Global Aridity Research Articles

Inulin biopolymer as a novel material for sustainable soil stabilization

The development of new urban areas necessitates building on increasingly scarce land, often overlaid on weak soil layers. Furthermore, climate change has exacerbated the extent of global arid lands, making it imperative to find sustainable soil stabilization and erosion mitigation methods. Thus, scientists have strived to find a plant-based biopolymer that favors several agricultural waste sources and provides high strength and durability for sustainable soil stabilization. This contribution is one of the first studies assessing the feasibility of using inulin to stabilize soil and mitigate erosion. Inulin has several agricultural waste sources, making it a sustainable alternative to traditional additives. Soil samples susceptible to wind erosion were collected from a dust-prone area in southwest Iran and treated with inulin at 0%, 0.5%, 1%, and 2% by weight. Their mechanical strength was evaluated using unconfined compressive strength tests and a penetrometer. In addition, wind tunnel tests (at 16 m/s) were performed to investigate inulin’s wind erosion mitigation potential. The durability of treated samples was evaluated after ten wetting–drying cycles to assess the effect of environmental stressors. The results indicated a 40-fold increase in the unconfined compressive strength (up to 8 MPa) of the samples treated with 2% inulin and only 0.22% weight loss after ten wetting–drying cycles. SEM images revealed the formation of biopolymer-induced particle-to-particle bonds. Moreover, Raman spectroscopy indicated molecular (hydrogen) bonding of the biopolymer hydrogel-soil particles facilitated by the hydroxyl groups of inulin. The deterioration in stiffness and strength of treated samples was less noticeable after 3rd dry–wet cycle, indicating the durability of the samples. The durability of samples against wet–dry cycles was attributed to molecular bonding of soil-biopolymer hydrogel, as revealed by FTIR analysis.

Advances in Sorghum Improvement for Climate Resilience in the Global Arid and Semi-Arid Tropics: A Review

Sorghum is a climate-resilient crop which has been cultivated as a staple food in the semi-arid areas of Africa and Asia for food and nutrition security. However, the current climate change is increasingly affecting sorghum performance, especially at the flowering stage when water availability is critical for grain filling, thus lowering the sorghum grain yield. The development of climate-resilient, biotic and abiotic stress-tolerant, market-preferred, and nutrient-dense sorghum varieties offers a potentially cost-effective and environmentally sustainable strategy for adapting to climate change. Some of the common technologies for sorghum improvement include mass selection, single seed descent, pure line selection, and marker-assisted selection, facilitated by backcrossing and genotyping using molecular markers. In addition, recent advancements including new machine learning algorithms, gene editing, genomic selection, rapid generation advancement, and recycling of elite material, along with high-throughput phenotyping tools such as drone- and satellite-based images and other speed-breeding techniques, have increased the precision, speed, and accuracy of new crop variety development. In addition to these modern breeding tools and technologies, enhancing genetic diversity to incorporate various climate resilience traits, including against heat and drought stress, into the current sorghum breeding pools is critical. This review covers the potential of sorghum as a staple food crop, explores the genetic diversity of sorghum, discusses the challenges facing sorghum breeding, highlights the recent advancements in technologies for sorghum breeding, and addresses the perceptions of farmers on sorghum production under the current climate change conditions.

Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid‐Machine Learning Model Approach

AbstractTerrestrial biosphere models offer a comprehensive view of the global carbon cycle by integrating ecological processes across scales, yet they introduce significant uncertainties in climate and biogeochemical projections due to diverse process representations and parameter variations. For instance, different soil water limitation functions lead to wide productivity ranges across models. To address this, we propose the Differentiable Land Model (DifferLand), a novel hybrid machine learning approach replacing unknown water limitation functions in models with neural networks (NNs) to learn from data. Using automatic differentiation, we calibrated the embedded NN and the physical model parameters against daily observations of evapotranspiration, gross primary productivity, ecosystem respiration, and leaf area index across 16 FLUXNET sites. We evaluated six model configurations where NNs simulate increasingly complex soil water and photosynthesis interactions against test data sets to find the optimal structure‐performance tradeoff. Our findings show that a simple hybrid model with a univariate NN effectively captures site‐level water and carbon fluxes on a monthly timescale. Across a global aridity gradient, the magnitude of water stress limitation varies, but its functional form consistently converges to a piecewise linear relationship with saturation at high water levels. While models incorporating more interactions between soil water and meteorological drivers better fit observations at finer time scales, they risk overfitting and equifinality issues. Our study demonstrates that hybrid models have great potential in learning unknown parameterizations and testing ecological hypotheses. Nevertheless, careful structure‐performance tradeoffs are warranted in light of observational constraints to translate the retrieved relationships into robust process understanding.

Cosmogenic 10Be‐ and 21Ne‐based model exposure ages of desert pavements in the Thar Desert, India

AbstractThe Thar Desert, India has desert pavements comprising angular‐subangular to well‐rounded gravels at marginally higher elevations than the surrounding terrain. Sedimentological and geomorphic analyses suggest that the pavements are lags of weathered Mesozoic and older bedrock. The presence of Palaeolithic artefacts on the pavement surfaces and occasionally within their matrix was used to infer their antiquity and landscape stability.This study presents the first surface exposure ages based on cosmic‐ray‐produced 10Be and 21Ne for pavements at four sites in the Thar Desert, viz. Bhojka, Hamira, Solanki and Jayal. The computation of model exposure ages assumed that (a) the gravels were derived from cemented conglomerates, uplifted by tectonics and thereafter disintegrated by climate, and (b) cosmogenic nuclide production in the gravels began when the conglomerates approached the surface and, continued during their disintegration, gravity sliding of individual gravels and storage, until the present. Assuming an average burial depth of 50 cm, 21Ne and 10Be data provide ages ranging from 1.30 to 2.92 Ma and 1.11 to 5.4 Ma, respectively, for the two nuclides.Published electron spin resonance ages of Thar calcretes suggest the presence of water and extreme seasonality since 1.54 Ma. Such conditions facilitated the mobilization and precipitation of carbonates. The pavement ages and the minimum age of the conglomerate at 2.51 Ma extend the time of such desertic conditions to > 2.51 Ma and suggest that the initiation of desertic conditions in the Thar was possibly linked to global aridity beginning around 3.6 Ma.Depending on assumptions, cosmic ray surface exposure (10Be) ages at Jayal range between 0.76 and 2.43 Ma. In the context of the Indian Palaeolithic, the presence of tools on the gravel surfaces and within dunes, suggests frequent occupation of this region from at least 0.76 Ma and, parallels Early to Middle Pleistocene Acheulian assemblages from Southern India.

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

Remote sensing for rangeland conservation monitoring: Impacts of livestock removal after 15 years

Abstract Degradation from overgrazing of global arid rangelands is of significant concern for this widespread terrestrial ecosystem, and the people who rely on its sustainable use. Addressing this degradation requires a better understanding of how livestock removal and conservation management actions affect the rehabilitation of land condition. However, the dominant effect of climate and meteorological variability on arid vegetation makes isolating management‐induced change a difficult task. We present an analytically simple method for measuring relative land condition by assessing differences in remote‐sensing‐derived persistent fractional vegetation between adjacent stocked and destocked regions over time. The 22‐year observation period focusses on vegetation recovery at a long‐established sheep station after the removal of livestock and the introduction of conservation‐focussed management. The study region, in the southern Australian arid rangelands, comprises sparsely vegetated chenopod shrublands and acacia woodlands across low‐lying plains. Comparison is made to immediately surrounding stocked properties in a spatial design where climate and biogeographic variables remain consistent across the study area. We found an unequivocal relative increase in vegetation cover, and reduction of bare ground cover at the destocked property that became evident approximately 8 years after a reduction in grazing pressure. Distinctly higher persistent vegetation cover was maintained, even during periods of drought. Non‐photosynthetic vegetation cover was most indicative of long‐term change and increased by 1.1% relative to surrounding areas. Photosynthetic vegetation cover increased by 0.5%, while bare ground cover decreased by 2.1%. These cover changes, particularly in the non‐photosynthetic vegetation and bare ground components spatially coincide with the fenced boundary of the destocked property. Synthesis and applications: The ability to quantify management‐related impacts in land condition using freely available remote sensing data could support improved management by all stakeholders in these regions. The method leverages readily available satellite remote sensing data in a study design that is not dependent on large geospatial and climatic datasets that are not necessarily available in remote regions.

Increasing aridity strengthens the core bacterial rhizosphere associations in the pan-palaeotropical C4 grass, Themeda triandra

Understanding belowground plant-microbial interactions is fundamental to predicting how plant species respond to climate change, particularly in global drylands. However, these interactions are poorly understood, especially for keystone grass species like the pan-palaeotropical Themeda triandra. Here, we used 16S rRNA amplicon sequencing to characterise microbiota in rhizospheres and bulk soils associated with T. triandra. We applied this method to eight native sites across a 3-fold aridity gradient (aridity index range = 0.318 to 0.903 = 87 % global aridity distribution) in southern Australia. By examining the relative contributions of climatic, edaphic, ecological, and host specific phenotypic traits, we identified the ecological drivers of core T. triandra-associated microbiota. We show that aridity had the strongest effect on shaping these core microbiotas, and report that a greater proportion of bacterial taxa that were from the core rhizosphere microbiomes were also differentially abundant in more arid T. triandra regions. These results suggest that T. triandra naturally growing in soils under more arid conditions have greater reliance on rhizosphere core taxa than plants growing under wetter conditions. Our study underscores the likely importance of targeted recruitment of bacteria into the rhizosphere by grassland keystone species, such as T. triandra, when growing in arid conditions. This bacterial soil recruitment is expected to become even more important under climate change.

Trends, turning points, and driving forces of desertification in global arid land based on the segmental trend method and SHAP model

ABSTRACT Desertification is a form of land degradation observed in arid, semiarid, and dry subhumid ecosystems. Assessing the global trends and drivers of desertification in arid land is crucial for developing effective land restoration policies and mitigating desertification. We aimed to evaluate global segmental trends in desertification in arid land using Moderate Resolution Imaging Spectroradiometer images from 2000 to 2022. By constructing a robust MSAVI-Albedo Desertification Distance Index (DDI), we assessed the segmented development characteristics of desertification on Google Earth Engine. Additionally, we employed the SHapley Additive exPlanations (SHAP) model and machine learning methods to analyze the individual and interactive driving mechanisms of desertification. The results indicated an overall reduction in desertification, with approximately 23.21 million km2 (39% of the global arid region area) exhibiting negative trends in DDI. Approximately 31% of the land area showed a DDI of −0.002/y. Precipitation was consistently the primary factor influencing desertification, with an average SHAP value of 11.42. Secondary influencing factors included potential evapotranspiration, soil moisture, and vapor pressure differences. Notably, the coupling between precipitation and soil moisture exhibited the most significant impact on the desertification process, with SHAP coupling values of approximately 3.28 and 5.06 before and after the DDI turning point, respectively. These findings provide new insights into desertification on a global scale and offer valuable scientific support for promoting effective prevention and control measures.

Recent Advances in Studies of Genomic DNA Methylation and Its Involvement in Regulating Drought Stress Response in Crops.

As global arid conditions worsen and groundwater resources diminish, drought stress has emerged as a critical impediment to plant growth and development globally, notably causing declines in crop yields and even the extinction of certain cultivated species. Numerous studies on drought resistance have demonstrated that DNA methylation dynamically interacts with plant responses to drought stress by modulating gene expression and developmental processes. However, the precise mechanisms underlying these interactions remain elusive. This article consolidates the latest research on the role of DNA methylation in plant responses to drought stress across various species, focusing on methods of methylation detection, mechanisms of methylation pattern alteration (including DNA de novo methylation, DNA maintenance methylation, and DNA demethylation), and overall responses to drought conditions. While many studies have observed significant shifts in genome-wide or gene promoter methylation levels in drought-stressed plants, the identification of specific genes and pathways involved remains limited. This review aims to furnish a reference for detailed research into plant responses to drought stress through epigenetic approaches, striving to identify drought resistance genes regulated by DNA methylation, specific signaling pathways, and their molecular mechanisms of action.

Ozone production over arid regions: insights into meteorological and chemical drivers

Arid urban areas are pivotal in the global landscape, and their air quality issues are highlighted by the complexities of tropospheric ozone production. Here, we use recent satellite observations from TROPOMI and a longer record of data from OMI to investigate the levels of ozone precursors (NO2 and CH2O) in 12 major cities in arid regions. Using a space-based CH2O/NO2 indicator, we identified the dominant chemical regime influencing ozone formation, revealing a clear temporal trend that aligns with previously reported economic trajectories as well as variation in emission control strategies implemented in these cities. Our results show that, NO2 concentrations decreased in cities with proactive regulatory policies, such as Madrid and Los Angeles in semi-arid and arid regions. A contrasting increase was observed in rapidly developing cities within arid and hyper-arid regions, such as Tehran and Cairo, where emission controls are less strict. An increase in CH2O levels was also apparent, requiring more attention to VOCs control. Furthermore, our analysis clearly shows that the interactions between ozone production and climatic factors such as temperature exhibit a nonlinear relationship, especially in arid climates. These findings highlight the importance of emission reduction strategies that consider the meteorological and chemical drivers of dry regions, particularly in light of the rising global aridity.

Dynamic simulation and projection of ESV changes in arid regions caused by urban growth under climate change scenarios.

Spatial simulation and projection of ecosystem services value (ESV) changes caused by urban growth are important for sustainable development in arid regions. We developed a new model of cellular automata based grasshopper optimization algorithm (named GOA-CA) for simulating urban growth patterns and assessing the impacts of urban growth on ESV changes under climate change scenarios. The results show that GOA-CA yielded overall accuracy exceeding 98%, and FOM for 2010 and 2020 were 43.2% and 38.1%, respectively, indicating the effectiveness of the model. The prairie lost the highest economic ESVs (192 million USD) and the coniferous yielded the largest economic ESV increase (292 million USD) during 2000-2020. Using climate change scenarios as urban future land use demands, we projected three scenarios of the urban growth of Urumqi for 2050 and their impacts on ESV. Our model can be easily applied to simulating urban development, analyzing its impact on ESV and projecting future scenarios in global arid regions.

Development of a new rotational irrigation model in an arid basin based on ecological zoning and sluice regulation

Precise and efficient allocation of existing water resources is of great significance to protect and restore desert riparian forests in inland river basins. However, the lack of detailed ecological zoning in most basins hinders the optimization of the current ecological sluice regulation scheme, leading to low effectiveness in vegetation restoration and water resources management. To address this problem, we proposed for the first time an ecological zoning method that integrates the vegetation gradient model and control of ecological sluices. Specifically, the mainstream of Tarim River basin (China) was classified into three types of ecological functional zones, i.e., key protection, key restoration, and ecologically sensitive zones, based on the kernel NDVI (kNDVI) gradient detection framework. On this basis, the basin was divided into 27 sluice group control areas determined by the control range of ecological sluices and the study area was further divided into five grades zones of ecological rotation irrigation. Based on the results of ecological zoning and the priorities of rotational irrigation, this study proposes a new rotational irrigation model in the basin to further promote vegetation recovery. The results showed that guarantee rate of ecological water supply to vegetation protection and restoration increased from 82.3% to 101.7% before and after rotational irrigation, respectively, indicating the effectiveness of this new method. The results of this study have a practical value and may significantly contribute to the optimal allocation and management of water resources in global arid basins.

Evaluation of the livability of arid urban environments under global warming: A multi-parameter approach

To support the United Nations Sustainable Development Goals, know the livability spatial distribution in the global arid regions, It is necessary to construct a comprehensive, accurate and timeliness spatial livability evaluation method. This study combined the perspective of subjective perceived environment and objective environmental supplying. Several parameters (land use type, traffic accessibility, vegetation coverage, available water resources, terrain (slope, altitude, and topographic relief) and land surface temperature) were chosen to form the livability evaluation system for arid urban environment. A case study was carried out based on Yinchuan City. Livability was calculated pixel by pixel and the study area was divided into five grades: Least livable, Unlivable, Generally livable, Relatively livable, and Livable. Results show that the influence of indexes on livability was as follows: LST > LUT > TA > VC > AWR >A >S > TR. Moreover, the spatial distribution of livability is highly consistent with the corresponding population distribution. The surface thermal environment of cities may decrease human comfort, which agrees with the urban development features of Yinchuan. The proposed method is demonstrated to be robust and reliable and can provide new ideas for better construction of urban environments.

Mapping center pivot irrigation systems in global arid regions using instance segmentation and analyzing their spatial relationship with freshwater resources

Ensuring freshwater availability and supply sufficient for socio-economic development is important for human health and productivity. Globally, increasing industrial agriculture is driving crop productivity, with increasing surface water and groundwater being withdrawn for irrigation. This study mapped center pivot irrigation systems (CPIS), which are indicators of intensified industrial agriculture and large-scale investment in agriculture, with satellite data using a convolutional neural network in global arid areas. A cascade instance segmentation network was adopted, and both the convolutional block attention module technique and PointRend technique were used to increase the performance of off-the-shelf algorithms. Geospatial analysis methods were used to derive the relationships between CPIS and freshwater resources. Overall there were about 10.26 million hectares (Mha) of irrigated areas equipped with CPIS in global arid areas in 2021, and there were 4.41 Mha of CPIS-equipped irrigated fields added in the past twenty years at a relative increase of 75% compared with those at the beginning of this century. Although the largest extent of increased CPIS-equipped irrigated fields was in North America, the other five continents had higher relative increases, with Africa having the highest relative growth of 253%. The analysis of spatial relationships between CPIS and freshwater resources indicates that the stress on freshwater resources from industrial agriculture is intensifying in arid areas globally. Overall, the world's arid regions with the highest water stress from CPIS are mostly located in western North America, northern Africa, and the Arabian Peninsula. The results facilitate a better understanding of the current status of freshwater stress and ecosystem sustainability in relation to the development of agricultural intensification in global arid areas. The outcomes support governments to judge better the environmental consequences of agricultural modernization and further help them make balanced agriculture and water management policies.

Spatiotemporal characteristics and driving mechanisms of PM10 in arid and semi-arid cities of northwest China

PM10 pollution is a pressing ecological concern within arid and semi-arid cities, adversely influencing the natural environment and human health. Based on the Geographically and Temporally Weighted Regression (GTWR) model, this study comparatively investigated the spatiotemporal dynamics of PM10 during non-dust and dust periods in five arid cities (Jiuquan, Jiayuguan, Zhangye, Jinchang, and Wuwei) and one semi-arid city (Lanzhou) in northwest China. Further, this study examined the critical mechanisms of PM10 via the GTWR and Geographic Detector model. A comparative analysis illustrated that dust period PM10 concentrations surpassed non-dust period concentrations. The annual average PM10 concentrations during the non-dust period wavily declined from 2015 to 2020, exhibiting significantly higher levels in the semi-arid city as opposed to arid cities. Conversely, during the dust period, PM10 levels rose initially between 2015 and 2017 and diminished from 2018 to 2020, with high concentration clusterings primarily observed in 2015–2016 in western arid cities, Jiuquan and Jiayuguan, in 2017–2019 in eastern cities, Wuwei and Lanzhou, and in 2020 in central arid cities, Jinchang and Zhangye. During the non-dust period from 2015 to 2020, the cumulative effects of single driving factors were ranked in the following order: soil moisture content (MOI), geographical location (LOC), precipitation (PRE), SO2, gross domestic product (GDP), wind speed (WS), and digital elevation (DEM) while during the dust period, the emphasized factors were ranked in the following order: MOI, LOC, PRE, WS, SO2, GDP, and DEM. The interaction between MOI and WS on PM10 was most noticeable in this study (non-dust: 0.787, dust: 0.832). Thus, MOI and WS are critical components in controlling PM10 pollution. The outcomes of this study can furnish theoretical foundations for ecological construction in Northwest China, unlock novel insights for ecological measures in other global arid and semi-arid cities.

Water use strategy of nitraria tangutorum shrubs in ecological water delivery area of the lower inland river: Based on stable isotope data

Excessive water use in the upper and middle reaches of arid inland river basins will inevitably lead to the drying up of the lower reaches. Water allocation and ecological water delivery have been widely used as water resources management strategies in global arid regions, and the implementation of such strategies has a great impact on the downstream ecosystem. We established an ecohydrological observation system at the lower reaches of the Shiyang River, an inland river in the arid region of China. The stable hydrogen and oxygen isotope technique was used to study the influence mechanism of artificial water delivery on the water use strategy of nitraria tangutorum shrubs, the typical dominant species around Qingtu Lake. The results showed that: (1)12.33% of the water used by the dominant species in the terminal lake area came directly from ecological water delivery, 77.88% from soil water formed by water delivery, and only 9.79% from precipitation. (2)With the increase of distance, nitraria tangutorum shrubs absorbed less surface soil water and lake water, and gradually turned to using deeper soil water and precipitation. (3) Ecological water delivery cycles affected the water use strategies of nitraria tangutorum shrubs. The proportion of deep soil water used by plants increased in the non-water delivery period, and the proportion of precipitation absorbed by plants also increased. Thus, Vegetation in the terminal lake region mainly depended on ecological water use, and the amount and time of water delivery directly determined the water use of dominant species. The results help us to understand the adaptation mechanism of plants under the background of artificial water delivery in the low reaches of arid inland river basins and provide a reference for the rational use of water resources and ecological construction.

Spatiotemporal variation of marsh vegetation productivity and climatic effects in Inner Mongolia, China

Net primary productivity (NPP) is a vital ecological index that reflects the ecological function and carbon sequestration of marsh ecosystem. Inner Mongolia has a large area of marshes, which play a crucial role in the East Asian carbon cycle. Under the influence of climate change, the NPP of Inner Mongolian marsh has changed significantly in the past few decades, but the spatiotemporal variation in marsh vegetation NPP and how climate change affects marsh NPP remain unclear. This study explores, for the first time, the spatiotemporal variation of marsh NPP and its response to climatic change in Inner Mongolia based on the MODIS-NPP and climate datasets. We find that the long-term average annual NPP of marsh is 339.85 g⋅C/m2 and the marsh NPP shows a significantly increasing trend (4.44 g⋅C/m2/a; p < 0.01) over Inner Mongolia during 2000–2020. Spatially, the most prominent increase trend of NPP is mainly distributed in the northeast of the region (Greater Khingan Mountains). The partial correlation results show that increasing autumn and summer precipitation can increase the NPP of marsh vegetation over Inner Mongolia. Regarding the temperature effects, we observe a strong asymmetric effect of maximum (Tmax) and minimum (Tmin) temperature on annual NPP. A high spring Tmax can markedly increase marsh NPP in Inner Mongolia, whereas a high Tmin can significantly reduce it. In contrast to spring temperature effects on NPP, a high summer Tmax can decrease NPP, whereas a high Tmin can increase it. Our results suggest different effects of seasonal climate conditions on marsh vegetation productivity and highlight the influences of day-time and night-time temperatures. This should be considered in simulating and predicting marsh carbon sequestration in global arid and semi-arid regions.

Spatiotemporal Evolution of Arid Ecosystems Using Thematic Land Cover Products

The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land cover change (LCC) were calculated, i.e., regional change intensity (RCI), rate of change in land cover (CR), evolutionary direction index (EDI), and artificial change percentage (ACP), to progressively derive the intensity, rate, evolutionary direction, and anthropogenic interferences of global arid ecosystems. The LCC from 1992 to 2020 and from 28 consecutive pair-years was observed at the global, continental, and country scales to examine spatiotemporal evolution in the Earth’s arid ecosystems. The following main results were obtained: (1) Global arid ecosystems experienced positive evolution despite complex LCCs and anthropogenic interferences. Cautious steps to avoid potential issues caused by rapid urbanization and farmland expansion are necessary. (2) The arid ecosystems in Australia, Central Asia, and southeastern Africa generally improved, as indicated by EDI values, but those in North America were degraded, with 41.1% of LCCs associated with urbanization or farming. The arid ecosystems in South America also deteriorated, but 83.4% of LCCs were in natural land covers. The arid ecosystems in Europe slightly improved with overall equivalent changes in natural and artificial land covers. (3) Global arid ecosystems experienced three phases of change based on RCI values: ‘intense’ (1992–1998), ‘stable’ (1998–2014), and ‘intense’ (2014–2020). In addition, two phases of evolution based on EDI values were observed: ‘deterioration’ (1992–2002) and ‘improvement’ (2002–2020). The ACP values indicated that urbanization and farming activities contributed increasingly less to global dryland change since 1992. These findings provide critical insights into the evolution of global arid ecosystems based on analyses of LCCs and will be beneficial for sustainable development of arid ecosystems worldwide within the context of ongoing climate change.

A global drought-aridity index: The spatiotemporal standardized precipitation evapotranspiration index

Climate change has increased the risk of drought in both arid and non-arid regions of the globe. Therefore, in order to monitor and evaluate global drought and aridity,based on the principle of standardized precipitation evapotranspiration index (SPEI), this paper selected precipitation and potential evapotranspiration data and used a frequency calculation method to develop a multi-scale global drought aridity index (GDAI) that concurrently considers spatial and temporal variation characteristics: the spatiotemporal standardized precipitation evapotranspiration index. The index was compared with the aridity index (AI) and SPEI. The results showed that the correlation coefficients between GDAI and AI were greater than 0.8 in most areas, and the relationship was closer and could reveal the global aridity and wetness zoning. In addition, the correlation between GDAI and SPEI was also better, which could capture the temporal change characteristics of drought. Moreover, this study also compared the grades of GDAI and SPEI with the actual drought grades in Yunnan from September 2009 to August 2010, and found that GDAI was better than SPEI, and GDAI was the most accurately evaluated at the 3 month time scale, with an accuracy rate of 86.11%. Therefore, this index is more helpful for the assessment and monitoring of global drought and aridity.

An overall consistent increase of global aridity in 1970–2018

An overall consistent increase of global aridity in 1970–2018