Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake ...Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.
•Central Asia experienced record-breaking drought event in 2021.•SPEI and random forest model were used in drought detection and impacts estimation.•2021 drought caused large loss in vegetation ...greenness and production.•Antecedent drivers amplify drought loss in vegetation greenness and production.•Soil moisture is primary driver in greenness changes while LAI for production loss.
In the growing season of 2021, a severe drought occurred in Central Asia impacting various local ecosystems and socio-economic conditions. However, there is limited research on the spatiotemporal characteristics of this drought and its effects on local vegetation. To address this gap, this study utilizes the standardized precipitation evapotranspiration index (SPEI) to quantitatively evaluate the spatiotemporal drought characteristics, and standardized anomalies to assess drought impacts on vegetation greenness and productivity. The drought-induced reduction in vegetation greenness and production was also decomposed into environmental drivers based on a random forest model. Our findings can be summarized as follows: During the 2021 growing season, Central Asia experienced one of the most severe drought events in the past 40 years, with approximately 42.57 % of the region facing record-breaking drought conditions. Abnormally low precipitation (PRE), prolonged high temperatures (TMP) and evapotranspiration also occurred during the drought event. The drought had a significant detrimental effect on vegetation, leading to an approximately 10 % reduction in vegetation greenness and around 13 % in productivity. Soil moisture (SM) was found to be the most critical factor influencing vegetation greenness loss during drought conditions. Leaf Area Index (LAI) and SM emerged as the primary drivers for the reduction of Gross Primary Productivity (GPP) and Solar-Induced Fluorescence (SIF). The antecedent environmental conditions had a significant impact on the decline in vegetation greenness and productivity during the 2021 drought event, accounting for approximately 30 %–35 % and 52–69 % of the respective losses. The findings of this study highlight the importance of taking into account antecedent climate factors when studying the impacts of drought on vegetation.
•Ten indicators assessed the ecological health of the Taitema Lake wetland.•The result witnessed a 15.6-fold area increase and a twentyfold vegetation increase.•The wetland health has improved since ...2000 but achieved a Good status in 2021.•Wetness increase and water body expansions promote the wetland recovery.
The Taitema Lake wetland in the lower reaches of the Tarim River (northwest China) is sensitive to hydrological changes and provides necessary ecosystem functions for biodiversity conservation. Monitoring and evaluating the long-term dynamics of the Taitema Lake wetland is essential for conserving and restoring regional wetland ecosystems. This paper used a dense time series of Landsat images from 1986 to 2021 to map the Taitema Lake wetlands. It analyzed the annual and seasonal variations of the wetlands by ten ecological indicators, which include area extent, vegetation, hydrology, and landscape features. A systematic assessment of the ecological quality of the wetlands over the past 36 years, along with their influencing factors, was conducted from the perspective of wetland health. The results showed that (1) the recovered water areas and the wetlands showed a clear trend of ecological restoration since 2000, accompanied by an increased fragmentation. The total wetland area increased from 69.95 km2 in 1986 to 1164.47 km2 in 2021, with a 15.6-fold increase, and the vegetation area increased twentyfold to 639.84 km2. (2) The wetness in the core zone increased consistently across seasons, coupled with a simultaneous vegetation expansion surrounding the core zone. The areas with increasing wetness levels account for 48 % during winter and spring, and the areas with vegetation expansion account for 81 % and 72 % in summer and autumn, respectively. (3) The wetland ecological health status recovered to a “Good” level in 2021 and has not reached the “Excellent” status. The expansion of the water body is the primary driver behind the promotion of wetland ecological recovery. Small water bodies within the area range of 0.05 to 0.1 km2 notably exhibited the most stimulating effects on the wetlands. Thus, increasing the count of smaller water bodies helps rehabilitate vegetation growth and mitigating the water supply’s stress to sustain large lacustrine bodies.
Concerns have increased regarding water quality deterioration in arid land water. Water age is a useful indicator of the susceptibility of water bodies to water quality deterioration and is helpful ...for knowing the basic mechanisms governing the transport of materials through water bodies. In the current study, the spatial distributions of water age in the small lake of Bosten Lake (hereinafter referred to as small lake) were investigated with a two-dimensional hydrodynamic model built on the basis of the Environmental Fluid Dynamics Code (EFDC) model. In particular, the influences of different water flow periods, farmland drainages, and wind directions on water age distributions in the small lake were investigated. The modeled water age in the small lake has high spatial variability. The water age is maximum at the northeastern part and minimum at the center of the small lake. The water age in the small lake is lower during wet periods and gets larger for dry periods. After five years’ simulation, the average water age in the whole small lake system was 594, 684, and 794 days under wet, normal, and dry periods. Increasing the hydraulic connectivity of the small lake can reduce its water age by opening its ecological gate inflow and Ahongkou gate outflow. This is the more favorable hydraulic conditions promoting water exchange in most regions of the small lake and can be used in hydraulic engineering to improve its water age. The farmland drainages should be controlled around the small lake. The mean water age of the whole small lake under the northwestern wind is lower than that under the southwestern wind. The simulated results provide important information for comprehending the water exchange efficiency, help in discovery of areas of the small lake most likely to experience water quality degradation, and can be used to design the engineering projects to improve or protect the water environment.
Alpine glaciers are sensitive indicators of regional climate change, which can affect regional ecological stability and social development. Variations in glacier mass balance (GMB) are an important ...parameter in studying glacier change. In this study, data from the Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), and CryoSat-2 (Ku-band) were combined, and high-resolution ALOS DEM was employed to denoise. After that, the polynomial fitting method was used to analyze the characteristics of glacier surface elevation (GSE) variations from 2003–2020 in the Tomur Peak Region of the Central Asian Tianshan Mountains and the regional GMB was calculated. Research results showed that: (1) From 2003–2020, the GSE of the Tomur Peak Region had an overall −8.95 ± 4.48 m variation, the average rate of which was −0.53 ± 0.26 m/yr (/yr is /year). Overall, elevations of most glaciers in the Tomur Peak Region had downward trends, with a rate of change of −0.5 to 0 m/yr. The fastest rate of elevation decline in the Koxkar Glacier Tongue was −1.5 m/yr. The elevation of some altimetric points in the Eastern Tomur Peak Region showed a rising state, with a maximum rate of variation of 1.0 m/yr. (2) From 2003–2020, the average GMB in the Tomur Peak Region was −1.51 ± 0.04 Gt/yr. In the region of elevation below 4000 m, small glaciers dominated, with a GMB of −0.61 ± 0.04 Gt/yr. With increasing elevation, the melting rate of glaciers gradually slowed down, but overall, the mass balance remained in a state of decline. (3) Climate was the main driving factor of GMB change in the study area. From 2003–2020, in the Tomur Peak Region, the average annual temperature continued to increase at a rate of 0.04 ± 0.02 °C/yr, and this was the main influencing factor for the negative GMB in the Tomur Peak Region. In the same period, the annual precipitation showed a rising trend with a linear variation rate of 0.12 ± 0.06 mm/yr, and the rising precipitation was the influencing factor for the gradually slowing change in the GMB in the study area.
The assessment of forest structural parameters is crucial for understanding carbon storage, habitat suitability, and timber stock. However, the labor-intensive and expensive nature of field ...measurements, coupled with inadequate sample sizes for large-scale modeling, poses challenges. To address the forest structure parameters in the Western Tianshan Mountains, this study used UAV-LiDAR to gather extensive sample data. This approach was enhanced by integrating Sentinel satellite and topographic data and using a Bayesian-Random Forest model to estimate forest canopy height, average height, density, and aboveground biomass (AGB). Validation against independent LiDAR-derived samples confirmed the model’s high accuracy, with coefficients of determination (R2) and root mean square errors (RMSE) indicating strong predictive performance (R2 = 0.63, RMSE = 5.06 m for canopy height; R2 = 0.64, RMSE = 2.88 m for average height; R2 = 0.68, RMSE = 62.84 for density; and R2 = 0.59, RMSE = 29.71 Mg/ha for AGB). Notably, the crucial factors include DEM, Sentinel-1 (VH and VV backscatter in dB), and Sentinel-2 (B6, B8A, and B11 bands). These factors contribute significantly to the modeling of forest structure. This technology aims to expedite and economize forest surveys while augmenting the range of forest parameters, especially in remote and rugged terrains. Using a wealth of UAV-LiDAR data, this outcome surpasses its counterparts’ by providing essential insights for exploring climate change effects on Central Asian forests, facilitating precise carbon stock quantification, and enhancing knowledge of forest ecosystems.
While the use of large tropical trees to predict aboveground biomass (AGB) in forests has previously been studied, the applicability of this approach in arid regions remains unquantified. In the ...natural forests of arid mountains of Northwestern China, this study collected individual tree data from 105 plots across 11 sites through field measurements. The objective was to assess the feasibility of using large trees for predicting plot AGB in these natural forests of arid mountains. This entailed determining the contribution of large trees, based on which a plot AGB prediction model was constructed. This study also aimed to identify the optimal number of large trees needed for accurate AGB prediction. The findings indicate that within the natural forests of arid mountains, only seven large trees (approximately 12% of the trees in a plot) are necessary to account for over 50% of the plot AGB. By measuring 18 large trees within a plot, this study achieved a precise plot AGB estimation, resulting in a model rRMSE of 0.27. The regression fit R2 for the predicted AGB and the estimated AGB was 0.79, effectively aligning the predicted and measured AGB. In the Tianshan Mountains’ natural forests, the prediction model yielded further improvements with an rRMSE of 0.13 and a remarkable regression R2 of 0.92 between predicted and estimated AGB. However, due to variances in tree size distribution and tree species biomass, the Altai Mountains’ natural forest was found to be unsuitable for predicting plot AGB using large trees. This study establishes that large trees can effectively represent plot AGB in the natural forests of arid mountains. Employing forest surveys or remote sensing to collect data from a few large trees instead of the entire tree population enables accurate plot AGB prediction. This research serves as the initial quantification of large tree utilization for plot AGB prediction in the natural forests of arid mountains, carrying substantial implications for future arid forest inventories, carbon accounting, and the formulation of prudent conservation strategies.
Antimony (Sb) pollution in the downstream farmland soil of the Sb mine area has been of a great environmental concern to the local residents. However, effects of Sb on the growth and physiology of ...crops are still not well known. In the present study, Sb uptake and its effect on growth, antioxidant defense system, and photosynthesis of maize (Zea mays) were investigated. Our results demonstrated that accumulation of Sb in the maize increased with increasing Sb level in the soil. Sb could be easily translocated from root to shoot with a translocation coefficient over 2.05. Plant growth and biomass were reduced due to Sb pollution. Under Sb stress, the activities of peroxidase (POD), superoxide dismutases (SOD), and catalase (CAT) responded differently. The activities of POD and SOD were inhibited when the soil Sb concentration was higher than 50 mg kg⁻¹. CAT activity showed an increasing trend with increasing soil Sb concentration. Chlorophyll synthesis and the maximum photochemical efficiency (F V/F M) were also inhibited significantly under stress of high-level Sb in soil.
Climate change can lead to seasonal surface elevation variations in alpine glaciers. This study first uses DEM (Digital Elevation Model) of Pamir glaciers to develop a denoising model for laser ...altimetry of ICESat-2 footprints, which reduces the standard deviation of the differences between ICESat-2 footprints and corresponding datum DEM from 13.9 to 3.6 m. Second, the study constructs a calibration processing model for solving the problem that laser footprints obtained at different times have inconsistent plane positions. We calculates plane position and elevation differences between the two laser footprints in the local area of 0.05 × 0.05° from 2018 to 2021. The elevations constructed by laser footprints shows a strong correlation with the datum elevation over the different periods, and effectively preserve the time-series variation information of glacier surface elevation (GSE). Based on these two models, the spatiotemporal variations of the surface elevation of the Pamir glaciers is established as a function of seasons. There are three main conclusions: (1) The GSE in the Pamir increased slightly from 2018 to 2021 at an average rate of +0.02 ± 0.01 m/year. The time series with elevation increase was located exactly on the glacial ablation zone, and the time series with elevation decrease occurred on the glacial accumulation zone. Both observations demonstrate the surge state of the glacier. (2) The Pamir eastern (Zone I) and northwestern (Zone III) regions had large glacier accumulation areas. GSE in these two regions has increased in recent years at yearly rates of +0.25 ± 0.13 and +0.06 ± 0.04 m/year, respectively. In contrast, the GSE of small glaciers in Zones II and IV has decreased at a yearly rate of −0.96 ± 0.37 and −0.24 ± 0.18 m/year, respectively. Climate was the primary factor influencing the increase in GSE in Zones I and III. The westerly circulation had been reinforced in recent years, and precipitation had increased dramatically at a rate of +0.99 mm/year in the northwestern section of the Pamir; this was the primary cause of the increase in GSE. (3) The increased precipitation and decreased temperature were both important factors causing an overall +0.02 ± 0.01 m/year variation of GSE in this region. The GSE in the four sub-regions showed different variation trends because of variations in temperature and precipitation. The external causes that affected the increase in GSE in the region included an average yearly temperature decrease at the rate of 0.54 ± 0.36 °C/year and a total yearly precipitation increase of 0.46 ± 0.29 mm/year in the study area from 2018 to 2021.
The spatio-temporal pattern of the global water resource has significantly changed with climate change and intensified human activities. The regional economy and ecological environment are highly ...affected by terrestrial water storage (TWS), especially in arid areas. To investigate the variation of TWS and its influencing factors under changing environments, the response relationships between TWS and changing environments (climate change and human activities) in Central Asia have been analyzed based on the Gravity Recovery and Climate Experiment (GRACE) data, Climatic Research Unit (CRU) climate data and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data products (MOD16A2, MOD13A3 and MCD12Q1) from 2003 to 2013. The slope and Pearson correlation analysis methods were used. Results indicate that: (1) TWS in about 77 % of the study area has decreased from 2003 to 2013. The total change volume of TWS is about 2915.6 × 10
8
m
3
. The areas of decreased TWS are mainly distributed in the middle of Central Asia, while the areas of increased TWS are concentrated in the middle-altitude regions of the Kazakhstan hills and Tarim Basin. (2) TWS in about 5.91% of areas, mainly distributed in the mountain and piedmont zones, is significantly positively correlated with precipitation, while only 3.78% of areas show significant correlation between TWS and temperature. If the response time was delayed by three months, there would be a very good correlation between temperature and TWS. (3) There is a significantly positive relationship between TWS and Normalized Difference Vegetation Index (NDVI) in 13.35% of the study area. (4) The area of significantly positive correlation between TWS and evapotranspiration is about 31.87%, mainly situated in mountainous areas and northwestern Kazakhstan. The reduction of regional TWS is related to precipitation more than evaporation. Increasing farmland area may explain why some areas show increasing precipitation and decreasing evapotranspiration. (5) The influences of land use on TWS are still not very clear. This study could provide scientific data useful for the estimation of changes in TWS with climate change and human activities.
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