI_PET Database” provides a set of data projections and tools available for a variety of scientific and practical applications, illustrating trends and magnitude of predicted climatic and eco-hydrological impacts on terrestrial ecosystems. The Future_Global_AI_PET Database is archived in the ScienceDB repository and available online at: https://doi.org/10.57760/sciencedb.nbsdc.00086

Stable isotopes reveal soil evaporation and its controlling factors in the Heihe River source area on the northeastern Qinghai-Tibetan Plateau

Study regionThe Heihe River source area in the Northeastern Qinghai-Tibetan Plateau, China. Study focusSoil evaporation, which is a key process in soil water loss, is influenced by various environmental factors. However, the identification of its main drivers on a large scale in alpine mountains remains challenging due to sampling constraints. This study examined the spatial distribution of soil evaporation and control factors during the growing season in the Heihe River source area. New hydrological insightsThe results indicated that soil evaporation, represented by lc-excess values, gradually increased from southeast to northwest, and then decreased, reaching a depth of 50 cm below the surface. Although the normalized difference vegetation index explained 54 % of the spatial variation in soil evaporation, the interaction between land surface temperature and soil water content (SWC) provided a more robust explanation. The soil evaporation losses exhibited the following pattern: cropland > grassland > forest land > shrubland. In forest land and shrubland, SWC and precipitation explained 68 % and 73.3 % of the spatial variations in soil evaporation, respectively. Grassland mainly relied on temperature and SWC, with 49.33 % of unexplained spatial variability by environmental factors. Meanwhile, the aridity index and aspect explained 45 % and 44.6 % of the spatial variations in cropland. These findings provided invaluable information for advancing our understanding of the ecohydrological processes in alpine mountains.

Regional Climate Change and Drought Dynamics in Tunceli, Turkey: Insights from Drought Indices

Global warming and climate change are causing temperatures to rise, which is having a negative impact on water resources. Climate change is a worldwide problem that affects and will continue to affect the frequency and intensity of natural disasters in many regions of the world. Tunceli region in Turkey, which until 10 years ago was known as an environmentally friendly city with abundant water resources and frequent rainfall, is experiencing a decrease in precipitation during the snowy winter season. This situation has made the investigation of climate change impacts an important issue in the region. Therefore, effective climate change adaptation strategies need to be developed. To determine these strategies, in this study, we assessed long-term drought conditions using multiple drought indices such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Normal Precipitation Index (PNI), and Aridity Index (AI). The SPI and RDI analyses were performed in annual reference periods on a time scale of 3, 6, 9, and 12 months using temperature, precipitation, and evaporation data. Consequently, the SPI and RDI results were compared, and both indices show similar behavior in dry, wet, and normal seasons. Nevertheless, RDI shows less variation between different time scales, which is an advantage over SPI and is probably due to the inclusion of potential evapotranspiration in RDI. The variations in PNI between humid and dry sub-humid categorizations throughout the years, combined with the AI results, indicate that the Tunceli region predominantly experiences a climate ranging from dry sub-humid to semi-arid. This study could help decision-makers take effective measures to become more resilient to climate change in temperate climate regions and take important steps toward sustainable water resources management.

Проблемы опустынивания в восточной зоне Ростовской области

The article presents an analysis of the dynamics of soil fertility, trace elements, pH, aridity index and normalized index. Salt lakes in the complex from 30-50 % occupy 68.3 thousand hectares. The humus content in the arable layer is low. There is a decrease in the content of humus (from 2.2% to 1.7%) and phosphorus (from 18.6 mg/kg to 18.2 mg). The content of exchangeable potassium is generally within the optimal range. The processes of loss of humus and nutrients in the soils of the area are a manifestation of the desertification process. The content of sulfur and trace elements (copper, zinc, manganese, cobalt) is low, insufficient for grain crops. The soils of the district are slightly alkaline (pH – 8.1). The aridity index of the Zavetinsky district is 0.41, and the normalized index is 0.59. According to the normalized aridity index – 0.59, the territory of the Zavetinsky district belongs to the moderately arid (NIA =0.5—0.6). Keywords: DESERTIFICATION, SALT LAKES, ARIDITY INDEX, SOIL FERTILITY, TRACE ELEMENTS

Assessing Drought Vulnerability in the Brazilian Atlantic Forest Using High-Frequency Data

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from using imputation methods due to potential bias. Drought quantification involved the computation of the aridity index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann–Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting that geographical location and elevation do not strongly influence observed dryness trends.

Aridity threshold of ecological restoration mitigated atmospheric drought via land‒atmosphere coupling in drylands

Land‒atmosphere coupling intensifies the vulnerability of ecosystems in drylands. However, whether and how ecological restoration would modify the land‒atmosphere coupling across drylands remains unclear. To address these gaps, here we use structural equation model to separate two pathways of land‒atmosphere coupling: vegetation and soil moisture pathways, and investigate the effect of ecological restoration in China’s drylands on land‒atmosphere coupling. Analysis reveals that, land‒atmosphere coupling regulates approximately 30% of atmospheric drought, among which soil moisture pathway contributes twice as much as vegetation pathway. Vegetation greening mitigates atmospheric drought in areas where the aridity index ranges from 0.3 to 0.5, while soil drying exacerbates atmospheric drought in areas where the aridity index ranges from 0.5 to 0.65. The findings identify the optimal regions where ecological restoration helps alleviate the vulnerability of ecosystems under anthropogenic warming. Additionally, the proposed method enhances the understanding of how restored ecosystems contribute to mitigating atmospheric drought.