Dryland changes under different levels of global warming

A novel index for vegetation drought assessment based on plant water metabolism and balance under vegetation restoration on the Loess Plateau

Vegetation is vital to the ecosystem, contributing to the global carbon balance, but susceptible to the impacts of climate change. Monitoring vegetation drought remains challenging due to the lack of widely accepted drought indices. This study focused on vegetation, and simulated the vegetation suitable water demand and soil available water supply (calculated by Remote-sensing-based Water Balance Assessment Tool model). The standardized Vegetation Water deficit Index (SVWDI) was established by calculating the vegetation water deficit, which reflects the response of vegetation to drought. We examined the spatiotemporal evolution of vegetation drought on the Loess Plateau and evaluated the applicability of standardized vegetation water deficit index. Our findings revealed that the standardized vegetation water deficit index demonstrated an overall upward trend across different time scales from 1991 to 2020. Drought conditions were concentrated in the first 20 years of the study period, but vegetation drought on the Loess Plateau has been alleviated in the past decade. Moreover, as the time scale extended, the trend of SVWDI generally decreased, with approximately 49.50 % (1-month scale), 46.66 % (3-month scale), 47.08 % (12-month scale), and 32.16 % (24-month scale) of the grid areas experiencing increased SVWDI. The correlation between SVWDI and tree-ring width index (TRWI) performed well under all precipitation gradients, but the Palmer drought severity index (PDSI) was only highly correlated with TRWI in regions with low precipitation. In terms of the relationship with vegetation health, SVWDI demonstrated the highest correlation with the normalized difference vegetation index (NDVI) across different time scales, followed by PDSI and standardized precipitation evapotranspiration index (SPEI). This study provides insights into the evolution of vegetation drought in response to climate change. The findings can guide initiatives such as returning farmland to forest and grassland on the Loess Plateau to aid climate change adaptation strategies.

Improvement of evapotranspiration simulation study in the Hailar River basin under the influence of vegetation dynamics

In arid and semi-arid areas with <400 mm of precipitation, evapotranspiration (ET) accounts for about 80% of precipitation and is the main water consumer in the watershed. However, vegetation greening in recent years will increase ET and exacerbate the aridity of the area by affecting soil moisture in the root system. Vegetation changes are regional and spatially heterogeneous, therefore, in order to characterize ET changes under vegetation dynamics, it is necessary to expand the spatial scale of ET simulation. However, widely used evapotranspiration simulation models, such as the Shuttleworth-Wallace model (SW model), are deficient in reflecting the direct and indirect effects of vertical (i.e., soil depths) and horizontal (i.e., vegetation dynamics) directions. Based on field sampling and constructed structural equation model (SEM), we found that vegetation dynamics affect evapotranspiration not only directly, but also indirectly by affecting soil moisture at different depths. On this basis, we defined the weighting coefficients of 0.85 and 0.15 for grassland vegetation zones, 0.3, 0.15, 0.20, 0.25, 0.10 for forest-grass interspersed zones, and 0.20, 0.55, 0.25 for forested zones, respectively, based on the SEM results. Different soil moisture weighting coefficients were defined within different vegetation type zones and the improved SW model is called S-W-α. Comparing the simulation results with the measured data, S-W-α improved the ET simulation accuracy in this region by 33.92% and the improved ET spatial trend can respond to the dynamic changes of vegetation. Replacing the ET module in the Block-wise use of TOPMODEL and Muskingum-Cunge method mode (BTOP model) with the modified S-W-α, the results show that the simulation accuracy of the improved model is increased by 25%, and the Nash is higher than 75% for both the rate period and the validation period, which realizes the extension of the model from the point scale to the basin scale. The modified model may provide technical support for simulation of evapotranspiration and management of ecosystem health in ecologically fragile areas.

Climate change and geo-environmental factors influencing desertification: a critical review

The problem of desertification (DSF) is one of the most severe environmental disasters which influence the overall condition of the environment. In Rio de Janeiro Earth Summit on Environment and Development (1922), DSF is defined as arid, semi-arid, and dry sub-humid induced LD and that is adopted at the UNEP's Nairobi ad hoc meeting in 1977. It has been seen that there is no variability in the trend of long-term rainfall, but the change has been found in the variability of temperature (avg. temp. 0-5 °C). There is no proof that the air pollution brought on by CO2 and other warming gases is the cause of this rise, which seems to be partially caused by urbanization. The two types of driving factors in DSF-CC (climate change) along with anthropogenic influences-must be compared in order to work and take action to stop DSF from spreading. The proportional contributions of human activity and CC to DSF have been extensively evaluated in this work from "qualitative, semi-quantitative, and quantitative" perspectives. In this study, we have tried to connect the drives of desertification to desertification-induced migration due to loss of biodiversity and agriculture failure. The authors discovered that several of the issues from the earlier studies persisted. The policy-makers should follow the proper SLM (soil and land management) through using the land. The afforestation with social forestry and consciousness among the people can reduce the spreading of the desertification (Badapalli et al. 2023). The green wall is also playing an important role to reduce the desertification. For instance, it was clear that assessments were subjective; they could not be readily replicated, and they always relied on administrative areas rather than being taken and displayed in a continuous space. This research is trying to fulfill the mentioned research gap with the help of the existing literatures related to this field.

Dryland microbiomes reveal community adaptations to desertification and climate change

Drylands account for 45% of the Earth's land area, supporting ~40% of the global population. These regions support some of the most extreme environments on Earth, characterized by extreme temperatures, low and variable rainfall, and low soil fertility. In these biomes, microorganisms provide vital ecosystem services and have evolved distinctive adaptation strategies to endure and flourish in the extreme. However, dryland microbiomes and the ecosystem services they provide are under threat due to intensifying desertification and climate change. In this review, we provide a synthesis of our current understanding of microbial life in drylands, emphasizing the remarkable diversity and adaptations of these communities. We then discuss anthropogenic threats, including the influence of climate change on dryland microbiomes and outline current knowledge gaps. Finally, we propose research priorities to address those gaps and safeguard the sustainability of these fragile biomes.

Temperature vegetation dryness index (TVDI) for drought monitoring in the Guangdong Province from 2000 to 2019

Drought monitoring is crucial for assessing and mitigating the impacts of water scarcity on various sectors and ecosystems. Although traditional drought monitoring relies on soil moisture data, remote sensing technology has have significantly augmented the capabilities for drought monitoring. This study aims to evaluate the accuracy and applicability of two temperature vegetation drought indices (TVDI), TVDINDVI and TVDIEVI, constructed using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) vegetation indices for drought monitoring. Using Guangdong Province as a case, enhanced versions of these indices, developed through Savitzky-Golay filtering and terrain correction were employed. Additionally, Pearson correlation analysis and F-tests were utilized to determine the suitability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in correlation with TVDINDVI and TVDIEVI. The results show that TVDINDVI had more meteorological stations passing both significance test levels (P < 0.001 and P < 0.05) compared to TVDIEVI, and the average Pearson'R correlation coefficient was slightly higher than that of TVDIEVI, indicating that TVDINDVI responded better to drought in Guangdong Province. Our conclusion reveals that drought-prone regions in Guangdong Province are concentrated in the Leizhou Peninsula in southern Guangdong and the Pearl River Delta in central Guangdong. We also analyzed the phenomenon of winter-spring drought in Guangdong Province over the past 20 years. The area coverage of different drought levels was as follows: mild drought accounted for 42% to 64.6%, moderate drought accounted for 6.96% to 27.92%, and severe drought accounted for 0.002% to 1.84%. In 2003, the winter-spring drought in the entire province was the most severe, with a drought coverage rate of up to 84.2%, while in 2009, the drought area coverage was the lowest, at 49.02%. This study offers valuable insights the applicability of TVDI, and presents a viable methodology for drought monitoring in Guangdong Province, underlining its significance to agriculture, environmental conservation, and socio-economic facets in the region.

Vegetation as the catalyst for water circulation on global terrestrial ecosystem

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water‑carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

New Functional Foods with Cactus Components: Sustainable Perspectives and Future Trends

The growing interest in a healthy lifestyle has contributed to disseminating perspectives on more sustainable natural resource management. This review describes promising aspects of using cacti in the food industry, addressing sustainable, nutritional, and functional aspects of the plant's production. Our study provides an overview of the potential of cacti for the food industry to encourage the sustainable cultivation of underutilized cactus species and their commercial exploitation. The commercial production of cacti has advantages over other agricultural practices by mitigating damage to ecosystems and encouraging migration to sustainable agriculture. The application of cactus ingredients in food development has been broad, whether in producing breads, jellies, gums, dyes, probiotics, and postbiotic and paraprobiotic foods. However, in the field of probiotic foods, future research should focus on technologies applied in processing and researching interactions between probiotics and raw materials to determine the functionality and bioactivity of products.

Dust emission response to precipitation and temperature anomalies under different climatic conditions

The dust-drought nexus has received more attention in recent years, particularly in light of global warming. This study aims to better understand the interacting role of temperature and precipitation climatology on global dust emission. Simulated global arid regions' dust emissions from 11 CMIP6 Earth System Models (ESMs) (65 years from 1950 to 2014) and MERRA2 reanalysis (41 years from 1980 to 2020) are analyzed. We calculate dust emission sensitivity to precipitation and temperature using multiple linear regression with near-surface (10 m) wind speed, precipitation, and 2 m air temperature as predictors. The results show that simulated dust emission from most models is significantly correlated with precipitation and temperature over large arid dust source regions. All ESMs show both positive and negative dust emission sensitivity to precipitation (β_P) and temperature (β_T). The climatological mean state affects the strength of the relationships. The precipitation sensitivity is larger over the cold regions while the temperature sensitivity is larger in wet regions. The precipitation sensitivity symmetrically increases with latitude due to the variability of temperature with latitude. Future changes to mean temperature and precipitation have competing effects on dust emission and are likely to alter the hemispheric balance of atmospheric dust.

The role of climate, vegetation, and soil factors on carbon fluxes in Chinese drylands

Drylands dominate the trend and variability of the land carbon (C) sink. A better understanding of the implications of climate-induced changes in the drylands for C sink-source dynamics is urgently needed. The effect of climate on ecosystem C fluxes (gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP)) in drylands has been extensively explored, but the roles of other concurrently changing factors, such as vegetation conditions and nutrient availability, remain unclear. We used eddy-covariance C-flux measurements from 45 ecosystems with concurrent information on climate (mean annual temperature (MAT) and mean annual precipitation (MAP)), soil (soil moisture (SM) and soil total nitrogen content (soil N)), and vegetation (leaf area index (LAI) and leaf nitrogen content (LNC)) factors to assess their roles in C fluxes. The results showed that the drylands in China were weak C sinks. GPP and ER were positively correlated with MAP, while they were negatively correlated with MAT. NEP first decreased and then increased with increasing MAT and MAP, and 6.6 °C and 207 mm were the boundaries for the NEP response to MAT and MAP, respectively. SM, soil N, LAI, and MAP were the main factors affecting GPP and ER. However, SM and LNC had the most important influence on NEP. Compared with climate and vegetation factors, soil factors (SM and soil N) had a greater impact on C fluxes in the drylands. Climate factors mainly affected C fluxes by regulating vegetation and soil factors. To accurately estimate the global C balance and predict the response of ecosystems to environmental change, it is necessary to fully consider the discrepant effects of climate, vegetation, and soil factors on C fluxes, as well as the cascade relationships between different factors.

Relations between physical and ecosystem service flows of freshwater are critical for water resource security in large dryland river basin

Freshwater ecosystem services are the link between ecological systems and social systems, which is an important guarantee of the freshwater safety particularly in dryland regions. However, more quantitative research has been based on the freshwater ecosystem services of static situations, and less on the flow conditions. We established a comprehensive modeling framework for the analysis of water security pattern based on the physical flow (PF) and ecosystem service flow (ESF) of freshwater. The results for Yellow River Basin showed that the water-scarce area have reduced in the past two decades. The PF of freshwater relieves water stress on an average of 52.1 % of the static water in scarce areas per year. The problem in water-deficient areas meanly lies on the water supply side. These results highlight the importance of PF from the upstream to downstream, which is critical for formulating sustainable management strategies in safeguarding long-term regional freshwater resource security.

Assessment of the spatiotemporal characteristics of vegetation water use efficiency in response to drought in Inner Mongolia, China

Ecosystem water use efficiency (eWUE) can be used to obtain a better comprehension of the ecosystem water-carbon cycle. This study aimed to characterize the regional-scale responses and adaptations of different vegetation categories to drought changes and the spatiotemporal characteristics of WUE and associated drought factors for nine vegetation categories in Inner Mongolia, China, from 2000 to 2020. This study estimated drought, the association between drought and eWUE among varying vegetation categories, and the differences in eWUE between the drought stage and the post-drought stage by analyzing the spatiotemporal variations in eWUE of different vegetation categories based on MODIS ET (evapotranspiration), GPP (gross primary productivity), and temperature vegetation drought index data. The results illustrated the following: (1) the multi-year mean eWUE from 2000 to 2020 was 1.03 g·m^-2·mm^-1, with an overall significantly increasing trend of 0.008 g·m^-2·mm^-1 and eWUE decreasing from northeast to southwest. (2) The rank of vegetation types in Inner Mongolia according to multi-year mean eWUE was evergreen coniferous forest > savanna > evergreen broadleaf forest > forested grassland > farmland > deciduous broadleaf forest > mixed forest > closed scrub > grassland. All vegetation categories illustrated an increasing trend in eWUE over time. (3) eWUE was inversely associated with drought in the drought stage and a clear effect of drought legacy was identified in which harsh drought impacted the eWUE of the ecosystem, whereas eWUE was positively associated with drought. (4) The eWUE values of ecosystems increased significantly after drought, indicating that ecosystems that are adapted to drought show high capacity to recovery from drought stress.

Potential distribution of three types of ephemeral plants under climate changes

BACKGROUND

Arid and semi-arid regions account for about 40% of the world's land surface area, and are the most sensitive areas to climate change, leading to a dramatic expansion of arid regions in recent decades. Ephemeral plants are crucial herbs in this area and are very sensitive to climate change, but it is still unclear which factors can determine the distribution of ephemeral plants and how the distribution of ephemeral plants responds to future climate change across the globe.

AIMS

Understanding the impact of climate change on ephemeral plant distribution is crucial for sustainable biodiversity conservation.

METHODS

This study explored the potential distribution of three types of ephemeral plants in arid and semi-arid regions (cold desert, hot desert, and deciduous forest) on a global scale using the MaxEnt software. We used species global occurrence data and 30 environmental factors in scientific collections.

RESULTS

Our results showed that (1) the average value of the area under the receiver operating curve (AUC) of each species was higher than 0.95, indicating that the MaxEnt model's simulation accuracy for each species was good; (2) distributions of cold desert and deciduous forest species were mainly determined by soil pH and annual mean temperature; the key factor that determines the distribution of hot desert species was precipitation of the driest month; and (3) the potential distribution of ephemeral plants in the cold desert was increased under one-third of climate scenarios; in the hot desert, the potential suitable distribution for Anastatica hierochuntica was decreased in more than half of the climate scenarios, but Trigonella arabica was increased in more than half of the climate scenarios. In deciduous forests, the ephemeral plant Crocus alatavicus decreased in nearly nine-tenths of climate scenarios, and Gagea filiformis was increased in 75% of climate scenarios.

CONCLUSIONS

The potential suitable distributions of ephemeral plants in the different ecosystems were closely related to their specific adaptation strategies. These results contribute to a comprehensive understanding of the potential distribution pattern of some ephemeral plants in arid and semi-arid ecosystems.

Hotspots, co-occurrence, and shifts of compound and cascading extreme climate events in Eurasian drylands

Eurasian drylands are the regions that are most vulnerable to climate change. Climate extremes have caused enormous or even devastating impacts on ecosystems and the social economy in this region, and the compound climate extremes (com_CEs, two or more extreme events occurring simultaneously) and cascading climate extremes (cas_CEs, two or more extreme events occurring successively) have exacerbated these problems. However, little is known about the occurrence patterns of com_CEs and cas_CEs in the Eurasian drylands. Based on the ERA5 reanalysis data range from 1979 to 2020, we improved the methodology for the extraction of co-occurrence events and identified high-frequency types, their hotspots, and occurrence rhythms (seasonally and annually) in Eurasian drylands. Our results showed that com_CEs and cas_CEs have high similarities in the types and spatial hotspots of extreme events; however, the former has a wider geographical and spatial distribution, and the latter has a longer duration. Specifically, co-occurring drought and heatwave events (DH) frequently appear in South Asia and western mid-latitude regions during summer, while in the winter, high latitude regions should be alert to the co-occurrence of drought and low-temperature events (DT). Central Asia and the Mongolian Plateau regions are prone to frequent drought and wind events (DW), and wind and high precipitation events (WP) in the spring and autumn. We have noticed that mid-latitude may suffer from extreme events that have never occurred before, such as com_DH being scattered sporadically in the first two decades and suddenly surging in West Asia and East Asia after the year 2000, and com_DT migrating from high-latitude areas such as the Arctic Ocean coast to mid-latitudes. Our results contribute to understanding hotspots of co-occurring CEs in Eurasian drylands, where more efforts will be needed in the future, especially in mid-latitudes which may suffer extreme climate events that have never occurred before.

Limited climate change mitigation potential through forestation of the vast dryland regions

Forestation of the vast global drylands has been considered a promising climate change mitigation strategy. However, its actual climatic benefits are uncertain because the forests' reduced albedo can produce large warming effects. Using high-resolution spatial analysis of global drylands, we found 448 million hectares suitable for afforestation. This area's carbon sequestration potential until 2100 is 32.3 billion tons of carbon (Gt C), but 22.6 Gt C of that is required to balance albedo effects. The net carbon equivalent would offset ~1% of projected medium-emissions and business-as-usual scenarios over the same period. Focusing forestation only on areas with net cooling effects would use half the area and double the emissions offset. Although such smart forestation is clearly important, its limited climatic benefits reinforce the need to reduce emissions rapidly.

Conceptualization of arid region radioecology strategies for agricultural ecosystems of the United Arab Emirates (UAE)

Since the last decade, ambitious nuclear power programmes have begun maturing in the Arab countries, most importantly in the United Arab Emirates (UAE). The region's environment and population, therefore, are prone to adverse, long-term impacts of radionuclide discharges. To calculate the associated exposure scenarios, to estimate doses and their consequences, and finally, to lay out a radiological emergency management plan, arid region radioecology is taking shape in the UAE as a major field of research. Geography, demography, food habits, weather, soil, water, flora, and fauna of the desert-marine regions are quite distinct from their temperate counterparts. This results in the need to increase the knowhows of environmental migration and bioaccumulation of radionuclides in the region's agricultural ecosystems. In this paper, we present a detailed review of the measured data from the UAE and nearby nations, generating insights for the soil radioactivity and soil-to-plant transfer phenomena under local arid conditions. In the literature, the radionuclide activity concentrations (in Bq kg^-1) vary over five orders of magnitude depending on nuclide type, sample type, and locality. Variabilities over six orders of magnitude are observed for transfer parameters too, and in-depth studies on the transfer mechanisms are usually scarce. We discuss the recent progresses made in root and foliar uptake studies with methods relatively new to sandy soils, like controlled potting, and soil K_d measurements. Showing the serious gaps in the data and interpretations, we provide a justification for the immediate experimentation in the understudied aspects of radioecology in the UAE and in arid lands in general.

How Do Taxonomic and Functional Diversity Metrics Change Along an Aridity Gradient in a Tropical Dry Forest?

Ecological indicators based on biodiversity metrics are valuable and cost-effective tools to quantify, track and understand the effects of climate change on ecosystems. Studying changes in these indicators along climatic gradients in space is a common approach to infer about potential impacts of climate change over time, overcoming the limitations of lack of sufficiently long time-series data. Here, we studied the response of complementary biodiversity metrics in plants: taxonomic diversity (species richness and Simpson index) and functional diversity (diversity and redundancy) in 113 sampling sites along a spatial aridity gradient (from 0.27 to 0.69 of aridity index-AI) of 700 km in a Tropical dry forest. We found different responses of taxonomic and functional diversity metrics to aridity. Species diversity showed a hump-shaped curve peaking at intermediate levels of aridity between 0.38 and 0.52 AI as an ecotone, probably because it is where most species, from both drier and more mesic environments, still find conditions to co-exist. Functional diversity showed a positive linear relation with increasing aridity, suggesting higher aridity favors drought-adapted species with diverse functional traits. In contrast, redundancy showed a negative linear relation with increasing aridity, indicating that drier sites have few species sharing the same functional traits and resource acquisition strategies. Thus, despite the increase in functional diversity toward drier sites, these communities are less resilient since they are composed of a small number of plant species with unique functions, increasing the chances that the loss of one of such "key species" could lead to the loss of key ecosystem functions. These findings show that the integration of complementary taxonomic and functional diversity metrics, beyond the individual response of each one, is essential for reliably tracking the impacts of climate change on ecosystems. This work also provides support to the use of these biodiversity metrics as ecological indicators of the potential impact of climate change on drylands over time.

Key Areas of Ecological Restoration in Inner Mongolia Based on Ecosystem Vulnerability and Ecosystem Service

Inner Mongolia is located in China’s arid and semi-arid regions, with sensitive and fragile ecosystems at risk of increased desertification, necessitating ecological restoration. However, economic resources for large-scale ecological restoration are often scarce, so it is vital to identify key areas for ecological restoration. Previous desertification research has focused mainly on the condition and changes in soil or vegetation. However, not all changes in soil or vegetation directly impact humans. New perspectives are increasingly needed to bridge the gap between biophysical and human well-being. We construct a framework to identify priority restoration areas based on ecosystem services and ecosystem vulnerability over a long time series. The results show that: (1) soil conservation services in northeast and southwest Inner Mongolia have degraded. Sand fixation services in central and eastern Inner Mongolia have shown a degradation trend. Habitat quality has been generally stable and sporadic in the past 20 years. (2) The areas with higher ecosystem vulnerability are concentrated in the northeast, mainly due to higher climate exposure and climate sensitivity but relatively lower climate resilience in the northeast. (3) Compared with the results of ecological restoration areas identified based on the trends of traditional vegetation indicators (fractional vegetation cover and net primary productivity), we found a greater proportion of land in northeastern Inner Mongolia in need of restoration. Additionally, there was identified a decreased restoration proportion in southwestern Inner Mongolia to ensure the self-restoration and regulation of desert ecosystems, which is conducive to realizing nature-based solutions.

Soil microbial communities of dryland legume plantations are more complex than non-legumes

Soil microorganisms play a crucial role in the vegetation restoration of dryland plantations and participating in biogeochemical cycles. However, how the co-occurrence networks of soil microbial communities respond to dryland legume and non-legume plantations is unclear. Here, we conducted a comparative analysis of legume (13-, 35-, and 55-years Caragana korshinskii) and non-legume (13- and 55-years Platycladus orientalis) plantations, including plant communities, soil physicochemical properties, and soil microbial communities, in the west of the Loess Plateau, China. The results showed higher richness and diversity, more keystone taxa and positive relationships, and larger connectivity and potential functions existed in soil bacterial and fungal communities of legume plantations. Meanwhile, richer plant communities and higher soil nutrients in legume plantations were found than those in non-legume plantations. We revealed that legume plantations shaped a more complex co-occurrence network, forming a virtuous cycling of "plant-soil-microbe" continuum in legume plantation ecosystems. Our results provided a new perspective on evaluating the ecological value and plantation stability of legume tree species in the vegetation restoration engineering of drylands.

Minimum Rainfall Inter-Event Time to Separate Rainfall Events in a Low Latitude Semi-Arid Environment

Water scarcity in dry tropical regions is expected to intensify due to climate change. Characterization of rainfall events is needed for a better assessment of the associated hydrological processes, and the proposition of adaptation strategies. There is still no consensus on the most appropriate method to separate rainfall events from a continuous database, although the minimum inter-event time (MIET) is a commonly used criterion. Semi-arid regions of low latitudes hold a distinct rainfall pattern compared to their equivalent at higher latitudes; these seasonally dry tropical forests experience strong spatial–temporal variability with intense short-duration rainfall events, which, in association with high energy surplus and potential evaporation, leads to an atmospheric water deficit. In this study, we identified the most adequate MIET based on rainfall data continuously measured at 5-min intervals over the last decade (2009–2020) in the semi-arid northeast of Brazil. The rainfall events were grouped according to different MIETs: 15 min, 1 h, 2 h, 3 h, 6 h, 12 h, and 24 h to determine rainfall depth, duration and intensity at intervals of 5, 30, and 60 min, time between events, and respective temporal distribution, with and without single tip events. Including single tip events in the dataset affected the number of rainfall events and respective characteristics up to a MIET of 3 h. A MIET of 6 h is the most appropriate to characterize the rainfall distribution in this tropical semi-arid region. Three classes were defined based on rainfall depth, duration, and intensity: I-small events (77% below 40 mm and 32 mm/h), II-high intensity events (3% between 36 and 76 mm/h), III-longer events of higher depth (20%). This study is useful for understanding how the MIET relates to other ecohydrological processes and provides more precise information on the rainfall characteristics at the event scale.

Soil moisture and species richness interactively affect multiple ecosystem functions in a microcosm experiment of simulated shrub encroached grasslands

Understanding relationships between biodiversity and ecosystem functions (BEF) and the way in which ecosystem functions respond to changing climatic conditions or community composition is useful for predicting ecological consequences of global changes. However, how soil moisture condition, plant species richness interactively affect ecosystem functions in shrub-encroached grasslands is poorly understood. We conducted a soil moisture × species richness microcosm experiment using semi-arid grassland species with a N-fixing shrub Caragana microphylla Lam. as the dominant species to evaluate how soil moisture and plant species richness affected ecosystem functions directly or indirectly via regulating community functional structure, such as community-weighted mean values (CWM) and functional dispersion (FDis). Soil moisture and species richness interactively affected soil functions (soil C-, N-, P cycles and soil multifunctionality), with greater species richness buffering the adverse effects of soil drought. Soil moisture and species richness showed opposite effects on FDis but similar effects on CWM. FDis mediated the indirect effect of soil moisture and species richness on ecosystem functions, while CWM only mediated the indirect effect of soil moisture. More specifically, both soil moisture and plant species richness were negatively associated with soil P cycle, and the CWM_PC1 contributed by traits related to resource-conservative strategy was positively associated with soil N cycle. Species richness showed a positive direct effect on total shoot biomass, which was mainly contributed by the complementarity effect of neighbor species richness. This study provides strong empirical support of how biomass and soil nutrient cycles respond to the changes of soil moisture and plant species richness in C. microphylla shrub-encroached grasslands, and insights on the mechanisms underlying the interactive effects of soil condition and community species composition on multiple ecosystem functions in N-fixing shrub encroached grasslands in semi-arid grassland regions.

Analyzing WSTP trend: a new method for global warming assessment

This paper tries to introduce a time-series of temperature parameters as a potential method for studying the global warming. So, we investigated the spatial-temporal variations of warm-season temperature parameters (WSTP), including start time, end time, length of season, base value, peak time, peak value, amplitude, large integrated value, right drive, and left drive, using a database of 30 years' period in different climates of Iran. We used daily temperature data from 1989 to 2018 over Iran to extract the parameters by TIMESAT software. We studied the trend analysis of WSTP through the Mann-Kendall method. Then, we considered the Pearson correlation coefficient to calculate the correlation between WSTP and time. We assessed the trends of the slope using a simple linear regression method. Then, we compared the results of the WSTP trend analysis in climatic zones. Our results accused the hyper-arid climatic zone has the longest warm season (194.89 days a year). The warm season in this region starts earlier than other regions and increases with moderate speed (left drive, 0.19 °C day^-1). Then, it reaches a peak value (31.3 °C) earlier than the different climatic zones. On the other hand, the humid regions' warm season starts with the shortest length and ends later than the other climatic zones (112.1 and 297.5 days a year for start and end times, respectively). We detected that the trend of the start time parameter has decreased by 98.02% of the study area during the last 30 years. The base value, length, and large integrated value parameters have an increasing trend of 66.47%, 80.11%, and 92.95% in Iran. The highest correlation coefficient with time was for start time and large integrated value parameters. Hence, the start time and large integrated value parameters have almost the most negative (< - 0.5) and positive (> 5) trend slope, among other parameters, respectively. In general, these results demonstrate that the studied region has faced global warming impacts over time by increasing the warm season and thermal energy, especially in arid and hyper-arid. We highlight the necessity of planning the land use under the high natural vulnerability of the studied local, especially in this new age of global warming.

Using AI-MCDM Model to Boost Sustainable Energy System Development: A Case Study on Solar Energy and Rainwater Collection in Guangdong Province

Rural areas in southern China receive ample rainfall annually as well as over 1600 h of annual sunshine. Despite a generally severe urban–rural development imbalance, these rural areas feature well-developed basic infrastructure and diverse economic activities. Rural revitalization policies in these areas have emphasized the development of cultural and ecological tourism, which has spurred economic development and given rise to a trend of villa construction. Residential buildings sit on large areas where natural resources are abundant. These advantages are conducive to the development and use of sustainable resources. This study proposes an incentive policy encouraging rural residents to renovate their buildings to include rainwater conservation and solar power generation. The Delphi method, an analytic hierarchy process, and fuzzy logic theory were combined to establish an AI-MCDM model, with applications of artificial intelligence and multiple-criteria decision making. Using Conghua District, Guangdong Province as an example, the study suggested that the model is beneficial to increasing the willingness of rural residents to reconstruct and renovate their residences, promoting the development of a low-carbon ecological region, Wenquan Township. We conducted the Delphi process twice to assess and validate incentives for installing natural resource conservation structures in agricultural areas. Nine criteria were identified, which can be divided into three main dimensions of participation situation, generating capacity, and storage facilities. The proposed AI-MCDM model developed using the Delphi–Fuzzy Analytic Hierarchy Process Model has high objectivity and can support rural areas in developing low-carbon, sustainable characteristics. The findings can serve as a reference for governments formulating incentives to encourage the installation of rainwater conservation and solar energy generation structures by rural households.

Long-term effects of grazing on the biological, chemical, and physical soil properties of the Caatinga biome

Soil degradation is a global issue that affects both plant productivity and human life. Intensive grazing practices can accelerate this process, mainly due to rapid removal of biomass from the soil surface. However, the long-term effects of grazing on biological, chemical, and physical properties remain poorly understood, particularly in tropical drylands, such as the Caatinga biome. Our aim was to evaluate the soil properties and combine both culture-dependent and -independent analyses to assess metabolic activity and bacterial community structure. We collected samples (0-20 cm) of three different types of soil in the Caatinga biome: secondary Caatinga forest (NC), grazing exclusion (GE), and degraded areas by overgrazing (OG). We sought to investigate how grazing affects soil properties to determine the effectiveness of grazing exclusion in the restoration of soil fertility/functions. Redundancy analysis demonstrated NC were positively correlated with organic carbon (λ = 0.18, p = 0.0012) and total nitrogen (λ = 0.16, p = 0.0011), while OG was correlated with harmful soil parameters such as Na⁺ (λ = 0.08, p = 0.0400), electric conductivity (λ = 0.13, p = 0.0060) and exchangeable acidity (λ = 0.11, p = 0.0030). In addition, GE showed lower aluminum content and saturation, reducing these harmful parameters by 48 % and 34 %, respectively. Also, GE showed the highest values for the β-glucosidase (63.62 mg ρ-nitrophenol kg^-1 h^-1) and arylsulfatase (5.8 mg ρ-nitrophenol kg^-1 h^-1) activities. Changes in bacterial community structure were significant (p = 0.0096), with a higher difference comparing GE and OG (p = 0.0135). The GE area showed 20 % more phosphate solubilizers than OG, but there were no differences for siderophores production. All isolates were halotolerant and had at least 60 % nitrogen fixers. Our findings indicate that while soil recovery is slow, with grazing-exclusion areas presenting 18 years of implantation, it seems to improve in subsequent years. Finally, our results provide evidence that microbe-based technologies can mitigate soil degradation in the Caatinga biome.

UAV RGB, thermal infrared and multispectral imagery used to investigate the control of terrain on the spatial distribution of dryland biocrust

Biocrusts (topsoil communities formed by mosses, lichens, bacteria, fungi, algae, and cyanobacteria) are a key biotic component of dryland ecosystems. Whilst climate patterns control the distribution of biocrusts in drylands worldwide, terrain and soil attributes can influence biocrust distribution at landscape scale. Multi-source unmanned aerial vehicle (UAV) imagery was used to map and study biocrust ecology in a typical dryland ecosystem in central Spain. Red, green and blue (RGB) imagery was processed using structure-from-motion techniques to map terrain attributes related to microclimate and terrain stability. Multispectral imagery was used to produce accurate maps (accuracy > 80%) of dryland ecosystem components (vegetation, bare soil and biocrust composition). Finally, thermal infrared (TIR) and multispectral imagery was used to calculate the apparent thermal inertia (ATI) of soil and to evaluate how ATI was related to soil moisture (r 2 = 0.83). The relationship between soil properties and UAV-derived variables was first evaluated at the field plot level. Then, the maps obtained were used to explore the relationship between biocrusts and terrain attributes at ecosystem level through a redundancy analysis. The most significant variables that explain biocrust distribution are: ATI (34.4% of variance, F = 130.75; p < 0.001), Elevation (25.8%, F = 97.6; p < 0.001), and potential solar incoming radiation (PSIR) (52.9%, F = 200.1; p < 0.001). Differences were found between areas dominated by lichens and mosses. Lichen-dominated biocrusts were associated with areas with high slopes and low values of ATI, with soil characterized by a higher amount of soluble salts, and lower amount of organic carbon, total phosphorus (Ptot) and total nitrogen (Ntot). Biocrust-forming mosses dominated lower and moister areas, characterized by gentler slopes and higher values of ATI with soils with higher contents of organic carbon, Ptot and Ntot. This study shows the potential to use UAVs to improve our understanding of drylands and to evaluate the control that the terrain has on biocrust distribution.

Annual precipitation explains variability in dryland vegetation greenness globally but not locally

Dryland vegetation productivity is strongly modulated by water availability. As precipitation patterns and variability are altered by climate change, there is a pressing need to better understand vegetation responses to precipitation variability in these ecologically fragile regions. Here we present a global analysis of dryland sensitivity to annual precipitation variations using long-term records of normalized difference vegetation index (NDVI). We show that while precipitation explains 66% of spatial gradients in NDVI across dryland regions, precipitation only accounts for <26% of temporal NDVI variability over most (>75%) dryland regions. We observed this weaker temporal relative to spatial relationship between NDVI and precipitation across all global drylands. We confirmed this result using three alternative water availability metrics that account for water loss to evaporation, and growing season and precipitation timing. This suggests that predicting vegetation responses to future rainfall using space-for-time substitution will strongly overestimate precipitation control on interannual variability in aboveground growth. We explore multiple mechanisms to explain the discrepancy between spatial and temporal responses and find contributions from multiple factors including local-scale vegetation characteristics, climate and soil properties. Earth system models (ESMs) from the latest Coupled Model Intercomparison Project overestimate the observed vegetation sensitivity to precipitation variability up to threefold, particularly during dry years. Given projections of increasing meteorological drought, ESMs are likely to overestimate the impacts of future drought on dryland vegetation with observations suggesting that dryland vegetation is more resistant to annual precipitation variations than ESMs project.

What factors affect the water saving behaviors of farmers in the Loess Hilly Region of China?

Water shortage has become one of the most serious challenges faced by humans. Thus, improving water use behaviors and saving water are critical. In this paper, based on the extended Value-Belief-Norm (VBN) theory, we used the structural equation model and the survey data of 558 households in the Loess Hilly Region to analyze the key factors affecting water saving behaviors of farmers. The domestic water consumption of farmers in Loess Hilly Region is mainly for diet and personal hygiene. The water-saving behaviors of farmers are significantly affected by their willingness, knowledge and age. Personal habits, policy incentives, egocentric values and biospheric values indirectly affect the willingness to save water by acting on personal water-saving norms. Gender and annual household income also have a significant impact on water-saving willingness of farmers. The estimation results of multi-group structural equation model and full-sample structural equation model are generally consistent, and there are significant differences in the influence of gender and water source adjustment variables on water-saving behaviors among different hypothetical paths. Among them, female, cellar water and well water groups have the greater impact on water-saving behaviors. These results provide references for policy makers to formulate effective rural water-saving strategies. Policymakers should strengthen the propagandize of water-saving knowledge and skills, popularize knowledge about water resources and ecological environment changes, encourage farmers to develop good water use habits, and actively promote the subsidy system for water-saving appliances. Then they should establish a supervision and management mechanism of water resources to stimulate farmers to save water, which realize the sustainable use of local water resources.

Use of A MODIS Satellite-Based Aridity Index to Monitor Drought Conditions in Mongolia from 2001 to 2013

The 4D disasters (desertification, drought, dust, and dzud, a Mongolian term for severe winter weather) have recently been increasing in Mongolia, and their impacts on the livelihoods of humans has likewise increased. The combination of drought and dzud has caused the loss of livestock on which nomadic herdsmen depend for their well-being. Understanding the spatiotemporal patterns of drought and predicting drought conditions are important goals of scientific research in Mongolia. This study involved examining the trends of the normalized difference vegetation index (NDVI) and satellite-based aridity index (SbAI) to determine why the land surface of Mongolia has recently (2001–2013) become drier across a range of aridity indices (AIs). The main reasons were that the maximum NDVI (NDVImax) was lower than the NDVImax typically found in other arid regions of the world, and the SbAI throughout the year was large (dry), although the SbAI in summer was comparatively small (wet). Under the current conditions, the capacity of the land surface to retain water throughout the year caused a large SbAI because rainfall in Mongolia is concentrated in the summer, and the conditions of grasslands reflect summer rainfall in addition to grazing pressure. We then proposed a method to monitor the land-surface dryness or drought using only satellite data. The correct identification of drought was higher for the SbAI. Drought is more strongly correlated with soil moisture anomalies, and thus the annual averaged SbAI might be appropriate for monitoring drought during seasons. Degraded land area, defined as annual NDVImax < 0.2 and annual averaged SbAI > 0.025, has decreased. Degraded land area was large in the major drought years of Mongolia.

Impact of Cropland Evolution on Soil Wind Erosion in Inner Mongolia of China

Understanding soil erosion responses to cropland expansion/shrinking plays a crucial role in regional agriculture sustainability development in drylands. We selected Inner Mongolia, a typical water resource constraints region with acute cropland expansion, as the study area in China. Spatial cropland evolution and its impact on wind-driven soil erosion were investigated with the help of field sampling data, remotely sensed retrieved data, and the revised wind erosion model (RWEQ). Results showed that the cropland area of Inner Mongolia presented an increased growth trend, with a net increase area of 15,542.9 km2 from 1990 to 2018. Cropland characteristics in Inner Mongolia presented continuous growth in its eastern region, basically constant growth in its central region, and declined in its western region. Most cropland declines occurred after 2000 when the Grain for Green project began, which means that acute cropland expansion happened from 1990 to 2000. The soil wind erosion modulus showed a net increase with cropland expansion. The reclamation of forests and grasslands contributed to an increase of 5.0 million tons of the soil wind erosion modulus, 80% of which was produced in the eastern part of the region. The conversion from croplands to grasslands/forests caused a decrease of approximately 2.7 million tons, 62% of which was in the east and 25% in the west of the region. Considering the constraints of water shortage and over-exploitation of groundwater, we provide a path based on a balance between ”resource-production-ecosystem” to achieve ecologically sustainable agriculture development in the drylands of China.

Assessment of global warming in Al Buraimi, sultanate of Oman based on statistical analysis of NASA POWER data over 39 years, and testing the reliability of NASA POWER against meteorological measurements

We performed a number of statistical analysis methods on the historical data for the air temperature at 2 m above the ground and its range, as reported by the database of NASA known as POWER, which stands for Prediction Of Worldwide Energy Resources. The point of analysis is the University of Buraimi, located in Al Buraimi Governate, in the Northwest of the Sultanate of Oman, near its border with United Arab Emirates (UAE). The data is in the form of a value per day, for every day in the year. The data analyzed span the period from January 3^rd, 1981 (earliest day available) to December 31^st, 2019 (latest end-of-year available).

The statistical analysis methods include: simple linear regression, F-test: two-sample for variances, analysis of variance (ANOVA): single factor, and t-test: two-sample assuming equal variances (pooled).

The results show that the mean of the local 2-meter air temperature is increasing at a rate of about 0.039 °C per year, starting from an estimated value of 27.4 °C in 1980.

For the standard deviation of the 2-meter air temperature, and the mean and standard deviation of its range; although a linear regression analysis suggests a decline over time, the regression coefficient is not significant. On the other hand, the analysis of variance for the 9 years 1981, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019 suggests existence of statistical difference among them collectively, for the 2-meter air temperature and its range.

In the second part of this work, NASA POWER data for the air temperature at 2 m above the ground, the atmospheric pressure, the relative humidity at 2 m above the ground, and the daily precipitation were compared with recorded sensor measurements at Manah meteorological station, located in Al Dakhiliyah Governate, in the Northeast of the Sultanate of Oman over all days of years 2011, 2012, 2015, and 2016. The statistical analysis and visual inspection suggest that NASA POWER data are reliable for the 2-meter air temperature, while showing about 2.1 kPa constant shift (underestimation) for the atmospheric pressure. The data show mild inaccuracy for the 2-meter relative humidity, but are largely unreliable for the precipitation, with significantly exaggerated values compared to real recordings.

Controlling gully- and revegetation-induced dried soil layers across a slope-gully system

The introduction of exotic plants and improper management strategies with regard to plant species can change the soil-water balance of deep soils, which in turn results in the formation of a dried soil layer (DSL) within the soil profile. The Loess Plateau (LP) of China has a complex terrain; however, only a few studies have evaluated the effects of the gully-induced DSL patterns, especially in hilly and gully regions of the northern LP. In this study, we collected soil-water content data to a depth of 5 m at 40 sampling sites in a slope-gully system to investigate and characterize DSLs and their spatial patterns. Results show that the DSL indices vary greatly in different slope positions. The thickness of DSLs (DSLT) and quantitative index (QI) in the gully were significantly (p < 0.05) higher than those in the non-gully areas. The relative contribution of soil properties was higher than those of terrain factors in the gully, whereas the contribution of terrain factors was higher than those of soil properties under shrubland. Gullies contributed to the complex spatial DSL patterns in the slope-gully system. Partial least squares regression (PLSR) was used to detect the relative significance of 10 selected environmental factors that affect spatial DSL patterns. Variable importance in projection (VIP) demonstrated that soil properties, especially Clay and Silt content, significantly influenced the DSL formation depth (DSLFD), DSLT, and QI. Land-use and slope position were the most important factors that influenced the mean soil-water content (SWC) within DSLs (DSL-SWC), which exhibited the highest VIP values. PLSR models simulated DSL indices accurately in DSL-SWC; the values for variation in response (R²) and goodness of prediction (Q²) were 0.94 and 0.92, respectively. Therefore, our findings provide a helpful base reference for DSL management and reclamation of hill and gully regions of the LP.

Local environmental variables are key drivers of ant taxonomic and functional beta-diversity in a Mediterranean dryland

The decomposition of beta-diversity (β-diversity) into its replacement (β_repl) and richness (β_rich) components in combination with a taxonomic and functional approach, may help to identify processes driving community composition along environmental gradients. We aimed to understand which abiotic and spatial variables influence ant β-diversity and identify which processes may drive ant β-diversity patterns in Mediterranean drylands by measuring the percentage of variation in ant taxonomic and functional β-diversity explained by local environmental, regional climatic and spatial variables. We found that taxonomic and functional replacement (β_repl) primarily drove patterns in overall β-diversity (β_tot). Variation partitioning analysis showed that respectively 16.8%, 12.9% and 21.6% of taxonomic β_tot, β_repl and β_rich variation were mainly explained by local environmental variables. Local environmental variables were also the main determinants of functional β-diversity, explaining 20.4%, 17.9% and 23.2% of β_tot, β_repl and β_rich variation, respectively. Findings suggest that niche-based processes drive changes in ant β-diversity, as local environmental variables may act as environmental filters on species and trait composition. While we found that local environmental variables were important predictors of ant β-diversity, further analysis should address the contribution of other mechanisms, e.g. competitive exclusion and resource partitioning, on ant β-diversity.

Imaging Spectroscopy for Conservation Applications

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

Global response of terrestrial gross primary productivity to climate extremes

Extreme climate events undoubtedly have essential impacts on ecosystem gross primary productivity (GPP), but the global spatio-temporal patterns of GPP responses to climate extremes are unclear. In this study, we analyzed the responses of GPP to temperature and precipitation extremes during historical (1901-2016) and future (2006-2100) periods using climate extreme indices (CEIs) developed by the Expert Team on Climate Change Detection and Indices. Eight temperature-related CEIs and eight precipitation-related CEIs were used for this analysis, along with three future greenhouse gas concentration trajectory scenarios generated by the IPCC: RCP 2.6, RCP 4.5, and RCP 8.5. Our results show that under RCP 4.5 and RCP 8.5, most climate extremes are increasing from the historical period into the future, indicating a warming globe with more frequent and more intense extreme climate events. But the increasing rate is only persistently enhanced with time under scenario RCP 8.5. GPP shows a continuous negative relationship with cold CEIs and positive relationship with wet CEIs from the historical period into the future. In all zonal scales, the changed magnitude of GPP responds strongly to extreme value-related temperature extremes under different scenarios. However, the precipitation-related extremes with the strongest GPP response are various in different regions. In the future, GPP is most sensitive to temperature extremes in upper northern latitudes and in high-altitude regions (e.g., Qinghai-Tibet Plateau) and to precipitation extremes in the tropical zone. This study may provide a basis for predicting how GPP responds to climate extremes and explaining the underlying changes in the carbon cycle.

The response of plant functional traits to aridity in a tropical dry forest

Drylands are experiencing an overall increase in aridity that is predicted to intensify in the future due to climate change. This may cause changes in the structure and functioning of dryland ecosystems, affecting ecosystem services and human well-being. Therefore, detecting early signs of ecosystem change before irreversible damage takes place is important. Thus, here we used a space-for-time substitution approach to study the response of the plant community to aridity in a Tropical dry forest (Caatinga, Brazil), and infer potential consequences of climate change. We assessed plant functional structure using the community weighted mean (CWM) and functional diversity, measured through functional dispersion (FDis), along a 700 km climatic gradient. We studied 13 functional traits, reflecting strategies associated with establishment, defense, regeneration, and dispersal of the most abundant 48 plant species in 113 sampling sites. Spearman correlations were used to test the relation between aridity and single-trait functional metrics. Aridity was a major environmental filter of the plant community functional structure. We found a higher abundance of species with deciduous leaves, zoochorous dispersal, fleshy fruits, chemical defense exudation and spinescence, and crassulacean acid metabolism towards more arid sites, at the expense of species with evergreen and thicker leaves, autochory dispersal, and shrub growth-form. The FDis of leaf type and thickness decreased with aridity, whereas FDis of fruit type, photosynthetic pathway, and defense strategies increased. Our findings provide functional indicators to early detect climate change impacts on Caatinga structure and functioning, to timely adopt preventive measures (e.g. conservation of forest remnants) and restoration actions (e.g. introduction of species with specific functional traits) in this threatened and unique ecosystem.

A review on maintenance strategies for PV systems

PV (PhotoVoltaic) systems gained popularity in being eco-friendly power supplies, reducing toxic gas emissions for energy production. Unfortunately it's been seen that efficiency reduction and output deficit are two common scenarios for PV systems when confronted with faulty events: this fact creates the crucially important need of knowing types of different possible faults and be able to detect them according to their ways of striking, timing and density of their leftovers. What comes after is fixing the damages, by preventing them from happening repeatedly and stop them from reaching the surface. Since knowing the fault without correcting it is counterproductive, maintenance strategies can reduce competencies of the system, accelerate operation time, and compensate the system in a way that prevents lowering in efficiency and consequently financial losses. This paper aims to present different schemes used in fault intervention categorized by preventive, corrective, predictive and urgent case faults maintenance methods with corresponding effects on environment. A critical comparison between different strategies is presented as well as an economical assessment of the maintenance costs and energy production increase. Each type is individually clarified to reveal what area of PV faults types it can withstand. With a recommendation on maintenance choice, an informative reference for researchers in the field is established to optimally maintain a sustainable PV system.

Use of a Satellite-Based Aridity Index to Monitor Decreased Soil Water Content and Grass Growth in Grasslands of North-East Asia

Numerous simulation studies of the effect of global warming on arid regions have indicated that increases in temperature and decreases in precipitation will trigger water shortages, drought, and further aridification. In north-east Asia, especially China and Mongolia, the area of degraded land has increased since 2000. Land use in arid regions is mainly natural grasslands for grazing. Growth in this land use is limited by the precipitation amount and intensity. To develop sustainable management of grasslands, it is essential to examine the relationship between water consumption and the growth patterns of the grasses. This study examined the applicability of a satellite-based aridity index (SbAI) as a way to measure the water consumption and growth of grasslands in China and Mongolia. The effective cumulative reciprocal SbAI was strongly correlated with the cumulative decreased soil water content in the root zone and changes in the normalized difference vegetation index in Shenmu, China. Application of the effective cumulative reciprocal SbAI to grasslands in Mongolia and in north-east Asia revealed a high correlation between the effective cumulative reciprocal SbAI and changes in the normalized difference vegetation index (NDVI). The effective cumulative reciprocal SbAI might be suitable for the detection of water consumption and growth in grasslands from satellite data alone.

Performance of the Remotely-Derived Products in Monitoring Gross Primary Production across Arid and Semi-Arid Ecosystems in Northwest China

As an important component to quantify the carbon budget, accurate evaluation of terrestrial gross primary production (GPP) is crucial for large-scale applications, especially in dryland ecosystems. Based on the in situ data from six flux sites in northwestern China from 2014 to 2016, this study compares seasonal and interannual dynamics of carbon fluxes between these arid and semi-arid ecosystems and the atmosphere. Meanwhile, the reliability of multiple remotely-derived GPP products in representative drylands was examined, including the Breathing Earth System Simulator (BESS), the Moderate Resolution Imaging Spectroradiometer (MODIS) and data derived from the OCO-2 solar-induced chlorophyll fluorescence (GOSIF). The results indicated that the carbon fluxes had clear seasonal patterns, with all ecosystems functioning as carbon sinks. The maize cropland had the highest GPP with 1183 g C m−2 y−1. Although the net ecosystem carbon exchange (NEE) in the Tamarix spp. ecosystem was the smallest among these flux sites, it reached 208 g C m−2 y−1. Furthermore, distinct advantages of GOSIF GPP (with R2 = 0.85–0.98, and RMSE = 0.87–2.66 g C m−2 d−1) were found with good performance. However, large underestimations in three GPP products existed during the growing seasons, except in grassland ecosystems. The main reasons can be ascribed to the uncertainties in the key model parameters, including the underestimated light use efficiency of the MODIS GPP, the same coarse land cover product for the BESS and MODIS GPP, the coarse gridded meteorological data, and distribution of C3 and C4 plants. Therefore, it still requires more work to accurately quantify the GPP across these dryland ecosystems.

Photosynthesis and Growth of Pennisetum centrasiaticum (C4) is Superior to Calamagrostis pseudophragmites (C3) During Drought and Recovery

Global warming and changes in rainfall patterns may put many ecosystems at risk of drought. These stressors could be particularly destructive in arid systems where species are already water-limited. Understanding plant responses in terms of photosynthesis and growth to drought and rewatering is essential for predicting ecosystem-level responses to climate change. Different drought responses of C₃ and C₄ species could have important ecological implications affecting interspecific competition and distribution of plant communities in the future. For this study, C₄ plant Pennisetum centrasiaticum and C₃ plant Calamagrostis pseudophragmites were subjected to progressive drought and subsequent rewatering in order to better understand their differential responses to regional climate changes. We tracked responses in gas exchange, chlorophyll fluorescence, biomass as well as soil water status in order to investigate the ecophysiological responses of these two plant functional types. Similar patterns of photosynthetic regulations were observed during drought and rewatering for both psammophytes. They experienced stomatal restriction and nonstomatal restriction successively during drought. Photosynthetic performance recovered to the levels in well-watered plants after rewatering for 6–8 days. The C₄ plant, P. centrasiaticum, exhibited the classic CO₂-concentrating mechanism and more efficient thermal dissipation in the leaves, which confers more efficient CO₂ assimilation and water use efficiency, alleviating drought stress, maintaining their photosynthetic advantage until water deficits became severe and quicker recovery after rewatering. In addition, P. centrasiaticum can allocate a greater proportion of root biomass in case of adequate water supply and a greater proportion of above-ground biomass in case of drought stress. This physiological adaptability and morphological adjustment underline the capacity of C₄ plant P. centrasiaticum to withstand drought more efficiently and recover upon rewatering more quickly than C. pseudophragmites and dominate in the Horqin Sandy Land.

Brazilian Dry Forest (Caatinga) Response To Multiple ENSO: the role of Atlantic and Pacific Ocean

The semi-arid region of Northeast Brazil (NEB) experiences severe droughts during El Niño Southern Oscillation (ENSO) years, with major impacts on the dynamics of the native vegetation (Caatinga). However, the effect of these droughts on carbon cycling is not well understood. Here, a numerical model is used to investigate the influence of variations in Pacific and Atlantic sea surface temperatures (SST) on drought and carbon dynamics of the Caatinga during past ENSO events. We demonstrate that precipitation reductions in the Caatinga have a strong influence on vegetation dynamics, with net primary production (NPP) remaining low throughout the droughts. Furthermore, the Caatinga acts as a carbon sink, even in years of severe drought. However, net ecosystem exchange (NEE) is lower in years of low NPP rates, resulting in long periods with limited ecosystem activity. The SST patterns indicate that extreme vegetation changes in the Caatinga are associated with the combination of ENSO events and North Atlantic SST warming.

TERRAenVISION: Science for Society. Environmental issues today

Our Planet suffers from human activities. As scientists, we know more and more about our environment, about processes, rates of change, new threats, and risks. However, the challenges we face seem to grow quicker than the solutions we can create. To achieve sustainability, the key is to make solutions not only functional from an environmental point of view, but also socially acceptable and economically viable. In this context, the TERRAenVISION conference series gathers diverse groups of scientists to discuss sustainability. The first TERRAenVISION meeting in January 2018 was framed around 7 themes: (1) Climate Change: Mitigation and Adaptation, (2) Water Resources: Quality and Quantity, (3) Land Degradation and Restoration, (4) Nature-based Solutions, (5) Fire in the Earth System, Effects, and Prevention, (6) Ecosystem Services and Health, and (7) Science Interface with Policy and Public. Among the works presented in the conference, this Special Issue collates 22 papers that illustrate the best, problems and solutions the scientific community is currently working on to achieve sustainability. Similar to the concept of the SDGs, paper subjects often intertwine and bridge the conference themes. The papers are grouped in two main chapters dealing with Water and Land, with two additional cross cutting chapters of Scientific Tools and Science-Policy Interface. Drawing from the conclusions of these works as well as those of the TERRAenVISION 2018 conference, we make recommendations regarding raising awareness, connecting scientific fields, and supporting robust economic and policy transitions.

Spatial Shift of Aridity and Its Impact on Land Use of Syria

Expansion of arid lands due to climate change, particularly in water stressed regions of the world can have severe implications on the economy and people’s livelihoods. The spatiotemporal trends in aridity, the shift of land from lower to higher arid classes and the effect of this shift on different land uses in Syria have been evaluated in this study for the period 1951–2010 using high-resolution monthly climate data of the Terrestrial Hydrology Research Group of Princeton University. The trends in rainfall, temperature and potential evapotranspiration were also evaluated to understand the causes of aridity shifts. The results revealed an expansion of aridity in Syria during 1951–1980 compared to 1981–2010. About 6.21% of semi-arid land was observed to shift to arid class and 5.91% dry-subhumid land to semi-arid land between the two periods. Analysis of results revealed that the decrease in rainfall is the major cause of increasing aridity in Syria. About 28.3% of agriculture land located in the north and the northwest was found to shift from humid to dry-subhumid or dry-subhumid to semi-arid. Analysis of results revealed that the shifting of drylands mostly occurred in the northern agricultural areas of Syria. The land productivity and irrigation needs can be severely affected by increasing aridity which may affect food security and the economy of the country.