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In the context of climate change, studying changes in rainfall patterns is a crucial area of research, remarkably so in arid and semi-arid regions due to the susceptibility of human activities to extreme events such as droughts. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applies two type of machine learning methods—long short-term memory (LSTM) and Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)—to develop and deploy artificial neural network models with the aim of predicting the regional standardized precipitation index (SPI) in four regions of Zacatecas, Mexico. The predictor variables were a set of climatological time series data spanning from 1964 to 2020. The results suggest that the N-HiTS model outperforms the LSTM model in the prediction and forecasting of SPI time series for all regions in terms of performance metrics: the Mean Squared Error, Mean Absolute Error, Coefficient of Determination and ξ correlation coefficient range from 0.0455 to 0.5472, from 0.1696 to 0.6661, from 0.9162 to 0.9684 and from 0.9222 to 0.9368, respectively, for the regions under study. Consequently, the outcomes revealed the successful performance of the N-HiTS models in accurately predicting the SPI across the four examined regions.

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery.

Decision support system tools such as crop models and considering the uncertainties associated with them are important for making an informed decision to fill the yield gap in farms and increase food security. This study’s objective was to identify and quantify the degree to which crop management practices, as well as climate and soil, affected the uncertainty of total biomass, evapotranspiration, and water productivity of silage maize by using a crop model and spatiotemporal input data. Using a calibrated crop model (DSSAT) and pSIMS platform, three planting dates by considering ten ensemble weather data and three soil profile data were simulated for the time period between 2002 and 2017 with a 2 km × 2 km resolution across maize production areas with arid and Mediterranean climates in Isfahan province, Iran. Additionally, the findings were used to determine the yield gap in the studied area to identify opportunities to boost food production. Our results showed larger uncertainty in Mediterranean climates than in arid climates, and it was more affected by planting date than weather parameters and soil profile. The accuracy of total biomass prediction by using pSIMS-CERES-Maize based on the spatiotemporal input data was 1.9% compared to field experimental data in the dry climate, and the yield gap based on the comparison of modified-pSIMS-CERES-Maize and reported biomass was 6.8 to 13 tons ha−1 in the arid and Mediterranean climate. Generally, all results represented the importance of using crop models and considering spatiotemporal data to increase reliability and accuracy, especially in Mediterranean climates, and their potential to increase food production in developing countries with limited water resources and poor agriculture management.

Transporting water to supply livestock is one of the great challenges of the drylands. Ranchers usually make impoundments, filled by runoff, to access freshwater for cattle supply in flat rangelands. The aim of this study was to understand rainfall-runoff generation and water storage temporal dynamics of impoundments in the Dry Chaco rangelands (Argentina). Thus, we instrumented six impoundments over three consecutive years and analyzed water storage data by developing a probabilistic model. For all impoundments, the rainfall event size thresholds to generate runoff presented values between 15 and 33 mm. Once they reached this threshold, the water gain response slopes presented values between 19 and 99 m3 mm−1. Loss patterns of water storage were described by exponential or linear functions. The predicted water storage dynamics presented high accuracy with the observed time series for all impoundments (RMSD between 380 and 1320 m3). The model only needs daily rainfall and air temperature to be run, making it easy to be used by scientists, ranchers, or local decision makers. It may be used to explore the hydrological functioning of small and seasonal water bodies of different sites of the world exposed to drought episodes caused by high climate variability and/or climate change.

There have been few attempts to estimate the effects of land use and land cover (LULC) on ecosystem services in desert-oasis ecotones, which are recognized as critical ecological barriers and buffers that prevent deserts from expanding into oases. This research investigated how remote sensing and geographic information technology may be used to monitor changes in LULC in the Cholistan desert and the Bahawalpur region of Pakistan between the years 2015 and 2022. The objective of this research was to identify thematic and statistical shifts in LULC in the study area due to various human interventions in the area. Landsat-8 images were processed using the maximum likelihood supervised classification technique using 500 training samples to categorize the study area into four LULC classes, i.e., desert/barren land, waterbodies, vegetation, and built-up areas, with an overall accuracy of 93% and 98% for 2015 and 2022, respectively. Results indicate a significant expansion in built-up area in 2022, which is up to 43%, agriculture and vegetation area declined by 8%, waterbodies decreased by 41%, and desert area decreased by 2% when compared with 2015. The change detection approach revealed that agricultural land was directly encroached on by rapidly increasing built-up area and urbanization as the area had an overall 19% rise in population growth within eight years with an annual growth rate of more than 3%. This study will be helpful to assess the quantity of spatial and temporal changes in the desert ecosystem, which is usually ignored by policymakers and governments due to less economic activity, although it plays a huge role in biodiversity conservation and balancing the regional ecosystem.

Satellite-derived vegetation fractional cover (VFC) has shown to be a promising tool for dryland ecosystem monitoring. This model, calibrated through biophysical field measurements, depicts the sub-pixel proportion of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil (BS). The distinction between NPV and BS makes it particularly important for drylands, as these fractions often dominate. Two Landsat VFC products are available for the Australian continent: the original Joint Remote Sensing Research Program (JRSRP) product, and a newer Digital Earth Australia (DEA) product. Although similar validation statistics have been presented for each, an evaluation of their differences has not been undertaken. Moreover, spatial variability of VFC accuracy within drylands has not been comprehensively assessed. Here, a large field dataset (4207 sites) was employed to compare Landsat VFC accuracy across the Australian continent, with detailed spatial and temporal analysis conducted on four regions of interest. Furthermore, spatiotemporal features of VFC unmixing error (UE) were explored to characterize model uncertainty in large areas yet to be field sampled. Our results showed that the JRSRP and DEA VFC were very similar (RMSE = 4.00–6.59) and can be employed interchangeably. Drylands did not show a substantial difference in accuracy compared to the continental assessment; however contrasting variations were observed in dryland subtypes (e.g., semi-arid and arid zones). Moreover, VFC effectively tracked total ground cover change over time. UE increased with tree cover and height, indicating that model uncertainty was low in typical dryland landscapes. Together, these results provide guiding points to understanding the Australian ecosystems where VFC can be used with confidence.

Plants provide long-term and sustainable solutions to mitigate particulate matter (PM) pollution in urban environments. We evaluated total, fine, coarse and large particle trapping abilities of an equal number of common trees (Carica papaya, Citrus limon, Moringa oleifera, Ozoroa paniculosa, Peltophorum africanum, Psidium guajava) and herbaceous species (Argemone ochroleuca, Catharanthus roseus, Gomphocarpus fruticosus, Ipomoea batatas, Senna italica, Tribulus terrestris) to identify dust accumulators for Sekhukhuneland, a mining–smelting region of South Africa where desertification is becoming problematic. Scanning electron microscopy techniques were used to count and measure particles and relate leaf surface micromorphology to dust accumulation. Three tree and three herbaceous species showed superior dust collection capacity (G. fruticosus > P. guajava > I. batatas > O. paniculosa > C. roseus > M. oleifera). Variations in accumulation of PM sizes were noted among these six species and between adaxial and abaxial leaf surfaces. Compared with large PM, all plants accumulated more fine and coarse fractions which are respirable and thus hazardous to human health. Leaf surface roughness, epicuticular wax and epidermal glands improved dust accumulation. The six preferred plants may serve as forerunner species to abate PM pollution in Sekhukhuneland and other arid regions facing similar climate change and pollution challenges.

This study encompasses the grain size distribution of the playa lakes (Pachpadra, Pokhran, and Didwana) of the Thar Desert in Rajasthan, India. The grain size of sediment particles is the most fundamental feature, giving essential information regarding their origin, transport history, and depositional conditions. The aeolian and fluvial transport processes were evaluated through environmentally sensitive grain size subpopulations to identify the differential sedimentary sources and dynamics in the playas. End-member modelling further determined the sediment grain size distribution through statistical analysis. The playa sediments mainly consist of very fine sand (46–54%) and very coarse silt (22–37%). The results show that the average fine fraction of Pachpadra, Pokhran, and Didwana playa was 46.29%, 66.11%, and 66.28%, respectively. In contrast, the average coarser fraction deposition in Pachpadra, Pokhran, and Didwana corresponds to 53.71%, 33.89%, and 33.72%, respectively. This suggests that the playas mostly contain aeolian sediment rather than fluvial sediment transported by dust/sand storms. Additionally, the textural pattern and depositional distribution of the sediments determined through the Passega CM diagram and bivariate plots indicate that 82% of the samples were poorly sorted, and 18% were very poorly sorted. Furthermore, an environmentally sensitive grain size component (ESGSC) was also assessed to identify the spatial variability and transport processes of sediment between these playas. Three ESGSCs in Pokhran (250 µ, 31 µ, and 2 µ) and Pachpadra (125 µ, 31 µ, and 4 µ), while two ESGSCs in Didwana playa (125 µ and 16 µ) were identified, indicating sediment deposition with moderate velocity in a low energy environment with a mixed sediment population transported by aeolian and fluvial activities.

Smart-farming practices can help to overcome the impacts of soil and crop variability on crop yield. This article studies the impact of fertilization treatment on the production of a major oilseed crop, mustard (Brassica campestris L.), in an arid region of Pakistan. Soil-nutrient sampling was performed, using 20 × 20 m grids to characterize and quantify the variation of mustard yield and soil nutrients. Based on the results of a significant correlation between yield and soil nutrients, management zones (MZs) were developed that use Variable Rate Fertilization (VRF) to increase crop production and profitability while reducing environmental risk.

A systematic synthesis of phreatophytes’ responses to groundwater drawdown would provide a more complete picture of groundwater-related research aimed at the sustainable management of groundwater-dependent ecosystems amid climate change. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the ecophysiological effects of groundwater drawdown on phreatophytes and methodological approaches were synthesized from peer-reviewed articles published from 1988 to 2022. The highest relative count of studies was found in arid and semi-arid high-income countries, such as Australia and North America (18–24%), while the lowest relative count to no data was found in hyper-arid countries, such as north African countries (0–3.65%). The groundwater depth effects on phreatophyte ecophysiology had the highest relative count (53.65%), followed by large-scale tree plantation effects on the groundwater characteristics (44.37%) and groundwater depth and biological invasion relationship (1.99%). The results revealed that as the groundwater depth increased, the phreatophytic vegetation growth, productivity, and community structure decreased across the ecosystem types. A groundwater withdrawal also had a significant impact on the physiology of the phreatophytes, specifically on the transpiration rate, xylem water potential, hydraulic conductance, and photosynthetic rate. Many of the reviewed studies concluded that large-scale tree plantations can deplete groundwater resources due to an increased evapotranspiration rate. Further, species’ diversity, evenness, dominance, composition, and distribution, as well as the Normalized Difference Vegetation Index (NDVI), are commonly measured parameters in the reviewed studies through vegetation and groundwater monitoring. Amid applied and contemporary problems, this synthesis may provide researchers with cues to conduct studies relevant to the integrated and sustainable conservation and management of groundwater-dependent ecosystems, particularly in data-poor, hyper-arid countries.

Releases of Aedes aegypti carrying Wolbachia bacteria are known to suppress arbovirus transmission and reduce the incidence of vector-borne diseases. In planning for Wolbachia releases in the arid environment of Jeddah, Saudi Arabia, we collected entomological data with ovitraps across a 7-month period in four locations. Herein, we show that mosquito presence in basements does not differ from that of non-basement areas of buildings. In modelling mosquito presence across the study sites, we found the spatial structure to be statistically significant in one of the four sites, while a significant spatial structure was found for egg production data across three of the four sites. The length scales of the spatial covariance functions fitted to the egg production data ranged from 143 m to 574 m, indicating that high productivity regions can be extensive in size. Rank-correlation analyses indicated that mosquito presence tended to persist from the dry to wet season, but that egg production ranks at locations could reverse. The data suggest that, in Jeddah, the quality of the local environment for breeding can vary over time. The data support the feasibility of dry season releases but with release numbers needing to be flexible depending on local rates of invasion.

Prickly pear is a species that shows great capability to grow in harsh environments with potential for being used as an energy resource. The aim of this paper was to characterize prickly-pear mature cladodes in terms of chemical, proximal, and fuel properties, as well as to study the drying kinetics of this biomass after pretreatments destined to expose the internal tissues of mature cladodes to different temperatures. The results show that prickly-pear biomass was a poor-quality solid biofuel due to a low calorific value (12.9 MJ/kg d.m.b.) and a high ash content (25.8 wt.%). When drying prickly-pear biomass, cutting the cladodes to expose the internal tissues significantly increased the drying rate, especially when temperatures of 60 °C and above were employed. Double exponential (three parameters) and Midilli were the models that best fitted the experimental curves of prickly-pear cladodes cut into straps and cubes when dried at 50–70 °C. Finally, the bulk density of the prickly-pear biomass was similar to the one obtained for herbaceous biomasses, thus suggesting that further densification will enhance the usability of this feedstock.

Soil organic carbon (SOC) pool has been extensively studied in the carbon (C) cycling of terrestrial ecosystems. In dryland regions, however, soil inorganic carbon (SIC) has received increasing attention due to the high accumulation of SIC in arid soils contributed by its high temperature, low soil moisture, less vegetation, high salinity, and poor microbial activities. SIC storage in dryland soils is a complex process comprising multiple interactions of several factors such as climate, land use types, farm management practices, irrigation, inherent soil properties, soil biotic factors, etc. In addition, soil C studies in deeper layers of drylands have opened-up several study aspects on SIC storage. This review explains the mechanisms of SIC formation in dryland soils and critically discusses the SIC content in arid and semi-arid soils as compared to SOC. It also addresses the complex relationship between SIC and SOC in dryland soils. This review gives an overview of how climate change and anthropogenic management of soil might affect the SIC storage in dryland soils. Dryland soils could be an efficient sink in C sequestration through the formation of secondary carbonates. The review highlights the importance of an in-depth understanding of the C cycle in arid soils and emphasizes that SIC dynamics must be looked into broader perspective vis-à-vis C sequestration and climate change mitigation.

Litter decomposition plays a critical role in carbon and nutrient cycling in terrestrial and aquatic ecosystems. However, the effects transient flooding and soil covering have on leaf litter decomposition remain unclear. The changes of litter mass loss and stoichiometric ratio of C:N (the ratio of carbon to nitrogen), C:K (the ratio of carbon to potassium), C:P (the ratio of carbon to phosphorus), N:P (the ratio of nitrogen to phosphorus), and N:K (the ratio of nitrogen to potassium) of fresh Populus euphratica (P. euphratica) leaves in surface, transient flooding, and soil covering treatments were studied using litterbags in a desert riparian forest in a field decomposition experiment for a period of 640 d. The results showed that there was a significant influence of disturbance type and incubation time on litter mass loss rate and stoichiometric ratios of C:N, C:K, C:P, N:P, and N:K of fresh P. euphratica leaves, but no significant influence of the interaction between disturbance type and incubation time on leaf litter mass loss. In three treatments, five sequential phases of leaf litter mass loss rate and different temporal change patterns of stoichiometric ratio were identified within 640 d. Transient flooding was shown to affect P. euphratica leaf litter mass loss phases compared to that in no-disturbance conditions, and especially promote leaf litter mass loss within 0–173 d of incubation time. It was also demonstrated that transient flooding and soil covering can influence leaf litter decomposition, which led to the leaf litter mass loss rate and the stoichiometric ratios of C:N, C:K, C:P, N:P, and N:K exhibiting varied patterns and phases in different treatments during decay.

Spiny thickets or xerophytic thickets (XTs) are a type of shrubby vegetation found in the far south and southwest of Madagascar, the driest parts of the island. This type of vegetation, which is rich in endemic animal and plant species, is endangered. Extensive local goat breeding (Capra hircus, for meat and milk production) based on XT browsing is an important source of household income. The aim of this paper is to analyse the possibility of using improved goat breeding as an alternative to wood charcoal (WC) production and slash-and-burn agriculture (SBA), which are unsustainable activities. The literature on (i) the impacts of SBA, WC production, and goat browsing on the XT ecosystem and (ii) the income provided by these three activities is reviewed to determine the sustainability level of improved goat husbandry. SBA and WC production reduced XT biomass and XT cover area, while goat browsing alone, at a stocking rate of one head per hectare, did not affect XT leaf biomass production and shrub regeneration. Furthermore, batch breeding and flushing techniques provided the highest annual income, mainly from the sale of surplus animal products. This improved goat husbandry may be a sustainable alternative to WC production. However, increasing the current stocking rate is necessary to surpass the combined income of WC production and SBA. An estimation of the XT carrying capacity would offer a basis to assess whether this ecosystem would support a higher stocking rate.

Rising crop risk for farmers and greater subsidy costs for governments are both associated with changing climatic conditions, including increased water scarcity. The resilience of Agave spp. in both hot and dry conditions, combined with their wide range of uses, position these plants as novel high-yielding crops suitable for both (i) a warming climate and (ii) agricultural regions with finite water resources. A simple model of the physiological response of Agave americana to variations in solar radiation, temperature, and precipitation was used to predict A. americana yields globally at a 4 km spatial resolution for both contemporary climate and high-end warming scenarios. The potential growing region for A. americana expanded by 3–5% (up to 3 million ha) and potential biomass production increased by 4–5% (up to 4 Gt of additional biomass) with climate warming scenarios. There were some declines in biomass with the climate warming projected in smaller dispersed locations of tropical South America, Africa, and Australia. The amount of water required for optimal A. americana yield is less than half of the current water required for other crops grown in semi-arid agricultural regions of the southwestern US, and a similar low water demand can be expected in other semi-arid regions of the world. Rock mulching can further reduce the need for irrigation and increase suitable cropland area for A. americana by 26–30%. We show that >10 Mg ha−1 y−1 of A. americana biomass could be produced on 27 million ha of cropland without requiring irrigation. Our results suggest that cultivation of A. americana can support resilient agriculture in a future with rising temperatures and water scarcity.

Accurate determination of annual leaf litter amount constitutes the basis of scientific leaf litter nutrient release assessment. In this study, we tried to establish an equation between leaf litter amount and relevant tree characteristics of Populus euphratica (P. euphratica) tree on an individual scale, and to find the leaf litter nutrient content variation within 760 d incubation experiment in the main channel of the Tarim River, China. Results showed that there was no proper equation between leaf litter amount and tree height or diameter at breast height. There was great difference in leaf litter amount on an individual scale. The mean annual leaf litter amount per tree was 10.2, 14.83 kg/y obtained by field survey and the equation between annual leaf litter amount and canopy area on an individual scale, respectively. Leaf litter mass changed over incubation time and exhibited three main phases: an initial slow decomposition phase (0–173 d) with mass loss; a rapid mass loss phase (173–290 d); and a second rapid mass loss phase (470–560 d). Overall, carbon (C) and potassium (K) content decreased, and nitrogen (N) and phosphorus (P) content increased in a fluctuating manner over time in the P. euphratica leaf litter.

At gauged watersheds, the time of concentration can be estimated using rainfall-runoff data; however, at ungauged watersheds, empirical methods are used instead. Large errors in the application of empirical methods may cause inaccurate modeling of floods and unreliable structure design. In this paper, methods for calculating the time of concentration (Tc) were compared to identify the best equation for estimating Tc in ungauged watersheds of an arid region. The graphical method, based on measured data, was compared to 15 empirical methods to determine which empirical method returned the best results. The graphical method was applied to 33 rainfall-runoff events in four rural sub-watersheds located in the central parts of Hormozgan province, Iran. A ranking-based procedure was used to select the best performing empirical methods. To minimize bias and improve accuracy, the best performing empirical methods were modified by adjusting their formulas. According to the study, three empirical methods: (1) Williams, (2) Pilgrim and Mac Dermott, and (3) Arizona DOT, performed the best in the study areas. The results also showed that the modified Williams and Arizona DOT’s formulas were able to estimate the time of concentration in ungauged watersheds with an error lower than 1%.

This work compares the performance of vertical subsurface flow treatment wetlands (VSSF TWs) for wastewater treatment, planted with Zantedeschia aethiopica (Za), here operated simultaneously under two different climate conditions, arid and Mediterranean. The experimental setup was divided into two treatment lines for each climate condition: three VSSF TWs planted with Schoenplectus californicus (Sc) (VSSF-S), as the control, and three VSSF TWs planted with Zantedeschia aethiopica (Za) (VSSF-Z), as the experimental unit. The four treatment systems were operated at a hydraulic loading rate of 120 mm/d during spring and summer seasons, in two locations, Iquique (Atacama Desert, Chile) and Talca (Central Valley, Chile). The water quality in effluents, plant development, and water balance were used as performance measures. In terms of the water quality, the influents’ characteristics were similar in both climates and classified as “diluted”. For the effluents, in both climate conditions, average COD and TSS effluent concentrations were below 50 mg/L and 15 mg/L, respectively. In both climate conditions, average TN and TP effluent concentrations were below 40 mg/L and 2 mg/L, respectively. Furthermore, only total nitrogen (TN) and total phosphorus (TP) in effluents to VSSF-Z had a significant effect (p < 0.05) in relation to the climate condition. Regarding plant development, Za showed a lower height growth in both climate conditions, with arid consistently 0.3 m and Mediterranean decreasing from 0.6 m to 0.2 m. However, the physiological conditions of the leaves (measured by chlorophyll content) were not affected during operation time in both climates. Water balance showed that it was not influenced by the climate conditions or plant, with water loss differences below 5%. Therefore, taking into account the water quality and water balance results, Zantedeschia aethiopica can be used in VSSF TWs in a way similar to traditional plants under arid and Mediterranean climates. However, its use has to be carefully considered because lower height could affect the esthetics for its implementation in the VSSF TWs.

Model evaluation against experimental data is an important step towards accurate model predictions and simulations. Here, we evaluated an energy-balance model to predict dew formation occurrence and estimate its amount for East-African arid-climate conditions against 13 months of experimental dew harvesting data in Maktau, Kenya. The model was capable of predicting the dew formation occurrence effectively. However, it overestimated the harvestable dew amount by about a ratio of 1.7. As such, a factor of 0.6 was applied for a long-term period (1979–2018) to investigate the spatial and temporal variation of the dew formation in Kenya. The annual average of dew occurrence in Kenya was ~130 days with dew yield > 0.1 L/m2/day. The dew formation showed a seasonal cycle with the maximum yield in winter and minimum in summer. Three major dew formation zones were identified after cluster analysis: arid and semi-arid regions; mountain regions; and coastal regions. The average daily and yearly maximum dew yield were 0.05 and 18; 0.9 and 25; and 0.15 and 40 L/m2/day; respectively. A precise prediction of dew occurrence and dew yield is very challenging due to inherent limitations in numerical models and meteorological input parameters.

Caves share unique conditions that have led to convergent adaptations of cave-dwelling animals. In addition, local factors act as filters on regional species-pools to shape the assemblage composition of local caves. Surveys of 35 Levantine caves, distributed along a climate gradient from the mesic in the north of Israel to hyper-arid areas in the south of Israel, were conducted to test the effect of cave characteristics, location, climate, bat presence, and guano level on the spider assemblage. We found 62 spider species and assigned four species as troglobites, 28 as troglophiles, and 30 as accidentals. Precipitation, elevation, latitude, minimum temperature, and guano levels significantly affected the composition of cave-dwelling spider assemblages. Caves situated in the Mediterranean region had higher species richness and abundance, as well as more troglobite and troglophile arachnids. These discoveries contribute to the knowledge of the local arachnofauna and are important for the conservation of cave ecosystems. By comparing spider assemblages of Levantine caves to European caves, we identified gaps in the taxonomic research, focusing our efforts on spider families that may have additional cryptic or yet to be described cave-dwelling spider species. Our faunistic surveys are crucial stages for understanding the evolutionary and ecological mechanisms of arachnid speciation in Levantine caves.

Nursery practices are considered major factors influencing seedling quality, which are likely to be maintained in the early establishment phase in the field. Here, we investigated the effects of container depth and substrate heterogeneity on the growth of Populus sibirica nodal seedlings to suggest an effective nursery practice for producing quality seedlings appropriate for forest establishment in a dry environment. We used two substrate heterogeneities (homogeneous and heterogeneous) and two container depth treatments (30 and 60 cm). Variations in root collar diameter (RCD) growth, height growth, stem and root biomass, root to stem ratio, and root mass in the first 15 cm depth from the soil surface across the treatments were computed. Results revealed that both substrate heterogeneity and container depth had no significant effects on the RCD and height growth of P. sibirica seedlings but significantly improved their root and stem biomass. Seedlings in the 60 cm containers generally accumulated higher root biomass than those in the 30 cm containers. There was an interaction effect of container depth and substrate heterogeneity treatments on root and total dry mass, such that seedlings grown in the 60 cm container using heterogeneous substrate resulted in the highest root and total biomass. Analyses of proportional root growth in the upper 15 cm of the containers compared to the total indicated that both the main effects of deeper containers (60 cm) and heterogeneous substrate have fewer roots at this depth, indicating a greater root density in the bottom of the deeper containers. Therefore, deeper containers and heterogeneous substrate may be used as an effective nursery practice to produce seedlings with root traits potentially suitable for harsh conditions, such as arid and semi-arid environments. However, further studies using other seedling morphological traits in conjunction with field-trial tests are needed for a definitive assessment of the effectiveness of deeper containers and heterogeneous substrate in producing good quality seedlings potentially suitable in a dry environment.

Shrub biomass estimation is valuable in assessing ecological health, soil, and water conservation capacity, and carbon storage in arid areas, where trees are scattered, and shrubs are usually dominant. Most shrub biomass estimation models are derived from trees designed for trees, yet shrubs and trees show significant differences in morphology. However, current biomass estimation methods specifically for shrubs are still lacking. This study aimed to test various predictors’ performance in estimating shrub biomass, particularly providing an improved cone frustum volume model as a new predictor. Seven different variables, including three univariates and four composite variables, were selected as predictors in allometric models. Six dominant shrub species of different sizes and morphology in the semi-arid Hunshandake Sandy Land in Inner Mongolia were selected as samples to test the seven predictors’ performances in above-ground biomass estimation. Results showed that the single measurements performed poorly and were not suitable for shrub biomass estimation. The allometric models, including crown-related volumes as predictors, performed much better and were considered ideal for common shrub biomass estimation. The improved cone frustum volume model had more flexible geometric for shrubs of different shapes and sizes, with high fitting accuracy and stability among all the volume predictors. Therefore, we recommend the volume of an inverted cone frustum with a crown diameter and ground diameter as the long and short diameters as an excellent predictor of shrub biomass estimation, especially when studies involve various shrub species, and a general model would be needed.

Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management.

Visible near-infrared reflectance spectroscopy (VNIRS) and laser-induced breakdown spectroscopy (LIBS) are potential methods for the rapid and less expensive assessment of soil quality indicators (SQIs). The specific objective of this study was to compare VNIRS and LIBS for assessing SQIs. Data was collected from over 140 soil samples taken from multiple agricultural management systems in New Mexico, belonging to arid and semiarid agroecosystems. Sampled sites included New Mexico State University Agricultural Science Center research fields and several commercial farm fields in New Mexico. Partial least squares regression (PLSR) was used to establish predictive relationships between spectral data and SQIs. Fifteen soil measurements were modeled including the soil organic matter (SOM), permanganate oxidizable carbon (POXC), total microbial biomass (TMB), total bacteria biomass (TBB), total fungi biomass (TFB), mean weight diameter of dry aggregates (MWD), aggregates 2–4 mm (AGG > 2 mm), aggregates < 0.25 mm (AGG < 0.25 mm), wet aggregate stability (WAS), electrical conductivity (EC), calcium (Ca), magnesium (Mg), sodium (Na), and iron (Fe). Overall, calibrations based on measurements irrespective of locations performed better for LIBS and combined VNIRS-LIBS. Measurements separated according to locations highly improved the quality of prediction for VNIRS as compared to combined locations. For example, the prediction R2 values for regression of VNIRS were 0.19 for SOM, 0.30 for POXC, 0.24 for MWD, 0.15 for AGG > 2 mm, and 0.13 for EC in combined datasets irrespective of location. When separated according to locations, for one of the locations, the predictive R2 values for VNIRS were 0.48 for SOM, 0.70 for POXC, 0.67 for MWD, 0.60 for AGG > 2 mm, and 0.51 for EC. The prediction values varied with the sampling time for both LIBS and VNIRS. For example, the prediction values of some SQIs using VNIRS were higher in samples collected in winter for measurements, including SOM (0.90), MWD (0.96), WAS (0.66), and EC (0.94). Using the VNIRS, the corresponding predictive values for the same SQIs were lower for samples collected in the fall (SOM (0.61), MWD (0.45), WAS (0.46), and EC (0.65)). While this study illustrates the prospects of VNIRS and LIBS for estimating SQIs, a more comprehensive evaluation, using a larger regional dataset, is required to understand how the site and soil factors affect VNIRS and LIBS, in order to enhance the utility of these methods for soil quality assessment in arid and semiarid agroecosystems.

Arid and semi-arid ecosystems are characterized by patchy vegetation and variable resource availability. The interplant spaces of these ecosystems are very often covered by cyanobacteria-dominated biocrusts, which are the primary colonizers of terrestrial ecosystems and key in facilitating the succession of other biocrust organisms and plants. Cyanobacterial biocrusts regulate the horizontal and vertical fluxes of water, carbon and nutrients into and from the soil and play crucial hydrological, geomorphological and ecological roles in these ecosystems. In this paper, we analyze the influence of cyanobacterial biocrusts on water balance components (infiltration-runoff, evaporation, soil moisture and non-rainfall water inputs (NRWIs)) in representative semiarid ecosystems in southeastern Spain. The influence of cyanobacterial biocrusts, in two stages of their development, on runoff-infiltration was studied by rainfall simulation and in field plots under natural rainfall at different spatial scales. Results showed that cover, exopolysaccharide content, roughness, organic carbon, total nitrogen, available water holding capacity, aggregate stability, and other properties increased with the development of the cyanobacterial biocrust. Due to the effects on these soil properties, runoff generation was lower in well-developed than in incipient-cyanobacterial biocrusts under both simulated and natural rainfall and on different spatial scales. Runoff yield decreased at coarser spatial scales due to re-infiltration along the hillslope, thus decreasing hydrological connectivity. Soil moisture monitoring at 0.03 m depth revealed higher moisture content and slower soil water loss in plots covered by cyanobacterial biocrusts compared to bare soils. Non-rainfall water inputs were also higher under well-developed cyanobacterial biocrusts than in bare soils. Disturbance of cyanobacterial biocrusts seriously affected the water balance by increasing runoff, decreasing soil moisture and accelerating soil water loss, at the same time that led to a very significant increase in sediment yield. The recovery of biocrust cover after disturbance can be relatively fast, but its growth rate is strongly conditioned by microclimate. The results of this paper show the important influence of cyanobacterial biocrust in modulating the different processes supporting the capacity of these ecosystems to provide key services such as water regulation or erosion control, and also the important impacts of their anthropic disturbance.

In Mexico, one hundred of the 188 most important aquifers dedicated to agriculture and human consumption are over-exploited and 32 are affected by seawater intrusion in coastal areas. Considering that Mexico relies on groundwater, it is vital to develop a portfolio of alternatives to recover aquifers and examine policies and programs regarding reclaimed water and stormwater. Managed Aquifer Recharge (MAR) may be useful for increasing water availability and adapting to climate change in semi-arid regions of Mexico. In this paper, we present an overview of water recharge projects that have been conducted in Mexico in the last 50 years, their methods for recharge, water sources, geographical distribution, and the main results obtained in each project. We found three types of MAR efforts: (1) exploratory and suitability studies for MAR, (2) pilot projects, and (3) MAR facilities that currently operate. This study includes the examination of the legal framework for MAR to identify some challenges and opportunities that Mexican regulation contains in this regard. We find that beyond the technical issues that MAR projects normally address, the regulatory framework is a barrier to increasing MAR facilities in Mexico.

The fuzzy oil drop model, a tool which can be used to study the structure of the hydrophobic core in proteins, has been applied in the analysis of proteins belonging to the jumonji group—JARID2, JARID1A, JARID1B and JARID1D—proteins that share the property of being able to interact with DNA. Their ARID and PHD domains, when analyzed in the context of the fuzzy oil drop model, are found to exhibit structural variability regarding the status of their secondary folds, including the β-hairpin which determines their biological function. Additionally, the structure of disordered fragments which are present in jumonji proteins (as confirmed by the DisProt database) is explained on the grounds of the hydrophobic core model, suggesting that such fragments contribute to tertiary structural stabilization. This conclusion is supported by divergence entropy measurements, expressing the degree of ordering in each protein’s hydrophobic core.

Groundwater depletion poses a significant threat in arid and semi-arid areas where rivers are usually ephemeral and groundwater is the major source of water. The present study investigated whether an effective water resources management strategy, capable of minimizing evaporative water losses and groundwater depletion while providing water for expanded agricultural activities, can be achieved through aquifer storage and recovery (ASR) implemented in conjunction with water storage in an ephemeral river. A regional development modeling framework, including both ASR and a dam design developed through system dynamics modeling, was validated using a case study for the Sirik region of Iran. The system dynamics model of groundwater flow and the comprehensive system dynamics model developed in this study showed that ASR was a beneficial strategy for the region’s farmers and the groundwater system, since the rate of groundwater depletion declined significantly (from 14.5 meters per 40 years to three meters over the same period). Furthermore, evaporation from the reservoir decreased by 50 million cubic meters over the simulation period. It was concluded that the proposed system dynamics model is an effective tool in helping to conserve water resources and reduce depletion in arid regions and semi-arid areas.

Land-use mapping is critical for global change research. In Central Arizona, U.S.A., the spatial distribution of land use is important for sustainable land management decisions. The objective of this study was to create a land-use map that serves as a model for the city of Maricopa, an expanding urban region in the Sun Corridor of Arizona. We use object-based image analysis to map six land-use types from ASTER imagery, and then compare this with two per-pixel classifications. Our results show that a single segmentation, combined with intermediary classifications and merging, morphing, and growing image-objects, can lead to an accurate land-use map that is capable of utilizing both spatial and spectral information. We also employ a moving-window diversity assessment to help with analysis and improve post-classification modifications.

Trace elements (As, Cd, Cr, Pb, Ba, Fe, Al, Mn and Ba) were uptaken by the leaves of the creosote bush (Larrea tridentata (DC.) Coville) in Nelson, Nevada, although at low concentrations. Samples were collected up-gradient of the mine tailings, the tailings, and down gradient from the source to measure spatial distribution. Data show that trace elements (As, Ba, Cr, Hg, Se) enter L. tridentata through root tissues, migrating to leaf tissue, but at significantly lower levels than that of the source sediments. Metalloid (As and Se) concentrations in the leaf tissues ranged from non-detect to greater than 44 mg kg−1 As and non-detect to over 34 mg kg−1 Se. For trace metals, Hg ranged from non-detect to 0.14 mg kg−1; Ba from 1.74 to 4.12 mg kg−1; and Cr from non-detect to 6.18 mg kg−1 while Ag, Cd, and Pb were not detected in the plant leaves. When comparing the ratio of sediment metal concentration to plant metal concentrations, the Techatticup Wash contained the highest levels of trace elements in the leaves of the L. tridentata, followed by the Carnation Wash, with the Eagle Wash containing the lowest concentrations of trace elements.

Expanding urbanization in highly fragile desert environments requires a thorough understanding of the current state and trends of land uses to achieve an optimal balance between development and the integrity of vital ecosystems. The objectives of this study are to quantify land use change over the 25-year period 1990⁻2015 and analyze temporal and spatial urbanization trends in the Middle Rio Grande Basin. We conclude by indicating how the results can inform on-going water resource research and public policy discussion in an arid region. Results show that the predominant upland mixed vegetation land cover category has been steadily declining, giving up land to urban and agricultural development. Urban development across the region of interest increased from just under three percent in 1990 to more than 11 percent in 2015, mainly around the major urban areas of El Paso, Ciudad Juárez, and Las Cruces. Public policy aspects related to results from this study include transfer of water rights from agriculture to land developers in cities, higher risk of flooding, loss of natural ecosystems, and increased water pollution from point and non-point sources. Various stakeholders can find the study useful for a better understanding of historical spatial and temporal aspects of urban development and environmental change in arid regions. Such insights can help municipal authorities, farmers, and other stakeholders to strike a balance between development needs and protecting vital ecosystems that support the much needed development, especially in regions that are endowed with transboundary natural resources that often are incompletely represented in single nation data.

Prosopis alba seedlings, that grew at the 45 dS m−1 salinity level in a previous study of growth and survival of Argentine and Peruvian Prosopis, were propagated by rooting cuttings and established in a seed orchard/long term evaluation trial on soils with low salinity (EC 5.1–7.5 dS m−1) but high pH (8.9 to 10.2). A pH gradient occurred in the field with values ranging from pH 9.4 in block 1 to pH 10.3 in block 5. After five years growth, almost all of the clones had a mean height greater than 4 m and one clone was more than 5 m. Ten of the 21 tested clones had significantly greater biomass growth than the three seed propagated check varieties. The broad-sense (i.e., clone mean) heritability was estimated to be 0.45 for biomass, 0.53 for diameter and 0.59 for height indicating that strong genetic gains should be possible by selecting and vegetatively propagating the best genotypes. In the block with the highest pH values, two clones that appear to be P. alba × P. ruscifolia hybrids (i.e., P. vinallilo) had the greatest biomass. Correlations between growth during the last two months in the high salinity hydroponic greenhouse selection system and growth in the field were significant (R2 = 0.262) and positive, although the relationship was negative for putative P. vinallilo clones (R2 = 0.938). The several fold increase in biomass of some of the clones over the three check varieties, suggests that the greenhouse screen was successful in identifying superior salt tolerant clones. Apparently whether the greenhouse seedlings had lesser (~1 cm) to greater (~3 cm) height growth was not as important as just having a healthy live apical meristem. The observed salt tolerance of the putative P. vinalillo clones may prove useful as rootstocks for recently described high pod producing P. alba clones.