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Drylands are vast and face threats from climate change and human activities. Traditional reviews cannot capture interdisciplinary knowledge, but bibliometric analysis provides valuable insights. Our study conducted bibliometric research of scientific production on climate change and land degradation in drylands using remote sensing. We examined 1527 Scopus-indexed publications to identify geographic and thematic hotspots, extracting leading authors, journals, and institutions. China leads in publications, followed by the US, Germany, and Australia. The US has the highest citation count. Collaboration networks involve the US, China, and European countries. There has been an exponential increase in remote sensing of land degradation in drylands (RSLDD) publications since 2011. Key journals include "International Journal of Remote Sensing" and "Remote Sensing of Environment". The analysis highlights the growing interest in the field, driven by Australia, the US, and China. Key areas of study are vegetation dynamics and land use change. Future perspectives for this scientific field involve promoting collaboration and exploring emerging technologies for comprehensive land degradation and desertification research. [ABSTRACT FROM AUTHOR]

This document discusses the challenges faced by plants in arid and semi-arid regions due to harsh environmental conditions such as limited rainfall, high temperatures, and poor soil nutrition. The document highlights the importance of understanding plant adaptation strategies to extreme environments in order to protect and manage local vegetation and ecosystems. The document presents research papers that cover various aspects of plant adaptation in arid regions, including water uptake mechanisms, physiological and biochemical adaptations, plant-soil microbial interactions, and ecosystem services. The research findings provide insights into how plants in arid regions adapt to environmental changes and maintain ecological balance. However, further research is needed to fully understand the processes and mechanisms of plant adaptation in extreme environments. [Extracted from the article]

As arid lands expand across the globe, scholars increasingly turn to the archaeological record for examples of sustainable farming in extreme environments. The arid north coast of Peru was the setting of early and intensive irrigation-based farming; it is also periodically impacted by sudden, heavy rainfall related to the El Niño Southern Oscillation (ENSO) phenomenon. While the sociopolitical effects, technologies, and engineering expertise of these irrigation systems have been thoroughly examined and theorized, little is known about how farmers managed periods of water stress. The aim of this study is to test whether arid zone farming was supported by hybrid, intermittent flood and perennial water source systems in the prehispanic past. An arroyo in the Chicama Valley was selected for preliminary data collection, and these data are presented here: (1) drone photography of the arroyo capturing the aftermath of a recent (2023) rain event; and (2) potassium (K) soil test kit results from samples collected near suspected prehispanic check dam features in the same area. The paper combines these data with comparative examples from the literature to suggest that the prehispanic features functioned as water-harvesting infrastructure. The paper concludes that sustainable, arid zone farming can be supported by hybrid, intermittent flood and perennial water source systems. [ABSTRACT FROM AUTHOR]

Water scarcity is a global problem that particularly affects islands located in arid regions or regions with limited water resources. This issue has prompted the development of non-conventional water sources such as fossil fuel-powered desalination systems. Concern about the high energy and environmental costs associated with this type of facility has created the ideal framework for the proliferation of desalination projects powered by renewable energies, especially wind energy due to the multiple advantages it offers. This article provides a bibliometric analysis to identify the advances made in wind-powered desalination on islands. While many studies explore wind-powered desalination, none compile references specific to islands. This paper analyses islands' desalination needs and showcases wind-powered systems, exploring their types and uses. Firstly, the most relevant international scientific journals are identified to allow the subsequent selection and quantitative and qualitative analysis of articles directly dealing with wind-powered desalination systems. A total of 2344 articles obtained from the Scopus database were analyzed, of which 144 including 181 case studies were selected. Among the results of this study, an increasing year-on-year trend is observed in the number of published studies tackling wind-powered desalination. Finally, this paper presents a series of maps showing the most relevant facilities, projects, and data in this field, and provides an overview of the lessons learned in the decarbonization of desalination. [ABSTRACT FROM AUTHOR]

Architecture can be very influential in enabling health and wellbeing in the residential built environment. In arid regions, health-supportive design would consider major environmental hazards, such as heat and dust, as well as social and cultural factors that influence household size and composition. Under current and projected conditions, the effects of climate change will increase, presenting opportunities and challenges to architects to demonstrate and deliver best practice in climate-appropriate and culturally appropriate housing design. This paper brings together a multidisciplinary team of researchers to identify the current and future needs for tropical semi-arid zone design with a focus on Indigenous communities in northern Central Australia. In a narrative review of the literature and exploratory fieldwork, current and projected impacts of climate change on housing, health, energy systems and behavioural practices are explored in a holistic way. Our findings demonstrate the growing awareness of climate-related issues for remote Indigenous housing but also the lack of place-based studies that might inform adaptation strategies. Overwhelmingly, there is limited understanding of local lived experiences that may be highly significant for developing resilience in a changing climate. Consideration of the beliefs, values and practices of Indigenous people will be integral to design solutions. [ABSTRACT FROM AUTHOR]

Based on an examination of the outlooks and practices held by past and present Negev desert viticulturists vis-à-vis their environs, the article offers insights into how the wine sector can bolster its response to current climate adversity. The discussion fuses bio-archaeological research on the evolution of ancient grape cultivars (Vitis vinifera) in the Negev with an extensive ethnographic inquiry into the ongoing wine revival in arid southern Israel. Ancient DNA analysis of relic grape material led to the identification of an historical Negev viticulture lineage. The findings provide a temporal framework for contextualizing tphe interrelated anthropological data obtained on contemporary dryland viticulturalists and wine makers in Israel. Since the climate in the Negev has remained relatively stable over the last several millennia, it makes for an apt point of comparison via which to gauge the outlooks held by both historical and modern viticulturalists towards the local landscape. We suggest enacting two evidence-based adjustments to counter ecological instabilities: the holistic revival and applied renewal of historical cultivars, and the attendant agronomic routines that sustained their growth in the past. A more nuanced understanding of the unique terroir arid viticulturalists are operating in, together with the learned incorporation of the hardy genetic traits of age-old cultivars, will benefit wine growers to attune their responses to the array of challenges wrought by climate change on the wine industry. It is therefore our hope that the research methodologies and heritage-oriented horticultural perspectives we present in this paper will prove beneficial to broader wine growing regions (pax-Mediterranean and beyond) who are fortunate to likewise possess their own rich viticultural histories and lineages. [ABSTRACT FROM AUTHOR]

Soil salinity assessment methods based on remote sensing data are a common topic of scientific research. However, the developed methods, as a rule, estimate relatively small areas of the land surface at certain moments of the season, tied to the timing of ground surveys. Considerable variability of weather conditions and the state of the earth surface makes it difficult to assess the salinity level with the help of remote sensing data and to verify it within a year. At the same time, the assessment of salinity on the basis of multiyear data allows reducing the level of seasonal fluctuations to a considerable extent and revealing the statistically stable characteristics of cultivated areas of land surface. Such an approach allows, in our opinion, the processes of mapping the salinity of large areas of cultivated lands to be automated considerably. The authors propose an approach to assess the salinization of cultivated and non-cultivated soils of arid zones on the basis of long-term averaged values of vegetation indices and salinity indices. This approach allows revealing the consistent relationships between the characteristics of spectral indices and salinization parameters. Based on this approach, this paper presents a mapping method including the use of multiyear data and machine learning algorithms to classify soil salinity levels in one of the regions of South Kazakhstan. Verification of the method was carried out by comparing the obtained salinity assessment with the expert data and the results of laboratory tests of soil samples. The percentage of "gross" errors of the method, in other words, errors when the predicted salinity class differs by more than one position compared to the actual one, is 22–28% (accuracy is 0.78–0.72). The obtained results allow recommending the developed method for the assessment of long-term trends of secondary salinization of irrigated arable land in arid areas. [ABSTRACT FROM AUTHOR]

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of "carbon neutrality" in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted "U" shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Simple Summary: Acute hepatitis, which is a rising public health issue globally, is mostly caused by the hepatitis E virus (HEV). There is a potential risk of camel-borne zoonotic HEV infection in the desert regions of the Middle East and Africa, where camels frequently interact with human populations and camel-derived food products constitute a component of the food chain. To better understand the current state of this subject, the current work's objective is to provide a scientific review of the detection of HEV genotypes seven and eight in camels around the world. Until today, no review paper has been published compiling and discussing the reports available on HEV in camels. More studies are required to ascertain the prevalence of HEV infection in camels worldwide. Additionally, because camels are utilized as a form of transportation in many countries and because HEV in these animals may pose a threat to public health, there is a possibility of foodborne transmission through contaminated camel products. Hepatitis E virus (HEV) represents a major cause of acute hepatitis and is considered an emerging public health problem around the world. In the Middle East's and Africa's arid regions, where camels frequently interact with human populations and camel-derived food products are a component of the food chain, camel-borne zoonotic HEV infection is a potential threat. To date, no review paper has been published on HEV in camels. As such, the purpose of the current work is to provide a scientific review of the identification of HEV genotypes seven and eight in camels worldwide to have a better understanding of the current status of this topic and to identify gaps in the current knowledge. Searches were carried out in the electronic databases PubMed, Mendeley, Web of Science, and Scopus, including studies published until 31 December 2022 (n = 435). Once the databases were checked for duplicate papers (n = 307), the exclusion criteria were applied to remove any research that was not relevant (n = 118). As a result, only 10 papers were found to be eligible for the study. Additionally, in eight of the ten studies, the rates of HEV infection were found to be between 0.6% and 2.2% in both stool and serum samples. Furthermore, four studies detected HEV genotype seven in dromedary camels, and two studies have shown HEV genotype eight in Bactrian camels. Interestingly, these genotypes were recently reported in camels from the Middle East and China, where one human infection with HEV genotype seven has been associated with the consumption of contaminated camel meat and milk. In conclusion, more research will be needed to determine the prevalence of HEV infection in camels around the world as well as the risk of foodborne transmission of contaminated camel products. As camels are utility animals in several countries, HEV in these animals may pose a potential risk to public health. [ABSTRACT FROM AUTHOR]

Accurate estimation of surface evapotranspiration (ET) in the Heihe River Basin using remote sensing data is crucial for understanding water dynamics in arid regions. In this paper, by coupling physical constraints and machine learning for hybrid modeling, we develop a hybrid model based on surface conductance optimization. A hybrid modeling algorithm, two physical process-based ET algorithms (Penman–Monteith-based and Priestley–Taylor-based ET algorithms), and three pure machine learning algorithms (Random Forest, Extreme Gradient Boosting, and K Nearest Neighbors) are comparatively analyzed for estimating the ET. The results showed that, in general, the machine learning model optimized by parameters was able to better predict the surface conductance of the hybrid model. Driver analyses showed that radiation, normalized difference vegetation index (NDVI), and air temperature had high correlations with ET. The hybrid model had a better prediction performance for ET than the other five models, and it improved the R2 of the two physical process-based algorithms to 0.9, reduced the root mean square error (RMSE) to 0.5 mm/day, reduced the BIAS to 0.2 mm/day, and improved the Kling–Gupta efficiency (KGE) to 0.9. The hybrid model outperformed the others across different time scales, displaying lower BIAS, RMSE, and higher KGE. Spatially, its ET patterns aligned with regional vegetation changes, with superior accuracy in annual ET estimation compared to the other models. Comparison with other ET products shows that the estimation results based on the hybrid model have better performance. This approach not only improves the accuracy of ET estimation but also improves the understanding of the physical mechanism of ET estimation by pure machine learning models. This study can provide important support for understanding ET and hydrological processes under different climatic and biotic vegetation in other arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

The combination of seasonal shutdowns, water conveyance, cold, and drought can easily lead to the deterioration of the anti-seepage system and loess foundation of the canal, which contributes to the destruction of the slope. To reveal the failure mechanism of the collapsible loess canal slope, this paper is based on the results of laboratory tests and adopts numerical simulations to analyze the stability of the canal slope under different conditions. The results show that the shear strength indexes and elastic modulus E of loess decrease following an exponential pattern with the increase in wetting-drying and freezing-thawing (WD-FT) cycles. The height of the seepage overflow point yields little effect on the water level behind the impermeable membrane, whereas the height of the water level has a significant effect. In the operation period, the slope under any working conditions is in a relatively stable state. However, the slope with a water level of 4.5 m behind the impermeable membrane tends to be unstable after three WD-FT cycles during the shutdown period. By replacing the surface-degraded loess with sand gravel and picking a depth of 0.9–1.2 m, the slope will maintain a long-term stable state. [ABSTRACT FROM AUTHOR]

The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features. [ABSTRACT FROM AUTHOR]

The Tarim Basin is a large inland arid basin in the arid region of northwest China and has been experiencing significant "warming and wetting" since 1987. As a result, the purpose of this paper is to determine whether the climate transition phenomenon occurred in the Tarim Basin as well as the role of atmospheric circulation in this process. We use meteorological data and atmospheric circulation indexes to study the seasonal trends of climate change in this region from 1987 to 2020 to understand how they are affected by atmospheric circulation. The findings show that, from 1987 to 2020, the Tarim Basin experienced significant warming and wetting; with the exception of the winter scale, all other seasonal scales exhibited a clear warming and wetting trend. From the perspective of spatial distribution, most of the areas showed a significant warming trend, and the warming amplitude around the basin is greater than that in the central area of the basin. However, there are significant regional differences in precipitation change rates. Meanwhile, wavelet analysis shows that there is a significant oscillation period of 17–20 years between climate change and the atmospheric circulation index during 1987–2020. The correlation analysis shows that the Pacific decadal oscillation (PDO) and El Niño-Southern Oscillation (ENSO) are the main influencing factors of climate change in the Tarim Basin at different seasonal scales, while the teleconnection of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) is low and the PDO dominates the summer and autumn temperature changes in the Tarim Basin. The research results of this paper show that, despite the warming and wetting trends since 1987 in the Tarim Basin, the climate type did not change. From 1987 to 2020, the main teleconnection factors of climate change in the Tarim Basin were PDO and ENSO. [ABSTRACT FROM AUTHOR]

Lanzhou City is located in the semi-arid region of northwest China, which experiences serious desertification. Moreover, the high intensity of land development, with the accelerated industrialization and urbanization, causes increasingly aggravated conflict between humans and the environment. Exploring the response of the ecological environment quality to the natural environment and anthropogenic activities is important to protect the sustainable development of urban economic construction and the environment. Based on the Google Earth Engine (GEE) platform, this paper constructed a modified Remote Sensing Ecological Index (MRSEI) model which could reflect the ecological environment quality by integrating the desertification index (DI) into the Remote Sensing Ecological index (RSEI) model. This paper explores the spatiotemporal variation in the environmental quality from 2000 to 2020 in Lanzhou, China, and analyzes the natural and anthropogenic factors affecting the environment quality in terms of temperature, precipitation, gross domestic product (GDP), land use, night lighting, and population. The results showed that the mean value of MRSEI ranged from 0.254 to 0.400. The area undergoing fast growth in ecological quality was in the northwestern part of Lanzhou, and the area of decrease was in the central part. Various factors have different degrees of influence on the ecosystem, with temperature, precipitation, and land use having a greater impact, and GDP and population having a limited impact. Precipitation and temperature showed a strong impact when interacting with other factors, demonstrating that precipitation and temperature were also key factors affecting MRSEI. Overall, climate change and the implementation of ecological restoration projects have led to an improvement in the quality of the ecological environment in Lanzhou. This study provides a reference for understanding the spatiotemporal changes in the ecological environment in semi-arid Lanzhou and is conducive to formulating proper protection strategies. [ABSTRACT FROM AUTHOR]

Atriplex canescens is widely cultivated as drought and salt-tolerant fodder in arid regions of Northwest China, which is used for photoremediation of degraded land and soil and water conservation. To explore the growth performance of A. canescens when exposed to drought and salt stress, seedlings were treated with a range of drought stress (WC1: 75 ± 3.6%, WC2: 49 ± 2.9% and WC3: 27 ± 2.5% of soil water content) and the corresponding drought stress with additional sodium salt supplementation (NaCl:Na2SO4 = 1:1 with the total concentration of Na+ set to 150 mM). The findings of this paper indicated that moderate sodium salt could stimulate the growth of A. canescens and effectively alleviate the deleterious impact of drought stress by increasing the turgor potential (ψt) and relative water content (RWC) and decreasing the leaf water osmotic potential (ψs). Furthermore, the photosynthetic capacity was improved and the negative effects of drought stress on photosystem II (PSII) were mitigated. The extra 150 mM sodium salt also markedly increased the contribution of Na+ to ψs and the contribution of betaine to ψs. In summary, these results indicate that A. canescens can adapt to drought stress by accumulating enough Na+ for osmotic adjustment (OA). Additionally, this paper is aimed to provide a fundamental basis for the utilization and cultivation of A. canescens as a favored pasture crop in the Qaidam basin, thus increasing the ecological and environmental benefits for arid regions worldwide. [ABSTRACT FROM AUTHOR]

The Forest Fire Danger Index (FFDI) is the main measure used in Australia for estimating fire risk. Recent work by the authors showed that the FFDI forms stable state regimes, nominated as fire climate regimes. These regimes shifted to greater intensity in southern and eastern Australia around the year 2000 and, a decade later, further north. Reductions in atmospheric moisture were the primary contributor. These changes have not been fully incorporated into future projections. This paper compares the recent regime shifts with the most recent national projections of FFDI, published in 2015. They show that for most states and regions, the 2030 upper limit is approached or exceeded by the recent shift, except for two states with large arid zones, South Australia and Western Australia. Methods for attributing past changes, constructing projections, and the inability of climate models to reproduce the recent decreases in atmospheric moisture, all contribute to these underestimates. To address these shortcomings, we make some suggestions to modify efforts aiming to develop seamless predictions and projections of future fire risk. [ABSTRACT FROM AUTHOR]

Simple Summary: This work aims to analyze flocks' management practices and sheep breeders' perceptions of the impact of climate change to enhance the resilience of the sheep production systems in the Tunisian semi-arid region. The results revealed three main sheep production systems: the agro-pastoral rain-fed system (AGPRF), the agro-pastoral irrigated system (AGPI), and the agro-sylvo-pastoral system (AGSP). Each production system is characterized by specific management and productive parameters that are used by farmers to build resilience actions. However, farmers' climate change perceptions are mostly the same across the three sheep production systems: a decrease in precipitation and an increase in temperatures and extreme events, which negatively influence feedstuff availability and costs. Global climate change inflicts unambiguous risks on agricultural systems and food security. Small ruminants are known for their ability to adapt to changing environmental conditions. This paper aims to characterize sheep production systems in a vulnerable agricultural zone and the breeders' perceptions of climate change to apprehend challenges that they are confronting, and formulate resilience actions. The data analysis is based on 94 semi-structured surveys of sheep farmers carried out in the Tunisian semi-arid region. The PCA analysis results revealed three main sheep production systems. The agro-pastoral rain-fed system (AGPRF) is dominant (55%), with large farms and common pastures integrating cereals and fodder. The agro-pastoral irrigated system (AGPI: 20%) is characterized by small-area and forage irrigation (1.8 ha) and a smaller number of ewes but a greater use of animal feed supplementation. The agro-sylvo-pastoral system (AGSP: 25%) is a system where grazing is based on common lands and using tree sub-products, while the agricultural area is exclusively used to cultivate cereal crops. Sheep breeders' climate perceptions are summarized as unpredictable climate events, a decrease in precipitation, and an increase in temperature. Resilience actions principally consist of reducing flocks' numbers, using alternative local feed, fodder, and water resources, and building more shelters and planting more trees in the grazing areas. Nevertheless, cost-effectiveness should be considered in such vulnerable zones to insure the sheep production systems' sustainability. [ABSTRACT FROM AUTHOR]

Floods destroy crop production; nevertheless, the extent of their impact on farmers' livelihoods in developing countries has been poorly investigated. This paper contributes to the growing evidence-based assessment of the impacts of shocks on communities. It assessed the post-disaster livelihood of farmers affected by the 2012 flooding in the semi-arid zone of Benin. To this end, a survey was conducted on 228 farmers in two municipalities of the flood-prone part of the semi-arid zone of Benin (Malanville, Karimama). Information on the well-being of households was collected using semi-structured interviews. Data were analyzed using income and consumption approaches focusing on poverty and on subjective assessment using happiness approach. Additionally, a probit model was used for a poverty assessment. The survey revealed that flooded farmers were amongst the poorest in the study system. Seven variables determined poverty in this study: household size, location, the percentage of the farm size that was flooded, fishing, the farmer's gender, farm size, and "holding a secondary activity". Regarding happiness, 99% of the flooded farmers were unhappier after the flood in 2012. The results clearly show that being subjected to floods increases the incidence of poverty. The capacity of flood risk management and governance should be strengthened in the study system. [ABSTRACT FROM AUTHOR]

In the process of agricultural development in arid and semi-arid areas, the carbon fixation capacity of vegetation can be affected to different degrees, but research on its driving factors is lacking. Consequently, this paper focuses on the Weiku Oasis in Xinjiang as its research area, in which the carbon fixation capacity of vegetation is estimated with the chemical equation of a photochemical reaction, using methods such as linear system models and Geodetector to analyze the relationship between cropland expansion characteristics and the carbon fixation capacity of vegetation from 1990 to 2020. The influence of land-use changes on the space differentiation of carbon fixation was elucidated through a time series relationship, and the synergistic effects of nine influencing factors on the carbon fixation capacity during the process of vegetation changes were discussed. The results were as follows: (1) In the process of agricultural development, the proportions of cultivated land area and spatial agglomeration had significant negative correlations with carbon sequestration, and the significance was rising, but the effect of cultivated land area proportion was more significant. (2) Through temporal sequential cooperativity analysis, when other land-use types were converted into cultivated land, the carbon fixation capacity of vegetation suddenly and significantly decreased in the initial year of the transformation, but the effect of cultivated land reclamation on the carbon fixation capacity of vegetation did not have a significant time lag. Moreover, after a certain period of time, cultivated land can gradually recover part of its lost carbon fixation capacity. (3) Among the nine driving factors, potential evapotranspiration is the most prominent in explaining the carbon fixation capacity of vegetation. This single-factor pairwise interaction presents the relationship between bivariate enhancement and nonlinear enhancement. When terrain factors interact with other factors, the enhancement effect of the influence on the carbon fixation capacity of vegetation has an obvious promotion effect. However, the change in the carbon fixation capacity of vegetation is more significantly influenced by potential evapotranspiration and the interaction between the normalized difference vegetation index (NDVI) and other factors. This research is helpful to understanding the basic theories related to the change in the carbon fixation capacity of vegetation during the process of agricultural development in arid and semi-arid areas, as well as providing theoretical reference for ecological environment construction and sustainable development. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [ABSTRACT FROM AUTHOR]

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data. [ABSTRACT FROM AUTHOR]

There is great uncertainty about the future effects of climate change on the global economic, social, environmental, and water sectors. This paper focuses on watershed vulnerabilities to climate change by coupling a distributed hydrological model with artificial neural networks and spatially distributed indicators for the use of a predictive model of such vulnerability. The analyses are complemented by a Monte Carlo evaluation of the uncertainty associated with the projections of the global circulation models, including how such uncertainty impacts the vulnerability forecast. To test the proposal, the paper uses current and future vulnerabilities of the Turbio River watershed, located in the semi-arid zone of Guanajuato (Mexico). The results show that nearly 50% of the watershed currently has medium and high vulnerabilities, and only the natural areas in the watershed show low vulnerabilities. In the future, an increase from medium to high vulnerability is expected to occur in urban and agricultural areas of the basin, with an associated uncertainty of ±15 mm in the projected precipitation. [ABSTRACT FROM AUTHOR]

With the warming and humidification process in the Northwest Arid Zone over the past 30 years, the runoff of a vast majority of rivers has been affected to different degrees. In this paper, the runoff from the Weigan River, a typical inland river in the arid zone of Northwest China, is taken as an example, and seven types of CMIP6 data are selected with the help of a SWAT model to predict the runoff volume of the Weigan River in the next 30 years under climate change. The results show that (1) the SWAT model can simulate the runoff from the Weigan River well and has good applicability in this study area. (2) With an increase in radiative forcing, the temperature, precipitation and runoff in the study area show an increasing trend. (3) Under the four radiative forcing scenarios in 2030 and 2050, the runoff from the Weigan River out of the mountain is predicted to be maintained at 25.68 to 30.89 × 108 m3, which is an increase of 1.35% to 21.91% compared with the current runoff, of which the contribution to the increase in future runoff caused by the changes in temperature and precipitation is 68.71% and 27.24%, respectively. It is important to explore the impact of climate change on the runoff from the Weigan River to understand the impact of climate change on the Northwest Arid Region scientifically and rationally, and to provide a scientific basis for evaluating the risk of climate change and formulating policies to deal with it. [ABSTRACT FROM AUTHOR]

The human population continues to grow, and specific efforts must be made in order to meet foreseeable food demands. In this paper, it is suggested that an IoT-based fuzzy control system be used for smart soil monitoring systems. This study is based on the semi-arid regions of India. A fuzzy classifier is used to categorize the real-time data into three parameters, such as sodium, potassium, and calcium, based on the proposed model, which gets trained from a dataset and then chooses the optimal solution. The real-time data are collected from NPK sensors, which are suitable for sensing the content of nitrogen, phosphorus, and potassium in the territory, which helps in determining the fertility of the soil by facilitating the systematic assessment of the soil condition. With the aid of this system, a farmer would be able to monitor soil health in a real-time environment and also track the growth of their plants. Farmers will be able to enhance productivity while decreasing resource waste with the aid of an IoT-enabled fuzzy system. Experimental data have been collected from Mahoba district, Uttar Pradesh provinces in India, and the results show that the suggested system is a more reliable and precise concept used for precision farming that will certainly enhance the overall production of crops with better quality. These results obtained with the help of the proposed model system have been compared with the existing one with data accuracy that has been improved and well accepted. [ABSTRACT FROM AUTHOR]

Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r2) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm/10 d to 8.48 mm/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP. [ABSTRACT FROM AUTHOR]

Accurately grasping the distribution and area of cotton for agricultural irrigation scheduling, intensive and efficient management of water resources, and yield estimation in arid and semiarid regions is of great significance. In this paper, taking the Xinjiang Shihezi oasis agriculture region as the study area, extracting the spectroscopic characterization (R, G, B, panchromatic), texture feature (entropy, mean, variance, contrast, homogeneity, angular second moment, correlation, and dissimilarity) and characteristics of vegetation index (normalized difference vegetation index/NDVI, ratio vegetation index/DVI, difference vegetation index/RVI) in the cotton flowering period before and after based on GF-6 image data, four models such as the random forests (RF) and deep learning approach (U-Net, DeepLabV3+ network, Deeplabv3+ model based on attention mechanism) were used to identify cotton and to compare their accuracies. The results show that the deep learning model is better than that of the random forest model. In all the deep learning models with three kinds of feature sets, the recognition accuracy and credibility of the DeepLabV3+ model based on the attention mechanism are the highest, the overall recognition accuracy of cotton is 98.23%, and the kappa coefficient is 96.11. Using the same Deeplabv3+ model based on an attention mechanism with different input feature sets (all features and only spectroscopic characterization), the identification accuracy of the former is much higher than that of the latter. GF-6 satellite image data in the field of crop type recognition has great application potential and prospects. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop analytical tools to find the effectiveness of using aerosols to mitigate the urban heat island effect. Specifically, mineral calcite would be placed in a plume over cities to reflect solar radiation. A secondary goal is to compare the relative efficiencies of releasing the particles from tower heights or from aircraft heights. The aim is to reduce daytime temperatures at the surface. The method was to use a one-dimensional model or a single-column model to predict temperatures and weather conditions at all altitudes over a period of one month. The SCAM6 code was altered to incorporate the new capabilities for introduced aerosols. The pre-existing code considered only windblown dust, so the code was enhanced to handle aerosols that were intentionally produced. The key findings are that calcite as an aerosol does affect the weather. The models predict that in humid regions, calcite is less effective because it interacts with water clouds. In arid regions, calcite should be more effective since there are fewer water clouds to interact with. The result is that it is possible to predict reductions in air temperatures if solar insolation can be reduced. It was shown that temperatures can be reduced by 4 °C in arid regions. The conclusions are that calcite aerosol should be effective at mitigating urban heat islands. However, further work is needed related to economic, health, and ecological concerns. [ABSTRACT FROM AUTHOR]

Slurries with quicklime for fissure restoration in earthen sites in northwest China can reduce dry shrinkage and reinforce the soil on both sides of fissure. However, their full-scale properties need to be investigated and a compatibility assessment needs to be carried out in order to proceed with further research. The paper pays attention to the full-scale properties of the slurry and typical site soils. A total of 3 slurries with different mix proportions and 35 earthen sites in northwest China are selected to test properties, including basic physical properties, mechanical properties, thermal properties, and water-physical properties. The test results show the properties of the slurries are close and that the properties of the site soils are obviously different. Then, a compatibility assessment is considered as a multi-criteria decision-making problem to solve. Three universal and necessary steps in the paper are to select assessment indicators, weigh each indicator, and sort all of the samples. The AHP, CRITIC, and TOPSIS methods are adopted in the process of the compatibility assessment. The results of assessment show that most site soils are commonly or highly compatible with three slurries; few site soils are very highly or lowly compatible; and that no site soils are very lowly compatible, which means that the three slurries with quicklime with different mix proportions are acceptable for most earthen sites in NW China. The paper provides much basic data of the slurries with quicklime and site soils widely distributed in arid region in NW China, and also builds a model of the compatibility assessment which has potential for the further application of slurries with quicklime. [ABSTRACT FROM AUTHOR]

In the context of global warming, snow cover changes have an extremely important impact on the hydrological cycle and the redistribution of water resources in arid and semi-arid regions. In this paper, based on the daily cloud-free snow area remote sensing product data in the source region of Yangtze River (SRYR) from 2000 to 2019, the snow phenology variables such as the snow cover day (SCD), snow onset date (SOD), snow end date (SED), and snow duration day (SDD) were extracted separately for each hydrological year, and the vertical distribution of snow cover area (SCA) in the SRYR was analyzed by combining with the digital elevation model (DEM). In addition, we also combined climate factors and land cover types to further explore the spatiotemporal variation characteristics of snow phenology in response to different influencing factors, in order to reveal the spatiotemporal variation patterns of snow cover in the SRYR. The results showed that: (1) The SCA in the SRYR has a more obvious vertical distribution, with the maximum SCA reaching 61.58% at high elevation, while at low elevation, the SCA is mostly below 20%. The distribution of SCD in the study area showed a significant exponential correlation with DEM (R2 = 0.87). (2) The area of SOD in the SRYR showed an advanced trend of about 63.37%, while the area of SED showed a delayed trend of about 69.59%, and the area which showed significant trends is 4.29% and 4.36%, respectively. Therefore, the trends of both SOD and SED showed insignificant changes. (3) Temperature change is the main factor affecting the change of snow cover in the SRYR. Among them, 90.9% of the regions showed a significant positive correlation between temperature and SCD, while precipitation showed a significant negative correlation with SCD in about 75.3% of the total area of SRYR. Under the stable snow area (SCD > 60), the land cover type is glacial or permanent snow about 1.5 × 103 km2, which covers almost the entire glacial or permanent snow of the SRYR. [ABSTRACT FROM AUTHOR]

In recent years, a new discipline known as Explainable Artificial Intelligence (XAI) has emerged, which has followed the growing trend experienced by Artificial Intelligence over the last decades. There are, however, important gaps in the adoption of XAI in hydrology research, in terms of application studies in the southern hemisphere, or in studies associated with snowmelt-driven streamflow prediction in arid regions, to mention a few. This paper seeks to contribute to filling these knowledge gaps through the application of XAI techniques in snowmelt-driven streamflow prediction in a basin located in the arid region of north-central Chile in South America. For this, two prediction models were built using the Random Forest algorithm, for one and four months in advance. The models show good prediction performance in the training set for one (RMSE:1.33, R2: 0.94, MAE:0.55) and four (RMSE: 5.67, R2:0.94, MAE: 1.51) months in advance. The selected interpretation techniques (importance of the variable, partial dependence plot, accumulated local effects plot, Shapley values and local interpretable model-agnostic explanations) show that hydrometeorological variables in the vicinity of the basin are more important than climate variables and this occurs both for the dataset level and for the months with the lowest streamflow records. The importance of the XAI approach adopted in this study is discussed in terms of its contribution to the understanding of hydrological processes, as well as its role in high-stakes decision-making. [ABSTRACT FROM AUTHOR]

Integration of renewable energy sources and water production technologies is a must when facing water scarcity problems in semiarid regions, such as Mediterranean regions. The use of additional water resources and production methods, such as reclaimed water and, more specifically, desalinated water, means present and necessary water resources to introduce in the water balances to attend to water demands within a global warming and droughting scenario. These solutions have the inconvenience of energy/power needs and costs. However, the development of renewable energies like photovoltaic solar energy, with lower and lower costs and greater efficiency, makes these economically feasible facilities, reaching competitive production costs for marine or sea desalinated water by around 50% of reduction in energy costs and 20–30% of savings in final water production cost. This paper presents a practical project or action focused on the integration of renewable energies and new water resources by introducing a Photovoltaic Energy Plant (PVEP) as an energy source to feed a Seawater Desalination Treatment Plant (SWDTP). The PV facility is designed to cover all the energy demanded using the SWDTP during the day, and even studying the possibility of selling the energy production exceeds and injecting them into the energy supply network, covering the needs of buying energy needed during the high period where there is no photovoltaic energy production. Thus, savings related to energy costs and even incomes coming from energy sales mean an important reduction in operation costs or expenditures (OPEX), which makes economically feasible and sustainable the investment and the final price of water produced within the Mutxamel SWDTP. The final reduction cost in water desalination reaches 25% on average. [ABSTRACT FROM AUTHOR]

Over the past two decades, China's spatial distribution of cultivated land has been stable, yet there remains an urgent need to amplify grain productivity. The central scientific question addressed in this study is: How can we effectively evaluate the natural resource thresholds of cultivated land at a regional scale? Despite existing systems, there is a noticeable gap, particularly in this area of inquiry. Recognizing the present state of these resources and introducing more efficient management strategies is pivotal. This paper aims to research the restrictive situation of the natural resources background quality (NRB-quality) of China's cultivated land by developing an innovative classification method and a short-board identification method and adopting cluster analysis and other technical methods. The results showed that (1) China mainly maintains medium-quality land (57.30%). The soil texture displayed a fine average grade of 1.39, while the biodiversity, soil pH, secondary land types, and cropping systems indicated poor conditions, with average scores of 2.01, 2.06, 2.26, and 2.33. (2) A notable difference in the restrictive factors of cultivated land quality emerged, with secondary land types of cultivated land (including paddy field, irrigated land, and dry land) being the only national short-board factor. Regionally, the cropping system, secondary land types of cultivated land, and natural region were identified as short-board factors in 31, 24, and 23 natural regions. (3) The regional difference in cultivated land quality leads to six different management strategies. Eastern regions emphasized stabilizing land distribution and infrastructure enhancement, while China's western areas advocated cautious development and heightened ecological protection. The findings demonstrated regional differences in the status of cultivated land quality factors; implementing classified management approaches for cultivated land quality factors based on their characteristics is vital to preventing irreversible decline. The study serves as an important basis for the protection and utilization of cultivated land in the new era to clarify the NRB-quality differences of cultivated land in China. [ABSTRACT FROM AUTHOR]

Water is the most important resource for human, animal, and vegetal life. Recently, the use of artificial intelligence techniques, such as Random Forest, has been combined with other techniques, such as models of logical–mathematical reasoning, to generate predictive water quality models. In this study, a rule-based inference technique to generate water quality labels is described, using historical physicochemical parameter data on seven water monitoring stations in Loa River, collected by the Chilean Ministry of the Environment. Next, a predictive model of water quality status was created, using Random Forest, physicochemical parameters, and expert knowledge. The validation of Random Forest results is described using three quality indicators from the machine learning model: accuracy (acc), precision (p), and recall (r). This paper describes dataset preparation, the refinement of the threshold values used for the physicochemical parameters most significant in the class, and the predictive model labeling water quality. The models obtained yielded the following mean values: acc = 0.897, p = 89.73, and r = 0.928. The ML model reported here is novel since no previous studies of this kind predict the water quality of Loa River, located in an extremely arid zone. This study also helps to create specific knowledge to predict freshwater quality. [ABSTRACT FROM AUTHOR]

An important problem currently faced by karst rocky desertification management is the degradation of vegetation fruits, which encourages the search for a solution for the sustainable growth of vegetation. Water and fertilizer are key factors affecting crop quality (the formation of soluble solids, protein, amino acids, soluble sugar content, etc.). A comprehensive review of research related to water–fertilizer coupling and crop quality improvement may be beneficial for rocky desertification control. This study analyzed 427 related papers, and the results showed that: (1) the number of published papers showed an "S" curve growth trend over time, indicating that research in the field of water–fertilizer coupling has entered a mature stage after rapid growth, yet it is still in the initial stage in karst areas; (2) the research was mainly theoretical, but also included technology research and development, model construction, demonstration and extension, and monitoring and evaluation; (3) except for research related to greenhouse facilities, the research areas are mainly located in arid and semi-arid regions. The study further revealed (4) the frontier theory of water–fertilizer coupling and crop quality research. Finally, it was found that the theoretical and technical studies related to water–fertilizer coupling and crop quality improvement in karst areas are insufficient. Whether the existing theories and techniques are applicable to karst areas needs further verification. In addition to the areas of selecting germplasm with high water/fertilizer utilization efficiency and tolerance to rocky desertification, further improvement in the dynamic model of water–fertilizer coupling and the construction of a more applicable water–fertilizer coupling model could also be future research hotspots. [ABSTRACT FROM AUTHOR]

Environmental problems may develop in groundwater basins when water levels change due to long-term wetter or drier climate or land development. A term related to water-level elevation is flow capacity, which develops in aquifers when the water table is at or very close to land surface. Non-capacity develops in systems where the water table is too deep for capillary water to reach the land surface. Flow capacity is the maximum amount of water that an aquifer can transmit. Sufficient moisture is not available for flow capacity to be established in most aquifers in arid zones and these aquifers are at non-capacity, but many aquifers in today's deserts were at flow capacity when paleoclimates were cooler and moister during the late Pleistocene. Climate change and anthropogenic activities can cause aquifers to move toward flow capacity but in the last 15,000 years, almost always toward non-capacity. This paper reviews environmental and geotechnical problems associated with the transition of groundwater basins from flow capacity to non-capacity, and vice versa. Five relevant topics are discussed and evaluated: (1) The effects of flow capacity and non-capacity on groundwater basins targeted for waste repositories; (2) The salt contamination of groundwater where flow capacity was present in the Late Pleistocene and is no longer present; (3) Trace element enrichment in salt crusts in playa sediments and environmental risks to groundwater when the flow systems transition from flow capacity to non-capacity; (4) The development and retention of environmental tracers in arid groundwater flow systems at flow capacity that cannot be explained under conditions of non-capacity; and (5) The relationship of flow capacity to fossil hydraulic gradients and non-equilibrium conditions where there is little groundwater extraction. A case example is provided with each of these topics to demonstrate relevance and to provide an understanding of topics as they relate to land management. [ABSTRACT FROM AUTHOR]

The global climate shows an obvious warming trend, and the impact on water resources is increasing. Abrupt temperature change and warming hiatus are two important states of temperature change. The quantitative impacts of temperature change and warming hiatus on surface runoff remain unclear. Based on the measured runoff data from 60 representative hydrological stations in China from 1956 to 2016 and the Water Balance Model developed by the Research Center for Climate Change (RCCC-WBC), this paper analyzes the quantitative impacts of abrupt temperature change and warming hiatus on surface runoff. The results showed that the effects of three types of abrupt temperature changes on runoff in different basins in China are significantly different. The effects of abrupt temperature changes and warming stagnation on runoff in northern China are greater than those in southern China, and the effects of abrupt temperature changes and warming stagnation on runoff in the upper, middle, and lower reaches of the same basin are also different. Before the abrupt change in temperature, the influence of temperature on the surface runoff was less than 9%, and the influence of temperature on the runoff in some southern areas was weaker, only affecting less than 3% of the runoff. When the temperature changes abruptly, the influence of air temperature on the surface runoff in a small part of the arid region is up to 30%. The abrupt change in mean maximum temperature has both positive and negative driving effects on runoff in China, and the negative driving effect is concentrated in the areas with abrupt warming, affecting about 8% of the runoff on average. The average influence of abrupt mean temperature change on runoff in China is about 10%, and the area with a large influence on runoff change is concentrated in the area north of 40° N. The abrupt change in temperature in the middle and lower reaches of the Yellow River Basin has a great influence on the runoff change, up to 13%. The maximum impact of abrupt mean minimum temperature on runoff is concentrated in Northeast China, ranging from 9% to 12%. During the period of temperature stagnation, air temperature and runoff showed an obvious reverse trend. During this period, the average negative influence of drastic changes in air temperature on runoff was about 15%, but precipitation and runoff still maintained a good consistency, which may be due to the effect of other influencing factors which offset the negative driving effect of air temperature. [ABSTRACT FROM AUTHOR]

With global warming, the frequency, intensity, and period of extreme climates in more areas will probably increase in the twenty first century. However, the impact of climate extremes on forest vulnerability and the mechanisms by which forests adapt to climate extremes are not clear. The eastern Tianshan Mountains, set within the arid and dry region of Central Asia, is very sensitive to climate change. In this paper, the response of Picea schrenkiana and Larix sibirica to climate fluctuations and their stability were analyzed by Pearson's correlation based on the observation of interannual change rates of climate indexes in different periods. Additionally, their ecological adaptability to future climate change was explored by regression analysis of climate factors and a selection of master control factors using the Lasso model. We found that the climate has undergone significant changes, especially the temperature, from 1958 to 2012. Around 1985, various extreme climate indexes had obvious abrupt changes. The research results suggested that: (1) the responses of the two tree species to extreme climate changed significantly after the change in temperature; (2) Schrenk spruce was more sensitive than Siberian larch to extreme climate change; and (3) the resistance of Siberian larch was higher than that of Schrenk spruce when faced with climate disturbance events. These results indicate that extreme climate changes will significantly interfere with the trees radial growth. At the same time, scientific management and maintenance measures are taken for different extreme weather events and different tree species. [ABSTRACT FROM AUTHOR]

In arid regions with scarce water resources, lakes play an extremely vital role in maintaining the ecological environment. Therefore, the Chinese government has launched an ecological water conveyance project in the Tarim River basin in Xinjiang with the aim of restoring the ecological environment of the area. In previous studies, there was no complete evaluation system used to quantify changes in the ecological environment of arid regions after ecological water conveyance. In this paper, Lake Taitema was selected as the study area, which is both a terminal lake in the Tarim River basin and an object of the ecological water conveyance project. This study utilized Landsat TM/OLI satellite remote sensing images and MODIS datasets to build a remote sensing ecological index model and systematically evaluated the changes in the ecological environment and land use types in the Taitema Lake area. A structural equation model was constructed to analyze the correlation between the area of Taitema Lake and its driving factors. The results show that over the selected 20 years, the proportion of pixels with an upward trend (Zc > 0) of the RSEI was 56.5%, while the proportion of pixels with a downward trend (Zc < 0) of the RSEI was 43.5%. The area proportion of regions with poor ecological environment quality decreased by about 40%, and the area proportions of regions with moderate, good, and excellent ecological environment quality increased by 29.7%, 10%, and 0.6%, respectively. By comparing the land use data from 2000 and 2020, the proportion of grassland increased by 6%, the proportion of water area increased by 4.4%, and the proportion of unused land decreased by 9.6%. In summary, after the implementation of the ecological water conveyance project, the ecological environment quality of the Lake Taitema area gradually improved, and ecological water conveyance was the main driving factor of the area change in Lake Taitema. [ABSTRACT FROM AUTHOR]

Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about −0.40 ± 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales. [ABSTRACT FROM AUTHOR]

Meeting current needs without compromising future generations' ability to meet theirs is the only path toward achieving environmental sustainability. As the most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation to mass losses brought on by salinization. These issues affect agricultural productivity and ecological balance, undermining sustainability and food security. Therefore, timely monitoring and accurate mapping of salinization processes are crucial, especially in semi-arid and arid regions where climate variability impacts have already reached alarming levels. Salt-affected soil mapping has enormous potential thanks to recent progress in remote sensing. This paper comprehensively reviews the potential of remote sensing to assess soil salinization. The review demonstrates that large-scale soil salinity estimation based on remote sensing tools remains a significant challenge, primarily due to data resolution and acquisition costs. Fundamental trade-offs constrain practical remote sensing applications in salinization mapping between data resolution, spatial and temporal coverage, acquisition costs, and high accuracy expectations. This article provides an overview of research work related to soil salinization mapping and monitoring using remote sensing. By synthesizing recent research and highlighting areas where further investigation is needed, this review helps to steer future efforts, provides insight for decision-making on environmental sustainability and soil resource management, and promotes interdisciplinary collaboration. [ABSTRACT FROM AUTHOR]

The arid region of northwest China is an extremely fragile area of natural ecology. With population growth and continuous expansion of urban scale, the ecosystem in the arid region is facing greater pressure. Scientific assessment and prediction of the value of ecosystem services in arid areas are necessary and of great significance for the sustainable development of regional ecological environments. In this paper, a parametric optimal geographic model is used to analyze the driving factors of ESV spatial dispersion in Jiuquan City as an example. The PLUS model is also used to simulate the spatial and temporal evolution of ESV in 2035 under the scenarios of natural development, urban development, water constraints and ecological conservation, based on the historical change pattern of land use and ecosystem service value (ESV). The results showed that from 1980 to 2020, the plowland, water and construction land area in Jiuquan City showed a significant increasing trend, while other land use types showed a decreasing trend. ESV has increased from CNY 139.394 billion to CNY 142.642 billion. The expansion of plowland and water area was the main reason for the increase in ESV. Elevation, temperature, and precipitation are the main driving factors of spatial differentiation of ESV in Jiuquan City. The interaction of natural and human factors enhances the explanatory power of each factor to the spatial differentiation of ESV. In 2035, the ESV in four development scenarios in Jiuquan City showed an upward trend, and the ESV of the ecological protection scenario was the highest, which was the best mode to realize a sustainable development in Jiuquan City in the future. This study can provide scientific basis and decision-making basis for Jiuquan City to formulate sustainable development strategy. [ABSTRACT FROM AUTHOR]

In this paper, a low-cost monitoring system for an off-grid photovoltaic (PV) system, installed at an isolated location (Sahara region, south of Algeria), is designed. The PV system is used to supply a small-scale greenhouse farm. A simple and accurate fault diagnosis algorithm was developed and integrated into a low-cost microcontroller for real time validation. The monitoring system, including the fault diagnosis procedure, was evaluated under specific climate conditions. The Internet of Things (IoT) technique is used to remotely monitor the data, such as PV currents, PV voltages, solar irradiance, and cell temperature. A friendly web page was also developed to visualize the data and check the state of the PV system remotely. The users could be notified about the state of the PV system via phone SMS. Results showed that the system performs better under this climate conditions and that it can supply the considered greenhouse farm. It was also shown that the integrated algorithm is able to detect and identify some examined defects with a good accuracy. The total cost of the designed IoT-based monitoring system is around 73 euros and its average energy consumed per day is around 13.5 Wh. [ABSTRACT FROM AUTHOR]

In this paper, we propose a stochastic weather model consisting of temperature, humidity, and precipitation, which is used to calculate a reconnaissance drought index (RDI) in Qatar. The temperature and humidity models include stochastic differential equations and utilize an adjusted Ornstein–Uhlenbeck (O–U) process. For the precipitation model, a first-order Markov chain is used to differentiate between wet and dry days and the precipitation amount on wet days is determined by a probability distribution. Five different probability distributions were statistically tested to obtain an appropriate precipitation amount. The evapotranspiration used in the RDI calculation incorporates crop coefficient values, depends on the growth stages of the crops, and provides a crop-specific and more realistic representation of the drought conditions. Five different evapotranspiration formulations were investigated in order to obtain the most accurate RDI values. The calculated RDI was used to assess the intensity of drought in Doha, Qatar, and could be used for the pricing of financial drought derivatives, a form of weather derivative. These derivatives could be used by agricultural producers to hedge against the economic effects of droughts. [ABSTRACT FROM AUTHOR]

In the last three decades, water shortages have become more common and have left an increasing and significant mark on the world economy. The lack of water in arid and semi-arid regions, along with the interest in water security in areas where the demand for water exceeds its availability, has caused water reuse to be considered as an alternative source in these areas and has prompted the authorities to change from the usual linear, take–make–use management approach to the circular management of resource preservation. This new approach to sustainable management, using the management of reduction, reuse, recycling, and recovery, is called the circular economy (CE). However, although the potential for the reuse of treated wastewater is still insipient, CE in the water sector has gained a lot of attention lately. The aim of this paper is to systematically examine existing research published in the last five years on CE implementation in the water and wastewater sector. A sample of 64 articles was found through interactive keyword selection. Using the dynamic reading technique, data were extracted, and articles were classified according to five research motivations, which include, political, economic, legal, institutional, technical, and environmental. Most publications were from institutions in Europe, followed by North America and Asia. A similar trend was observed in terms of the distribution by authors, countries, and regions. [ABSTRACT FROM AUTHOR]

It has become undeniable that global land surface temperature (LST) has continued to rise in recent years. The threat of extreme heat to humans has become self-evident, especially in arid regions. Many studies have clarified the temperature rise/fall mechanism of LST from the perspective of influencing factors. However, there are few studies on mitigating LST from the standpoint of regional networks. This paper first combines Landsat 8 with Sentinel-2 imagery for LST downscaling based on the Google Earth engine as a way to match local climate zone (LCZ) with 17 classification types. Then, the thermal environment resistance surface is constructed according to LCZ, and the essential cold sources are identified using morphological spatial pattern analysis (MSPA) and circuit theory to form the thermal environment green corridor and obtain the pinch point and barrier point areas. The results show that (1) The downscaling of LST based on random forest (RF) for the Urumqi–Changji–Wujiaqu metropolitan area has an R2 of 0.860 and an RMSE of 3.23, with high downscaling accuracy. (2) High temperature (HT), medium temperature (MT), and low temperature (LT) have the largest proportions in the study area; HT dominates in Urumqi, LT in Changji, and MT in Wujiaqu. (3) The natural types (LCZ-D, LCZ-C, and LCZ-F) in the LCZ classification occupy a large area, and the building types are mainly concentrated in Urumqi; LCZ-D, LCZ-G, and LCZ-A contribute the most to the cooling of LST, and LCZ-F, LCZ-C, and LCZ-10 contribute the most to the warming of LST. (4) After identifying critical cold source patches according to MSPA to arrive at 253 green corridors, subsensitive corridors and sensitive corridors need to take certain measures to prevent corridor blockage; pinch point areas, as well as barrier point areas, need to be protected and repaired according to their respective characteristics. In summary, corresponding cooling measures to specific areas can improve the connectivity between cooling sources and slow down the temperature increase of the whole area. This study and experimental approach can provide new insights for urban planners and climate researchers. [ABSTRACT FROM AUTHOR]

Global climate change affects biodiversity patterns, especially in arid and semi-arid regions such as the Mongolian plateau, one of the most ecologically fragile regions in the world. Three dynamic habitat indices (DHIs) were related to the productivity hypothesis and calculated based on FAPAR, including cumulative productivity ( D H I c u m indicates the availability of resources such as food supply and habitat in a year, representing available energy), minimum productivity ( D H I m i n indicates the limitations of food and habitat resources in a year, representing environmental stress), and seasonal productivity ( D H I s e a denotes the change in productivity in a year, representing environmental stability). In this paper, we investigated the distribution pattern of species richness on the Mongolian Plateau based on the productivity hypothesis. We constructed models of the richness of three species (mammals, birds, and amphibians) using DHIs and climate variables to explain patterns of species richness on the Mongolian Plateau. The results revealed that, on the Mongolian plateau, there is a relatively high correlation between DHIs and species richness, especially with D H I c u m (R = 0.59 for mammals, R = 0.73 for birds, and R = 0.58 for amphibians). There was a significant non-linear relationship between DHIs and species richness, as the model predictive power was significantly enhanced with GAM and RF. The inclusion of climate variables significantly improved the explanatory power of various models for the mammal, bird, and amphibian species richness on the Mongolian Plateau, with the best results for RF (0.89, 0.94, and 0.91, respectively). The influence of climate variables on species richness patterns in the importance ranking was higher than that of DHIs. Climate also has an influence on species richness. Vegetation productivity and climatic factors are good determinants of species richness on the Mongolian Plateau and should be carefully considered in future studies. [ABSTRACT FROM AUTHOR]

The spatial-temporal differentiation characteristics and driving mechanisms of ecosystem services are of great significance for optimizing the pattern of land spatial protection and realizing regional sustainable development. Existing studies seldom consider the segmental influence mechanism of various influencing factors on different levels of ecosystem service value (ESV). Therefore, this paper analyzes the temporal and spatial differentiation evolution characteristics of ESV in semiarid regions through an improved ESV evaluation model. The spatial panel quantile regression (SPQR) model was introduced to explore the relationship between various types of influencing factors and ESV in different intervals. The results showed the following: (1) The changes in ESV in Baotou City from 2000 to 2018 tended to be stable, but the spatial differentiation of ESV intensified. The aggregation feature of the low-ESV region is significant and gradually expanding. (2) Precipitation was the dominant factor increasing the ESV in each interval, and temperature had a significant negative impact on the low-ESV area. (3) Higher land use integrity accelerates the decline of ESV in the surrounding areas of built-up areas. The high-ESV area was more sensitive to the intensity of human activity. The direction of human activities should be effectively controlled, and the structure of comprehensive land use should be optimized to enhance the service function of regional ecosystems. This research provides new thinking for the ecological restoration zoning of regional territorial spatial planning and the sustainable development of resource-based cities. [ABSTRACT FROM AUTHOR]

Ongoing climate change processes and increasing environmental pressure suggest the need to adjust the wine production systems worldwide to the new conditions while reducing their environmental pressure. The grapes' cultivation phase may be influenced by expected changes. It follows that existing grape wine cultivation systems should be analyzed to identify major 'hotspots' and opportunities for change. Several studies have analyzed materials, energy inputs, and related emissions along the grape wine life cycle. However, most research focuses on traditional grape wine growing areas, and no study has yet focused on grape wine grown in unconventional desert areas. The research presented in this paper analyzed the carbon footprint (CF) of grapes grown in the Mediterranean, semi-arid, and arid climatic regions in the state of Israel. It revealed that, on average, a ton of grapes generates 342 kg CO2 eq from the cradle to the farm gate. The product was analyzed using a life-cycle approach, with the aim of studying the CF of each phase according. Most emissions were found to be related to the use of fertilizers (37%), fuel for transportation and mechanization (19%), and water supply (17%). The CF of grapes in the arid region was found to be the highest at 361 kg CO2 eq compared to 317 kg CO2 eq in the semi-arid region and 346 kg CO2 eq in the Mediterranean region. The analysis emphasizes the arid and semi-arid potential to reduce its CF by implementing farm management practices, including the choice of grape varieties, changing vineyard infrastructure, fertilizers, water management, and more. As presented here, understanding cropping systems in these regions can promote a better adaptation of the cropping systems to the changing conditions around the world. [ABSTRACT FROM AUTHOR]

Climate change is exacerbating adverse impacts of water stress in rainfed agriculture. This paper seeks to identify safe sowing windows for smallholder farmers in the Sudanian region of West Africa (WA). We hypothesize that the traditional focus on the onset of the season to start sowing leads to crop losses in years of high rainfall intermittency. AquaCrop, an FAO crop model, is used to simulate the yield response of maize (Zea mays L.) to sowing dates ranging from the 1st of May to the 30th of November at 20 locations in WA. We find that sowing directly after the first rains carries a higher risk of water stress, hampering crop development due to insufficient buildup of soil water storage to overcome dry spells. Based on three years of data per station on average, we identify safe sowing windows across the Sudanian region that secure optimal yield in 97% of all cases. We find that delaying sowing to mid-June (savanna and western part of the region) and to July (semi-arid region) ensures optimal yields. Of the three commonly applied local onset approaches covered in our evaluation, only LO10mm (10 mm/day on four consecutive days) achieves a similar yield result. The advantage of the safe window approach is that it is accessible for smallholders, who in many cases do not have access to local rainfall information. [ABSTRACT FROM AUTHOR]

Xinjiang is a typical continental arid climate zone and ecologically fragile zone. Drought has caused damage to the local social economy, agricultural production, and the ecological environment. However, the study of drought is more difficult due to the complex topography and the lack of monitoring information. In this paper, based on the meteorological data of 94 meteorological stations in Xinjiang from 1961 to 2020, we used the precipitation and potential evapotranspiration (ET0) to calculate the aridity index (AI); the Mann–Kendall test, Morlet wavelet analysis, and Kriging interpolation were used to identify the trend, period, and spatial distribution. The results showed that (1) the average change rate of the precipitation in Xinjiang was 8.58 mm/10a, the average change rate of the ET0 was −14.84 mm/10a, and the average change rate of the AI was −1.94/10a; (2) the periods of precipitation, ET0, and AI in Xinjiang were 39, 29, and 14 years, respectively, and the abrupt changes occurred in 1986, 1971, and 1987, respectively; (3) The Moran index of precipitation and temperature are 0.41 and 0.33, respectively, indicating that precipitation and temperature in Xinjiang are positively correlated in spatial distribution and have spatial clustering characteristics; and the z-values are both greater than 2.58 (p < 0.01), indicating that the spatial autocorrelation of precipitation and temperature in Xinjiang is significant. This study can provide a reference for the diagnosis of the meteorological drought mechanism and the coping with climate change in Xinjiang. [ABSTRACT FROM AUTHOR]