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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]

Arid zone grassland is a crucial component of terrestrial ecosystems and plays a significant role in ecosystem protection and soil erosion prevention. However, accurately mapping grassland spatial information in arid zones presents a great challenge. The accuracy of remote sensing grassland mapping in arid zones is affected by spectral variability caused by the highly diverse landscapes. In this study, we explored the potential of a rectangular tile classification model, constructed using the random forest algorithm and integrated images from Sentinel-1A (synthetic aperture radar imagery) and Sentinel-2 (optical imagery), to enhance the accuracy of grassland mapping in the semiarid to arid regions of Ordos, China. Monthly Sentinel-1A median value images were synthesised, and four MODIS vegetation index mean value curves (NDVI, MSAVI, NDWI and NDBI) were used to determine the optimal synthesis time window for Sentinel-2 images. Seven experimental groups, including 14 experimental schemes based on the rectangular tile classification model and the traditional global classification model, were designed. By applying the rectangular tile classification model and Sentinel-integrated images, we successfully identified and extracted grasslands. The results showed the integration of vegetation index features and texture features improved the accuracy of grassland mapping. The overall accuracy of the Sentinel-integrated images from EXP7-2 was 88.23%, which was higher than the accuracy of the single sensor Sentinel-1A (53.52%) in EXP2-2 and Sentinel-2 (86.53%) in EXP5-2. In all seven experimental groups, the rectangular tile classification model was found to improve overall accuracy (OA) by 1.20% to 13.99% compared to the traditional global classification model. This paper presents novel perspectives and guidance for improving the accuracy of remote sensing mapping for land cover classification in arid zones with highly diverse landscapes. The study presents a flexible and scalable model within the Google Earth Engine framework, which can be readily customized and implemented in various geographical locations and time periods. [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]

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]

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]

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]

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]

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]

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]

Introduction: Understanding the impact of deep groundwater depth on vegetation communities and soil in sand dunes with different underground water tables is essential for ecological restoration and the conservation of groundwater. Furthermore, this understanding is critical for determining the threshold value of groundwater depth that ensures the survival of vegetation. Method: This paper was conducted in a semi-arid region in eastern China, and the effects of deep groundwater depth (6.25 m, 10.61 m, and 15.26 m) on vegetation communities and soil properties (0-200 cm) across three dune types (mobile, semi-fixed, and fixed dunes) were evaluated in a sand ecosystem in the Horqin Sandy Land. Results: For vegetation community, variations in the same species are more significant at different groundwater depths. For soil properties, groundwater depth negatively influences soil moisture, total carbon, total nitrogen, available nitrogen, available phosphorus concentrations, and soil pH. Besides, groundwater depth also significantly affected organic carbon and available potassium concentrations. In addition, herb species were mainly distributed in areas with lower groundwater depth, yet arbor and shrub species were sparsely distributed in places with deeper groundwater depth. Discussion: As arbor and shrub species are key drivers of ecosystem sustainability, the adaptation of these dominant species to increasing groundwater depth may alleviate the negative effects of increasing groundwater depth; however, restrictions on this adaptation were exceeded at deeper groundwater depth. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lakes are sensitive indicators of climatic change and are important resources for regional economic development. In recent years, there have been many studies related to the changes in lake area and lake volume. However, further studies are still required to understand the responses of lakes to climatic change and human activities. This paper utilizes lakes in the Qaidam Basin, an inland arid region of China, as the object of study and investigates the characteristics of variability in lake changes and its driving forces by combining multi-source remote sensing, model simulations and historical data. We first analyzed the spatiotemporal pattern of climatic change in the basin under the background of global warming. The response of lake water volume to climatic change and human activities is then discussed. Finally, the main factors that affect the change in lake water volume in different regions of the basin are delineated. The water volume of lakes in the Qaidam Basin increased by 3.81 km3 from 1990 to 2020. Particularly since the 21st century, the water volume of lakes has increased rapidly, and an increasingly abrupt change appeared around 2015. The increases in precipitation and vegetation area are the main and secondary factors that led to the increase in total lake water volume in the basin, respectively. However, the main influencing factors still vary in different regions. The impact of air temperature, evaporation, and changes in the cropland area on the change in lake water volume is generally not obvious. Human activities, such as the development of salt lakes and damming, have led to substantial changes in the spatial pattern of lakes in the middle of the basin and are associated with the replacement, genesis, and disappearance of Yiliping Lake, Yahu Lake and West Taijinar Lake, respectively. This study reveals the changing characteristics of climate and lake water volume in inland arid basins in China, which are highly important to understand the responses of lakes to climatic change and human activities, and provides a scientific basis for the rational development and utilization of lake resources in arid basins. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flood disaster is one of the natural disasters which cause the most serious economic losses, the most casualties, and the greatest social impact. Flood frequency analysis is very important for reducing flood disaster. In this paper, based on the flood data of Manas River and tools of Box–Cox and Johnson normal transformation, the nonparametric statistical method for flood frequency analysis is studied in order to analyze the adaptability between it and the rivers in arid region of north-western China. The calculation result of the fitness index is divided into two parts: high flood discharge and low flood discharge. One of the two evaluation indexes has an advantage in fitting, and the number of advantages of the three methods in each part has been counted. After analysis, for the flood peak discharge frequency of rivers in arid region of north-western China, the frequency curve of Johnson transformation fits best with empirical data. The high flood discharge advantage is 6, and the low flood discharge is 4. For the flood volume frequency of rivers in arid region of north-western China, Box–Cox transform fits well with empirical data at the high flood discharge frequency curve, and its advantage is 12; Johnson transformation has a better fit between the low flood discharge frequency curve and empirical data, and its advantage is 12. Therefore, it is the way of improving the precision of flood frequency analysis to use the method of P-III distribution and normal transformation comprehensively. [ABSTRACT FROM AUTHOR]

Water system management is a worldwide challenge, especially in arid and semi-arid regions. Ecological water conveyance projects aim to raise the groundwater table, thereby saving natural vegetation and curbing ecological deterioration. Since 2000, these projects have been implemented in the arid zone of northwest China, with generally successful outcomes. Taking a portion of the lower reaches of the Tarim River as the study area, this paper analyzes in detail the ecohydrological effects which have occurred since the launching of artificial water conveyance 20 years ago. The results show that the groundwater table in the upper, middle and lower segments of the Tarim River's lower reaches has been raised on average 4.06, 4.83 and 5.13 m, respectively, while the area of surface water bodies connected to those sections has expanded from 49.00 km2 to 498.54 km2. At the same time, Taitema Lake, which is the terminal lake of the Tarim River, has been revived and now boasts a water area of 455.27 km2. Other findings indicate that the surface ecological response is extremely sensitive and that the area of natural vegetation has expanded to 1423 km2. Furthermore, the vegetation coverage, vegetation index (NDVI), and Net Primary Productivity (NPP) have increased by 132 km2, 0.07 and 7.6 g C m−2, respectively, and the Simpson dominance, McIntosh evenness, and Margalef richness indices have risen by 0.33, 0.35 and 0.49, respectively, in the monitored sample sites. As well, the carbon sink area has expanded from 1.54% to 7.8%. Given the increasing intensity of the occurrence of extreme hydrological events and successive dry years, similar ecological water conveyance projects should be considered elsewhere in China and in other parts of the world. The water conveyance scheme has generally proven successful and should be optimized to enhance the benefits of ecological water conveyance under water resource constraints. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Poverty and disasters are globally prevalent in ecologically fragile areas. Ecological migration is regarded as an effective way to address these issues. This paper investigates the spatial pattern of ecological migration and the corresponding spatiotemporal changes in land use and vegetation restoration in Gulang County, located in northwest China, between 2010 and 2018. For this purpose, we calculated three indicators: the transfer matrix of land use, normalized difference vegetation index (NDVI), and vegetation restoration degree (VRD). We found that ecological migrants in Gulang County moved from the Qilian Mountain National Park to the intersection between flat area and desert. The spatial patterns ranged from high-altitude to low-altitude, and the slopes became less steep. The distribution of the resettlements is more clustered and shaped by the traffic conditions and guided by the local governments. Unused land in the whole-village migration area and construction land in the resettlement area were dramatically impacted by ecological migration (625% and 279.3%, respectively). The cropland and construction land in the outmigration areas were mainly replaced with grassland and forest. In contrast, the grassland and unused land in the resettlement area were transferred to cropland and construction land. After ecological migration, the mean NDVI and VRD in Gulang County significantly increased, indicating that the vegetation in the outmigration areas quickly recovered. Moreover, the VRD in the whole-township migration areas was greater than that in the whole-village migration areas (121% > 68%). The main contribution to the increase in NDVI was the conversion of forest to grassland, accounting for 33%. In addition, the transition from other types of land to grassland made a larger contribution to the NDVI than conversions to forest. [ABSTRACT FROM AUTHOR]

Taking metropolitan areas as space carriers has become the engine of the Chinese government in its promotion of high-quality development, and this has also become an important measure by which to balance regional development. We used Zipf's law and the gravity model to study the urban scale distribution characteristics of the Kashgar Metropolitan Area (KMA) in this paper. We also constructed a spatial structure judgment vector for the KMA and put forward the development objectives of different circles. The findings show the following: (1) large cities have a high primacy of development, while small and medium-sized cities are underdeveloped. At present, the KMA is a concentrated monocentric-pattern metropolitan area, with Kashgar City as its core city. (2) The urban built-up area of Kashgar City is expanding to the east and south, where it has broken through the administrative boundary and become integrated with the urban built-up area of Shule County. The spatial structure characteristics of the KMA have been further clarified. The KMA forms three circles: core, middle, and outer. (3) Tumxuk City, Bachu County, Yecheng County, Shache County, and other counties are far from the core city and cannot be connected with Kashgar, but they are closely related to the surrounding cities, forming the Bachu–Tumxuk Urban Group and the Shache–Zepu–Yecheng Urban Group. This study contributes to the understanding of the characteristics of urban scale distribution and the spatial structure of metropolitan areas in arid regions, as well as providing guidance for the formulation of policies for the development of different circles in the KMA. [ABSTRACT FROM AUTHOR]

Glacier mass balance can visually indicate the degree of glacier response to climate change. The mountain glaciers are an essential source of recharge for rivers in arid regions and play a vital role in maintaining regional ecological stability and production life. This paper drives a spatially distributed degree-day model using multi-source remote sensing data such as MOD11C3 and TRMM3B43 to simulate the mass balance in the Tianshan Mountains' south and north slope basins. The spatiotemporal heterogeneity of the mass balance was compared and attributed using a Geographical detector. The results show that: (1) The glaciers in the north and south basins are mainly distributed at an altitude of 3900–4300 m, and the total glacier area accounts for 85.71%. The number of less than 1 km2 glaciers is the most in the whole region. (2) During the study period, the glaciers in the north and south basins were negative (−465.95 mm w.e.) an entire interannual change rate was −28.36 mm w.e./a. The overall trend of ablation can be divided into two stages: from 2000 to 2010a, the persistence increased, and from 2010 to 2016a, the volatility decreased. (3) In the attribution of mass balance, the factors affecting glacier mass balance can be divided into two parts: climate and topography. The cumulative contribution rate of climate factors in Kaidu is nearly 20% higher than that of topographic factors, but the contribution rate of climate factors in Manas is only 7.3% higher. Therefore, the change of glacier mass balance in the Kaidu river basin is more driven by climate factors, while the glacier mass balance in the Manas river basin is more affected by the combination of climate and topographic factors. (4) The climate accumulation is the dominant factor in the Manas river basin (69.55%); for the ablation area, the Kaidu river basin is dominated by climate (70.85%), and the Manas river basin is dominated by topographic factors (54.11%). Due to the driving force of climate and topographic factors and the different coupling modes, glacier mass balance's spatiotemporal heterogeneity in the north and south slope basins is caused. This study contributes to analyzing the mechanism of regional changes in the glacier mass balance. It provides a scientific basis for investigating the characteristics of water resource changes and water resource regulation in the north and south slope basins of the Tianshan Mountains. [ABSTRACT FROM AUTHOR]

The arid region of northwest China (ARNC) is one of the most sensitive areas to global warming. However, the performance of new Global Climate Models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating climate in this region, especially in the subregions, is not clear yet. Based on the temperature dataset from historical runs of CMIP6, this paper analyzed and evaluated the simulation ability of 29 GCMs in reproducing the annual mean temperature (tas), annual mean maximum temperature (tasmax) and annual mean minimum temperature (tasmin) in the ARNC and subregions from 1961 to 2014. The results show that (1) the correlation coefficients (CCs) between simulation and observation time series for the mean of two model ensembles (MME for equal-weight multi-model ensemble and PME for preferred-model ensemble) are generally better than those of 29 individual GCMs, with CCs ranging from 0.38 to 0.87 (p < 0.01). (2) All the models can simulate the significant warming trend of the three temperature elements in the study area well. However, the warming magnitude simulated by most of the models (41%) is smaller than the observations except for tasmax, which is also shown in the MME. (3) The spatial pattern of the three temperature elements can be better reflected by most models. Model simulation ability for the ARNC is better compared to that of the four subregions, with a spatial CC greater than 0.7 (p < 0.01). Among the subregions, the simulation performance of the north of Xinjiang for spatial pattern is superior to that of the other regions. (4) The preferred models for each subregion are various and should be treated differently when used. Overall, the PME outperforms both the MME and the individual models; it can not only simulate the linear trend accurately but also reduce the deviation effectively. [ABSTRACT FROM AUTHOR]

In this paper, Changji, Xinjiang, northwest China, was selected as the study area, and platinum group elements (PGEs) in PM2.5 were quantified by ICP-MS using microwave digestion. The results indicated that the average concentrations (and range) of Rh, Pd, and Pt in PM2.5 were 0.21 (n.d. −1.41) ng/m3, 8.09 (n.d. −59.50) ng/m3, and 0.12 (n.d. −0.83) ng/m3, respectively. The concentration of Pd was significantly higher than Rh and Pt. Moreover, the seasonal variations of Rh and Pd were the same: highest in summer and lower in other seasons. However, the seasonal variation of Pt was opposite to that of Rh and Pd: highest in winter and lower in other seasons. Seasonal differences in emission sources of PGEs and the climatic characteristics of arid regions played important roles in the seasonal changes of PGEs. Rh and Pd had a common source and similar diurnal variation. The major influencing factors were traffic volume and meteorological conditions. The diurnal variation regularity of Pt was different from Rh and Pd. The superimposed effect of vehicle exhaust emissions and coal-fired emissions was the main reason why the diurnal variation of Pt was more complicated than those of Rh and Pd. The diurnal concentration of Pt varied with the seasons. It is caused by seasonal coal combustion and meteorological conditions. [ABSTRACT FROM AUTHOR]

• This paper constructed a framework for green water assessment in the Loess Plateau gully region using MIKE SHE model. • A new concept and extraction method for green water components was defined based on existing research. • A new concept of the potential of green water was defined in relation to the dried soil layers' classification indicators. • We assessed the dependence on green water components and potential for environmental changes. Exponentially increasing water demand and increasingly uncertain hydrological conditions due to climate change (CC) and land use/cover change (LUCC) undoubtedly pose significant challenges to the sustainability of water systems in arid and semi-arid regions worldwide, and even more so in the rain-fed agricultural area of the Loess Plateau (LP) in China, where green water supports all ecological landscapes and most agriculture. Therefore, an accurate assessment of green water resources is crucial for regional development. This paper constructed a comprehensive framework for assessing green water resources based on the MIKE SHE model, defined a new concept of green water components (GWCs) that can be extracted based on existing studies, proposes the potential of green water (GW P), and present a research methodology. To validate this research framework, we applied it to a typical basin in the LP gully area of the Nanxiaohegou (NXHG) basin. The model was calibrated using runoff data from basin outlet stations to extract four GWCs: high-efficiency green water (GW H), low-efficiency green water (GW L), unused green water (GW U), ineffective green water (GW I). We then assessed the GW P of the basin and analyzed its trends, consistency, and dependence on changing environments. The assessment framework is simple and easy to use, providing valuable information for regional revegetation and basin water management, and is highly recommended for replication in other regions. [ABSTRACT FROM AUTHOR]

In the dryland area of the Loess Plateau in northwest China, long-term excessive fertilization has led to soil compaction and nutrient loss, which in turn limits crop yield and soil productivity. To address this issue, we conducted experiments using environmentally friendly organic fertilizer and bacterium fertilizer. Our goal was to investigate the effects of additional organic and bacterium fertilizer inputs on soil water migration, crop root architecture, and yield formation. We implemented six different fertilizer strategies, namely: Nm (mulching, N 30 kg/ha), NPK1m (mulching, N 60 kg/ha; P 30 kg/ha; K 30 kg/ha), NPK2m (mulching, N 90 kg/ha; P 45 kg/ha; K 30 kg/ha), NPKOm (mulching, N 90 kg/ha; P 45 kg/ha; K 30 kg/ha; organic fertilizer 2 t/ha), NPKBm (mulching, N 60 kg/ha; P 30 kg/ha; K 30 kg/ha; bacterium fertilizer 10 kg/ha), and N (N 30 kg/ha; no mulching). The results revealed that the addition of bacterium fertilizer (NPKBm) had a positive impact on soybean root system development. Compared with the other treatments, it significantly increased the total root length, total root surface area, and total root length density by 25.96% ~ 94.89%, -19.63% ~ 36.28%, and 9.36% ~ 28.84%, respectively. Furthermore, NPKBm enhanced soil water consumption. In 2018, water storage during the flowering and podding periods decreased by 12.63% and 19.65%, respectively, while water consumption increased by 0.97% compared to Nm. In 2019, the flowering and harvest periods decreased by 23.49% and 11.51%, respectively, while water consumption increased by 0.65%. Ultimately, NPKBm achieved high grain yield and significantly increased water use efficiency (WUE), surpassing other treatments by 76.79% ~ 78.97% and 71.22% ~ 73.76%, respectively. Subsequently, NPK1m also exhibited significant increases in yield and WUE, with improvements of 35.58% ~ 39.27% and 35.26% ~ 38.16%, respectively. The use of bacterium fertilizer has a profound impact on soybean root architecture, leading to stable and sustainable grain yield production. [ABSTRACT FROM AUTHOR]

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users' understanding of error characteristics and sources in IMERG. [ABSTRACT FROM AUTHOR]

High-resolution oasis maps are imperative for understanding ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline, there are no high-precision datasets related to oases in the world to date. To fill this gap, detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel-2 was conducted in summer 2020, and for the first time, a high-precision dataset of China's oases (abbreviation HDCO) with a resolution of 1 meter was constructed. HDCO comprises 1,466 oases with a total area of 277,375.56 km2. The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. In addition, information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis was added to the attribute table of the dataset. This dataset enables researchers to quantitatively monitor location and area of oases, fosters exploration of the relationship between oases and human under climate change and urbanization. [ABSTRACT FROM AUTHOR]

Semiarid regions play a pivotal role in global ecosystem and environmental governance. Changing environment (climate and land use variation) has led to the disruption of hydrology in semi-arid regions and ecological degradation in some area. Understanding their hydrological response to changing environments is crucial for ecological rehabilitation and optimal water resource allocation. This research, conducted in a Chinese semiarid watershed, integrates multi-field models about hydrology, meteorology and geography to investigate hydrological processes in typical hydrological years. The results indicate that the climate in this region is drying, and the likelihood of extreme weather events is increasing with global warming. Projecting changes from 2010 to 2060, 9.21% of grassland converting to 5.63% of forest and 3.58% of gully built-up land. As a result, the flood peak increases by 22.99% in typical drought years, while it decreases by 36.77% in normal years. Based on our analysis of the rainfallrunoff process in typical drought and normal years, we identify two distinct effects: the "local rainfall-runoff effect" and the "global rainfall-runoff effect", respectively. However, in typical wet years, the streamflow state is primarily controlled by intense and concentrated precipitation. This research provides insights into the effect of ecological restoration and typical climate variations on hydrological cycle in semiarid regions, thus contributing to more informed decision-making in environmental management and water resource planning. [ABSTRACT FROM AUTHOR]

Vegetation restoration in dryland regions may be a powerful way to control desertification and promote local habitat recovery. In these artificial revegetation systems, ecosystem processes and functioning are strongly determined by nitrogen (N) availability, while soil inorganic N (SIN) is the major available N form and is absorbed as a main N source for plants. However, SIN dynamics, their influencing factors, and their relative importance to the ecosystem of these N‐limited systems are not well understood. A field investigation was conducted to examine the monthly variations in SIN and its response to planting age and vegetation type in a typical artificial sand‐fixing system in northwestern China. The SIN content in topsoil (0–20 cm) during the growing season ranged from 7.10 to 95.65 mg kg−1, with a mean value of 27.14 mg kg−1. Soil nitrate N had a dominant role in determining SIN monthly dynamics, which showed a fluctuating trend with two peak values in early July and late August. SIN showed a significant increasing trend with the planting age of sand‐fixing vegetation, and it was highest for Nitraria sphaerocarpa (55.66 ± 5.76 mg kg−1), followed by Haloxylon ammodendron (29.81 ± 3.47 mg kg−1) and interspace (11.39 ± 1.13 mg kg−1). SIN displayed a hump‐shaped relationship with air temperature (R2 = 0.96, p < 0.01) and had a maximum at 19.88°C. The change rate of SIN was positively correlated with accumulated precipitation (R2 = 0.99, p < 0.05). SIN was correlated significantly with soil organic carbon (SOC), total nitrogen (TN), and soil clay content. Our results revealed that climatic (air temperature and precipitation), abiotic (soil texture), plant (planting age and vegetation type), and nutrient‐related (SOC and TN) factors regulate SIN dynamics in artificial sand‐fixing vegetation systems. Climate was the predominant factor affecting soil ammonium N, and soil nutrients (SOC and TN) were the predominant factor affecting soil nitrate N. Therefore, these factors should be integrated into optimizing regional vegetation establishment and improving ecosystem management practices in sand‐fixing lands. [ABSTRACT FROM AUTHOR]

Drought seriously restricts people's lives and social–economic development. An accurate understanding of the evolution of drought characteristics and future changes in cultivated land exposure can reduce the risk of drought. There is evidence that increased CO2 concentrations alter the physiological properties of vegetation and, thus, affect drought evolution. In this study, both changes and differences in drought (i.e., characteristics and cropland exposure) with and without the CO2 effect over the arid region of China are investigated, using seven CMIP6 outputs and land-use under seven shared-socioeconomic-pathway (SSP)-based scenarios. The results show that: (1) drier conditions will be more severe in 2015–2100 under SSP5-8.5, especially if the CO2 effect is neglected. Moreover, the CO2 effect will increase with increasing emission concentrations; (2) drought intensity will be greater than in the baseline period (1995–2014, approximately −1.45) but weaker than that without the CO2 effect under all scenarios; (3) drought frequency will decrease, and will generally decline faster if the CO2 effect is not considered; (4) drought duration will increase and the difference between the presence and absence of the CO2 effect will always be smallest under SSP1-1.9 and largest under SSP5-8.5; (5) drought acreage will also increase, and neglecting the CO2 effect is always higher than that considering CO2. The difference between the two algorithms will increase with time; and (6) cropland exposure to drought will increase, and can even reach 669,000 km2 and 524,000 km2 considering and ignoring the CO2 effect, respectively. Our findings suggest that ignoring CO2 in drought evaluations will result in significant overestimations of drought projections. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) is an important factor that needs to be considered in regional water management and allocation; thus, the reasonable estimation of PET is an important topic in hydrometeorology and other related fields. There is evidence that increased CO2 concentration alters the physiological properties of vegetation and thus affects PET. In this study, changes in PET with and without the CO2 effect over China is investigated using seven CMIP6-GCMs outputs under seven shared socioeconomic pathways (SSPs) based scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5), as well as the contribution rate of CO2 on PET in different climatic regions. Changes in estimated PET based on modified Penman–Monteith (PM) method that considers the CO2 effect is compared with the traditional PM method to examine how PET quantity varies (differences) between these two approaches. The results show that the PET values estimated by the two methods explored opposite trends in 1961–2014 over entire China; it decreases with consideration of CO2 but increases without consideration of CO2. In the future, overall PET is projected to increase under all scenarios during 2015–2100 for China and its three sub-regions. PET generally tends to grow slower when CO2 is taken into account (modified PM approach), than when it is not (traditional PM method). In terms of differences in the estimated PET by the two methods, the difference between the two adopted methods increased in China and its sub-regions for the 1961–2014 period. In the future, the difference in estimated PET is anticipated to continuously increase under SSP3-7.0 and SSP5-8.5. Spatially, a much greater extent of difference is found in the arid region. Across the arid region, the PET difference is projected to be the highest at 138% in the mid-term (2041–2060) with respect to the 1995–2014 period, whereas it tends to increase slower in the long-term period (2081–2100). Importantly, CO2 is found to be the most dominant factor (−154.2% contribution) to have a great effect on PET changes across the arid region. Our findings suggest that ignorance of CO2 concentration in PET estimation will result in significant overestimation of PET in the arid region. However, consideration of CO2 in PET estimation will be beneficial for formulating strategies on future water resource management and sustainable development at the local scale. [ABSTRACT FROM AUTHOR]

Drought prediction plays an important guiding role in drought risk management. The standardized precipitation index (SPI) is a popular meteorological drought indicator to measure the degree of drought. The SPI time series is non-stationary, whereas the conventional artificial neural network (ANN) model has limitations to predict non-stationary time series. To overcome this limitation, it is essential to investigate input data preprocessing to improve the ANN model. In this paper, a hybrid model coupled with singular spectrum analysis (SSA) and backpropagation ANN is proposed (SSA-BP-ANN). The advantage of this model is that the SSA of finite-length SPI sequences does not require the adoption of boundary extensions to suppress boundary effects, while the most predictable components of the SPI can be efficiently extracted and incorporated into the model. The proposed SSA-BP-ANN model is tested in case studies at three meteorological stations in Northern Shannxi Province, China. The results show that the SSA-BP-ANN model can produce more accurate predictions than the BP-ANN model. In addition, the performance improvement of SSA on the BP-ANN model is slightly better than wavelet decomposition and empirical mode decomposition. This new hybrid prediction model has great potential for promoting drought early warning in arid regions. [ABSTRACT FROM AUTHOR]

Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale. [ABSTRACT FROM AUTHOR]

• Quantitatively describes the groundwater recharge process for lakes. • Constructed a model for the groundwater table-lake area relationship in groundwater-dependent lakes in arid regions. • Suggested a groundwater depth of 1.73 m to maintain stable lake area within the Hetao Irrigation District. • Constructed model can serve as an effective reference tool for groundwater management and the protection of lake ecosystems. The groundwater table constitutes a significant factor in sustaining the ecosystems of lakes in arid regions. To elucidate the mechanism by which variations in groundwater tables affect lake areas, this paper presents a case study of a typical groundwater-fed lake in China's Hetao Irrigation District. An analytical model correlating groundwater tables with lake areas was developed to simulate and predict the impact of changes in groundwater tables on lake areas. The findings indicate that the analytical model adeptly simulates the lake area dynamics of the groundwater-fed lake cluster (Taerhu lake cluster) from 1998 to 2018, achieving a Nash-Sutcliffe efficiency (NSE) value exceeding 0.6 and a root mean square error (RMSE) value below 0.1 square kilometers. Maintaining a groundwater depth at 1.73 m ensures the stability of the lake area; a decrease in groundwater depth to 1.93 m would result in a 79.5 % reduction in lake area. The model constructed in this study offers a crucial tool for the ecological protection of lakes and groundwater management in arid regions. [ABSTRACT FROM AUTHOR]

Whether there is a transition underway, from a warm-dry climate to a warm-wet climate in Northwest China remains a controversial and scientifically significant issue. Will this trend continue in the future? Another hot issue is whether the climate in Northwest China will continue to be warm and humid over the next few decades. In this paper, eight CMIP6 models were employed to investigate temperature and precipitation changes under five principal Shared Socioeconomic Pathway (SSP) scenarios (from 2015 to 2099) to project the future warming and humidification in Northwest China using the SPEI (standardized precipitation evapotranspiration index) method. The results revealed that (1) the simulated temperature and precipitation of eight CMIP6 models were consistent with that of observed data during 1961-2014, which showed an increase of approximately 28.2 mm, while simulated data revealed an increase of approximately 9.4 mm. The annual precipitation gradually decreased from Eastern Inner Mongolia and the Southern Northwest Mongolia region (.700 mm) to the Central Northwest Mongolia region (,100 mm) from 1961 to 2014; (2) the MME significantly overestimated the temperature and slightly underestimated the precipitation in Northwest Mongolia. The temperature difference between the simulated and observed data was approximately 0.4 °C. The observed data showed an increase of approximately 0.9 °C from 1961 to 2014, whereas the simulated data revealed an increase of approximately 0.7 °C; (3) in the SSP5-8.5 scenario, the percentage of precipitation anomalies at 1.5, 2, 3, and 4 °C were 166.64, 190.58, 226.44, and 274.56%, respectively; thus, alleviating the drought situation while facilitating the warm-dry to warm-wet climate transition; (4) the water balance between rising temperatures and increased evapotranspiration resulting from increased precipitation suggested that not all sites will be wet in the future. There was still a drying trend in some areas, where drought was more severe under the high emissions scenario than the low emissions scenario. [ABSTRACT FROM AUTHOR]

Introduction: With global warming, the disaster losses caused by extreme precipitation events are increasing. The poor natural conditions and climate change make the arid and semi-arid mountainous grassland area a sensitive region of climate change. The study on the spatio-temporal variation characteristics of extreme precipitation events in this region is helpful to improve the ability of climate prediction and disaster prevention and reduction in grassland. Methods: Based on the daily precipitation data of four meteorological monitoring stations in the Yinshanbeilu from 1970 to 2020, the trend analysis, M-K test and wavelet analysis were used to select seven typical extreme precipitation indicators to analyze the temporal and spatial characteristics of extreme precipitation. Results and Discussion: The results showed that the precipitation in the Yinshanbeilu increased in the past 51a, and the number of heavy rain days increased significantly. The significance test of CDD and CWD showed that the number of continuous dry days and continuous wet days decreased abruptly. The spatial analysis showed that the high value areas of R95p, R95d and PRCRTOT were all located in Siziwang Banner, and it could be concluded that the extreme precipitation risk was the highest in Siziwang Banner, while the low value areas of SDII, Rx1day, R95p and PRCRTOT were all located in Sonid Right Banner, which could be inferred that the Sonid Right Banner was relatively dry. The first main cycle of the seven indexes of extreme precipitation almost runs through the whole time series, and the starting point of the minimum main cycle changes inconsistent. In addition to the number of consecutive dry days, the other indices have a good correlation with annual precipitation, flood season and monthly precipitation from June to September, and July is the peak period of extreme precipitation events. [ABSTRACT FROM AUTHOR]

Research on endophytic fungi in desert plants, particularly the epiphytic or endophytic fungi of leaves, remains limited. In the extremely arid regions of northwest China, the ultra-xerophytic desert plant Haloxylon ammodendron harbors white fungi on its assimilating branches during autumn. The hyphae of these fungi intertwine, both internally and externally, comprising superficial, bridging, and endophytic types. The superficial hyphae attach to the surface of the assimilating branches and continuously grow and intersect, forming a thick layer of felt-like hyphae. This thick, felt-like layer of hyphae facilitates the adsorption of atmospheric water vapor on the surface of the hyphae or the assimilating branches, allowing H. ammodendron to capture atmospheric moisture, even under low humidity. Some superficial hyphae penetrate the cuticle into the epidermis, becoming bridging hyphae, which can rapidly transport water from the outside of the epidermis to the inside. The endophytic hyphae shuttle within the epidermis, achieving rapid water transfer within the epidermis of the assimilating branches. The presence of these three types of hyphae not only enables the assimilating branches of H. ammodendron to achieve rapid water absorption and transmission, but also facilitates the uptake of atmospheric water vapor under low humidity conditions. We discuss the mechanism by which the hyphae promote water absorption from the perspectives of hyphal composition, the formation of felt-like structures, and environmental conditions. We consider the presence of fungal hyphae on the surface of the H. ammodendron assimilating branches as an inevitable ecological process in arid environments. This study provides important theoretical insights into the mechanisms underlying the strong drought resistance of desert plants in extremely arid regions and offers strategies for desertification control. [ABSTRACT FROM AUTHOR]

Soil amendments have been proposed as an effective way to enhance soil carbon stocks on degraded soils, particularly in dryland farming areas. Soil organic carbon (SOC) plays an important role in improving soil quality, and soil aggregates are known to be crucial in sequestering and protecting SOC. However, how aggregation and protection of SOC by aggregates respond to a single application of bentonite combined with maize straw remains unknown, especially in the sandy soil of a semi-arid region. A three-year field experiment with four treatments [no amendment (CK), maize straw amendment addition only (T1, 6 Mg ha−1), bentonite amendment addition only (T2, 18 Mg ha−1), and maize straw combined with bentonite amendment (T3, 6 Mg ha−1 maize straw plus 18 Mg ha−1 bentonite)] was conducted in the Loess Plateau of China to assess the effects of bentonite and maize straw on aggregation and SOC. The results indicated that soil bulk density decreased by 2.72–5.42%, and soil porosity increased by 3.38–8.77% with three years of T3 application, especially in the 20–40 cm layer, compared with CK. T3 increased the amount of C input, SOC stock, and SOC stock sequestration rate by 1.04 Mg ha−1 y−1, 0.84–1.08 Mg ha−1, and 0.49 Mg ha−1 y−1, respectively, and it increased the mass proportions and aggregate-associated C stock of >0.25 mm aggregates by 1.15–2.51- and 1.59–2.96-fold compared with CK. Correlation analysis showed a positive correlation of total SOC stock with the C concentration of >2 mm, 0.25–2 mm, and 0.053–0.25 mm aggregates. Aggregates of various sizes in sandy soils have the potential for greater SOC stock. Our findings suggest that the application of maize straw (6 Mg ha−1) combined with bentonite (18 Mg ha−1) would be an effective management strategy to enhance the bulk soil C pools by improving the soil structure and thereby improving soil fertility. [ABSTRACT FROM AUTHOR]

Background: Drought stress severely impedes plant growth, and only a limited number of species exhibit long-term resistance to such conditions. Pinus sylvestris var. mongolica, a dominant tree species in arid and semi-arid regions of China, exhibits strong drought resistance and plays a crucial role in the local ecosystem. However, the molecular mechanisms underlying this resistance remain poorly understood. Results: Here, we conducted transcriptome sequence and physiological indicators analysis of needle samples during drought treatment and rehydration stages. De-novo assembly yielded approximately 114,152 unigenes with an N50 length of 1,363 bp. We identified 6,506 differentially expressed genes (DEGs), with the majority being concentrated in the heavy drought stage (4,529 DEGs). Functional annotation revealed enrichment of drought-related GO terms such as response to water (GO:0009415: enriched 108 genes) and response to water deprivation (GO:0009414: enriched 106 genes), as well as KEGG categories including MAPK signaling pathway (K04733: enriched 35 genes) and monoterpenoid biosynthesis (K21374: enriched 27 genes). Multiple transcription factor families and functional protein families were differentially expressed during drought treatment. Co-expression network analysis identified a potential drought regulatory network between cytochrome P450 genes (Unigene4122_c1_g1) and a core regulatory transcription factor Unigene9098_c3_g1 (PsNAC1) with highly significant expression differences. We validated PsNAC1 overexpression in Arabidopsis and demonstrated enhanced drought resistance. Conclusions: These findings provide insight into the molecular basis of drought resistance in P. sylvestris var. mongolica and lay the foundation for further exploration of its regulatory network. [ABSTRACT FROM AUTHOR]

Water vapor adsorption on soil, a crucial non-rainfall water resource in arid regions, warrants further experimental investigation, particularly on two typical land surfaces: bare soil and gravel. This study examined the formation characteristics and influencing factors of vapor adsorption in an arid region of Northwestern China. Observations and analyses were conducted on adsorption and evaporation measurements taken by two small weighing lysimeters (SLSs); soil temperature at a depth of 5 cm; surface temperature; relative humidity; and air temperature at a height of 30 cm above the ground from 2019 to 2020. The adsorbed water in this area was more abundant at night and less abundant during the day, with a stable nightly adsorption rate of 0.013 mm/h. Adsorption was more frequent in spring and winter (from January to June and November to December), accounting for about 90% of the total annual adsorption. In 2019 and 2020, the ratio values of adsorption to evaporation were 0.16 and 0.10 for bare soil, and 0.10 and 0.12 for gravel, respectively. Adsorption was more likely to occur when the soil moisture content was less than 13%; the highest adsorption frequency was close to 20% when the RH was between 75 and 95%; low soil temperatures were more conducive to the occurrence of adsorption. The effect of temperature differences (Ta−Ts) on adsorption was stronger than that of relative humidity. The adsorption frequency generally showed a bimodal change with increasing temperature difference, but the effect of temperature differences was less effective for gravel than bare soil. When the relative humidity was high and the temperature difference was weakly positive, the maximum adsorption intensity could reach 0.18 mm/h. [ABSTRACT FROM AUTHOR]

To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study thoroughly investigated the spatial and temporal variations in phenological characteristics and vegetation health indices in the abdominal part of Maowusu Sandland in China over the past 20 years. Additionally, it established a linear correlation between vegetation health and temperature indices in the arid zone. To address the issue of predicting long-term trends in vegetation drought changes, we have developed a method that combines the Informer deep learning model with seasonal and Seasonal Trend decomposition using Loess (STL) and empirical mode decomposition (EMD). Additionally, we have utilized the linearly correlated indices of vegetation health and meteorological data spanning 20 years to predict the Normalized Difference Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI). The study's findings indicate that over the 20-year observation period, there was an upward trend in NDVI, accompanied by a decrease in both the frequency and severity of droughts. Additionally, the STL-EMD-Informer model successfully predicted the mean absolute percentage error (MAPE = 1.16%) of the future trend in vegetation drought changes for the next decade. This suggests that the overall health of vegetation is expected to continue improving during that time. This work examined the plant growth circumstances in dry locations from several angles and developed a complete analytical method for predicting long-term droughts. The findings provide a strong scientific basis for ecological conservation and vegetation management in arid regions. [ABSTRACT FROM AUTHOR]

Background: Accurate estimation of potential evapotranspiration (PET) is the key for studying land-air interaction hydrological processes. Several models are used to estimate the PET based on standardized meteorological data. Although combination-based models have the highest level performance estimation of PET, they require more meteorological data and may therefore be difficult to apply in areas lacking meteorological observation data. Results: The results showed significant differences in the spatial trends of PET calculated by different models in China, the Doorenbots–Pruitts model revealed the highest PET (1902.6 mm), and the Kuzmin model revealed the lowest PET (349.6 mm), with the largest difference being 5.5 times. The Romanenko and the Rohwer models were the recommended temperature-based and aerodynamic-based models. On the other hand, the Abtew model was more suitable for arid and semi-arid regions, while the Priestley–Taylor model was more suitable for humid regions. Combination-based models revealed ideal calculation accuracies, among which the Penman–Monteith model was the best option for PET calculation. Conclusions: The accuracy range of Romanenko, Rohwer, Abten, Priestley Taylor, and Penman Monteith models improved in MPZ and TCZ is higher than that improved in TMZ and SMZ. This does not mean that the improved models have higher accuracy in MPZ and TCZ than in TMZ and SMZ. On the contrary, the original model performed poorly in MPZ and TCZ, so the improved accuracy was relatively large. The unimproved model was already more suitable in TMZ and SMZ, so the improved accuracy was relatively small. Therefore, regional calibration of the PET models can improve the accuracy and applicability of PET calculation, providing a reference for studying hydrological processes in different climatic zones. [ABSTRACT FROM AUTHOR]

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide. • An integrated framework combining DBN, SD, PLUS and InVEST models was proposed. • Impact of natural and socioeconomic factors on ESs was determined under climate scenarios. • The spatial pattern of ESs was optimized and partitioned using DBNs. • ESs in arid oasis urban agglomerations are mainly affected by natural factors. [ABSTRACT FROM AUTHOR]

This study is nationwide multicenter epidemiological research, aimed at investigating the distribution changes and seasonal patterns of various airborne allergens among preschool children with allergic rhinitis (AR) in different regions of China, and analyzing the clinical correlation between sensitization to various airborne allergens and AR symptoms in children. Information on children was collected through standard questionnaires, and total IgE (tIgE) and specific IgE (sIgE) for 11 inhalant allergens were tested. The results showed that dust mites are the primary allergens for preschool AR children (39%). Among pollen allergens, Amb a had the highest positivity rate (8.1%), followed by Art v (7.8%). The sensitization rates for two mites peaked in May (46.9% and 40.6%). Art v peaked in August (21.5%), while Amb a had peaks in May (12.7%) and August (17.8%). The sensitization peaks for various tree pollens mainly occurred in August. In the Eastern monsoon region, the sensitization rate to mites was significantly higher than in the Northwest arid and semi-arid regions; whereas, for pollen allergens, the sensitization rates to Amb a, Pla a, Pin a, Pop d, and Bet v were significantly higher in the Northwest arid and semi-arid regions than in the Eastern monsoon region. The correlation among various tree pollens, specifically between Pla a, Pin r, Pop d, and Bet v was strong (0.63 ~ 0.79), with a cross-overlapping percentage of 53.9%. Children with multiple pollen sensitizations had higher cumulative nasal symptom scores than those negative for pollen (P < 0.01). Children with only pollen sensitization had higher cumulative rhinitis symptom scores than the all-negative group (P < 0.0001) and the mite-only sensitization group [P < 0.05], while the mite-only sensitization group also had higher scores than the all-negative group [P < 0.05], and the group sensitized to both pollen and mites had lower scores than the pollen-only group [P < 0.05]. This study indicates that sensitization to mites and grass pollens exhibits significant regional differences, with grass pollen allergies primarily occurring in autumn, sensitization to pollens in general exhibits a pronounced seasonal pattern. Moreover, pollen sensitization aggravates nasal and ocular symptoms in AR children. [ABSTRACT FROM AUTHOR]

Revegetation is one of the most effective ways to combat desertification and soil erosion in semiarid and arid regions. However, the impact of the perturbation of revegetation on ecohydrological processes, particularly its effects on the interplay between hydrological processes and vegetation growth under water stress, requires further investigation. This study evaluated the effects of revegetation on the energy, water, and carbon fluxes in a desert steppe in Yanchi County, Ningxia Province, northwest China, by simulating two vegetated scenarios (shrub–grassland ecosystem and grassland ecosystem) using the STEMMUS–SCOPE (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil–Soil Canopy Observation of Photosynthesis and Energy fluxes) model. The model was validated by field observations from May to September of 2016–2019. The evaluation of revegetation effects relied on comparing simulated fluxes between two vegetated scenarios in 2016 and 2019. In both scenarios, turbulent energy was dominated by latent heat flux, which was stronger in the shrub–grassland ecosystem (+ 7 %). A higher leaf area index and root water uptake of C 3 shrubs (Caragana intermedia) resulted in increased carbon fixation (+ 83 %) and transpiration (+ 72 %) of the shrub–grassland ecosystem compared to the C 3 grassland ecosystem. Accompanied by a marked increase in root water uptake (+ 123 %), revegetation intensified water consumption beyond the levels of received precipitation. These results highlight the critical importance of considering both energy and water budgets in water-limited ecosystems during ecological restoration to avert soil water depletion. [ABSTRACT FROM AUTHOR]