Desert Research Articles

Two-Year Hypertension Incidence Risk Prediction in Populations in the Desert Regions of Northwest China: Prospective Cohort Study.

Hypertension is a major global health issue and a significant modifiable risk factor for cardiovascular diseases, contributing to a substantial socioeconomic burden due to its high prevalence. In China, particularly among populations living near desert regions, hypertension is even more prevalent due to unique environmental and lifestyle conditions, exacerbating the disease burden in these areas, underscoring the urgent need for effective early detection and intervention strategies. This study aims to develop, calibrate, and prospectively validate a 2-year hypertension risk prediction model by using large-scale health examination data collected from populations residing in 4 regions surrounding the Taklamakan Desert of northwest China. We retrospectively analyzed the health examination data of 1,038,170 adults (2019-2021) and prospectively validated our findings in a separate cohort of 961,519 adults (2021-2023). Data included demographics, lifestyle factors, physical examinations, and laboratory measurements. Feature selection was performed using light gradient-boosting machine-based recursive feature elimination with cross-validation and Least Absolute Shrinkage and Selection Operator, yielding 24 key predictors. Multiple machine learning (logistic regression, random forest, extreme gradient boosting, light gradient-boosting machine) and deep learning (Feature Tokenizer + Transformer, SAINT) models were trained with Bayesian hyperparameter optimization. Over a 2-year follow-up, 15.20% (157,766/1,038,170) of the participants in the retrospective cohort and 10.50% (101,077/961,519) in the prospective cohort developed hypertension. Among the models developed, the CatBoost model demonstrated the best performance, achieving area under the curve (AUC) values of 0.888 (95% CI 0.886-0.889) in the retrospective cohort and 0.803 (95% CI 0.801-0.804) in the prospective cohort. Calibration via isotonic regression improved the model's probability estimates, with Brier scores of 0.090 (95% CI 0.089-0.091) and 0.102 (95% CI 0.101-0.103) in the internal validation and prospective cohorts, respectively. Participants were ranked by the positive predictive value calculated using the calibrated model and stratified into 4 risk categories (low, medium, high, and very high), with the very high group exhibiting a 41.08% (5741/13,975) hypertension incidence over 2 years. Age, BMI, and socioeconomic factors were identified as significant predictors of hypertension. Our machine learning model effectively predicted the 2-year risk of hypertension, making it particularly suitable for preventive health care management in high-risk populations residing in the desert regions of China. Our model exhibited excellent predictive performance and has potential for clinical application. A web-based application was developed based on our predictive model, which further enhanced the accessibility for clinical and public health use, aiding in reducing the burden of hypertension through timely prevention strategies.

Integrated metabolomics, transcriptomic, and phytohormonal analyses to study the effects of water stress and foliar abscisic acid application in Thymus species using LC-MS/MS

Thyme species, including Thymus vulgaris, T. kotschyanus (drought-tolerant) and T. serpyllum (drought-sensitive), are valuable medicinal herbs. They are often grown in arid regions and are increasingly suffering from water stress due to climate change. Here, we analyzed the metabolome and expression of selected genes in leaves of these species under drought stress with and without treatment with the phytohormone abscisic acid (ABA). Among the terpenes, dominant metabolites in thyme, thymol was the most important terpenoid component, followed by thymoquinone, carvacrol and p-cymene in all three species. Drought stress reduced terpene concentrations, while moderate ABA levels increased them. T. kotschyanus showed the highest concentrations of thymol and carvacrol after combined treatment with drought and ABA. Metabolite accumulation was partially correlated with genes related to terpenoid biosynthesis. The combined treatment of drought stress and ABA resulted in a significant reduction of the stress hormone jasmonic acid and an increase of its biosynthetic precursor, OPDA (cis-12-oxophytodienoic acid), in all species. The present research results indicate that ABA treatment at moderate concentrations could be used as a measure to increase the production of some pharmaceutically active phenolic monoterpenes in T. vulgaris, T. serpyllum and T. kotschyanus and increase the stress resistance of the plants.

Research on the Method of Extracting Water Body Information in Central Asia Based on Google Earth Engine

This study evaluates water body changes in Central Asia (2000–2019) using Landsat 7 data on Google Earth Engine, comparing the Modified Normalized Difference Water Index (MNDWI), OTSU algorithm, and random forest (RF). The random forest algorithm demonstrated the best overall performance in water body extraction and was selected as the analysis tool. The results reveal a significant 11.25% decline in Central Asia’s total water area over two decades, with the Aral Sea shrinking by 72.13% (2000–2019) and southern Kyrgyzstan’s glaciers decreasing by 39.23%. Pearson correlations indicate strong links between water loss and rising temperatures (−0.5583) and declining precipitation (0.6872). Seasonal fluctuations and permanent degradation (e.g., dry riverbeds) highlight climate-driven vulnerabilities, exacerbated by anthropogenic impacts. These trends threaten regional water security and ecological stability, underscoring the urgent need for adaptive resource management. The RF-GEE framework proves effective for large-scale, long-term hydrological monitoring in arid regions, offering critical insights for climate resilience strategies.

Advancing pearl millet yield forecasting: Comparative analysis of individual and ensemble machine learning approaches over Rajasthan, India.

Pearl millet (Pennisetum glaucum L.) is a resilient crop known for its ability to thrive in arid and semi-arid regions, making it a crucial staple in regions prone to drought. Rajasthan, a state in India, emerged as the top producer of pearl millet. This study enhances yield forecasting for pearl millet using machine learning models across nine districts viz. Jaipur, Ajmer, Jodhpur, Bikaner, Bharatpur, Alwar, Sikar, Jhunjhunu and Nagaur in Rajasthan, India. Data from 1997-2019 (23 years), including yield data from the Directorate of Economics and Statistics and weather data from the NASA POWER web portal, were analysed. The study employed individual machine learning methods (GLM, ELNET, XGB, SVR and RF) and their ensemble combinations (GLM, ELNET, Cubist and RF). Discerning the overall best performing model across all locations remained challenging. For instance, while ensemble models exhibited subpar performance in Barmer and Nagaur, their performance ranged from satisfactory to commendable in other locations. To identify the best model, all models were ranked based on their R2 and nRMSE (%) values. Combined average ranks during training and testing revealed the model performance ranking as I-XGB (3.83) > I-GLM (4.28) > E-ELNET (4.32) > I-RF (4.67) > E-GLM (4.88) > I-SVR (4.90) > I-ELNET (4.94) > E-RF (6.03) > E-Cubist (7.15), where I denotes individual model, while E denotes ensemble model. Intriguingly, while individual GLM and XGB models demonstrated superior performance during calibration, they exhibited poorer performance during validation, potentially indicating issues of data overfitting. Hence, the ensemble ELNET approach is recommended for accurate prediction of pearl millet yield, followed by the individual RF model. These performances underscore the importance of tailored model selection based on specific geographic and environmental conditions.

Assessment of ecological risk under different SSP-RCP scenarios of the Xinjiang province in China

The ecological research of regional land use and land cover change (LULCC) under the Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP) scenarios proposed by the IPCC has become a prominent topic. This study investigates the spatial distribution of ecological risks associated with land use and land cover changes in the arid and semi-arid regions of Xinjiang under future SSP-RCP scenarios. In this paper, LUCC data, climate data, and soil and topographic data under different scenarios in 2100 were adopted to construct the land use/land cover quality index (LQI), the climate quality index (CQI), and the soil quality index (SQI) respectively. Using Xinjiang as a case study, an integrated ecological risk model was constructed through the LQI, CQI and SQI. By 2100, LQI risk areas in Xinjiang will dominate, accounting for approximately 70% of the total area. Central Xinjiang, home to the largest desert in China—the Taklimakan Desert—predominantly consists of desert and Gobi, where LQI is generally better under the SSP-RCP126 scenario compared to other scenarios. In 2100, high CQI regions are primarily concentrated in the mountainous, Gobi, and desert areas of Xinjiang. The spatial distribution of SQI in Xinjiang remains consistent across different scenarios in 2100. Under the SSP-RCP126 and SSP-RCP245 scenarios, global warming is effectively mitigated, leading to a relatively favorable overall ecological risk scenario in Xinjiang. However, under the SSP-RCP370 and SSP-RCP585 scenarios, moderate, high, and extremely high ecological risk areas expand, covering approximately 50% of the total area.

Microplastic-Induced Alterations in Soil Aggregate-Associated Carbon Stabilization Pathways: Evidence from δ13C Signature Analysis.

Microplastics (MPs) are known to affect soil carbon stability in a numerous ways. However, the mechanisms by which they alter the carbon stability within soil aggregates remain unclear . Herein, a one-year field experiment was conducted in an arid agricultural region employing stable isotope techniques to evaluate the soil organic carbon flow in the presence of both persistent (PE, PVC) and biodegradable (PLA, PHA) MPs. PE and PVC reduced the stability of soil aggregates, while PLA and PHA maintained it. Additionally, organic carbon content increased in microaggregates but decreased in small macroaggregates for PE and PVC treatments. By contrast, treatment with PLA and PHA enhanced organic carbon content across aggregates. The δ13C values of PE- and PVC-treated aggregates ranged from -25.34 to -20.85‰, while those of PLA and PHA ranged from -16.29 to -9.26‰. Notably, MPs altered the direction of carbon flow between aggregates, reduced carbon flux, and accelerated carbon emissions. RFP and PLS-PM analyses revealed that persistent MPs affected carbon flow primarily via abiotic factors, whereas biodegradable MPs influenced it via biotic factors. These findings provide insights into the mechanisms by which MPs impact aggregate-associated carbon, highlighting their effects on soil ecosystem services.

Evaluation of satellite-based rainfall estimates over the IGAD region of Eastern Africa

Satellite precipitation (rainfall) products have become increasingly valuable in the delivery of climate services in recent years, particularly in Africa, where the network of synoptic stations has been gradually declining. The Satellites-based Rainfall Estimates (SREs) are estimates and measurements; thus, accuracy is of major concern. This study gives a detailed long-term comparison and evaluation of nine (9) SREs from 2001 to 2020 over East Africa. The study used 105 rain gauge observations and gridded 9 SRE products. The statistical methods such as rainfall totals, annual cycle, 24 continuous, categorical, and volumetric metrics, scatter plots, the Cumulative Distribution Function (CDF), and colored code portraits were employed to assess the temporal and spatial patterns of SREs performance. The evaluation was conducted at each 105-rain gauge spatially and in five sub-regions for different metrics and performance comparisons during March–May (MAM), June–September (JJAS), and October-December (OND). Our findings demonstrate that relying on a single metric for validating the performance of SREs is not sufficient. Instead, it is necessary to utilize multiple metrics to assess rainfall performance, especially in areas with complex topography such as mountains and diverse climatic zones. The spatial patterns of validation of nine SREs showed CMORPH RT, CHIRPS v2.0, CPC-RFEv2, and GPM-IMERG are the top four best-performing SREs. The CMORPH RT emerged the best-performing SRE, followed by CHIRPS v2.0. Also, the satellite products tend to slightly underestimate the rainfall throughout the region. Geographically, SREs performed well over highlands compared to desert and semi-arid regions, while seasonally, the accuracy of SRE was lower over JJAS compared to MAM and OND. This study further demonstrated that the density and distribution of synoptic stations (rain gauges) in a country play a significant role in the accuracy of validation. These findings shows that SREs play complementary roles in the accurate monitoring and analysis of precipitation and provide comprehensive coverage, especially in remote areas where ground-based measurements are sparse. These findings serve as guidance to climate service providers and end-users on how to select suitable alternative rainfall datasets for different applications and feedback to the algorithm developers to improve the SRE products.

An Epidemiological Study on Knowledge, Attitude And Practices of Breast-Feeding And Newborn Care among Mothers in Pali district of Western Rajasthan, 2024

Introduction: Breastfeeding is one of the most important determinants of child survival, birth spacing, and prevention of childhood infections. As per NFHS- 5, in Rajasthan, the current IMR and NMR are at 35.2 and 24.9 per 1000 live births respectively. Safe delivery, early breastfeeding ,new born care within first few days and correct weaning practices are very crucial for the health of mothers and their baby. With so many interventions on Maternal and child health among rural population improvements are apparent but socio cultural norms still dictate practices in arid regions of Western Rajasthan. The study findings aim to orient the breastfeeding promotional activities and channelize efforts where gaps still exist in correct breast-feeding and newborn care practices. Objectives To study knowledge and attitude about breastfeeding and newborn care in rural areas of XXX district of Western Rajasthan . To study factors affecting breastfeeding and newborn care practices including weaning patterns in the selected rural area. Methods: An analytical cross sectional study among mothers of less than one year old infants was conducted using a pretested questionnaire in villages of catchment area of RHTC of GMC Pali using convenient sampling until the sample size of 384 was reached. Results : Most mothers 98% had hospital deliveries, 56% initiated breastfeeding within first hour, scissors were used to cut the umbilical cord in 70% of the home deliveries and 86% gave prelacteals. Colostrum was fed by 80%of the mothers interviewed. Exclusive breastfeeding was just 12% in our study. Weaning foods consisted of goat’s milk with wheat chapattis or biscuits in goats milk among others. No tinned powder milk formulas or tetra packs were. Early bathing and use of kajal were other incorrect practices . Immunization and growth monitoring showed coverage of >80%.There was statistically significance between parity, and type of delivery with the good practices (with p-value <0.05) respectively. Among participants 77% had good knowledge but only 47% followed correct practices. Conclusions : Significant improvement was seen in practices regarding safe delivery ,initiation of breast feeding , colostrum feeding and cord care. Pre lacteal feeds , non-exclusive breast feeding, unhealthy weaning foods were points of concern . Existing gaps between knowledge and action can be filled by repeated emphasis on desired good practices.

Dipole pattern in aerosol-induced atmospheric warming trends over the Indian subcontinent in the last two decades

Abstract Understanding the patterns of aerosol-induced perturbation in radiation budget and its drivers is crucial in climate science. Here, we examined spatio-temporal trends in aerosol-induced atmospheric warming and the top-of-the-atmosphere (TOA) and surface cooling over the Indian Subcontinent under clear-sky and all-sky conditions using clouds and the earth’s radiant energy system data for the period 2000–2021. Overall, the regional mean TOA and surface cooling were found to increase by 0.06 W m−2 yr−1 and 0.09 W m−2 yr−1, respectively. Over the last two decades, the aerosol-induced atmospheric warming in all-sky conditions increased over the subcontinent landmass and outflow regions over the ocean while it declined over dust-dominated arid regions. This dipole pattern was driven by a combination of an overall increase in aerosol optical depth, a gradual increase in the fraction of scattering aerosols over the Indian landmass dominated by anthropogenic sources, a decline in dust loading over the arid sources. As a result, atmospheric warming efficiency declined in most parts of the Indian subcontinent. A comparative meta-analysis revealed that aerosol-induced atmospheric warming was over-estimated by the existing studies where aerosol direct radiative forcings were estimated by 1-D radiative transfer model utilizing modeled optical properties based on incomplete information about in-situ physico-chemical properties derived from ground-based measurements. Our analysis showed that TOA and surface cooling by aerosols were higher in clear-sky conditions relative to the actual all-sky condition by up to 11 W m−2 and 16 W m−2, respectively; therefore, atmospheric warming reported for clear-sky conditions would be biased high over the subcontinent. As India embarked on a clean air mission, changes in aerosol loading and its composition are expected to alter the dipole pattern further in the future, impacting the regional climate via dynamic feedback.

An Automated Framework for Interaction Analysis of Driving Factors on Soil Salinization in Central Asia and Western China

Soil salinization is a global ecological and environmental problem, which is particularly serious in arid areas. The formation process of soil salinity is complex, and the interactive effects of natural causes and anthropogenic activities on soil salinization are elusive. Therefore, we propose an automated machine learning framework for predicting soil salt content (SSC), which can search for the optimal model without human intervention. At the same time, post hoc interpretation methods and graph theory knowledge are introduced to visualize the nonlinear interactions of variables related to SSC. The proposed method shows robust and adaptive performance in two typical arid regions (Central Asia and Xinjiang Province in western China) under different environmental conditions. The optimal algorithms for the Central Asia and Xinjiang regions are Extremely Randomized Trees (ET) and eXtreme Gradient Boosting (XGBoost), respectively. Moreover, precipitation and minimum air temperature are important feature variables for salt-affected soils in Central Asia and Xinjiang, and their strongest interaction effects are latitude and normalized difference water index. In both study areas, meteorological factors exhibit the greatest effect on SSC, and demonstrate strong spatiotemporal interactions. Soil salinization intensifies with long-term climate warming. Regions with severe SSC variation are mainly distributed around the irrigation water source and in low-terrain basins. From 1950 to 2100, the regional mean SSC (g/kg) varies by +20.94% and +64.76% under extreme scenarios in Central Asia and Xinjiang, respectively. In conclusion, our study provides a novel automated approach for interaction analysis of driving factors on soil salinization in drylands.

Sustainable Weed Management: The Effects of Applying Pre- and Post-Emergence Herbicides to Medicago ruthenica

Medicago ruthenica is a forage legume crop that is widely used as fodder and for ecological restoration in arid and semi-arid areas in Northcentral Asia. During the seedling stage, weeds challenge the growth and development of M. ruthenica, especially in fields sown for seed production. However, strategies to effectively control weeds in crops of M. ruthenica using herbicides have not been investigated. We evaluate the efficacy of different herbicides that control pre- and post-emergence of weeds in M. ruthenica. The results indicated that the most effective pre-emergence herbicides, imazethapyr (1530 mL ha−1) and flumetsulam (120 mL ha−1), resulted in crop safety and soil microbial community equivalent to a weed-free check. The most effective post-emergence herbicides are imazethapyr + haloxyfop-P (1800 + 600 mL ha−1) and 2,4-DB + haloxyfop-P (2250 + 600 mL ha−1). These herbicide treatments demonstrate effective control of most weeds (A. retroflexus, C. album, and grasses) while ensuring crop safety. Application of these herbicides to control weeds in M. ruthenica prior to or after their emergence represents a viable strategy for their control and also improve agricultural viability and crop yield and quality. Our research contributes to sustainable agriculture and ecological restoration in arid regions.

Water production efficiency and economic benefits under diversified planting modes of intercropping-multiple cropping in arid regions.

Diversified planting patterns are important measures to improve the comprehensive grain production capacity, alleviate the contradiction between grain crops and forage planting, and enhance water production efficiency. In order to explore the suitable diversified planting modes in the Yellow River irrigation area of Ningxia, a total of 4 treatments were designed, including wheat-maize silage intercropping and multiple planting of sorghum-sudangrass hybrid after wheat harvest (T1); wheat-cabbage intercropping, multiple planting of oil sunflower after wheat harvest, followed by maize silage planting after cabbage harvest (T2); sole wheat, after the harvest of wheat, half of the land is used for multiple plantings of maize silage, and the other half is used for multiple plantings of oil sunflower (T3); and sole maize silage (T4). The results showed that all diversified planting modes can increase biomass and land equivalent ratio compared to the control monoculture. The T2 had the highest total biomass and land equivalent ratio, the two-year average was 79.68t/ha and 2.27, respectively. The highest biological yield per cubic meter of water was achieved by T3, with an average of 12.65kg/m3 over two years. T1 achieved the highest output value per cubic meter of water, with 16.81 Chinese Yuan/m³ over 2years. In both T1 and T2, due to the increased spacing between intercropping crops, as well as sufficient water and fertilizer supply and developed root system, maize silage is at a competitive advantage, with a interspecific relative competitive ability between 0.059-0.234. When maize silage and oil sunflower are planted simultaneously, due to the fast growth rate of oil sunflower, it is the dominant crop, the interspecific relative competitive ability in 2022 and 2023 were 0.164 and 0.137, respectively. The net benefit of T1 was the highest, with an average total net income of 84,950 Chinese Yuan/ha over 2years. It can be seen that diversified planting patterns can improve the yield and economic benefits per unit land area, and are a highly promising planting pattern.

Statistical Model Development for Estimating Soil Hydraulic Conductivity Through On-Site Investigations

In arid regions, irrigated agriculture is mainly dependent on groundwater. In Pakistan, 73% of agricultural land is directly or indirectly irrigated through groundwater. In Punjab (Pakistan), 1.2 million private tube wells are operating, mainly extracting 90% of the country’s groundwater. Most of these wells are poorly designed due to improper site investigations and poor estimations of the aquifer’s hydraulic parameters. As a result, most wells become dry, causing considerable financial losses to farmers. Hence, optimizing the well-designed parameters through proper soil investigations is essential. This research aims to develop a statistical model for estimating the hydraulic conductivity of soil through on-site investigation: five sites were selected in Multan (Pakistan), and seven samples were collected at each location from 3, 6, 9,12,15,18, and 21 m depth. For hydraulic conductivity, soil texture, and porosity, soil laboratory tests were carried out. Finally, a statistical model was developed using hydrological parameters such as average grain size distribution (D50), uniformity coefficient (U), and porosity (n). Statistically computed hydraulic conductivity was verified with experimentally measured and empirically derived hydraulic conductivity. Statistically measured hydraulic conductivity showed closer agreement with experimentally measured hydraulic conductivity than the empirically measured hydraulic conductivity: root mean square error (RMSE), correlation coefficient (Cc), and mean absolute error (MAE) are, respectively, equal to 0.013, 0.93, and 0.011.

Conservation Biodiversity in Arid Areas: A Review

Drylands cover a vast area, and biodiversity conservation in these regions represents a major challenge. A bibliometric study of published research highlighted several key aspects, including publication types, research fields, years of publication, contributing countries, institutions, languages, journals, publishers, authors, and frequently used keywords. The analysis also included plants related to biodiversity conservation in arid areas, animals related to biodiversity conservation in arid areas, and causes of biodiversity decline in arid regions, effects of biodiversity loss in these regions, and restoration methods aimed at improving biodiversity conservation in arid areas. A total of 947 publications were identified, starting from 1994, authored by researchers from 99 countries, primarily from Australia, the USA, China, Spain, and South Africa, and published in 345 journals, with the most prominent being Journal of Arid Environments, Biodiversity and Conservation, and Biological Conservation. The most commonly appearing keywords included biodiversity, conservation, diversity, vegetation, and patterns, with recent years showing an increased use of terms related to the causes and effects of aridification: climate change, land use, and ecosystem services. The causes of biodiversity loss in drylands are primarily linked to human activities and climatic changes, while the effects impact the entire ecosystem. Methods to improve biodiversity include traditional agroforestry systems, tree plantations and other plant species, grazing management, and other approaches. Combined actions among stakeholders and ecologically appropriate nature-based solutions are also recommended. Improvements in conservation biodiversity in arid areas are very important also for achieving the sustainability goals in these areas. However, numerous aspects of this topic remain to be studied in greater detail.

Water Reuse—Retrospective Study on Sustainable Future Prospects

In recent decades, societies and economies across the globe have started to show signs of stress associated with water shortages. Meeting the sustainability benchmarks in arid and semi-arid regions has caused water reuse to be considered a viable alternate source to augment the existing water supply resources. Water reuse, resource recovery, and recycling are extensions of the concept of a circular economy that has been practiced in other fields. Globally, the U.S. has played a leadership role in the development of guidance and regulations for various water reuse applications. Other countries and organizations have also developed similar programs. This paper aims to propose a review of the existing literature and provide a broader perspective of water reuse focusing on the most pressing issues such as direct potable reuse with the backdrop of viral pathogens and perfluorinated compounds. The global history of statutory developments to regulate the selected contaminants has also been discussed by covering the recent advancement in water reuse applications. Technological developments and regulatory trends are chronicled in the context of emerging contaminants linked with an imminent social, industrial, and agricultural prospectus. The proposed high viral log removal credit for water reuse is a challenging task especially at regular intervals; therefore, the treatment requirements must be verified to ensure public safety. The extreme persistence of PFAS, their tendency for buildup in biotic systems, and their removal is another challenging task which requires development of cost effective and efficient technologies. Disparity in the financial and technological capabilities of regional or internal stakeholders of shared watershed or aquifer is a bottleneck in tangible advancements in this area. The role of public–private partnerships in addressing the impending water sustainability challenges is discussed as a model for future direction in funding, managing, and public acceptance.

Spatiotemporal Variations Affect DTPA-Extractable Heavy Metals in Coastal Salt-Affected Soils of Arid Regions

The concept of metal bioavailability in soils is increasingly becoming the key to addressing potential risks. Yet, space–time variations of heavy metal concentrations in salt-affected soils is still vague. The current work, therefore, is the first attempt to address spatial and seasonal analyses of heavy metals in a Mediterranean arid agroecosystem. This study was conducted in a coastal area in northeastern Egypt as an example. The DTPA-extractable concentrations of Cr, Co, Cu, Fe, Pb, Mn, Ni, and Zn in addition to the main properties of 70 georeferenced soil samples (0–30 cm) were determined during the wet (March) and dry (September) seasons. The results revealed that except for Cu, the concentrations of all the determined metals stood below the safe limits. On average, the concentrations of Cu were 4.1- and 5-fold the acceptable limit of 0.20 mg kg−1, respectively. The statistical analysis indicated that seasonal variations greatly affect the concentrations of Mn, Ni, and Zn. Compared with the wet season, significant increases of 1.25, 1.50, and 1.28-fold in the concentrations of these metals occurred during the dry season, respectively. The principal component analysis affirmed that the presence of Cr, Co, Fe, and Ni was closely related to geogenic factors; meanwhile, agronomic practices were likely the main inputs of Cu, Pb, and Zn. The geostatistical analysis illustrated that the geographic variability of Cr, Fe, Mn, and Zn was due to interactions of natural and stochastic processes. Farming practices controlled the spatial variability of Ni, Pb (in the wet period), and Co (in the dry period). The effect of natural processes during the wet period was evident for Cu, which showed strong spatial variability. The kriged maps showed that the concentrations of Co, Fe, and Ni tended to increase seaward and were found to be affected by pH, salt ions, and exchangeable Na+. Moreover, both silt and organic matter content had profound impacts on the spatial distribution of Cr, while the distributions of Cu, Pb, and Zn were linked to that of CaCO3 content. The suggested mechanisms governing metal bioavailability were sorption and complexation with ligands (for Co, Fe, and Ni), redox potential (for Cr), dissolution–precipitation (for Mn), and ion exchange (for Cu, Pb, and Zn). The results of this study affirm that drying–wetting cycles and spatial distribution affect the bioavailability of heavy metals in coastal salt-affected soils of arid regions. These findings imply that seasonality (wet and dry) and spatiality should be considered for monitoring and rehabilitation of degraded soils under similar ecological conditions.

Mapping Groundwater Potential in Arid Regions: A Geographic Information System and Remote Sensing Approach for Sustainable Resource Management in Khamis Mushayt, Saudi Arabia

Groundwater is a critical resource in arid regions such as Khamis Mushayt, located in southwestern Saudi Arabia, where surface water availability is limited. This study integrates various geospatial and environmental datasets to delineate groundwater potential zones (GWPZs) using Geographic Information Systems (GISs) and remote sensing (RS) techniques. Key parameters considered include lithology, slope, drainage density, precipitation, soil type, and vegetation index (NDVI). The influence of each theme and subunit/class on groundwater recharge was evaluated by weighted overlay analysis, including previous studies and field data. The results reveal three distinct groundwater potential zones: poor, moderate, and good. Areas with good groundwater potential account for 8.2% of the study area (16.3 km2) and are predominantly located in the eastern and central parts of the study area, in valleys and low-lying regions with permeable geological formations such as alluvial deposits, supported by higher drainage density and favorable precipitation. Conversely, poor-potential zones represent 27.6% (54.50 km2), corresponding to areas with steep slopes and impermeable rock formations. Moderate-potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and account for 64.2% (127.0 km2). Validation using existing well data demonstrates strong agreement between the identified potential zones and actual groundwater availability. These findings provide a strong framework for sustainable water resource management, urban planning, and agricultural development in Khamis Mushayt and similar arid regions.

The First Botanical Garden: A Technical Design for a Sustainable City in Arequipa, Peru

Arequipa, situated in the arid Atacama Desert, is experiencing significant biodiversity loss due to unchecked urban growth. This study presents the design of a Botanical Garden to address this issue by serving as a conservation center for native flora. A baseline assessment was conducted to evaluate meteorological conditions, soil properties, and the quality of treated wastewater from the “La Escalerilla” plant. The analysis revealed a semi-arid climate with low precipitation and suitable soil conditions. A total of 685 plant species were identified and proposed for conservation within the garden, with a high proportion of endemic species. To protect the future plant species, a living fence composed of 700 individuals was installed, considering resilient species such as Molle serrano (Schinus molle), Cantuta (Cantua buxifolia), and Huaranguillo (Tephrosia cinerea), which were installed at an average height of 50 cm and have grown to an average of 80 cm after 8 months. This study demonstrates the feasibility of establishing a botanical garden in Arequipa’s arid environment and serves as a model for future botanical garden projects in arid regions at the national and regional levels, providing a foundation for natural landscapes in urban areas.

Evaluation of the Soil Conservation Service Curve Number (SCS-CN) Method for Flash Flood Runoff Estimation in Arid Regions: A Case Study of Central Eastern Desert, Egypt

Flash floods are highly destructive natural disasters, particularly in arid and semi-arid regions like Egypt, where data scarcity poses significant challenges for analysis. This study focuses on the Wadi Al-Barud basin in Egypt’s Central Eastern Desert (CED), where a severe flash flood occurred on 26–27 October 2016. This flash flood event, characterized by moderate rainfall (16.4 mm/day) and a total volume of 8.85 × 106 m3, caused minor infrastructure damage, with 78.4% of the rainfall occurring within 6 h. A significant portion of floodwaters was stored in dam reservoirs, reducing downstream impacts. Multi-source data, including Landsat 8 OLI imagery, ALOS-PALSAR radar data, Global Precipitation Measurements—Integrated Multi-satellite Retrievals for Final Run (GPM-FR) precipitation data, geologic maps, field measurements, and Triangulated Irregular Networks (TINs), were integrated to analyze the flash flood event. The Soil Conservation Service Curve Number (SCS-CN) method integrated with several hydrologic models, including the Hydrologic Modelling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and European Hydrological System Model (MIKE-SHE), was applied to evaluate flood forecasting, watershed management, and runoff estimation, with results cross-validated using TIN-derived DEMs, field measurements, and Landsat 8 imagery. The SCS-CN method proved effective, with percentage differences of 5.4% and 11.7% for reservoirs 1 and 3, respectively. High-resolution GPM-FR rainfall data and ALOS-derived soil texture mapping were particularly valuable for flash flood analysis in data-scarce regions. The study concluded that the existing protection plan is sufficient for 25- and 50-year return periods but inadequate for 100-year events, especially under climate change. Recommendations include constructing additional reservoirs (0.25 × 106 m3 and 1 × 106 m3) along Wadi Kahlah and Al-Barud Delta, reinforcing the Safaga–Qena highway, and building protective barriers to divert floodwaters. The methodology is applicable to similar flash flood events globally, and advancements in geomatics and datasets will enhance future flood prediction and management.

Estimation of Manning’s roughness coefficient using observational data

ABSTRACT The inability to accurately estimate the Manning roughness coefficient (n) and the use of a constant value of n is a major source of uncertainty in flood simulations and flow-depth calculations. The main purpose of present study is to more precisely determine n for the Aji-Chai River upstream of the Vanyar hydrometric station. Results show that the lowest value of n is 0.034, corresponding to the discharge of 180 m3/s, while the highest value of n, corresponding to a discharge of 2.083 m3/s, is 0.119. As the discharge decreases, the roughness coefficient increases. The functional relationship between the roughness coefficient and discharge yields R2 = 0.80. The relationship between the hydraulic radius and the discharge yields R2 = 0.944, indicating a significant relationship. The roughness coefficient and the hydraulic radius have an inverse relationship. Every flood results in a different roughness with different sedimentation depending on variations in river bed particle diameter. Usually, rivers in arid regions are temporary and in the descending limb of the hydrograph, they leave coarser materials in the bed which cause errors in estimating n. In discharge where the flow depth is lower than D 90, the Manning coefficient reaches its maximum value.