Semi-arid Environments Research Articles

Unlocking nature’s drought resilience: a focus on the parsimonious root phenotype and specialised root metabolism in wild Medicago populations

Abstract Background and aims Crop wild relatives, exposed to strong natural selection, exhibit effective tolerance traits against stresses. While an aggressive root proliferation phenotype has long been considered advantageous for a range of stresses, it appears to be counterproductive under drought due to its high metabolic cost. Recently, a parsimonious root phenotype, metabolically more efficient, has been suggested to be better adapted to semiarid environments, although it is not clear that this phenotype is a trait exhibited by crop wild relatives. Methods Firstly, we analysed the root phenotype and carbon metabolism in four Medicago crop wild relatives adapted to a semiarid environment and compared them with the cultivated M. truncatula Jemalong (A17). Secondly, we exposed the cultivated (probably the least adapted genotype to aridity) and the wild (the most common one in arid zones) M. truncatula genotypes to water deficit. The carbon metabolism response in different parts of their roots was analysed. Results A reduced carbon investment per unit of root length was a common trait in the four wild genotypes, indicative of an evolution towards a parsimonious root phenotype. During the water deficit experiment, the wild M. truncatula showed higher tolerance to drought, along with a superior ability of its taproot to partition sucrose and enhanced capacity of its fibrous roots to maintain sugar homeostasis. Conclusion A parsimonious root phenotype and the spatial specialization of root carbon metabolism represent two important drought tolerance traits. This work provides relevant findings to understand the response of Medicago species roots to water deficit.

Particulate matter retention and removal efficiency in ten tree species of semi-arid environment

Modern era has witnessed particulate matter (PM) become one of the biggest threats for the existence of biological species. Therefore, a study was performed in Faisalabad city to evaluate PM retention and removal efficiency of ten local tree species. Branch samples were collected from urban, sub-urban and rural areas in September 2020 (183 days after rain), and in August 2021 (30 days after rain). Results showed that total PM load, PM>10 and PM10-2.5 retention was the highest in urban followed by sub-urban and rural area. PM>10, PM10-2.5, total PM, and PM deposition rate decreased significantly in the following order, F. benghalensis > T. arjuna > S. cumini > A. scholaris > F. religiosa > E. camaldulensis > D sissoo > C lancifolius > B. ceiba > M. alba during both years 2020, and 2021. During the artificial rainfall experiment, total PM removed by the species also followed the same order however, PM removal efficiency was the highest in B. ceiba and M. alba followed by E. camaldulensis, C. lancifolius, D. sissoo, T. arjuna, S. cumini, A. scholaris, F. religiosa and F. benghalensis. Therefore, it can be concluded that species selection must be done skillfully for congested urban environment.

Quantifying the rainfall variability effects on crop growth and production in the intensified annual forage - winter wheat rotation systems in a semiarid region of China

Replacing summer fallow period (July to September, SF) with annual short-season forages in the traditional fallow-winter wheat (Triticum aestivum L.) system may maintain grain yield and improve productivity in the semi-arid environments. But the uneven and variability rainfall led to instable productivity of the annual forage–winter wheat cropping system. The aims of this study were to 1) quantifying rainfall variability effects on annual forage–winter wheat system crop growing process and productivity; 2) determine the optimal annual forage–winter wheat production system that will response better to future climate change. A four-year (2016–2020) field experiment was conducted to investigate the impact of replacing summer fallow period with annual forages including oat (FO, Avena sativa L.), soybean (SB, Glycine max L.), and vetch (FV, Vicia sativa L.) on plant height (H), leaf area index (LAI), and above-ground biomass (AByield) growth index dynamics under three different levels of rainfall manipulation i.e. 30 % of ambient rainfall exclusion (R-30 %), natural rainfall (CK), and 30 % of ambient rainfall increase (R+30 %). Additionally, we assessed the correlations between forage and winter wheat production with growing season precipitation across 12 rainfall scenarios. Average forage biomass values of oat, soybean, and vetch were 5.50, 4.29, and 2.82 t ha−1, respectively during summer fallow period. The average winter wheat grain yield values in SF, FO, SB, and FV were 3.78, 3.12, 4.02, and 3.18 t ha−1, respectively. Integrating oat into fallow period had negative effects on wheat growth and production, and the H, LAI, and AByield for FO were 63.7 %, 50.9 %, and 29.9 % lower than SF in dry year, but the wheat grain yield in SB were 18.2 % and 24.8 % greater than SF in normal and wet years. Across the four growing seasons, the forage and wheat yields were shown to be strongly related to precipitation, and increasing precipitation significantly enhanced the production. In 2016–2017 growing season, LAI of wheat in SF, FO, SB, and FV with R+30 % scenario was increased by 30.2 %, 21.7 %, 32.7 %, and 19.8 % and that with R-30 % scenario decreased by 23.2 %, 17.8 %, 24.7 %, 16.5 % compared CK, respectively. The traditional summer fallow practice had advantage for maintaining stability in wheat gain production, especially under dry years. In consideration of forage and wheat production to rainfall variability, integrating soybean into fallow season may be an efficient option to maintain wheat yield and produce high forage amount under future climate change on the Loess Plateau and similar semi-arid regions.

Evaluating the spatiotemporal patterns of drought characteristics in a semi‐arid region of Limpopo Province, South Africa

Drought is a complex phenomenon resulting from below-average rainfall and is characterized by frequency, duration, and severity, occurring at a regional scale with dire consequences, especially in semiarid environments. This study used the Reconnaissance Drought Index (RDI) to assess drought severity in two district municipalities in Limpopo Province. Rainfall and air temperature data from 12 stations covering 1970–2020 were obtained from the Agricultural Research Council. The calculation of RDI relies on the monthly accumulation ratio of total rainfall to potential evapotranspiration (PET). For this study, PET was estimated using the Hargreaves and Samani temperature-based approach. The RDI results showed a high spatial–temporal variation in drought characteristics over the study area. All stations experienced extreme drought conditions in different years, with the maximum drought severity (-3.40) occurring from 2002–2003 in the western parts of the study area, indicating extreme drought. Furthermore, the results revealed continuous drought conditions over various periods, including severe droughts between 1995 and 1998 and between 2014 and 2016, with the severity varying between mild and moderate drought conditions. The results reveal notable but nonuniform drought patterns as the climate evolves, with potential implications for water availability and livelihoods. The study's findings underscore the significance of adopting multidimensional approaches to drought assessment that encompass meteorological and hydrological factors to inform strategies for adaptive water management and policy formulation in the face of a changing climate.

Response of Alternaria and Fusarium Species to Low Precipitation in a Drought-Tolerant Plant in Morocco

The plant mycobiome plays a crucial role in the host life cycle, influencing both healthy and diseased states, and is essential for plant tolerance to drought. In this study, we used ITS metabarcoding to investigate the fungal community of the drought-resistant plant Malva sylvestris L. in Morocco along a gradient of precipitation, encompassing subhumid and semi-arid environments. We sampled three biotopes: rhizosphere, bulk soil, and root endosphere. Our findings revealed an absence of beta-diversity differences between bulk soil and rhizosphere, indicating that the plant does not selectively influence its rhizosphere mycobiome. Additionally, ASVs belonging to the genus Alternaria represented up to 30% of reads in the plant’s roots and correlated with drought (p = 0.006), indicating a potential role for this fungal genus in mitigating drought, possibly as part of the dark septate endophyte group. Root staining and microscopic observation revealed extensive colonization by fungal hyphae and microsclerotia-like structures. Furthermore, ASVs identified as Fusarium equiseti were also correlated with low precipitation and recognized as a hub taxon in the roots. However, it remains uncertain whether this species is pathogenic or beneficial to the plant. These insights contribute to our understanding of the plant mycobiome’s role in drought tolerance and highlight the importance of specific fungal taxa in supporting plant health under varying environmental conditions. Future research should focus on characterizing these taxa’s functional roles and their interactions with the host plant to further elucidate their contributions to drought resistance.

Cutting-edge approaches for judging surface water dynamics in semi-arid environments: Integrating landsat 8 OLI/TIRS and HYDROSAM model

This research addresses the critical need to assess surface water dynamics in semi-arid regions of Andhra Pradesh, India, by using advanced Spectral Indices for hydrological applications and methodologies. It aims to answer how remote sensing data from Landsat 8 OLI/TIRS can be effectively used to monitor and classify surface water bodies. The study applies indices such as Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Thermal Index (NDTI), and Water Ratio Index (WRI). Additionally, the Weighted Composite Index (WCI) and Normalized Composite Index (NCI) are integrated with Principal Component Analysis (PCA) for multi-criteria decision making. The HYDROSAM model successfully classifies and maps various categories of surface water bodies, including non-water features (53.62%), urban water zones (37.89%), seasonal water bodies (4.32%), transitional zones (2.17%), permanent water bodies (1.05%), and river bodies (0.95%). The resultant map was validated using the AUC-ROC curve, achieving an AUC of 0.820, indicating a high level of accuracy. This methodology provides a nuanced understanding of water resource distribution and availability in the region. The findings demonstrate the robustness and reliability of the HYDROSAM model in accurately assessing surface water characteristics, thereby providing critical insights for informed water resource management, strategic land-use planning, and effective ecological conservation. This innovative methodology not only fosters sustainable water resource management in semi-arid regions but also sets a precedent for advancing research in similar ecosystems globally.

Enhanced mass scattering efficiencies of background dust aerosols over East Asia following the passage of dust plumes

The transported dust particles significantly impact global weather and climate. Previous studies focused primarily on the physical and chemical properties of dust plumes, but the interaction mechanism between these dust plumes and background dust aerosols remains unclear. We explored this issue using in-situ observation data of the East Asia dust from the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) during January 2013. We identified a dust plume originating from the Gurbantunggut Desert, which triggered a four-day dust event with an average dust concentration of 67.2 μg m−3. Notably, this dust event led to a significant increase in mass scattering efficiencies of dust, shifting from an inverted U-shape to a continuously increasing pattern with wavelength. The enhanced dust mass scattering efficiency inhibited the development of the planetary boundary layer. These changes persisted for at least two weeks after the event, primarily due to the resuspension of deposited dust particles altering the size of background dust particle. Our findings highlight the ability of dust plumes to enhance the scattering efficiency of background dust aerosols and providing new insights into the complex interactions between dust and the atmosphere.

Effect of different concentrations of soil and foliar applied zinc, boron and iron fertilizers on seedling growth, chlorophyll content and productivity of chickpea seedlings under semi-arid environment

The effectiveness of zinc (Zn), boron (B), and iron (Fe) is reduced in semi-arid regions, which can lead to a deficiency of these nutrients and inhibit chickpea productivity. In this work, three field experiments were executed over two years where soil and foliar applications of Zn, B, and Fe were carried out, including controls (Zn0, B0, and F0), soil application at 4.125 kg/ha (Zn-1, B1, and F1) and 8.25 kg/ha (Zn-2, B2, and F2), and foliar spay at 0.3% at flowering initiating (Zn-3, B3, and F3) and one week after flowering initiation (Zn-4, B4, and F4, respectively). The results indicate that the deficiency of these nutrients inhibited chickpea growth and yield, leading to a reduction in the pigment contents. Nonetheless, soil and foliar application of Zn, B, and Fe significantly improved growth, chlorophyll contents, and yield, showing a dose-dependent effect. The best results were recorded for Zn-3, B2, and F2 treatments which significantly (P<0.05) increased shoot length (20.96-85.19%), root length (42.85-93.65%), shoot fresh (23-76%) and root fresh weight (45-90.32%), compared with the control treatment. Chlorophyll parameters, including chlorophyll a and b contents, showed similar trends. Zn-3, B2, and F2 treatments significantly increased biological and grain yields, which were associated with higher values of the number of pods per plant and the number of seeds per pod. In a nutshell, we suggest that Zn foliar application at 0.3% at flowering initiation and soil application of B and Fe at 25 kg/ha are beneficial for improving the growth, pigment content, and overall productivity of chickpea.

Measurement and modelling of kinetic energy and erosivity of rainfall and throughfall in a tropical semiarid region

Empirical relationships have been widely used to estimate kinetic energy of rainfall (KE) and its erosivity potential (EI30) due to scarcity of direct and continuous measurements. Two disdrometers were installed − one in an open field and another under the canopy − in a fragment of Tropical Dry Forest (TDF) in a semiarid region, Northeast of Brazil, to quantify the role of the canopy on attenuating rain kinetic energy and erosivity potential, for all events and at the seasonal scale. The data was also used to test available KE and EI30 models, as well as to recalibrate the models for the study area. Data were collected from December/2019 to July/2021, totaling 95 events. Rainfall (R) was higher than throughfall (TF) due to canopy interception, but rainfall duration and the total number of drops were higher under the canopy. The TDF canopy reduces rainfall kinetic energy by 30% and its erosivity potential by 39%, with higher KE and EI30 attenuation being observed during the high leaf density stage, which highlights the major seasonal role of this vegetation in protecting the soil surface from the impact of raindrops and, consequently, against erosion. The relationship between KE and mean intensity of R and TF is best expressed as a function of time (J m−2 h−1), by a linear model (r2 > 0.98 – p-value = 0.000). The Wischmeier and Smith-WS model for KE and EI30 was recalibrated for the semiarid region, underestimating KE of open field rainfall by 2% and overestimated TF KE by 1%, against underestimations of 24% and 20% of the original model for R and TF, respectively. The results presented here contribute to understanding the role of dry forests in attenuating the erosivity potential of the rainfall in semiarid environments, as well as to improving the capacity on predicting rainfall and throughfall erosivity under such conditions.

Leveraging machine learning and Landsat time series for high-resolution mapping of mining-induced vegetation changes in Ouagadougou, Burkina Faso

Quarries are open pit mines, where construction materials such as granite and clay are extracted from the earth's surface, leading to a major disturbance of vegetation and subsequent degradation of ecosystem services. Assessing mining-associated degradation over time remains a challenge. This paper uses a temporal series of Landsat satellite image extracted from Google Earth Engine (GEE) for monitoring mining effects on land cover around the West African city of Ouagadougou, Burkina Faso. We conducted a binary classification of quarry and non-quarry using Landsat 9 images. To account for varying quarry patterns, we applied the LandTrendr algorithm to analyze images from 1990 to 2022, mapping annual vegetation disturbance and recovery. This generated high-resolution land cover maps with over 90 % accuracy across 257 quarries (38 km²). LandTrendr identified disturbance years with 87 % accuracy and assessed recovery in abandoned quarries. Remote sensing accuracy was validated through fieldwork and analysis of historical aerial photos. Our findings revealed that major disturbances coincided with new quarrying activities, particularly during urban expansion from 2005 to 2019, with NDVI decreasing by 0.3 in active mining areas. Vegetation recovery was observed in 23 % of the abandoned quarries, with NDVI increases by 0.15 within five years post abandonment, particularly in clay quarries. The strongest recovery occurred in given up abandoned from 2015 to 2019, aligned with increased conservation efforts. Our approach offers valuable insights into mining impacts and vegetation resilience in semi-arid environments, informing sustainable urban planning and environmental management in rapidly transforming regions of West Africa.

Cover Crop Systems Impact on Biomass Production, Carbon-to-Nitrogen Ratio, Forage Quality, and Soil Health in a Semi-arid Environment

Cover cropping is a soil restorative strategy that can save degraded soils and offer additional benefits relative to the traditional fallow-based practice in semi-arid cropping systems. This study aimed to (i) quantify the above (shoot)- and belowground (root) biomass production, nutritive value, and tissue carbon and nitrogen concentrations from different annual cool-season cover crop systems, and (ii) determine their effects on soil organic carbon, total nitrogen, soil respiration, and soil microbial population biomass in a semi-arid environment. Treatments used were monocultures of annual ryegrass (Lolium multiflorum Lam.), oat (Avena sativa L.), faba bean (Vicia faba L.), yellow sweetclover [Melilotus officinalis (L.) Lam], winter pea (Pisum sativum L.), two three-species mixtures (Mix 1: annual ryegrass + faba bean + yellow sweetclover and Mix 2: Oat + faba bean + winter pea), and a fallow laid out in a randomized complete block design with three replications. Averaged across years, Mix 2 produced greater shoot biomass (9714 kg DM ha-1; SE = 699) than all other cover crop systems except, the monoculture of oat (7970 kg DM ha-1; SE = 699). The plant tissue C/N ratio of the mixtures and monoculture legumes was mostly similar (range = 19.4 – 29.1). Overall, legumes produced superior relative feed value (RFV; 112 – 161) compared to grass monocultures and mixtures (RFV; 80 – 95). Soil gram-negative bacteria biomass was greatest under the yellow sweetclover monoculture. Based on the results of this study, the mixed cover crop systems (Mix 1 and Mix 2) offered a better chance of fulfilling the dual role of soil health improvement and feed quality for livestock in this semi-arid environment.

Agro-biochemical, microbiological parameters and economics of mustard (Brassica juncea L.) as influenced by nutrient sources under semi-arid environment

Agro-biochemical, microbiological parameters and economics of mustard (Brassica juncea L.) as influenced by nutrient sources under semi-arid environment

Dissipation and persistence behaviour of fipronil and its metabolites in chilli fruits using GC-ECD, confirmed by GC-MS, under semi-arid conditions

Chilli, one of the most popular vegetables in the world is infested by many insect-pests and diseases. Fipronil, a phenylpyrazole class insecticide is used to manage insect-pests on chilli. The present study aimed to analyze the dissipation patterns and residual concentrations of the fipronil and its metabolites in chilli fruits during the Kharif season of 2020–21, in semi-arid environment. In the study, fipronil was applied to the plants twice, with a 10-day gap between treatments, using a 5 % suspension concentrate. The applications were undertaken using two separate concentrations i.e. the lower dosage of 40 g a.i. ha−1 and higher dosage of 80 g a.i. ha−1. There were four sets of data for each concentration. The chilli crop was systematically sampled at predetermined intervals after the application of the second spray. The procedure encompassed utilizing the modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) technique for extraction and purification, followed by analyzing the resulting residues using Gas Chromatography Electron Capture Detector. Gas Chromatography- Mass Spectrometry was subsequently conducted for the confirmation of the findings. The research revealed that the mean initial deposit of fipronil and its metabolites (desulfinyl, sulphide, and sulfone) at the authorized dosage was determined to be 0.574, 0.123, 0.031, and 0.180 mg kg−1, respectively. In contrast, when administered at twice the prescribed dosage, the mean first deposit was 1.204, 0.230, 0.067, and 0.382 mg kg−1. The half-life values of these residues exhibited a range of 1.2–4.1 days for both dosages. A prudent waiting duration was determined for the doses, leading to the conclusion that an average interval of 7 days is deemed safe for harvesting chilli peppers. The significance of this discovery is related to the maximum residue limits of 0.001 mg kg−1 for fipronil in green chilli, as established by the Food Safety and Standards Authority of India. This study provides significant insights into fipronil's persistence and proper management in chilli plant cultivation, emphasizing the importance of following prescribed dosages and designated waiting intervals to ensure the safety of food products.

Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine

In semi-arid agro-pastoral environments of Africa, beekeeping is widely recognized as an important activity to improve and diversify livelihoods. Although the scientific identification of suitable honey bees (Apis mellifera ssps.) forages may guide beekeepers to set up apiaries or to timely move honey bee colonies to exploit bee forage resources available in various landscapes, the characterization and mapping of bee forage classes is challenging. We evaluated how various data sources and classification algorithms in Google Earth Engine (GEE) affect the accuracy of honey bee forage class mapping in a semi-arid region of Ethiopia. Predictors derived from multi-source satellite data, such as high-resolution Planet imagery (P), Sentinel 1 RADAR (S1), Sentinel 2 multispectral (S2), and Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) were tested and best predictors were identified using Forward Feature Selection (FFS). Four machine learning algorithms (Gradient Tree Boost (GTB), Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM)), all available in GEE, were compared and ensembled for honey bee forage class mapping. The results show that the highest accuracy is obtained by all four algorithms when combining P, S1, S2, and DEM compared to using predictors from a single data source or any other combinations. GTB had higher overall accuracy (90.9%) than RF (88.2%), CART (85.5%), or SVM (79.9%). Nonetheless, the highest overall accuracy (94.7%) was obtained when integrating the four machine learning algorithms in an Ensemble Learning Approach (ELA). Applying ELA improved the classification accuracy by 3.8%, 6.5%, 9.2%, and 14.8% compared to single learner classification algorithms (i.e., GTB, RF, CART, and SVM, respectively). This study demonstrates an improved classification accuracy for honey bee forage class mapping in tropical rangeland by applying ELA, which can provide a better approach for monitoring and managing bee forage resources.

Insights into Archaeological and Modern Neotropical Biomes: Examining Diet and Shape Variation through White-Tailed Deer Lower Third Molar

The white-tailed deer (Odocoileus virginianus Zimmermann 1780) holds significant ecological importance across the Americas, both historically and in modern times. This species ranges from southern Canada to Brazil and exhibits polytypic characteristics, adapting well to diverse habitats including temperate, subtropical, semi-arid, rainforest, and savanna environments. In paleontology and archaeology, the comparison of dental characteristics between extant mammal species with known diets is commonly employed to infer the feeding behaviors of their ancient counterparts. This method assumes that extant and fossil species share similar dietary preferences, aiding in the identification of past environmental contexts. Consequently, we employed a multiproxy approach, combining the study of dental wear and 2D geometric morphometrics, to investigate potential relationships between molar shape, diet, and biomes among extant white-tailed deer populations across the Americas. Our analysis included a comparison with archaeological data from Panama. We sampled 274 extant lower second molar specimens for micro- and mesowear analysis, along with 105 lower third molar specimens from natural science museums for 2D geometric morphometric analysis. These were compared with a sample of 65 archaeological specimens from Panama. Our findings revealed distinct variations in the shape of lower m3 molars among extant white-tailed deer populations across different biomes, with notable differences observed in the archaeological samples as well. Micro- and mesowear analyses also indicated biome-related differences, suggesting a general browsing diet for white-tailed deer with nuanced variations across biomes. Mesowear analysis further suggested a dietary spectrum ranging from pure browsers to browser-mixed feeders. These findings offer valuable insights for the interpretation of fossil deer specimens recovered from archaeological sites.

Dynamic simulation modeling for sustainable water management with climate change in a semi-arid environment

Climate change is a significant challenge for sustainable water management in arid and semi-arid environments. The practical importance of the study lies in its use of system dynamics (SD) methodology for sustainable water management in semi-arid regions under climate change. This approach could provide a valuable tool for policy makers and stakeholders to mitigate the impacts of climate change on water resources. The objective of this study is to investigate the impacts of climate change on irrigation water withdrawals (IWW) in the southeast New Mexico (SENM), USA, and to evaluate some adaptation practices such as improving irrigation efficiency and changing cultivation patterns. By evaluating the effectiveness of these practices in reducing IWW, this study may also contribute to improving the resilience of water systems under climate change. The study utilized the SENM model, which is based on SD modeling. The model assesses the impacts of climate change impacts and proposed adaptation practices on IWW, aiming to enhance our understanding of their trends and behavior over the simulation period. Eight dynamic simulations were conducted to analyze and evaluate the performance of IWW reduction over the time span from 2000 to 2100. The model simulation results indicate that climate change will lead to increased IWW. The annual IWW have increased by an average of 1.18 % to 1.97 % due to climate change. A range of IWW reductions from 3 % to 57 % was associated with different combinations of improved conveyance and field irrigation efficiencies and changes in crop-cultivated areas that need significant amounts of irrigation water. The practices aimed at reducing the larger irrigated crop areas were most effective in decreasing IWW. Changing the cultivation pattern has a greater impact on IWW than improving irrigation efficiency practices alone. Decreasing cultivated areas reduced IWW by an average of about 42 %, and improving irrigation efficiency reduced them by an average of 12 %. However, combining both approaches result in the greatest net reduction in IWW. The combined practices of cropping and irrigation management are associated with greatly reduced IWW by an overall average reduction of 52 %. The study also shows that SD modeling enhances our understanding and evaluation of climate change’s impact on water resources in the SENM.

The implications of climate change on freshwater resources in the arid and semiarid Mediterranean environments using hydrological modeling, GIS tools, and remote sensing.

Precipitation partitioning in arid and semiarid environments is not well understood due to scanty precipitation, its temporal distribution, and the lack/absence of adequate measurements of the hydrometeorological components. Simulation methods have the potential to bridge the data gap, thereby providing a window to estimate the water balance components. The present investigation evaluates the water balance components of a typical watershed situated in the southeastern Mediterranean for the period 1979 through 2019 using daily meteorological data and a grid spacing of 250 m. Generated runoff results were commensurate with corresponding values obtained using the SWAT model. Computed groundwater recharge is also compatible with recharge values calculated using the chloride mass balance method. Results show that average runoff and groundwater recharge for the entire period was ⁓24 mm a-1 and 19 mm a-1, giving a precipitation ratio of 9.5% and 7.5%, respectively. Substantial interannual variability in the water balance components was observed during the study period which reflected the significant precipitation fluctuations typifying the Eastern Mediterranean. Results show that the period extending from 1998/1999 through 2018/2019 witnessed an 18% drop in annual precipitation, while surface runoff and groundwater recharge experienced a reduction of ⁓34% and ⁓67%, respectively. Although groundwater recharge is a complex function of numerous meteorological and geological factors, the NDVI can provide an excellent indicator of groundwater recharge in marginal Mediterranean environments. This is highly beneficial in areas where climate records are scanty or absent. The presented results emphasize the significant impacts of global warming and aridification on the future availability of water resources in the semiarid marginal climates in the Eastern Mediterranean and point out clearly that water resources in this area will become scarcer, leading to multiple security threats at national and regional levels.

Global-scale water security and desertification management amidst climate change.

Deserts and semi-arid environments are habitats torare species, rich cultural heritage, and essential ecological processes. Approximately 46% of the world's surface area is covered by drylands (arid, semi-arid, and dry sub-humid areas), where 3 billion people live and unfortunately witness water insecurity and desertification implications. In this context, the present study argued that reduced dryland ecosystem services and decreased ecosystem health have resulted from the individual and compounding impacts of desertification, water scarcity, and climate change. At 1.5°C, 2°C, and 3°C of global warming, under the shared socio-economic pathway SSP2, the number of people living in drylands who will be affected by various effects on water, energy, and land sectors is projected to reach 951 million, 1152 million, and 1285 million, respectively. Due to combinations of land use change, rainfall variations, fire suppression, and CO2 fertilization, as well as unsustainable management, widespread woody encroachment has occurred in many shrublands and savannas in Africa, Australia, North America, and South America. This hasaltered biodiversity and reduces ecosystem services, such as water availability and grazing potential. The north side of the Mediterranean, southern Africa, and North and South America are projected to have the most semiarid expansion. Contrarily, drylands are expected to shrinkin India, northern China, eastern equatorial Africa, and the southern Sahara. Growing research evidence highlights the adoption of policy frameworks deriving the solutions from soil land management (SLM), indigenous and local knowledge (ILK), early warning systems coupled with adaptation and mitigation responses, and targets of sustainable development goals (SDGs).

Contrasting regulation of leaf gas exchange of semi-arid tree species under repeated drought.

Predicting how plants respond to drought requires an understanding of how physiological mechanisms and drought response strategies occur, as these strategies underlie rates of gas exchange and productivity. We assessed the response of 11 plant traits to repeated experimental droughts in four co-occurring species of central Australia. The main goals of this study were to: (i) compare the response to drought between species; (ii) evaluate whether plants acclimated to repeated drought; and (iii) examine the degree of recovery in leaf gas exchange after cessation of drought. Our four species of study were two tree species and two shrub species, which field studies have shown to occupy different ecohydrological niches. The two tree species (Eucalyptus camaldulensis Dehnh. and Corymbia opaca (D.J.Carr & S.G.M.Carr) K.D.Hill & L.A.S.Johnson) had large reductions in stomatal conductance (gs) values, declining by 90% in the second drought. By contrast, the shrub species (Acacia aptaneura Maslin & J.E.Reid and Hakea macrocarpa A.Cunn. ex R.Br.) had smaller reductions gs in the second drought of 52 and 65%, respectively. Only A. aptaneura showed a physiological acclimatation to drought due to small declines in gs versus ᴪpd (0.08 slope) during repeated droughts, meaning they maintained higher rates of gs compared with plants that only experienced one final drought (0.19 slope). All species in all treatments rapidly recovered leaf gas exchange and leaf mass per area following drought, displaying physiological plasticity to drought exposure. This research refines our understanding of plant physiological responses to recurrent water stress, which has implications for modelling of vegetation, carbon assimilation and water use in semi-arid environments under drought.

Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning

Understanding the dynamics of Fractional Vegetation Cover (FVC) is crucial for effective environmental monitoring and management, especially in regions like Pakistan that are sensitive to climate change. This study employs an innovative approach using MODIS NDVI data and the Pixel Dichotomy Model (PDM) to analyze the spatiotemporal dynamics of FVC across Pakistan from 2003 to 2020. Our findings reveal an overall increasing trend in FVC, with the highest value recorded in 2017 (0.37) and the lowest in 2004 (0.26). The Hurst exponent analysis (R/S ratio = 0.718) indicates a degree of long-term memory in the FVC time series. Rainfall was found to positively correlate with FVC (r = 0.6), while Land Surface Temperature (LST) and the Compounded Night Light Index (CNLI) exhibited negative correlations (r = −0.59 and r = −0.43, respectively). The Random Forest regression model highlighted CNLI as the most influential predictor (importance = 62.4%), emphasizing the need to consider human-induced factors in environmental management. These results provide critical insights for sustainable land management and contribute to understanding vegetation-climate interactions in arid and semi-arid environments."