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Groundwater plays a critical water resources role in arid and semi-arid areas. Quantitative evaluation of groundwater recharge is crucial in hydrogeological investigations. Under the complex hydrogeological and climatic conditions in China's arid and semi-arid regions, groundwater recharge shows significant spatio-temporal heterogeneity. Although there has been much research, including hydrochemical analysis, isotope tracing, in-situ tests and numerical simulations, accurate quantitative groundwater recharge is still a great challenge. This paper reviews the progress in research on groundwater recharge in arid and semi-arid regions of China, focusing on the principles and current applications of the main techniques, as well as the achievements in nine typical arid and semi-arid regions (Tarim Basin, Junggar Basin, Shule River Basin, Heihe River Basin, Yinchuan Plain, Badain Jaran Desert, Loess Plateau, Ordos Plateau, North China Plain). The paper reveals that the chloride mass balance method is a popular method to estimate groundwater recharge rate, whereas stable isotopes and hydrochemistry analyses, as well as the joint use of both, have been widely used in the understanding of groundwater recharge processes and mechanisms in the arid and semi-arid regions of China. Numerical modeling and remote sensing technology will become important approaches to quantify large-scale groundwater recharge. It is also suggested that various estimation methods should be used in combination, in order to improve the reliability of recharge estimates. Furthermore, this review shows that the theoretical research so far still cannot meet the needs of regional development, and the paper highlights some scientific problems that need to be further explored. [ABSTRACT FROM AUTHOR]

Groundwater evapotranspiration (ETg) is a significant component of water and energy balance analyses in arid and semiarid environments; however, it is also one of the most challenging items to estimate. Estimation of ETg using diurnal groundwater-level fluctuation is considered cost-effective and straightforward, while more accurate estimation technology continues to be developed due to the complex set of factors that influence the variation of groundwater level. This paper proposes a method for calculating ETg that combines the analytical solution of the Boussinesq equation and dynamic harmonic regression analysis, which is a nonstationary approach to groundwater-level signal extraction. The presented approach can calculate ETg values with a high time resolution consistent with the groundwater-level monitoring frequency, and it is robust enough to handle large amounts of measurement data. More importantly, this method eliminates the influence of recharge during the groundwater-level-rise stage. The method is tested in bushland dominated by Sminthopsis psammophila in Mu Us Desert (northern China), which has a shallow water table. The results show that this method is easy to perform and it obtains a reasonable estimate of ETg in the unfrozen seasons, regardless of the disturbance of recharge that results in a sharp rise in groundwater level. However, the method does not work well in the frozen seasons because of the physical mechanisms of change in groundwater movement in the vadose zone and a strong barometric effect. Overall, the method provides a step toward accuracy of ETg estimation using diurnal groundwater-level fluctuation, and this paper provides guidelines for its use. [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]

Altay Prefecture, a typical arid region in northwestern China, has experienced the climate transition from warming-drying to warming-wetting since 1980s and has attracted widespread attention. Nonetheless, it is still unclear how climate change has influenced the distribution of vegetation in this region. In this paper, a reaction–diffusion model of the climate–vegetation system is proposed to study the impact of climate change (precipitation, temperature and carbon dioxide concentration) on vegetation patterns in Altay Prefecture. Our results indicate that the tendency of vegetation growth in Altay Prefecture improved gradually from 1985 to 2010. Under the current climate conditions, the increase of precipitation results in the change of vegetation pattern structures, and eventually vegetation coverage tends to be uniform. Moreover, we found that there exists an optimal temperature where the spot vegetation pattern structure remains stable. Furthermore, the increase in carbon dioxide concentration induces vegetation pattern transition. Based on four climate change scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6), we used the power law range (PLR) to predict the optimal scenario for the sustainable development of the vegetation ecosystem in Altay Prefecture. [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]

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 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]

Drought seriously endangers the development of agro‐pastoral ecotone in arid and semi‐arid regions. However, drought evolution characteristics of semi‐arid sandy grassland and its drivers are not well understood in the complex climate change context. Thus, in this paper, the revised standardized precipitation evaporation indices on one‐month (SPEI‐1) and growing‐season (SPEI‐6) scale, flash drought and dry spells (DS) were calculated as drought indicators, and the temporal variabilities and meteorological drivers of drought in Horqin Sandy Land during the growing period (April–September) from 2007 to 2021 were studied. The results showed that SPEI‐1 and SPEI‐6 exhibited negative trends and periodic change. This drought aggravation was manifested in increased grades and numbers of drought months. Flash droughts can occur every month during the growing season. And its occurrence was not only positively correlated with all drought events in frequency but also synchronized in time. The increased number of DS showed an asymmetrical shift towards 7‐day‐and‐longer DS. SPEI sensitivity to precipitation (P) was noticeably higher than to atmospheric evapotranspiration demand (AED) at both monthly and growing season scales. SPEI sensitivity to AED was modulated by P amounts, which was higher during low P period. Apart from the amounts of P and AED, their temporal variability also strongly determined how they affect the drought severity. P was also the main driver for flash drought and long DS, but AED with larger amount and higher variability is the reason for their aggravation. Our results demonstrate the complexity of drought intensification in semi‐arid sandy grasslands and highlight the important role of AED anomalies in it. [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]

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]

[Objective] The present paper aimed to analyze the effects of sowing date and film mulching on yield and quality of direct seeding winter rapeseed to provide a theoretical basis for suitable sowing and high-yielding cultivation of winter rapeseed in dryland in southwest China. [Method] A two-factor completely randomized block design was adopted to explore the effect of sowing date(20th September, 30th September, 10th October, 20th October, 30th October, 10th November, 20th November) and film mulching (film mulching and no film mulching) on field soil temperature, dry matter accumulation, agronomic characteristics, yield and quality of Chuanyou 36. [Result] Film mulching effectively increased the 0-20 cm soil temperature, dry matter accumulation, plant height, length of main inflorscence and effective pods, thereby increasing rapeseed yield by 3.61%-44.47%, which was more obvious with the delay of sowing date. However, the sowing date delayed longer, dry matter accumulation and agronomic traits of rapeseed decreased significantly. The regression equation showed that the rapeseed yield rose first and then declined with the delay of sowing date, which had a significant polynomial relationship with sowing date. The optimal sowing date for rapeseed was 10th October, which was 2.13%, 47.97%, 74.19% and 136.64% higher than 30th September, 30th October, 10th November and 20th November, respectively. In addition, the delay of sowing date effectively reduced the content of linolenic acid, erucic acid and glucosinolates in rapeseed, but also reduced the oil content, while film mulching effectively decreased the difference in rapeseed quality resulted from the delay of sowing date. [Conclusion] 30th September to 10th October was a suitable sowing date for high yield of direct seeding rapeseed in dry land in southwest China, and a proper delay of the sowing period in combination with film mulching technology was more conducive to achieving goals of high yield, efficiency and quality. [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]

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]

Ecological vulnerability is one of the core issues in the study of achieving sustainable development and is important for ecological conservation and environmental management, especially for ecologically vulnerable regions. Scientific assessment of ecological vulnerability can improve decision-making capacity and provide appropriate information for sustainable development. The Hexi Corridor is located in the inland northwest of China, and its ecological environment is very fragile. Under the influence of climate change and human activities, the Hexi Corridor is facing a crisis of ecological degradation. Therefore, this paper assessed the influence of various factors on ecological vulnerability, followed the principles of scientificity and data accessibility, and constructed a comprehensive assessment framework to reflect the spatio-temporal characteristics of ecological vulnerability in the Hexi Corridor from three aspects: natural factors, socio-economic factors and human interference factors so as to provide theoretical and practical guidance for regional ecological protection. The results showed that: (1) The ecological vulnerability of the Hexi Corridor was dominated by extreme vulnerability, mainly in the arid desert region, while mildly and lowly vulnerable areas were mainly distributed in the southern Qilian Mountains and the plain oasis region, which was closely related to its physical geography. The index values showed an overall decreasing trend, and the environmental situation was improving from 2000 to 2020. (2) The distribution of ecological vulnerability had obvious directional characteristics, and it gradually decreased from north to south. Moreover, it exhibited a significant positive spatial correlation, and its spatial aggregation pattern was basically consistent with the spatial distribution state. (3) Based on the spatio-temporal patterns of ecological vulnerability, the Hexi Corridor was divided into five zones: ecological core protection zone, integrated ecological monitoring zone, ecological optimization concern zone, ecological restoration management zone and ecological potential governance zone. Different environmental protection policies have been taken for different zones according to its basic conditions. Spatial principal component analysis (SPCA) method was used to analyze the ecological vulnerability factors based on the GIS technology. The partitions of ecological vulnerability for configuration and optimization has been classified and practical application. Geographic detector and national policies were considered for ecological vulnerability driver factors. [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]

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]

• 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]

• 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]

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]

Understanding the origins of groundwater and its movement from mountain to plain during different high intensity rainfall events is critical for conserving water supplies, determining water-use policies and controlling pollution. These factors are also the keys for understanding the dominant processes in hydrological models. In this study, groundwater resources and recharge processes during heavy precipitation were explored by using stable isotope tracers in the hilly area of Taihang Mountain. It was found that the δ2H and δ18O values of precipitation exhibited obvious precipitation amount effect during different precipitation intensity events. The stable isotopic values in groundwater and river water showed significantly varied during the single extreme heavy precipitation and the continuous heavy precipitation events. In the rainy season, precipitation amounts greater than 40 mm/d could effectively recharge the shallow groundwater in the study area. By comparing the spatial isotopic distribution of groundwater, soil water, and river water with precipitation, we showed distinct groundwater recharge patterns in terms of their water resources, timing, and the degree of river water and groundwater interaction during the single extreme heavy precipitation and the continuous heavy precipitation. After the single extreme heavy precipitation, the δ2H and δ18O values of groundwater, soil water, and river water showed stable over time and had the same similar variations, suggested that the groundwater recharge was mainly dominated by precipitation with preferential flow or bypass flow. While after the continuous heavy precipitation, the variation of δ2H and δ18O in all water is consistent with the previous precipitation, which shown a mixing effect of previous enrich precipitation and depleted heavy precipitation, suggested that groundwater source was dominated by a continuous recharge of previous heavier precipitation with translatory flow. The groundwater main recharge mechanism is not constant, but changes with rainfall intensity. The rainfall intensity play an important role in groundwater recharge change affecting runoff process. Overall, this paper presents a new insight to understand the effect of rainfall intensity on hydrological process, which could be used to provide vital information in the semi-humid and semi-arid regions where water resources are critical in climate change adaptation strategies. [Display omitted] • We identified the controls on groundwater recharge during different precipitation intensity events. • Precipitation greater than 40 mm/d could effectively recharge shallow groundwater. • Bypass flow after extreme precipitation produced stream water and groundwater recharge. • Groundwater recharge was by a continuous recharge with translatory flow after the continuous heavy precipitation. [ABSTRACT FROM AUTHOR]

The arid regions of northwestern China are dominated by extensive areas of mobile sand dunes. These features cause environmental problems and exert global climatic impacts; however, due to the lack of systematically-applied indicators of aeolian activity, direct evidence of past changes in aeolian activity is unreliable, especially for aeolian sand source regions. In this paper, we analyze the end-member (EM) distributions of various types of surface soil across the Tengger Desert in northwestern China, and devise a sensitive proxy for aeolian activity using a spatio-temporal substitution approach. This proxy is then used to evaluate a Quaternary aeolian succession and to understand the response of aeolian activity to regional climate change. Our results suggest that gradual increases in the coarse-grained EM components 3–5 reflect enhanced dune mobility and reduced vegetation coverage, which are a direct function of the intensity of regional aeolian activity. In the aeolian section, coarse- and medium-grained sand layers resemble modern semi-mobile and mobile dunes, whereas fine-grained sand layers are analogous to vegetated fixed/semi-fixed or semi-mobile dunes. Combining this record with independent multi-proxy evidence from the same section leads us to conclude that strong aeolian activity coincided with a dry climate, sparse vegetation cover, and strong surface winds during the Last Glacial Maximum, and that these conditions gradually attenuated at the beginning of the Last Deglacial. These inferences are in accord with independent paleoclimate records from this region, confirming that the intensity of aeolian activity in the southeastern Tengger Desert is closely associated with large-scale climate change. Our findings show that our EM proxy can be used to infer the intensity of aeolian activity and to reliably reconstruct the evolution of deserts in northwestern China. [Display omitted] • Coarse grain-size End-members (EM) 3–5 are a proxy of the strength of aeolian activity. • This proxy is applied to a palaeo-aeolian sequence to reconstruct the history of desert evolution. • Aeolian activity was closely associated with large-scale climate change. [ABSTRACT FROM AUTHOR]

Ecosystems in arid regions with sandy substrates are vulnerable to various environmental challenges. Understanding the vegetation evolution of such regions over a long timescale and the possible drivers of change is important for optimizing ecological restoration. In this paper, we present multi-proxy data to understand Holocene vegetation dynamics and their drivers in the Horqin Sandy Land in northern China. Phytoliths are used to reconstruct vegetation; grain size and magnetic susceptibility are used to reconstruct the climate; and sedimentary charcoal contents and the numbers of regional archaeological sites are used to reconstruct the fire activity and human activity intensity, respectively. Results indicate that the Horqin Sandy Land was covered mainly by grassland vegetation during the Holocene, and that the trajectory of vegetation evolution was as follows: sparse and species-poor Leymus chinensis grassland from 9300 to 6150 cal yr BP, relatively sparse and species-rich mesophytic herb grassland from 6150 to 2300 cal yr BP, and dense and species-rich L. chinensis grassland from 2300 to 250 cal yr BP. Climate change was the main driver of vegetation evolution, but fire and human activities also influenced the vegetation to varying degrees. Fire made the greatest contribution to the vegetation change during the 6150–2300 cal yr BP interval, and human activities contributed throughout the 8200–2300 cal yr BP interval. The evolution of plant species richness and the number of C 3 plants show a significant ∼1000-yr periodicity during 9300–4500 cal yr BP, which we speculate was caused by the effect of solar activity on East Asian monsoon intensity, which likely controlled the vegetation change on a millennial timescale. • The Holocene vegetation in Horqin Sandy Land is qualitatively reconstructed. • Climate dominated the vegetation evolution, while fire and human activity were additional influences. • The vegetation change shows a ∼ 1000-yr periodicity during 9300–4500 cal yr BP. • The vegetation change on a millennium cycle was likely driven by the solar activity. [ABSTRACT FROM AUTHOR]

The Soil and Water Assessment Tool (SWAT) model is diffusely applied to areas with uniform precipitation distribution and gentle slopes. However, the SWAT has apparent defects in the process of ecohydrological simulation in mid-high latitudes and arid regions. This model can merely be used in agricultural catchments with gentle slopes and uniform rainfall, and the simulation is not suitable for high-altitude alpine catchment. To this point, this paper makes appropriate improvements to the runoff module, and specifically corrects the key sensitive parameters. The global sensitivity and performance assessment of the model's parameters were carried out before and after the improvement using the monthly data of runoff and evaporation from 1990 to 2018 in the Kaidu River basin in northwestern China. It is indicated that the Modified SWAT simulates the performance of monthly runoff and evapotranspiration better than the original SWAT, which overestimates the runoff volume throughout hydrological changes in different periods. Compared with the monthly water production under different land use/cover conditions, the highest water production was found in forest and grassland. By modifying the SWAT algorithm, the structural non-determinacy of the Modified SWAT is significantly reduced, and the adaptability of the model is improved. Thus, the Modified SWAT shows a better simulation performance in arid areas with considerable terrain variation and scarce rainfall data. [Display omitted] [ABSTRACT FROM AUTHOR]

• Optical images are easily affected by clouds and cloud shadows. • Fmask is introduced into the GEE platform and modified for Sentinel-2 images. • The improved SNOWL algorithm considers unstable snow cover areas. • Snow cover extraction accuracy in flat surface and shady slope is the highest. • Snow cover in mountainous is reconstructed by combing the Fmask, Sen2Cor and the improved SNOWL algorithms. Snow is an important type of surface cover and an important freshwater resource in the arid regions of northwest China, and optical satellites are an important means for monitoring variation in snow cover. However, the radiation values measured by satellite sensors are affected by cloud cover and cloud shadows; thus, the reconstruction of snow cover under clouds and cloud shadows becomes a bottleneck problem especially in mountains areas. Based on the digital elevation model (DEM), in this paper, an integrated method of snow cover reconstruction on Sentinel-2 images is created by combining the improved SNOWL (snow line) algorithm considering unstable snow cover areas, Fmask cloud detection algorithms, and the Sen2Cor clear sky snow cover detection tool with a geospatial processing platform, GEE (Google Earth Engine). Considering the Babao River Basin as the study area, 474 Sentinel-2 scenes (10 m) were selected to obtain the snow cover spatiotemporal variations of 158 days from November 2016 to March 2021. Compared with the high-resolution GF-2 satellite images, the integrated method can extract snow under clouds and cloud shadows well, and the improved SNOWL algorithm improves the overall accuracy from 66.05 % to 84.26 %. The experimental results show that approximately half of the Babao River Basin is unstable snow cover, and areas above 3977 m are covered with snow for at least half a year. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proportion of vegetation improvement area in QLMs reached 65.06% in the past 21 years, while 52% of the area will be continuously improved in the future. • The distribution of the NDVI in the QLMs maintained a high spatial agglomeration. • Annual sunshine duration is the main meteorological factor affecting vegetation change in Qilian Mountains. • The interaction between different driving factors can improve the explanatory power of vegetation changes compared with a single driving factor. Vegetation is an important indicator reflecting ecological environment stability, and monitoring vegetation change and understanding its potential driving force have important guiding significance for the adjustment and implementation of ecological restoration measures. As an ecological security barrier in northwest China, the Qilian Mountains (QLMs) play an important role in promoting the green development of social and economic development. Currently, the comprehensive driving effects of natural and anthropogenic factors on vegetation change in the QLMs are not clear. Based on the normalized difference vegetation index (NDVI), the spatiotemporal characteristics and trend changes in vegetation in the QLMs were systematically analyzed from 2000 to 2020 in this paper. The effects of natural and anthropogenic driving factors on vegetation change were explored using a geographic detector (GeoDetector). The results showed that the vegetation has improved continuously in the past 21 years, but the overall vegetation coverage was still low. The vegetation distribution was highly clustered, with a decreasing trend from east to west. Annual sunshine duration (q statistic = 0.3347) and distance to the rivers (q statistic = 0.2649) had the greatest explanatory power for vegetation change, while slope, aspect, and landform type had the least explanatory power. The interaction between elevation and sunshine duration, temperature and precipitation, temperature and sunshine duration, elevation and precipitation had the most explanatory power for vegetation change. Finally, we determined the ranges or categories of driving factors that were most suitable for vegetation growth by using a risk detector. The results of this study can help us further understand the potential driving mechanism of vegetation coverage variation in the QLMs and provide theoretical guidance for relevant managers to formulate ecological restoration measures and land management policies in the next step. It is of great significance to maintain the stability of the fragile ecological environment and prevent land degradation in arid and semiarid areas of Northwest China. [ABSTRACT FROM AUTHOR]

• Spatiotemporal variation in vegetation on the Loess Plateau was investigated. • Precipitation was the greatest driving factor. • There was a bivariate or nonlinear enhancement between all factors. • Key factors' adaptation range or type suitable for vegetation cover was identified. Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. Studying vegetation cover dynamics variation and its drivers is critical to understanding the interactions between vegetation and ecosystems. The Loess Plateau (LP) is located in a semi-arid and semi-humid region with severe soil erosion and fragile ecology. This paper used the annual maximum Normalized Difference Vegetation Index (NDVI ymax) and growing season mean NDVI (NDVI gsmean) as the vegetation cover indicator. The vegetation cover variation of LP from 2000 to 2020 was analyzed using Sen's slope and Mann-Kendall test. Then the influence of natural and anthropogenic factors on the driving mechanisms of spatial vegetation differentiation was explored by the Geodetector model. The results revealed that the growth trends of NDVI ymax and NDVI gsmean were 0.075/10 years and 0.038/10 years, respectively, and the areas with improved vegetation cover accounted for 92.67 % and 88.58 % of the total area. The vegetation cover of the southeastern and northern parts of the LP decreased significantly; however, the central, southwestern, and northeastern parts exhibited a remarkable improvement. Precipitation, vegetation type, soil type, temperature, and land use type were the key driving forces, ranked differently on NDVI ymax and NDVI gsmean. However, precipitation was the most critical factor both on NDVI ymax and NDVI gsmean. The interaction detection showed non-linear and mutual enhancement, with no independent factor. The findings of our study can assist in identifying the vegetation cover status of the LP, as well as the driving forces, which can provide theoretical support for the formulation of environmental conservation policies. [ABSTRACT FROM AUTHOR]

• Both GPP and GPP max in Chinese grasslands showed increases at different space–time scales. • Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands. • GPP max × CUP is a robust indicator to explain annual-scale variation in GPP. At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO 2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPP max also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPP max are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that 'monotonically increasing' was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPP max × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change. [ABSTRACT FROM AUTHOR]

In their thoughtful paper entitled "An equable subtropical climate throughout China in the Miocene based on palaeofloral evidence", Qing Wang, Yan Li, David K. Ferguson, Wen-Bo Mo, and Nan Yang (2021) imply that China exhibited negligible latitudinal mean annual precipitation (MAP) gradients and lacked arid regions during the Miocene. They also speculate that there was no abrupt change in elevation of the Tibetan Plateau, nor that the plateau attained its present altitude or orography during that period. These conclusions were derived mainly by using a co-existence approach (CA) and 'incomplete data'. Here, we use CA with training data and also integrate a coexistence likelihood estimation, a pollen-MAP boundary, a desert-MAP boundary, and other newly published MAP data to show that Northwest China consistently exhibited drier conditions than South China and East China during the entire Miocene, with our new values being 300–500 mm lower than those of Wang et al. (2021). This implies that there were large spatial gradients of MAP in China during the Miocene, which may be directly linked with the growth of the Tibetan Plateau, the global cooling, as well as other factors. We recommend the comprehensive consideration of multiple methods and proxies for reconstructing past climate conditions as opposed to a single proxy. [ABSTRACT FROM AUTHOR]

Desertification and salinization are both threats to the ecosystem services in inland river oases of arid regions. Previous studies focus on either desertification or salinization, and there is a lack of joint studies on the two issues. The essential cause of desertification in a transition zone is usually concentrated irrigation water use, which leads to shrink of the subsurface flow field of groundwater, decline of the groundwater level, and loss of groundwater supply to the vegetation. The salinization problem in an oasis area is mainly caused by the local excess groundwater in the oasis, referring to secondary salinization, which leads to salt migration with the groundwater level rise to form salt crystallization at the land surface. Thus, the processes of desertification and secondary salinization are connected, and the solutions to the two problems can be complementary, i.e., by transporting the excess groundwater in the local secondary salinization area to the transition zone area where water is scarce. This paper, taking Luocheng Irrigation District in the Heihe River Basin of northwestern China as an example, estimates 1.76–4.70 million m3 of excess groundwater that can be extracted in the salinized area. Using this amount of water through engineering regulation, it is estimated that the transition zone nearby the irrigation district, which is under desertification threat, can be restored with an area of 23–212 km2. An engineering system is designed for coordinated groundwater regulation and the implementation with an experimental farm in the irrigation district is demonstrated. • Providing a new method to mitigate salinization and desertification in arid areas through groundwater regulation. • Estimating the amount of drainage from salinized oasis area in an irrigation district and the transition zone area to be recovered from desert. • Designing an engineering system for coordinated groundwater regulation. [ABSTRACT FROM AUTHOR]

[Display omitted] • SIF data that characterizes the growth of sandy vegetation is more sensitive. • SIF can well capture the changes in surface moisture and groundwater. • Vegetation growth has a strong correspondence with SM and a relatively weak correlation with TWS. • Vegetation growth in the late period of the mutation year is generally better than that in the early period. • Similarities and differences of vegetation-moisture relationship in four sandy lands. Hulunbuir, Otindag, Horqin and Mu Us are the four greater sandy lands in China. They are typical ecologically fragile areas with limited ability to resist interference and are susceptible to changes in the external environment. With climate change in recent decades, these areas have experienced severe land desertification and decreased groundwater levels. The ecological environment is at risk of further deterioration, and continuous vegetation restoration and management should be adapted to the local climate and maintain the water balance with natural precipitation. This paper combines multi-source satellite observations to explore the relationship between vegetation growth and water supply. The results show four main points. (1) Although the vegetation growth in Otindag and Horqin had degraded before 2010, it improved after 2010. The vegetation growth and soil moisture (SM) of the four sandy lands both generally increased from 2003 to 2016. The SM of the Horqin increased significantly (84% anomaly > 1.4 × 10−2 cm3/cm3). Other than the Hulunbuir, the other sandy lands show a downward trend in terrestrial water storage (TWS), especially the Mu Us declined the most (97% anomaly < −40 mm). (2) Solar-induced chlorophyll fluorescence (SIF) has specific advantages in characterizing the sandy vegetation productivity and water composition changes compared to vegetation index (EVI) or gross primary productivity (GPP). (3) The regional pattern of the vegetation-moisture relationship is mainly affected by the precipitation-driven SM and depends on the types of underlying vegetation functions. (4) Both SM and TWS have approximately one-month persistence in the four sandy lands, especially the Mu Us has the longest persistence (0.94 months and 1.1 months). The persistence in the early period is generally longer than that in the late period, implying that the resilience of water components after the mutation year is stronger than before it. This study provides a scientific basis for the management and restoration of sandy lands. It is recommended that different vegetation restoration and protection strategies should be developed for specific sandy land moisture conditions in arid/semi-arid regions of northern China, which includes the improvement of water use efficiency to maintain soil moisture and the reduction of excessive groundwater uses. [ABSTRACT FROM AUTHOR]

The aim of this study was to clarify the distribution of irrigated drylands in arid and semi-arid areas, where complex terrain, diverse crops and staggered cultivated lands exist. This paper studied the classification methods of irrigated drylands based on temperature, precipitation, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Landsat data in the one-harvest area of the northern Loess Plateau of China by using the Google Earth Engine (GEE) platform. An extraction method was proposed for irrigated drylands in arid and semi-arid regions of northwest China. In addition, the change types of irrigated and rainfed drylands in the two periods were classified, and a method was also put forward to directly classify the change types by using the image differences between the two periods combined with the classification results of each period. It was found that combining the ratio of NDVI and NDWI with the accumulated values of temperature and precipitation of the 30 days before imaging could effectively improve the classification accuracy. Moreover, directly classifying the gaps of remote sensing factors in the time dimension before combining spatial clustering information could yield a more accurate type of change, because the accumulation of errors in the change maps obtained from the overlay analysis of distribution maps of the two periods could be avoided. The accuracy of classification could be improved by introducing the dynamic information of time dimension into the classification of historical periods. This study complements the extraction method for this type of irrigated dryland, and the classification results can improve the accuracy of existing products in terms of spatial resolution, which can fill the shortage of detailed distribution data for irrigated and rainfed drylands in this region. [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]

The determination of spatiotemporal variation in ecosystem service value and its drivers is fundamental to ecosystem service management and decision-making. This paper selects a typical oasis irrigation district in the arid regions of northwest China as the research object. Using the benefit transfer method to evaluate the ecosystem service value variation caused by land use and land cover change and characteristics of its spatial distribution based on multi-temporal land use and land cover data sets (1977, 1987, 1997, 2007, 2017). Meanwhile, the contributions of factors driving ecosystem service value and their interactions were explored using geographical detector. The results showed the following: 1) The land use and land cover structure was stable from 1977 to 2017, and the overall ecosystem service value increased slightly. The services provided by the oasis ecosystem dominated the fluctuations in ecosystem service value throughout the study region. 2) Ecosystem service value exhibited a strong positive spatial autocorrelation. The high values were concentrated in the oasis area in the north of the study area, while the low values mainly appeared in the desert ecosystem. 3) The land use degree and human activity intensity index of human factors are the main factors leading to the differentiation of ecosystem service value. Synergized interactions among human activities, changes in landscape patterns, and natural factors produced the spatial differentiation in ecosystem service value of the study region. The results suggest that in future decision-making for ecosystem management, the direction of human activities within the ecological environment should be controlled. Improve the diversity of patches, reduce the degree of landscape fragmentation, improve the ecosystem service function of LULC, optimize the allocation of ecological landscape resources. [ABSTRACT FROM AUTHOR]

[Display omitted] • Grassland degradation is the main reason for the decline in carbon storage. • The carbon storage of farmland has increased significantly. • The risk of carbon storage reduction in the future is still high. The carbon storage of terrestrial ecosystems is closely related to global climate change. Therefore, assessing the impact of land-use changes on carbon storage is instructive to reduce global carbon emissions. Taking an arid region in northwest China as an example, this paper analyzed the land-use changes in arid region from 1980 to 2015. It used the CA-Markov model to predict the land-use pattern from 2020 to 2050, the carbon storage and sequestration module in Integrated Valuation of Ecosystem Services and Tradeoffs (Invest) mode to assess and predict ecosystem carbon storage over the past 35 years and in the next 30 years and discusses the impact of land-use changes on the carbon storage of terrestrial ecosystems. The results showed that: (1) The high-value areas of carbon storage in arid region were primarily concentrated in grassland, forests and farmland, and the low-value areas of carbon storage were mainly in desert areas. (2) The carbon storage in arid region showed a downward trend. From 1980 to 2015, the carbon reserves of terrestrial ecosystems in arid region decreased by 90.95 Tg, and the net loss of carbon reserves from 2020 to 2050 was 14.72 Tg. Grassland degradation was the main reason for the reduction of carbon storage in arid region. (3) The carbon storage of farmland had increased significantly, but the expansion of farmland dominated by high carbon density grassland, wetland and forest, had reduced the overall carbon storage of the arid region. [ABSTRACT FROM AUTHOR]

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity. [ABSTRACT FROM AUTHOR]