Dynamics of algal blooms in typical low-latitude plateau lakes: Spatiotemporal patterns and driving factors.

The alpine lakes distributed on the plateau are crucial for the hydrological, and biogeochemical cycle, and also serve as a guarantee for regional economic development and human survival. However, under the influence of human interference and climate fluctuations, lakes are facing problems of eutrophication and subsequent algal blooms (ABs) with acceleration, and the development and driving factors of this phenomenon need to be considered as a whole. In this study, ten lakes located on the Yunnan-Guizhou Plateau were selected as the study area to analyze the spatiotemporal distribution of ABs and possible controlling forces. The FAI (Floating Algae Index) derived from multiple MODIS products and water quality data under high-frequency monitoring were selected as the data sources for characterizing ABs. Three nutrient parameters and five meteorological variables were used to explore the driving factors affecting ABs. Various methods of trend detection and correlation analysis have been applied. The main results are as follows: (1) Dianchi Lake (in lake area) and Xingyun Lake (in area proportion) are the two lakes with the most serious ABs in the historical period; (2) ABs are mainly distributed on the shoreline and northern edge of lakes, and tend to stay away from the lake center during high-temperature periods of the day; (3) Six lakes show a decreasing trend in ABs, especially after 2018, while other lakes (including Fuxian, Chenghai, Yangzong, and Erhai) are increasing, not only in peak value but also in duration; (4) Lakes with severe ABs are all P-restricted lakes, the minimum temperature is the most sensitive meteorological factor, while the impact of precipitation against ABs has a time lag; (5) Establishing a warning system of temperature and nutrient concentration is critical in ABs adaptive strategy. This study is expected to provide scientific references for regional water management and the restoration of the eutrophic aquatic ecosystem.

Ecological security warning in Central Asia: Integrating ecosystem services protection under SSPs-RCPs scenarios.

Ecological security patterns (ESPs) are designed to enhance ecosystem structure and functionality while preserving vital ecosystem services (ESs). This study not only integrated the ES trade-offs related to ecological security warning, but also considered the effects of future climate changes and human activities on ESPs. By combining the revised universal soil loss equation (RUSLE), the revised wind erosion equation (RWEQ), the dry sedimentation (DS) model, the recreation opportunity map (ROM) and the integrated valuation of ESs and trade-offs (InVEST) model, this study projected provisioning services, regulation services and cultural services in Central Asia (CA) for historical periods (1995-2014) and future scenarios (2021-2099). An ecological security early-warning (source - corridor - barriers) framework was constructed based on the protection of ESs under the SSP126, SSP245 and SSP585 scenarios. The ordered weighted averaging method (OWA) was applied to this framework to identify ecological sources. The Minimum cumulative resistance model (MCR) and circuit theory were used to construct ecological corridors and barriers. Our results revealed that ES hotspot areas will decrease by 11.75 % to 16.42 % in CA under the SSP126, SSP245, and SSP585 scenarios. Under the ecological warning framework, the ecological source warning area will reach 792 km2-1942 km2 and 6591 km2-17,465 km2 under the SSP126 and SSP585 scenarios, respectively. In particular, in the 2050s under the SSP245 scenario, the number of key ecological corridor warnings will exceed 50 % of the total number of corridors. We found that ecological barrier warnings will mainly be distributed in desert areas with low vegetation coverage in southwestern CA. Building upon the reorganization of ESs within the ESP framework, we propose an ecological early warning strategy referred to as "one axis, two belts, two cores, and three zones". This novel approach aims to enhance our ability to predict and respond to ecological threats and challenges.

Recent impacts of water management on dryland's salinization and degradation neutralization.

Reducing soil salinization of croplands with optimized irrigation and water management is essential to achieve land degradation neutralization (LDN). The effectiveness and sustainability of various irrigation and water management measures to reduce basin-scale salinization remain uncertain. Here we used remote sensing to estimate the soil salinity of arid croplands from 1984 to 2021. We then use Bayesian network analysis to compare the spatial-temporal response of salinity to water management, including various irrigation and drainage methods, in ten large arid river basins: Nile, Tigris-Euphrates, Indus, Tarim, Amu, Ili, Syr, Junggar, Colorado, and San Joaquin. In basins at more advanced phases of development, managers implemented drip and groundwater irrigation and thus effectively controlled salinity by lowering groundwater levels. For the remaining basins using conventional flood irrigation, economic development and policies are crucial for establishing a virtuous circle of "improving irrigation systems, reducing salinity, and increasing agricultural incomes" which is necessary to achieve LDN.

Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Indicator-based assessments of the coupling coordination degree and correlations of water-energy-food-ecology nexus in Uzbekistan.

Grappling with the global ecological concern of the Aral Sea disaster, Uzbekistan exemplifies the urgent necessity of unravelling and addressing the complex Water-Energy-Food-Ecology (WEFE) nexus conflicts in arid regions, a critical task yet largely uncharted. Through the strategic process of 'Indicator Articulation - Weight Calibration - Nexus Coordination Quantification - Correlational Analysis', this work has developed a tailored framework that integrates a novel, context-specific indicator system, enabling an illumination of the intricate dynamics within the WEFE nexus in arid regions. During 2000-2018, the WEFE Nexus in Uzbekistan showed low-level coordination, indicating systemic imbalances. The Aral Sea crisis was the central disruptor, resulting in a moderately disordered ecological subsystem. Concurrently, disorder was observed in water resources, signaling inadequate management and potential overutilization. Furthermore, Coordination for energy and food were barely coordinated and under primary coordination respectively, underlining critical challenges in energy efficiency and food security. Over the last two decades, the WEFE Nexus has evolved towards a tighter interlinkage, yet the stability of this coupling coordination has experienced increased fluctuations, indicating that Uzbekistan's policies in the WEFE subsystems have been less stable in the last two decades and are in need of further adjustment and improvement. To address the challenges, we recommend a comprehensive approach that integrates technological, infrastructure, and policy solutions is needed. Specifically, promoting water-saving irrigation technology, renewing and maintaining outdated energy facilities, and raising public awareness of ecological protection are part of the essential measures. Furthermore, alleviating the contradiction between economic growth and ecological conservation remains a major challenge. Collectively, our constructed WEFE Nexus framework, with its extendable and context-specific indicators, holds significant potential for broad application in the analysis of multi-sectoral sustainability, particularly within arid regions globally, and forms a solid foundation for the formulation of effective, targeted policies and sustainable development strategies.

Interannual and seasonal relationships between photosynthesis and summer soil moisture in the Ili River basin, Xinjiang, 2000-2018.

Soil moisture (SM) is essential for controlling terrestrial carbon uptake, as it directly provides moisture for photosynthesis, especially in arid and semiarid regions. We selected the arid and semiarid Ili River basin (IRB) of Xinjiang as the study area, and investigated the spatial and temporal characteristics and interrelationships with SM and photosynthesis from 2000 to 2018 using the ERA5 products and solar-induced chlorophyll fluorescence (SIF). SM and photosynthesis showed a decreasing trend during the study period. Compared with those in spring and autumn, the variation of summer SM and SIF was more consistent with the interannual variation. Anomaly analysis showed that negative SM anomalies were most profound in 2012-2015, 2008, and 2014. Additionally, we quantified the effect of seasonal SM deficits on photosynthesis by performing model-based experiments. The results indicated that the gross primary productivity (GPP) simulated by the P-model could capture the characteristics of photosynthesis in the IRB, which had a high correlation with SIF (R2 = 0.82, p < 0.001). In 2012-2015, 2008, and 2014, SM deficits caused more GPP reduction in the summers than in the springs or the autumns. The trends were mainly visible in the northern IRB, where GPP was below 40 % of the multi-year mean, and SM was below 23 %. GPP decreased more significantly in grassland than in the forest under the influence of SM deficit. This study reveals seasonal differences in the effects of SM deficit on photosynthesis and emphasizes that the summer SM deficit was the main factor responsible for decreases in GPP in the IRB during the study period. These findings contribute to a better understanding of the relationships between photosynthesis and environmental factors, and provide a reference for an accurate assessment of the regional carbon cycle.

Shifting in the global flood timing.

Climate change will have an impact on not only flood magnitude but also on flood timing. This paper studies the shifting in flood timing at 6167 gauging stations from 1970 to 2010, globally. The shift in flood timing and its relationship with three influential factors (maximum 7-day precipitation, soil moisture excess, and snowmelt) are investigated. There is a clear global pattern in the mean flooding date: winter (Dec-Feb) across the western Coastal America, western Europe and the Mediterranean region, summer (Jun-Aug) in the north America, the Alps, Indian Peninsula, central Asia, Japan, and austral summer (Dec-Feb) in south Africa and north Australia area. The shift in flood timing has a trend from - 22 days per decade (earlier) to 28 days per decade (delayed). Earlier floods were found extensively in the north America, Europe and northeast Australia while delayed floods were prevailing in the Amazon, Cerrado, south Africa, India and Japan. Earlier flood timing in the north America and Europe was caused by earlier snowmelt while delayed extreme soil moisture excess and precipitation have jointly led to delayed floods around the monsoon zone, including south Africa, India and Japan. This study provides an insight on the shifting mechanism of flood timing, and supports decisions on the global flood mitigation and the impact from future climate change.

Impacts of climate change and evapotranspiration on shrinkage of Aral Sea.

The massive desiccation of the Aral Sea, the fourth largest lake in the world, has led to severe ecological problems, expansion of cropland was thought to be the main factor driving that shrinkage. But this study performed a long-term land cover and use change assessment for Aral Sea Basin (ASB) to show that the cropland has stopped expanding in 2000, of which the cropland in the ASB plain area has decreased significantly (-140 km2/year) from 2001 to 2019. By contrast, this study finds the hydrological cycle in the ASB has intensified through a spatial and temporal scale approach based on Earth observation. Specifically, there is a 7.21 % (+304.56 × 108 m3) increase in annual total precipitation and a 10.13 % (+376.21 × 108 m3) increase in annual total actual evapotranspiration (AET) for the whole ASB during 1980-2019. In particular, the total annual AET in the ASB plain area has increased by 37.81 % (+718.92 × 108 m3), which almost depletes the water that should have flowed into the Aral Sea. Therefore, the Aral Sea shrank by 5625 × 108 m3 (or 42,944.32km2) from 1980 to 2019. Changing climate and increasing AET have accelerated the desiccation of the Aral Sea, and the expansion of cropland is no longer the main factor of that shrinkage. After more water was conserved in the ASB plain area, evapotranspiration plays a more vital role in the Aral Sea shrinkage. Reducing AET and unproductive water losses are key initiatives in future projects to save the Aral Sea. This study explores the causes of Aral Sea shrinkage from an integrated perspective of climate-land-water-ecological change across the ASB, bridging the limitations of previous studies that have focused on Aral Sea waters and subbasins.

Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia.

The increasing frequency and intensity of droughts in a warming climate are likely to exacerbate adverse impacts on ecosystems, especially for water-limited regions such as Central Asia. A quantitative understanding of the impacts of drought on vegetation is required for drought preparedness and mitigation. Using the Global Inventory Modeling and Mapping Studies NDVI3g data and Standardized Precipitation Evapotranspiration Index (SPEI) from 1982 to 2015, we evaluate the vegetation vulnerability to drought in Central Asia based on a copula-based probabilistic framework and identify the critical regions and periods. Furthermore, a boosted regression trees (BRT) model was also used to explore the relative importance of environmental factors and plant traits on vegetation response to drought. Additionally, we also investigated to what extent irrigation could alleviate the impacts of drought. Results revealed that months from June to September was the critical period when vegetated areas were most vulnerable to drought stress. The probabilities of vegetation loss below 20th quantile under extremely dry in these months were 68.7%, 69.4%, 71.0%, and 67.0%, respectively. Regarding vegetation-vulnerable regions, they shifted with different growth stages. During the middle of the growing season, semi-arid areas were the most vulnerable regions, whereas the highest drought-vulnerable regions were observed in arid areas during other periods. The BRT results showed that plant traits accounted for a large fraction (58.9%) of vegetation response to drought, which was more important than ambient soil environment (20.8%). The analysis also showed that mitigations from irrigation during July to September were smaller than in other months. The results of this paper provide insight into the influences of drought on vegetation and may contribute to drought mitigation and land degradation measures in Central Asia under accelerating global warming.

Phenology-based seasonal terrestrial vegetation growth response to climate variability with consideration of cumulative effect and biological carryover.

Vegetation growth is influenced not only by climate variability but also by its past states. However, the differences in the degree of the climate variability and past states affecting vegetation growth over seasons are still poorly understood, particularly given the cumulative climate effects. Relying on the Normalized Difference Vegetation Index (NDVI) data from 1982 to 2014, the vegetation growing season was decomposed into three periods (sub-seasons) - green-up (GSgp), maturity (GSmp), and senescence (GSsp) - following a phenology-based definition. A distributed lag model was then utilized to analyze the time-lag effect of vegetation growth response to climatic factors including precipitation, temperature, and solar radiation during each sub-season. On this basis, the relative importance of climatic factors and vegetation growth carryover (VGC) effect on vegetation growth was quantified at the phenology-based seasonal scale. Results showed that the longest peak lag of precipitation, temperature, and solar radiation occurred in the GSmp, GSsp, and GSgp, with 1.27 (1.13 SD), 0.89 (1.02 SD), and 0.80 (1.04 SD) months, respectively. The influence of climate variability was strongest in the GSgp, and diminished over the season, while the opposite for the VGC effect. The relative influence of each climatic factor also varied between sub-seasons. Vegetation in more than 58% of areas was more affected by temperature in the GSgp, and the proportion decreased to 34.00% and 31.78% in the GSmp and GSsp, respectively. Precipitation and solar radiation acted as the dominant climatic factors in only 28.80% and 20.88% of vegetation areas in the GSgp, but they increased to 35.21%, 32.61% in the GSmp, and 38.20%, 30.02% in the GSsp, respectively. The increased regions influenced by precipitation were mainly in dry areas especially for the boreal and cool temperate climate zones, while increased regions influenced by solar radiation were primarily located in moist areas of mid-high latitudes of the Northern Hemisphere. By introducing the cumulative climate effect, our findings highlight seasonal patterns of vegetation growth affected by climate variability and the VGC effect. The results provide a more comprehensive perspective on climate-vegetation interactions, which may help us to accurately forecast future vegetation growth under accelerating global warming.

Quantifying the response of surface urban heat island to urban greening in global north megacities.

Urban heat island, a phenomenon that urban temperature is higher than the rural area nearby, affects directly citizens' human health and well-being. However, the cooling effect from urban green space (UGS) and the attribution of the different land processes to surface urban heat island intensity (SUHI) under different background climates remains unclear. The coarse-grained model was used to estimate summer SUHI in three different background climatic zones and for seven agglomerations (BTH, JP, LD, NAAC, NAGL, YZ, UQ). Results indicate that (1) the temperate zone had the highest daytime SUHI (0-10 °C), while the arid zone has the lowest daytime SUHI (-1-2 °C). In both temperate and cold zone, the daytime SUHI was higher than the nighttime SUHI. The SUHI in downtown was higher (more than 2 °C) than in the suburbs. (2) The increasing precipitation can enhance daytime SUHI while can weaken nighttime SUHI in all three climatic zones. The increasing temperature tends to enhance SUHI in both daytime and nighttime (exclude UQ). (3) The cooling effects of UGS in daytime SUHI were highly dependent on the background climate (cold > temperate > arid). (4) The nighttime SUHI could be effectively offset when UGSFs were greater than 0.48, 0.82, 0.97, 0.95 in NAAC, NAGL, YZ, and UQ. This article highlights the different feedback of urban green space to UHII and supports green infrastructure intervention as an effective means of reducing urban heat stress at urban agglomeration scales.

Assessing vegetation stability to climate variability in Central Asia.

The dramatic climate change has far-reaching impacts on vegetation in drylands such as Central Asia. Recent attempts to assess vegetation stability to short-term climate variability often account solely for vegetation sensitivity or resilience but ignore the composite effects of these two indicators. Meanwhile, our understanding of the vegetation stability at the seasonal scale remains insufficient. In this study, considering the cumulative effects of vegetation response to three key climate factors, we assessed the stability of vegetation in Central Asia using normalized difference vegetation index (NDVI) and the meteorological data from 1982 to 2014 by integrating vegetation sensitivity and resilience, and further identified the critical regions and seasons of vegetation that experience high risks of pending change. The results show that the sensitivity of vegetation has a strong correlation (R2 = 0.83, p < 0.001) with the aridity index (AI), with the vegetation of drier areas having lower sensitivities to climate variability. At the temporal scale, the sensitivity of vegetation to climate variability varied among different seasons. The average vegetation sensitivity index (VSI) is 41.17, 33.32 and 28.63 in spring, summer and autumn, respectively. Spatially, a trade-off between vegetation sensitivity and resilience is found both for the growing season (R2 = 0.67) and seasonal scale (R2 = 0.71, 0.32 and 0.43 for spring, summer and autumn, respectively), regions with high vegetation sensitivity were always accompanied by strong resilience. Based on the relationship between vegetation sensitivity and resilience, we further identify the critical regions and periods of vegetation with high change risk in Central Asia. Results suggest that herbaceous plants in semi-arid areas present high instability, especially in summer. This study offers a comprehensive perspective to assess vegetation stability to climate variability and the results will facilitate the protection of ecosystems and the implementation of sustainable development goals in Central Asia.

Identification of conservation priorities in the major basins of Central Asia: Using an integrated GIS-based ordered weighted averaging approach.

Ecosystem services (ESs) provided by the major basins of Central Asia are critical to human well-being and have attracted the attention of the international community. The identification of conservation priorities is of great significance for the maintenance and protection of key ESs. In this study, we quantified the spatiotemporal changes of net primary productivity (NPP), soil conservation (SC), water yield (WY) and habitat quality (HQ) in the major basins of Central Asia from 1995 to 2015. In addition, a GIS-based ordered weighted averaging (OWA) multi-criterion valuation method was adopted to identify potential conservation areas under 11 scenarios. Conservation priorities were determined by comparing the conservation efficiency under each scenario. Then, a broad range of indicators were considered to distinguish the driving factors affecting ESs in conservation priorities. The results show that the average conservation efficiency in the Issyk-Kul Basin was the highest, followed by the Am Darya Basin, Ili-Balkhash Basin and Syr Darya Basin. We observed that the conservation efficiency of the four ESs declined continuously in the Ili-Balkhash Basin from 1995 to 2015, while it changed steadily in the other three basins. Correlation analysis indicated that natural factors (e.g., precipitation and topography) were the main driving factors of WY, SR and NPP in conservation priorities, while HQ was more affected by socio-economic factors (e.g., population density and both cropland and urban percentages). The identification of conservation priorities and their driving factors plays an important role in ensuring the ecological security of the lower reaches, regulating the regional water balance and stabilizing the climate pattern.

Attribution of changes in the trend and temporal non-uniformity of extreme precipitation events in Central Asia.

Extreme precipitation events exhibit an increasing trend for both the frequency and magnitude on global and regional scales and it has already proven the impact of man-made global warming on the extreme precipitation amplification. Based on the observed datasets and global climate model (GCM) output, this study has evaluated the impact from anthropogenic forcing on the trend and temporal non-uniformity (i.e. increase in unevenness or disparity) of the precipitation amounts (PRCPTOT), extremes (R95p and RX5day) and intensity (SDII) in Central Asia (CA) from 1961 to 2005. Results indicate that radiative forcing changes, mainly driven by human activities, have significantly augmented the extreme precipitation indices in CA. The median trend with the influence of anthropogenic activities for the PRCPTOT, SDII, R95p and RX5day amounted to 2.19 mm/decade, 0.019 mm/decade, 1.39 mm/decade and 0.21 mm/decade during the study period, respectively. A statistically insignificant decrease in non-uniformity was noticed for the PRCPTOT, SDII and RX5day in Central CA (CCA) and Western CA (WCA), while Eastern CA (ECA) was the only region with a statistically significant increase in non-uniformity of the PRCPTOT, SDII, R95p and RX5day by 4.22%, 3.98%, 3.73% and 3.97%, respectively from 1961 to 2005 due to anthropogenic forcing. These results reflect the difference in various regions regarding the impact of anthropogenic forcing on the non-uniformity of extreme precipitation events in CA, which might help to fully understand the role of anthropogenic forcing in the changes of the precipitation extremes in CA and contribute to the development of water resource management strategies.

Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data.

Beach Surface Moisture (BSM) is a key attribute in the coastal investigations of land-atmospheric water and energy fluxes, groundwater resource budgets and coastal beach/dune development. In this study, an attempt has been made for the first time to estimate BSM from terrestrial LiDAR intensity data based on the Support Vector Regression (SVR). A long-range static terrestrial LiDAR (Riegl VZ-2000) was adopted to collect point cloud data of high spatiotemporal resolution on the Ostend-Mariakerke beach, Belgium. Based on the field moisture samples, SVR models were developed to retrieve BSM, using the backscattered intensity, scanning ranges and incidence angles as input features. The impacts of the training samples' size and density on the predictive accuracy and generalization capability of the SVR models were fully investigated based on simulated BSM-intensity samples. Additionally, we compared the performance of the SVR models for BSM estimation with the traditional Stepwise Regression (SR) method and the Artificial Neural Network (ANN). Results show that SVR could accurately retrieve the BSM from the backscattered intensity with high reproducibility (average test RMSE of 0.71% ± 0.02% and R2 of 0.98% ± 0.002%). The Radial Basis Function (RBF) was the most suitable kernel for SVR model development in this study. The impacts of scanning geometry on the intensity could also be accurately corrected in the process of estimating BSM by the SVR models. However, compared to the SR method, the predictive accuracy and generalization performance of SVR models were significantly dependent on the training samples' coverage, size and distribution, suggesting the need for the training samples of uniform distribution and representativeness. The minimum size of training samples required for SVR model development was 54. Under this condition, SVR performed similarly to ANN with a test RMSE of 1.06%, but SVR still performed acceptably (with an RMSE of 1.83%) even using extremely few training samples (only 16 field samples of uniform distribution), far better than the ANN (with an RMSE of 4.02%).

Climate change versus land-use change-What affects the ecosystem services more in the forest-steppe ecotone?

The Ecosystem services (ESs), which play an important role in the balance of the natural ecosystem and social-economic development, are suffering from degradation caused by human activities and climate change. However, the manner in which the ESs respond to the land use/cover changes (LUCCs) and the climatic factors respectively remain elusive, especially in the forest-steppe ecotone, which is highly sensitive to climate change and anthroponotic activities. Based on the remote sensing data and in situ meteorological data, we comprehensively modeled and compared 4 key ESs changes caused by 3 LUCC types, land-use change fraction, and climate changes through two simple comparative experiments. Our results showed that: the Grain for the Green Project improved the mean soil conservation, carbon sequestration, and water yield but reduced the sand fixation. The cropland expansion had a positive influence on the water yield and sand fixation, but it induced a decline in soil conservation and carbon sequestration. The urbanization very likely increased the water yield and decreased soil conservation, carbon sequestration, and sand fixation. The unequal change fractions of the same land-use conversion may affect the ESs differently. The ESs changes have different responses to climate change in different landscapes due to the ecological process. The water yield could be well explained by the temperature, precipitation, radiation, and wind speed. Climate change had a stronger effect on the water yield and carbon sequestration than the land use/cover changes but sand fixation and soil conservation were more likely to be affected by LUCCs. The impact of three types of land-use changes and climate change on the ecosystem services should be considered when formulating land-use policies. This paper might aid the decision-makers in achieving ESs sustainable management and develop land-use strategies in the forest-steppe ecotone.

The use of historical sources in a multi-layered methodology for karez research in Turpan, China.

In this article, we will present an overview of possible research methods to handle historical sources, in the specific case of karez landscapes. A karez system is an underground water collection system, prevalent in the Turpan basin of China. Sources and the associated methodology have become more important today, because of contemporary issues such as modernisation, urbanisation and agricultural expansion. These problems make it harder to read the landscape, which is why we have to start extracting our data from maps, reports, photographs, and satellite imagery. We will give a short overview of sources, each with an explanation of their processing method. Despite certain cautions that should be taken into account, these methods clearly complement the current state of knowledge on the Turpan karez. As this paper is part of a special issue, Water History in the time of COVID-19, it has undergone modified peer review.

Sub-Daily Simulation of Mountain Flood Processes Based on the Modified Soil Water Assessment Tool (SWAT) Model.

Floods not only provide a large amount of water resources, but they also cause serious disasters. Although there have been numerous hydrological studies on flood processes, most of these investigations were based on rainfall-type floods in plain areas. Few studies have examined high temporal resolution snowmelt floods in high-altitude mountainous areas. The Soil Water Assessment Tool (SWAT) model is a typical semi-distributed, hydrological model widely used in runoff and water quality simulations. The degree-day factor method used in SWAT utilizes only the average daily temperature as the criterion of snow melting and ignores the influence of accumulated temperature. Therefore, the influence of accumulated temperature on snowmelt was added by increasing the discriminating conditions of rain and snow, making that more suitable for the simulation of snowmelt processes in high-altitude mountainous areas. On the basis of the daily scale, the simulation of the flood process was modeled on an hourly scale. This research compared the results before and after the modification and revealed that the peak error decreased by 77% and the time error was reduced from ±11 h to ±1 h. This study provides an important reference for flood simulation and forecasting in mountainous areas.

Atmospheric nitrogen pollution in urban agglomeration and its impact on alpine lake-case study of Tianchi Lake.

The atmospheric pollution caused by human activities has been recognized as an important factor affecting the water quality of freshwater bodies. The process of the human factors' impact on high-altitude lakes in inland regions is not clear up to now. In this research, regions around Tianchi Lake were taken as a case study to explore the relation between the urban air pollution and alpine lake water quality. Multi-scale station observed data were analyzed for the urban NO2 pollution by means of relevance analysis and trend analysis, the field measured data were analyzed for the lake total nitrogen (TN) pollution using multiple methods including the water quantity and quality balance, remote sensing retrieval and nutrient load assessment. The sources and occurrence conditions of atmospheric pollution and lake pollution were identified by a multi-method driving factor analysis. As a result, there is sufficient direct and indirect evidence to prove that the serious air pollution in surrounding cities is an important cause of the nitrogen pollution in Tianchi Lake.

Identifying climate change impacts on water resources in Xinjiang, China.

Water resources have an important role in maintaining ecological fuctions and sustaining social and economic development. This is especially true in arid and semi-arid areas, where climate change has a large impact on water resources, such as in Xinjiang, China. Using a combination of precipitation and temperature bias correction methods, we analyzed projected changes in different hydrological components in nine high-alpine catchments distributed in Xinjiang using the Soil and Water Assessment Tool (SWAT). The impacts of elevation, area and aspect of the catchments were analyzed. The results suggested an overall warming and wetting trend for all nine catchments in the near future, with the exception of summer precipitation decreasing in some catchments. The total runoff discharge, evapotranspiration and snow/ice melting will generally increase. Warming temperature plays a more important role in the changes of each hydrological component than increasing precipitation. However, northern Xinjiang was more sensitive to predicted precipitation changes than southern Xinjiang. These results also indicate that the overall increases in water resources are not sustainable, and the impacts of climate change are associated with the elevation, area and slope aspect of the catchments.