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.
Due to high altitudes, Central Asian alpine lakes can serve as indicators of localized climate change. This article monitored the water volume time series trends of the ungauged alpine Lake Karakul, ...which is typical because of the abundance of glaciers in the basin, from 1990 to 2020 via multiple source remote sensing data. The “Global-Local” multi-scale lake extraction method is used to delineate the boundary of Lake Karakul. Consistency analysis was performed on the altimetry data of CryoSat-2, ICESat-1 and ICESat-2, assuming that the lake surface was flat; a threshold value was set to remove gross error, and then 3σ was used to remove the surface elevation anomaly. Based on the pyramid volume model, the lake area and surface elevation information were used to reconstruct the water volume time series of Lake Karakul. The influencing factors of water volume temporal variation were discussed. The results show that Lake Karakul has been on an expansionary trend in recent years: The lake area increased from 394.9 km2 in 1988 to 411.4 km2 in 2020; the rate of increase is 0.74 m/year. The surface elevation increased from 3886.6 m in 2003 to 3888.6 m in 2020; the rate of increase is 0.11 km2/year. The lake water volume accumulated was 0.817 km3 in 2003–2020, with an accumulation rate of 0.059 km3/year. The Lake Karakul basin is developing towards dry heat, with a cumulative temperature variation rate of +0.38 °C/year; the average rate of variation in annual cumulative precipitation is −3.37 mm/year; the average evapotranspiration in the watershed is on a fluctuating increasing trend, with a rate of variation of +0.43 mm/year; glaciers in the lake basin have a retreating trend, with an average annual rate of variation of −0.22 km2/year from 1992 to 2020. Lake Karakul is more sensitive to temperature variations, and the runoff from retreating glaciers in the basin is an important contribution to the expansion of Lake Karakul.
In this study, the Amu Darya river basin, Syr Darya river basin and Balkhash lake basin in Central Asia were selected as typical study areas. Temporal/spatial changes from 2002 to 2016 in the ...terrestrial water storage (TWS) and the groundwater storage (GWS) were analyzed, based on RL06 Mascon data from the Gravity Recovery and Climate Experiment (GRACE) satellite, and the sum of soil water content, snow water equivalent and canopy water data that were obtained from Global Land Data Assimilation System (GLDAS). Combing meteorological data and land use and cover change (LUCC) data, the joint impact of both human activities and climate change on the terrestrial water storage change (TWSC) and the groundwater storage change (GWSC) was evaluated by statistical analysis. The results revealed three findings: (1) The TWS retrieved by CSR (Center for Space Research) and the JPL (Jet Propulsion Laboratory) showed a decreasing trend in the three basins, and the variation of TWS showed a maximum surplus in spring (March–May) and a maximum deficit in autumn (September–November). (2) The decreasing rates of groundwater storage that were extracted, based on JPL and CSR Mascon data sets, were −2.17 mm/year and −3.90 mm/year, −3.72 mm/year and −4.96 mm/year, −1.74 mm/year and −3.36 mm/year in the Amu Darya river basin, Syr Darya river basin and Balkhash lake basin, respectively. (3) In the Amu Darya river basin, annual precipitation showed a decreasing trend, while the evapotranspiration rate showed an increasing trend due to an increasing temperature, and the TWS decreased from 2002 to 2016 in most areas of the basin. However, in the middle reaches of the Amu Darya river basin, the TWS increased due to the increase in cultivated land area, water income from flooded irrigation, and reservoir impoundment. In the upper reaches of the Syr Darya river basin, the increase in precipitation in alpine areas leads to an increase in glacier and snow meltwater, which is the reason for the increase in the TWS. In the middle and lower reaches of the Syr Darya river basin, the amount of evapotranspiration dissipation exceeds the amount of water replenished by agricultural irrigation, which leads to a decrease in TWS and GWS. The increase in precipitation in the northwest of the Balkhash lake basin, the increase in farmland irrigation water, and the topography (higher in the southeast and lower in the northwest) led to an increase in TWS and GWS in the northwest of the Balkhash lake basin. This study can provide useful information for water resources management in the inland river basins of Central Asia.
The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential ...prerequisite for controlling sand and dust activity. However, few relevant indicators reported in this current study can accurately describe and measure wind erosion intensity. A novel wind erosion intensity (WEI) of a pixel resolution unit was defined in this paper based on deformation due to the wind erosion in this pixel resolution unit. We also derived the relationship between WEI and soil InSAR temporal decorrelation (ITD). ITD is usually caused by the surface change over time, which is very suitable for describing wind erosion. However, within a pixel resolution unit, the ITD signal usually includes soil and vegetation contributions, and extant studies concerning this issue are considerably limited. Therefore, we proposed an ITD decomposition model (ITDDM) to decompose the ITD signal of a pixel resolution unit. The least-square method (LSM) based on singular value decomposition (SVD) is used to estimate the ITD of soil (SITD) within a pixel resolution unit. We verified the results qualitatively by the landscape photos, which can reflect the actual conditions of the soil. At last, the WEI of the Aral Sea from 23 June 2020, to 5 July 2020 was mapped. The results confirmed that (1) based on the ITDDM model, the SITD can be accurately estimated by the LSM; (2) the Aral Sea is experiencing severe wind erosion; and (3) the middle, northeast, and southeast bare areas of the South Aral Sea are where salt dust storms may occur.
The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human ...intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse vegetation coverage, the microwave surface scattering model is very suitable for describing the variation of topsoil property during the process of desertification. However, the microwave backscattering coefficient (MBC) trend of the soil during the desertification process is still unclear now. Moreover, the MBC of a resolution unit usually involves the contribution of soil and vegetation. These problems seriously limit the application of microwave remote sensing technology in desertification identification. In this paper, we studied the soil MBC change trend during the desertification process and proposed a microwave backscattering contribution decomposition (MBCD) model to estimate the soil MBC of a resolution unit. Furthermore, a simple microwave backscattering threshold (SMSBT) model was established to describe the severity of desertification. The MBCD and SMSBT models were verified qualitatively through landscape photos of sampling points from a field survey in November 2018. The results showed that the MBC would gradually decline with the deepening degree of desertification. The MBCD model and the corresponding least squares method can be used to estimate the soil MBC accurately, and the SMSBT model can accurately distinguish different degrees of desertification. The results of desertification classification showed that more than 68% of the dry bottom of the Aral Sea is suffering from different degrees of desertification.
The artificial young forest is an important component of ecosystems, and biomass models are important for estimating the carbon storage of ecosystems. However, research on biomass models of the young ...forest is lacking. In this study, biomass data of 96 saplings of three tree species from the southern foot of the Qilian Mountains were collected. These data, coupled with allometric growth equations and the nonlinear joint estimation method, were used to establish independent, component-additive, and total-control compatible models to estimate the biomass of artificial young wood of Picea crassifolia (Picea crassifolia Kom.), Sabina przewalskii (Sabina przewalskii Kom.), and Pinus tabulaeformis (Pinus tabuliformis Carr.). The distribution characteristics of the biomass components (branch, leaf, trunk, and root biomass) and the goodness of fit of the models were also analyzed. The results showed that (1) the multiple regression models with two independent variables (MRWTIV) were superior to the univariate models for all three tree species. Base diameter was the best-fitting variable of the univariate model for Picea crassifolia and Pinus tabulaeformis, and the addition of base diameter and crown diameter as variables to the MRWTIV can significantly improve model accuracy. Tree height was the best-fitting variable of the univariate model of Sabina przewalskii, and the addition of tree height and crown diameter to the MRWTIV can significantly improve model accuracy; (2) the two independent variable component-additive compatible model was the best-fitting biomass model. The compatible models constructed by the nonlinear joint estimation method were less accurate than the independent models. However, they maintained good compatibility among the biomass components and enabled more robust estimates of regional biomass; and (3) for the young wood of Picea crassifolia, Sabina przewalskii, and Pinus tabulaeformis, the aboveground biomass ratio of each component to total biomass was highest for leaf biomass (26%–68%), followed by branch (10%–46%) and trunk (11%–55%) biomass, and the aboveground biomass was higher than the underground biomass. In conclusion, the optimal biomass model of artificial young forest at the sampling site is a multivariate component-additive compatible biomass model. It can well estimate the biomass of young forest and provide a basis for future research.
Satellite precipitation estimates (SPEs) provide important alternative precipitation sources for various applications especially for regions where in situ observations are limited or unavailable, ...like central Asia. In this study, eight SPEs based on four different algorithms, namely, the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks are evaluated by using an improved evaluation system over central Asia with respect to their performance in capturing precipitation occurrence and magnitude. Both satellite‐only and gauge‐corrected versions are assessed against gauge‐gridded reference from June 2001 to May 2006. Main results show that all SPEs have difficulties in accurately estimating mountainous precipitation with great overestimation/underestimation in both winter and summer. In winter, CMORPH products fail to capture events over ice‐/snow‐covered region. In summer, large overestimations dominated by positive hit bias and missed precipitation are found for all products in northern central Asia. Interestingly, 3B42 and CMORPH products show great false alarm percentages (up to 90%) over lake region, which is more significant in summer than in winter. Significant elevation‐dependent errors exist in all products, especially for the high‐altitude regions (>3,000 m) with missed error and hit error being the two leading errors. Satellite‐only products have large systematic and random errors, while the gauge‐corrected products demonstrate significant improvements in reducing random errors. Generally, the gauge‐corrected GSMaP performs better than others with good skills in reducing various errors.
Key Points
Eight widely used satellite products are evaluated over central Asia
An improved error‐component evaluation system is used in this study
GSMaP_Gauge shows the best overall performance in central Asia
The Aral Sea was the fourth largest lake in the world but it has shrunk dramatically as a result of irrational human activities, triggering the “Aral Sea ecological crisis”. The ecological problems ...of the Aral Sea have attracted widespread attention, and the alleviation of the Aral Sea ecological crisis has reached a consensus among the five Central Asian countries (Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan, and Turkmenistan). In the past decades, many ecological management measures have been implemented for the ecological restoration of the Aral Sea. However, due to the lack of regional planning and zoning, the results are not ideal. In this study, we mapped the ecological zoning of the Aral Sea from the perspective of ecological restoration based on soil type, soil salinity, surface water, groundwater table, Normalized Difference Vegetation Index (NDVI), land cover, and aerosol optical depth (AOD) data. Soil salinization and salt dust are the most prominent ecological problems in the Aral Sea. The Aral Sea was divided into seven first-level ecological restoration subregions (North Aral Sea catchment area in the downstream of the Syr Darya River (Subregion I); artificial flood overflow areas downstream of the Aral Sea (Subregion II); physical/chemical remediation area of the salt dust source area in the eastern part of the South Aral Sea (Subregion III); physical/chemical remediation areas of severe salinization in the central part of the South Aral Sea (Subregion IV); existing water surface and potential restoration areas of the South Aral Sea (Subregion V); Aral Sea vegetation natural recovery area (Subregion VI); and vegetation planting areas with light salinity in the South Aral Sea (Subregion VII)) and 14 second-level ecological restoration subregions according to the ecological zoning principles. Implementable measures are proposed for each ecological restoration subregion. For Subregion I and Subregion II with lower elevations, artificial flooding should be carried out to restore the surface of the Aral Sea. Subregion III and Subregion IV have severe soil salinization, making it difficult for vegetation to grow. In these subregions, it is recommended to cover and pave the areas with green biomatrix coverings and environmentally sustainable bonding materials. In Subregion V located in the central and western parts of the South Aral Sea, surface water recharge should be increased to ensure that this subregion can maintain normal water levels. In Subregion VI and Subregion VII where natural conditions are suitable for vegetation growth, measures such as afforestation and buffer zones should be implemented to protect vegetation. This study could provide a reference basis for future comprehensive ecological management and restoration of the Aral Sea.
Quantifying the effects of alpine GMB (Glacier Mass Balance) on river runoff is an important content of climate change. Uncertainty exists in GMB monitoring when applying remote-sensing technology. ...There are several reasons for these uncertainties, such as terrain deviation co-registration among different topographic data, the mismatch between GSE (Glacier Surface Elevation) from satellite monitoring and the GMB that comprises the physical glacier properties, the driving factors of GMB, and the response patterns of the runoff within the basin. This paper proposed a method based on the ridge line co-registration of DEMs (Digital Elevation Models), and the Tailan River basin, which is a typical glacier melt runoff recharge basin located in the southern Tianshan Mountains, was selected. Abnormal values in GSE changes were removed using ice thickness data, and the GSE results were optimized based on the regularity of the GSE change with altitude to estimate the GMB. The driving factors of the GMB and the response characteristics of the runoff in the basin were also explored. The results showed that the accuracy of the optimized GSE results across different periods has improved by more than 25%. The mean annual thinning value of GSE in the basin from 2000 to 2022 was −0.25 ± 0.02 m·a−1, corresponding to a GMB value of −0.30 ± 0.02 m w.e.a−1, indicating a consistent GMB loss state. Combined with climate data, the glaciers in the basin were impacted by rising temperatures, and the smallest increase in annual precipitation in the basin was insufficient to compensate for the GMB loss. Moreover, in the past 22 years, glacier meltwater accounts for 46.15% of the total runoff in the Tailan River basin.
DMSP-OLS stаble nighttime light dаta version 4 (1 km resolution) from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) temporal nighttime light dataset provides ...a new way of information to monitor human activities on a global scale. However, the DMSP-OLS sensor data cannot be used directly to compare with other satellite datasets for human activities because it has no onboard calibration mechanism. This study used a newly developed systematically correcting temporal multi-satellite nightlight data from 1992 to 2013 for Kyrgyzstan, validation with different data sources (Landsat, Global Human Settlements, and population datasets). The results revealed the effectiveness of the proposed validation and data correction method for urban growth dynamics with reducing errors, signal distortion, and discrepancies from the nightlight dataset and improved the quality and comparability of data with other datasets. The urban expansion dynamics are computed for Kyrgyzstan with average accuracy and kappa statistics of about 0.80% and 0.61%. Furthermore, the results showed that the stable nightlight dataset provides valuable information for monitoring urban expansion and its impacts on land cover dynamics. The study provides a useful preliminary information tool for urban planners and policy and decision-makers for the better management of urban planning in the main cities of Kyrgyzstan in the context of the day-by-day increasing population trend.
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