Interannual Trends Research Articles

Spatiotemporal Changes in and Driving Factors of Potential Evapotranspiration in a Hyper-Arid Locale in the Hami Region, China

Potential evapotranspiration (PET) is a crucial variable for implementing adaptation measures to mitigate the potential impacts of climate change on water resources. In the context of global warming, PET is essential for predicting water resource supply and demand, guiding irrigation and water management decisions. However, there is limited understanding of the spatiotemporal changes in PET and its driving factors in the hyper-arid regions of Northwest China. In this study, the Hargreaves model was employed to estimate PET in the Hami region from 1991 to 2020. By combining relevant climate data and partial correlation analysis, we investigated the spatiotemporal distribution patterns of PET within the study area and analyzed the factors influencing these patterns. The results showed the following: (1) From 1991 to 2020, the overall PET in the Hami region demonstrated a tendency to rise. The interannual trend rates of PET for the full year, spring, summer, autumn, and winter were 0.933, 2.744, 0.906, 0.488, and −0.406 mm·a-1, respectively. Despite a decreasing trend in winter PET, the other seasonal PET values and the annual PET values exhibited an increasing trend. (2) The spatial distribution of both annual and seasonal PET showed significant regional heterogeneity, following a consistent pattern marked by lower values in the central part and higher values in the surrounding areas. The southern region tended to have relatively high PET, while the northwestern region experienced comparatively low PET. (3) Partial correlation analysis indicated significant differences in the impact of various climatic factors on PET. The maximum temperature emerged as the dominant factor influencing annual PET variation, while precipitation played a leading role in influencing autumn PET variation. This study underscores the influence of climate change on PET in the Hami region, contributing to an enhanced comprehension of PET variations.

Climate change is rapidly deteriorating the climatic signal in Svalbard glaciers

Abstract. The Svalbard archipelago is particularly sensitive to climate change due to the relatively low altitude of its main ice fields and its geographical location in the higher North Atlantic, where the effect of Arctic amplification is more significant. The largest temperature increases have been observed during winter, but increasing summer temperatures, above the melting point, have led to increased glacier melt. Here, we evaluate the impact of this increased melt on the preservation of the oxygen isotope (δ18O) signal in firn records. δ18O is commonly used as a proxy for past atmospheric temperature reconstructions, and, when preserved, it is a crucial parameter to date and align ice cores. By comparing four different firn cores collected in 2012, 2015, 2017 and 2019 at the top of the Holtedahlfonna ice field (1100 m a.s.l.), we show a progressive deterioration of the isotope signal, and we link its degradation to the increased occurrence and intensity of melt events. Our findings indicate that, starting from 2015, there has been an escalation in melting and percolation resulting from changes in the overall atmospheric conditions. This has led to the deterioration of the climate signal preserved within the firn or ice. Our observations correspond with the model's calculations, demonstrating an increase in water percolation since 2014, potentially reaching deeper layers of the firn. Although the δ18O signal still reflects the interannual temperature trend, more frequent melting events may in the future affect the interpretation of the isotopic signal, compromising the use of Svalbard ice cores. Our findings highlight the impact and the speed at which Arctic amplification is affecting Svalbard's cryosphere.

Evaluation of three contrasting models in estimating primary production from ocean color remote sensing using long-term time-series data at oceanic and coastal sites

Accurate estimates of depth-integrated Net Primary Production (NPP, mg C m−2 d−1) and the creation of a robust climate data record of NPP for the global oceans are essential goals of the ocean color remote sensing community. Here, we take advantage of in situ NPP measurements from three long-term time-series sites, the HOT (Hawaii Ocean Time-series), BATS (Bermuda Atlantic Time-series Study) and CARIACO (Ocean Time-Series Program from the Cariaco basin), spanning over 30 years to evaluate three contrasting models in estimating NPP from ocean color remote sensing. These models for NPP estimation include the Absorption-based Model (AbPM), which relies on phytoplankton absorption coefficient, the Vertically Generalized Production Model (VGPM), which centers on chlorophyll-a concentration, and the Carbon-based Productivity Model (CbPM) centering on phytoplankton carbon. In addition to the accuracy of NPP estimation from these models, we laid great emphasis on evaluating their skills in capturing the monthly to seasonal variations and interannual trends in NPP at the three sites. Comparison with in situ NPP at all three long-term sites (∼20 years) showed that AbPM yielded the highest coefficient of determination (R2 = 0.67) and the lowest uncertainties (Bias = 0.03 and unbiased root mean square difference = 0.17). Seasonal and interannual variations apparent in the in situ NPP time-series records were best captured by AbPM. These results showcase the robust capabilities of AbPM and its superiority for global carbon cycling and climate change studies, largely because it takes into account optical and photosynthetic parameters of local phytoplankton.

Analysis of Spatiotemporal Variation of Oceanographic Parameters in the North Atlantic of Morocco

Analysis of the spatiotemporal evolution of oceanographic parameters such as sea surface temperature (SST) and chlorophyll concentration (CHL) in the North Atlantic between 2018 and 2022 revealed interesting seasonal and interannual trends. Seasonal variations in SST and CHL are associated with factors such as sunlight, winds, and ocean currents. These two parameters showed an inverse relationship caused by several factors, of which the coastal upwelling is the most important. Interannual variations are influenced by weather conditions, extreme climatic events, and changes in ocean currents. The study of the spatiotemporal evolution of these two parameters is essential to understand environmental changes, assess the state of health of marine ecosystems and predict future changes. It also contributes to the establishment of appropriate management measures for the conservation of ocean ecosystems and to assess the impact of environmental disturbances.

A new global time-series GPP production: DFRF-GPP

Understanding the global ecosystem carbon cycle requires a quantitative estimation of terrestrial gross primary production (GPP). Although most existing GPP estimates can accurately reflect the spatial distribution, interannual trends and even seasonal cycles of GPP under average conditions to some extent, the GPP estimates of different models under extreme climate conditions are still unsatisfactory. In this study, based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. The quality of DFRF-GPP was evaluated by comparison with current commonly used GPP datasets and GPP observations at FLUXNET flux sites. The results showed that DFRF-GPP is in great agreement with other GPP products and can reproduce the spatial and temporal patterns and interannual trends of global GPP, with optimal performance in regions such as those north of 30° in the Northern Hemisphere where there are more FLUXNET flux sites. DFRF-GPP has higher accuracy and excellent performance on drought and high-temperature samples, with higher sensitivity to drought and high temperature. This study not only provides a set of time-series products of global gross primary production but also provides a convenient and practical solution for enhancing the applicability of GPP estimation products in large-scale extreme climate studies.

Diagnosis of the Evolutionary Characteristics and Variability of Meteorological Drought in the Yellow River Basin

Frequent occurrences of meteorological droughts in the Yellow River Basin (YRB) result from the combined impact of global climate change and human activities. To address the problems of unclear evolution mechanism and difficulty in accurately determining the variability time of meteorological droughts in the basin, this study uses multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) and integrates wavelet analysis with the Mann-Kendall to investigate the evolution patterns and variability characteristics of meteorological droughts in the basin. The findings indicate that: (1) Meteorological drought shows a trend of shifting from the upper to the middle and lower reaches of the river, with clear seasonal and interannual variability trends. (2) The basin experiences six primary drought periods, with durations ranging from 2 to 6 months and 10 to 18 months, the latter being particularly noteworthy. (3) The main periods of meteorological drought variability are concentrated from 03/2001 to 10/2014, from 06/2006 to 08/2006, from 10/2017 to 01/2019, and 11/2019. This study furnishes a scientific groundwork for enhancing water resource management in the YRB and elucidating the mechanisms underlying meteorological drought evolution. These insights contribute to fostering ecological preservation and facilitating high-quality development within the basin.

Climate variability and sesame production in the rural community of Bérégadougou, Burkina Faso

In Burkina Faso, sesame (Sesamum indicum L.) is the second most important export crop after cotton. In the rural commune of B?r?gadougou, it is the second most important cash crop after groundnuts. This oilseed is essentially a rainfed crop and is there-fore largely dependent on rainfall. The aim of this article is to analyze sesame production in a context of climatic variability. To achieve this, the research methodology is based on a global geographical approach that integrates meteorological data (1991-2021), climatic parameters and fieldwork. The results show a rainfall pattern with a rainy season (May to October) and a dry season (November to April). Inter-annual rainfall trends over the analysis period show that rainfall is always above 500mm, whatever the year. Similarly, whatever the year, the average temperature is always above 27?C. These variations in rainfall and temperature have no impact on sesame production. Family farmers also gave 92% of their produce as evidence of their acceptance of climatic variability.

Trends in the elements of the carbonate system of Lake Ladoga

During the observation period in 2003 – 2021, it was shown that the total concentration of dissolved inorganic carbon, represented in the water of Lake Ladoga mainly HCO3ˉ and CO2 has been increasing in recent years. The concentration of CO2 has a sufficiently large spatial and temporal variability and, in terms of carbon at the stations under consideration, can range from 0.6 to 47 % of the total concentration of dissolved inorganic carbon, depending on the change in the ratio of production and destruction processes in different seasons at different horizons. At the same time, a significant interannual trend of an increase in the total concentration of dissolved carbon dioxide and, accordingly, a decrease in the hydrogen index was revealed. Calculation of the balance of dissolved in water and atmospheric CO2 showed that, in general, CO2 concentrations in water exceed equilibrium, however, at the peak of the growing season, the surface layer of the lake in some cases, on the contrary, can absorb carbon dioxide from the air. This effect of the «biological pump» is most clearly manifested in the nutrient-rich and heated shallow part of the lake. However, in the main body of water, there is an interannual trend of increasing the calculated concentrations of dissolved carbon dioxide relative to its equilibrium concentrations with the atmosphere. Due to the shortage of nutrients, primarily phosphorus, an increase in temperature does not lead to an increase in productivity and CO2 consumption. Stoichiometric ratios of apparent oxygen and carbon deficiency, in turn, demonstrate that the quantitative change in the content of dissolved carbon in water can be fully explained by oxidation or synthesis of organic matter in only a third of the cases considered. In the remaining 2/3 of cases, the increase in the concentration of dissolved inorganic carbon may be associated with an increase in carbon runoff from the catchment area into Lake Ladoga, or with intra-reservoir processes associated with the peculiarities of accumulation and redistribution of CO2 in the water column.

Seasonal spatial-temporal variability in radar penetration depth

A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using the Differential Interferometric Synthetic Aperture Radar (DInSAR) processing technique was developed and tested. Among the several factors that affect DInSAR’s precision, such as temporal decorrelation and atmospheric noise, it was observed that seasonal spatial-temporal variability in radar penetration depth is the greatest contributor to the loss of precision of the long-term deformation rate measurement. This spatial-temporal signal is described for a specific region in the northern US; however, it is widely observed in multiple regions. In these regions, a gradual penetration depth increase is observed in fall and winter, and an abrupt penetration depth decrease is observed in spring. The latter effect is likely caused by rapid melting of ice and snow that, at the same time, significantly reduces interferometric coherence. Within a specific geographic region, radar penetration depth spatially varies proportionally to the elevation due to an elevation-dependent temperature gradient, producing artifacts that correlate well with the topography. This signal does not extend to high-elevation regions (above ~2500 m) that do not experience seasonal thawing, which allows for distinguishing it from the topography-dependent atmospheric signals. Without accounting for the spatial-temporal variability in radar penetration depth, deformation time series in these areas can contain either an annual periodic signal with an amplitude of over 0.1 m or, if the abrupt penetration depth decrease produced by melting is not resolved due to lower coherence, an erroneous interannual trend. Spatial-temporal variability in radar penetration depth needs to be better understood and corrected to improve the precision of long-term deformation rate products, particularly in regions susceptible to seasonal freezing and thawing.

Spatial and Temporal Variation of Satellite-Derived Chlorophyll-a in Bohai Sea over the Past 20 Years

The Chlorophyll-a (Chl-a) concentration in the surface layer of the Bohai Sea from 2003 to 2022 was obtained after processing the MODIS data with the improved shortwave infrared (SWIR) atmospheric correction algorithm and the Clack three-channel method. Its spatial and temporal variation were analyzed. The results show that it has obvious interannual spatial distribution characteristics, showing a trend of being high in nearshore waters and low in offshore waters, high in the estuarine bays and low in other sea areas. The interannual Chl-a variation shows an inverted U-shape. The year-by-year seasonal average spatial characteristics are basically consistent, with high in nearshore waters and low in other waters. The Chl-a in spring and summer was higher than that in autumn and winter. The temporal trend of the four seasons was similar to the interannual trend, showing an inverted U-shaped trend of first increasing, then stabilizing and then decreasing.

Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo - Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute 0.5∘ standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003–2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections.

Multi-elemental stoichiometric ratios of atmospheric wet deposition in Chinese terrestrial ecosystems

Intense human activities have significantly altered the concentrations of atmospheric components that enter ecosystems through wet and dry deposition, thereby affecting elemental cycles. However, atmospheric wet deposition multi-elemental stoichiometric ratios are poorly understood, hindering systematic exploration of atmospheric deposition effects on ecosystems. Monthly precipitation concentrations of six elements—nitrogen (N), phosphorus (P), sulfur (S), potassium (K), calcium (Ca), and magnesium (Mg)—were measured from 2013 to 2021 by the China Wet Deposition Observation Network (ChinaWD). The multi-elemental stoichiometric ratio of atmospheric wet deposition in Chinese terrestrial ecosystems was N: K: Ca: Mg: S: P = 31: 11: 67: 5.5: 28: 1, and there were differences between vegetation zones. Wet deposition N: S and N: Ca ratios exhibited initially increasing then decreasing inter-annual trends, whereas N: P ratios did not exhibit significant trends, with strong interannual variability. Wet deposition of multi-elements was significantly spatially negatively correlated with soil nutrient elements content (except for N), which indicates that wet deposition could facilitate soil nutrient replenishment, especially for nutrient-poor areas. Wet N deposition and N: P ratios were spatially negatively correlated with ecosystem and soil P densities. Meanwhile, wet deposition N: P ratios were all higher than those of ecosystem components (vegetation, soil, litter, and microorganisms) in different vegetation zones. High input of N deposition may reinforce P limitations in part of the ecosystem. The findings of this study establish a foundation for designing multi-elemental control experiments and exploring the ecological effects of atmospheric deposition.

Changes in wintertime visibility across China over 2013–2019 and the drivers: A comprehensive assessment using machine learning method

Effective emission reduction measures have largely lowered the particulate concentration in China, but low-visibility events still occur frequently, greatly affecting people's daily life, travel, and health. In the context of carbon neutrality strategy and climate change, the mechanisms governing visibility changes may be undergoing a transformation. To address this critical issue, we have undertaken a comprehensive assessment by employing a novel approach that combines site observations, model-derived datasets, and machine learning techniques. Our analysis of the dataset shows varying degrees of improvement in wintertime visibility in regions such as North China, South China, and the Fenwei Plain over 2013–2019, but an unexpected deterioration (approximately 1 km yr−1) in central and southern China (CSC). We further elucidate key roles of PM2.5 reduction in these regions with visibility improvement; whereas the unsatisfactory visibility trend in CSC was caused by combined effect of relative humidity (RH) increase (47 %), aerosol hygroscopicity (κ) enhancement (9 %), and boundary layer (BLH) reduction (8 %), which greatly overwhelms the effect of PM2.5 reduction recently. Moreover, the study reveals a growing influence of RH on the wintertime visibility, reaching 40 % ± 24 % to the total contribution in 2019, while that of PM2.5 declined to 18 % ± 19 % and is expected to further diminish with emission reduction. Note those often-neglected factors-temperature, wind speed, BLH, and κ, account for over 40 % of the total contribution. Though the importance of aerosol hygroscopic growth to visibility was found decreasing recently, it retains non-negligible impacts on driving inter-annual visibility trends. This study yields innovative insights for air pollution control, underscoring the imperative of region-specific strategies to mitigate low-visibility events.

Synergistic evolution and attribution analysis of water-sediment in the middle and lower reaches of the Yangtze River

Study area: The Yangtze RiverStudy focus: Climatic variation and anthropogenic factors have a far-reaching impact on the evolution of runoff and sediment in the middle and lower Yangtze River. We introduced IHA-RVA and Copula functions, and used Budyko and sediment decomposition method. Under the effect of climatic variation and anthropogenic factors, the co-evaluation system and attribution analysis of runoff and sediment change are established. This study clarified the synergistic evolution law of water-sediment.New hydrological insights for the area: We found that the interannual variation trends of sediment transport at various stations in the middle and lower reaches of the Yangtze River during the period from 1954 to 2019 were more pronounced compared to the variations in runoff. Moreover, the degree of changes in water-sediment consistency varied among these stations, with water-sediment consistency gradually recovering with an increase in distance from the upstream cascade reservoirs. Human factors contributed 52.68–74.16% to the changes in runoff and 89.40–94.15% to the changes in sediment transport. Variation of water-sediment and its consistency in the middle and lower reaches of the Yangtze River are mainly related to human factors. The research results can provide some assistance to decision-makers in formulating regional water-sediment regulation planning frameworks.

Toward a real validation of passive microwave snowmelt detection algorithms over the Antarctic Ice sheet

Antarctic Ice Sheet (AIS) surface snowmelt can profoundly impact climatological and hydrological processes, as well as energy and mass balances. Passive microwave remote sensing has proven highly effective in detecting snowmelt, and the accuracy of the algorithms is typically assessed using air temperature (Tair). Nonetheless, the occurrence of snowmelt cannot be solely determined by Tair being above the melting point, it can only provide evaluation of the algorithms rather than a real validation. This study is the first to conduct a reliable validation of six commonly used algorithms by using the melt rate data from a surface energy balance model, including the cross-polarized gradient ratio (XPGR) algorithm, the diurnal amplitude variation (DAV) algorithm, the Microwave Emission Model of Layered Snowpacks based (MEMLS-based) algorithm, the constant threshold algorithm (CTA), the Ashcraft and Long Algorithm (ALA), and the M + 30 K (M30 hereafter) algorithm. Results suggest that the M30 algorithm and MEMLS-based algorithm have overall better performances, with high overall accuracies (with a value of 95.81 % and 95.56 %) and high kappa coefficient (with a value of 0.69 and 0.68). Though Tair is not reliable for evaluating the algorithms, a threshold of Tair = − 0.8 ℃ can serve as an alternative criterion in the absence of melt rate data. Additionally, two algorithms with the highest accuracy were selected to investigate interannual snowmelt trends in the Antarctic and the sub-regions. The results show a significant increase in melt extent and density in the Wilkes and Adelie sub-region during the period 1979–2020.

Coastal eutrophication driven by long-distance transport of large river nutrient loads, the case of Xiangshan Bay, China

With accelerating anthropogenic activities, the overloading of land-derived nutrients and the resultant eutrophication are threatening coastal aquatic habitats worldwide. In semi-enclosed coastal bays, eutrophication is always considered a local problem that can be mitigated by nutrient reduction at a regional scale. However, as the main nutrient drains major global river discharges can have far-reaching effects over hundreds of kilometers alongshore, which are usually not precisely recognized in local coastal zone management. Here, we first quantified the contributions from both local and remote nutrient sources in Xiangshan Bay (XSB), a eutrophic semi-enclosed bay in China 200 km south of the mouth of the Changjiang River (CJR, the world's third largest river), employing a salinity-based conservative mixing model. We found that the nutrients in Xiangshan Bay were mainly supplied by intruded coastal water fed by CJR discharge, contributing 63 % of dissolved inorganic nitrogen (DIN), 65 % of dissolved silicon (DSi), and 49 % of dissolved inorganic phosphorus (DIP) during the summer of 2017, and 75 % of DIN, 75 % of DSi and 60 % of DIP during the winter of 2019. Additionally, long-term interannual trends in the nutrient concentrations of XSB were generally synchronous with those of the downstream portion of the CJR, indicating that CJR discharge seems to be a strong driver of the eutrophication observed in XSB. In contrast, the impact of local nutrient inputs, such as riverine sewage drainage, aquaculture, biogenic activities, and elemental recycling, was much lower and was regionally limited to the inner bay. Interestingly, the DIP contributions of the local and remote sources were similar, indicating the greater relevance of the internal process. Overall, to mitigate eutrophication in large river-adjacent coastal bays, the inter-regional united practices for nutrient source regulation and ecosystem restoration should be permanently applied along the entire river basin-estuary-coastal continuum.

Winter Subtropical Highs, the Hadley Circulation and Baroclinic Instability

AbstractSubtropical highs have a profound influence on the weather and climate of adjacent continents. In this study, we use reanalysis data to investigate the interannual variability and trends in winter subtropical highs from 1979 to 2021. We find dynamical relationships between subtropical high intensity, the Hadley and Ferrel Circulation intensity, and the Eady Growth Rate (EGR). A poleward shift of the maximum in EGR is associated with a strengthening of the descending branches of the Ferrel and Hadley Cells, with subtropical troposphere adiabatic warming and an increased intensity and poleward movement of the subtropical highs. Shifts in the poleward EGR are dominated by changes in vertical wind shear which, in turn, are in thermal wind balance with variations and trends in temperature. The mechanism for the intensification of the subtropical highs involves feedbacks from high‐frequency transient eddies. Strong North Pacific and South Pacific Subtropical highs are associated with La‐Niña conditions. We also show that the mechanisms for interannual variations are similar to those for trends in the highs.

Response of grassland growing season length to extreme climatic events on the Qinghai-Tibetan Plateau

Extreme Climatic Events (ECEs) are increasing in intensity, frequency, and duration as the earth warms, which greatly affects the vegetation phenology. However, the response of vegetation phenology to different types of ECEs (e.g., extreme hot, extreme cold, extreme drought, and extreme wet) has not been extensively studied. To fill this knowledge gap, we investigated the relationship between the length of growing season (LOS) of grassland and ECEs on the Qinghai-Tibetan Plateau (QTP). First, we analyzed the spatial distribution and interannual trends of phenology based on the MODIS Normalized Difference Vegetation Index (NDVI). Second, we used Coincidence Rate (CR) analysis to quantify the relationship between LOS anomalies and ECEs. Finally, we analyzed the sensitivity of LOS to the intensity of ECEs. The results indicated that the spatial distribution of LOS was closely related to local hydrothermal conditions, with longer LOS in places with more precipitation or higher temperatures during the growing season, and LOS extended by 0.28 days/year from 2000 to 2022. Moreover, we found that the CR of negative LOS anomalies to ECEs notably exhibited variations along climatic gradients, with higher CR to extreme hot generally occurring in warmer areas. Meanwhile, the CR of extreme wet increased while the CR of extreme drought decreased with increasing precipitation. We also found that the sensitivity of LOS to ECEs changed more markedly, along the climatic gradients, in alpine ecoregions compared to temperate ecoregions. Overall, the sensitivities of LOS ranked in descending order of absolute sensitivity to extreme drought, extreme wet, extreme hot, and extreme cold. This study furthers our understanding of the grassland response to ECEs under different hydrothermal conditions, which can provide valuable reference for the management and conservation of grassland ecosystems in QTP under future climate change scenarios.

Rebound of surface and terrestrial water resources in Mongolian plateau following sustained depletion

Water resources have always played an important role in ensuring industrial and agricultural production, as well as maintaining ecosystem security in the Mongolian Plateau (MP), a typical arid to semi-arid region. Previous studies have reported the considerable shrinkages of surface water bodies affected by intense human disturbance in the MP before 2010. However, it is still unclear about the effects of those key ecological restoration efforts (e.g., the construction of ecological civilization since 2012) on water resources in the recent decade. Here, using all the available Landsat-5/7/8 surface reflectance observations, a robust water mapping algorithm based on spectral indices and thresholds, and the Google Earth Engine (GEE) cloud computing platform, we examined the changes in surface water area (SWA) in the MP during 1991–2021. In addition, based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) Mascon data products, we investigated the inter-annual variability and trends of terrestrial water storage (TWS) from 2002 to 2021. We found that SWA experienced remarkable increases (85.5 km2/yr) since 2009 after continuous shrinkage of surface water bodies (-205.9 km2/yr) for over 20 years, in which Inner Mongolia played a dominant role in the recovery of SWA (72.2 km2/yr). Also, TWS had undergone continuous decline before 2012 and fluctuating rebound after that. The most significant recovery of TWS mainly happened in the northern part of the MP. Quantitative attribution analyses showed that the key ecological restoration projects in China, especially the construction of ecological civilization since 2012, were the major drivers for the recovery of surface and terrestrial water resources. While previous studies reported the considerable decline of surface water resources induced by human activities in the MP since the 1990s, our research provided gratifying satellite evidence for the significant recoveries of surface and terrestrial water resources in the plateau during the past decade under the influence of ecological restoration efforts.

Increased Vegetation Productivity of Altitudinal Vegetation Belts in the Chinese Tianshan Mountains despite Warming and Drying since the Early 21st Century

Gaining a deep understanding of how climate change affects the carbon cycle in dryland vegetation is of utmost importance, as it plays a pivotal role in shaping the overall carbon cycle in global ecosystems. It is currently not clear how plant communities at varying elevations in arid mountainous regions will respond to climate change in terms of their productivity. The aim of this study was to investigate the effect of climate change on vegetation productivity in different altitudinal vegetation belts of the Tianshan Mountains between 2000 and 2021, utilizing satellite-derived vegetation productivity and climate data. The findings suggest a notable increase in vegetation productivity across diverse altitudinal vegetation belts. The productivity of vegetation in the coniferous forest and alpine meadow belts displayed a notably higher interannual trend compared to other vegetation belts. Notably, an increase in vegetation productivity was accompanied by warming and drying. The productivity of altitudinal vegetation belts, however, appears to be resilient to current climate trends and was not significantly impacted by the severity of atmospheric drought. The trend of increased vegetation productivity was primarily driven by CO2 fertilization. Our results highlight that the extent of climate change may need to reach a threshold to noticeably affect variations in vegetation productivity in arid mountainous.