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.

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.

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.

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.

Full hierarchic versus non-hierarchic classification approaches for mapping sealed surfaces at the rural-urban fringe using high-resolution satellite data.

Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84.

Improving Distributed Runoff Prediction in Urbanized Catchments with Remote Sensing based Estimates of Impervious Surface Cover.

The amount and intensity of runoff on catchment scale are strongly determinedby the presence of impervious land-cover types, which are the predominant cover types inurbanized areas. This paper examines the impact of different methods for estimatingimpervious surface cover on the prediction of peak discharges, as determined by a fullydistributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe Rivercatchment in the southeastern part of Brussels. The study shows that detailed informationon the spatial distribution of impervious surfaces, as obtained from remotely sensed data,produces substantially different estimates of peak discharges than traditional approachesbased on expert judgment of average imperviousness for different types of urban land use.The study also demonstrates that sub-pixel estimation of imperviousness may be a usefulalternative for more expensive high-resolution mapping for rainfall-runoff modelling atcatchment scale.

Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels.

Urban growth and its related environmental problems call for sustainable urban management policies to safeguard the quality of urban environments. Vegetation plays an important part in this as it provides ecological, social, health and economic benefits to a city's inhabitants. Remotely sensed data are of great value to monitor urban green and despite the clear advantages of contemporary high resolution images, the benefits of medium resolution data should not be discarded. The objective of this research was to estimate fractional vegetation cover from a Landsat ETM+ image with sub-pixel classification, and to compare accuracies obtained with multiple stepwise regression analysis, linear spectral unmixing and multi-layer perceptrons (MLP) at the level of meaningful urban spatial entities. Despite the small, but nevertheless statistically significant differences at pixel level between the alternative approaches, the spatial pattern of vegetation cover and estimation errors is clearly distinctive at neighbourhood level. At this spatially aggregated level, a simple regression model appears to attain sufficient accuracy. For mapping at a spatially more detailed level, the MLP seems to be the most appropriate choice. Brightness normalisation only appeared to affect the linear models, especially the linear spectral unmixing.