Assessment of wetland fragmentation in the Tarim River basin, western China

Trade-offs and synergies among ecosystem services in Inland River Basins under the influence of ecological water transfer project: A case study on the Tarim River basin

Ecosystem services (ESs) are the largest benefits that humans derive directly or indirectly from ecosystems. Inland river basins in arid zones have a variety of key ecosystem functions. At present, inland river basins are experiencing a decline in ESs, such as shrinking lakes, land degradation, and rapid biodiversity loss. In order to address these problems, several ecological restoration projects (ERPs) have been implemented. Therefore, this study selected the Tarim River Basin (TRB), which is highly affected by the ecological water transfer project (EWTP), as the study area, and quantified the differences in ESs caused by the implementation of the EWTP through different scenarios of simulation, and discussed the impact of the EWTP in changing the ESs of the basin. Compared to the pre-EWTP period (1990-1999), the major ESs within the basin showed varying degrees of improvement. Water yield increased by 18 %, carbon sequestration increased by 2 %, wind prevention and sand fixation increased by 13 %, habitat quality increased by 8 %, and food production increased by 35 %. EWTP has shown positive impacts by directly or indirectly affecting runoff, vegetation, evapotranspiration, and landscape patterns, which in turn improves the comprehensive benefits of ESs in the TRB. The implementation of EWTP plays an important role in restoring ESs in inland river basins, and this study provides a key reference for the restoration of ESs in inland river basins in arid zones.

Landscape Pattern Evolution Processes and the Driving Forces in the Wetlands of Lake Baiyangdian

The spatiotemporal features of land use changes and the evolution process of landscape pattern from 1980 to 2017 were investigated using historical satellite images from a Landsat Thematic Mapper (TM) for 1980, 1990, 2000, 2005, 2010 and 2017 in the wetlands of Lake Baiyangdian in the North China Plain (NCP). Landscape pattern indices were used to quantify landscape changes in wetlands, and a redundancy analysis (RDA) was conducted to analyze the driving forces and quantitatively explain the effects of human activities and natural changes on wetland fragmentation. The results showed that the total wetland area was 234.4 km2 in 1980 but it decreased by 8.1% at an average decrease rate of 0.5 km2 per year. The dominant transition between land use types was from natural wetlands to artificial wetlands, and wetland conversion to dry land and residential land. The RDA results suggested that agricultural activities and total population were the main driving factors affecting wetland landscape. Additionally, climate change provided a potentially favorable environment for agricultural development, due to the increased temperatures and decreased wind speeds. Additionally, governmental policy changes and dam construction also played the roles in land use changes.

Application of Landsat derived indices and hydrological alteration matrices to quantify the response of floodplain wetlands to river hydrology in arid regions based on different dam operation strategies

The response of floodplain wetlands to changes in river hydrology caused by different dam operation strategies in arid regions has attracted worldwide attention in recent decades. However, less is known about quantifying the effects of dam operations on wetlands in different lateral zones located in low-gradient floodplains in arid regions using remote sensing and hydrological time series. In this study, hydrological time series from 1975 to 1985 and 2008-2014 were used to quantify 67 flow metrics during different dam operation periods. Time series of remote sensing data (39 periods in total) from 1975 to 1977 and 2008-2014 were analysed to quantify the spatiotemporal dynamics in floodplain wetland types (river, bare wet land and wetland vegetation areas). In addition, a correlation analysis was performed to quantify the relationship between each wetland type and the flow metrics in different lateral zones. The results indicate that approximately 90% of the water and bare wet land area were located in the continuously and frequently inundated zones. Vegetation was the main wetland type in the regularly and extremely rarely inundated zones, and a 45.7% decrease in the vegetation area of these two belts occurred; in contrast, a 20.14 km² of increase in vegetation occurred in the frequently inundated zone after the uniform scheduling was implemented for the entire river. Linear correlation and regression analysis showed that the different dam operation strategies resulted in various wetland changes in the different lateral zones. The decrease in high flows and increase in low flows caused a decrease in the vegetation cover area in the regularly and extremely rarely inundated zones and led to vegetation encroachment in the frequently inundated zone. Our study can improve the management of wetlands and water resources in arid region floodplains.

Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products

Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. However, the vast area of Northwest China, its complex geographical environment, and its scarce observation data make it difficult to deeply understand the temporal and spatial variation of precipitation. This paper establishes a statistical downscaling model to downscale the monthly precipitation in the inland river basin of Northwest China with the Tarim River Basin (TRB) as a typical representation. This method combines polynomial regression and machine learning, and it uses the batch gradient descent (BGD) algorithm to train the regression model. We downscale the monthly precipitation and obtain a dataset from January 2001 to December 2017 with a spatial resolution of 1 km × 1 km. The results show that the downscaling model presents a good performance in precipitation simulation with a high resolution, and it is more effective than ordinary polynomial regression. We also investigate the temporal and spatial variations of precipitation in the TRB based on the downscaling dataset. Analyses illustrate that the annual precipitation in the southern foothills of the Tianshan Mountains and the North Kunlun Mountains showed a significant upward trend during the study periods, while the annual precipitation in the central plains presented a significant downward trend.

Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990-2030)

Land use/cover (LCLU) is considered as one of the most serious environmental challenges that threatens developed and less developed countries. LCLU changes' monitoring using the integration of remote sensing (RS) and geographical information systems (GIS) and their predicting using an artificial neural network (ANN) in the western part of the Tarim River Basin (Aksu), north-western Xinjiang-China, from 1990 to 2030 have been investigated first time through satellite imageries available. The imageries of 1990, 2000, 2005, 2010, and 2015 were downloaded from GLCF and USGS websites. After digital image processing, the object-oriented image classification approach was applied. The ANN method with MOLUSCE Plugin was used to simulate the LCLU changes in 2020, 2025, and 2030. GIS has also been used to calculate the distance from the road and water and etc. The simulation results of 2010 and 2015 were validated using classification data with Kappa coefficient. The results showed high accuracy of the classification and prediction as the validation of simulated 2010 and 2015 maps to the referenced maps have high accuracy of Kappa 84 and 88%, respectively. The results revealed that the land cover classes forest-, grass-, wet-, and barren land have been decreased from 50.01, 13.06, 8.24, and 1.06% in 1990 to 32.03, 3.06, 6.26, and 0.97% in 2015, respectively, while the land use classes, crop or farm land, and urban land have been increased almost double from 25.5 and 2.13% in 1990 to 53.71 and 3.86% from the total area in 2015, respectively. For the prediction, forest- and wetlands will loss more than half of their areas by 2030, the grass land will be cleared completely to be only 1.3% from the total study area, while the urban land will be increased to be 4.4% or the double of 1990. These results are attributed to population growth and expanding of agriculture land on the grass land, but the effect of climate was weak as the rainfall increased during the study period. Causes and effects of the LCLU changes were briefly discussed. The output of the study serves as useful tools for policy and decision makers combatting natural resources misused in arid lands.

Continuous Monitoring of the Spatio-Temporal Patterns of Surface Water in Response to Land Use and Land Cover Types in a Mediterranean Lagoon Complex

Mediterranean coastal lagoons and their peripheral areas often provide a collection of habitats for many species, and they often face significant threats from anthropogenic activities. Diverse human activities in such areas directly affect the spatio-temporal dynamic of surface water and its ecological characteristics. Monitoring the surface water dynamic, and understanding the impact of human activities are of great significance for coastal lagoon conservation. The Regional Natural Park of Narbonne includes a typical Mediterranean lagoon complex where surface water dynamic and its potential link with local diverse human activities has not yet been studied. In this context, based on all the available Landsat images covering the study area during 2002–2016, this study identified the water and non-water classes for each satellite observation by comparing three widely used spectral indices (i.e., NDVI, NDWI and MNDWI) and using the Otsu method. The yearly water frequency index was then computed to present the spatio-temporal dynamic of surface water for each year, and three water dynamic scenarios were also identified for each year: permanent water (PW), non-permanent water (NPW) and non-water (NW). The spatial and inter-annual variation in the patterns of the three water scenarios were characterized by computing the landscape metrics at scenario-level quantifying area/edge, shape, aggregation and fragmentation. Finally, the quantitative link between different land use and land cover (LULC) types derived from the LULC maps of 2003, 2012 and 2015 and the surface water dynamic scenarios was established in each of the 300 m × 300 m grid cells covering the study area to determine the potential impact of human activities on the surface water dynamic. In terms of the inter-annual variation during 2002–2016, PW presented an overall stability, and NPW occupied only a small part of the water surface in each year and presented an inter-annual fluctuation. NPW had a smaller patch size, with lower connectivity degree and higher fragmentation degree. In terms of spatial variation during 2002–2016, NPW often occurred around PW, and its configurational features varied from place to place. Moreover, PW mostly corresponded to the natural lagoon, and salt marsh (as a part of lagoons), and NPW had a strong link with arable land (agricultural irrigation) and salt marsh (salt production), sand beach/dune, coastal wetlands and lagoon for the LULC maps of 2003, 2012 and 2015. However, more in-depth analysis is required for understanding the impact of sand beach/dune, coastal wetlands and lagoon on surface water dynamics. This study covers the long-term variations of surface water patterns in a Mediterranean lagoon complex having intense and diverse human activities, and the potential link between LULC types and the water dynamic scenarios was investigated on different dates. The results of the study should be useful for environmental management and protection of coastal lagoons.

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350-2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745-910 nm and 1,911-2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSE t and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSE t was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems.

Selecting sites for converting farmlands to wetlands in the Sanjiang Plain, Northeast China, based on remote sensing and GIS

Wetlands in the Sanjiang Plain are rich in biodiversity and natural resources in the northeast of China. However, this wetland area has decreased in size and deteriorated in quality owing to expanded agricultural activities since the 1950s. Converting farmlands to wetlands is necessary to improve these conditions. Using Remote Sensing (RS) and Geographic Information Systems (GIS) technologies, we derived farmland productivity data and hydrology data for the Sanjiang Plain. The farmland productivity data were derived from land use and net primary productivity (NPP) data of the MODIS products. We obtained three productivity farmland classes (low, medium, and high) through the NPP anomaly percentage method. We were only concerned with the low-productivity farmland. Hydrology data were modeled with a wetness index, which was derived from Digital Elevation Model (DEM) data. Based on these two data layers, we identified and prioritized sites for the conversion of farmlands to wetlands. The areas with low farmland productivity and medium or high wetness values have potential to support the conversion of farmlands to wetlands. Potential sites were prioritized in terms of patch size and proximity to natural wetlands and water bodies. We obtained three priority classes, among which the high-priority class would be used as the areas for the recent conversion of farmlands to wetlands. The area of this class was 75,888 ha and accounted for 1.3% of the total farmland area.

Rehabilitating China's largest inland river

Wetlands are particularly important for conserving China's biodiversity but riparian wetlands in the Tarim River basin in western China have been reduced by 46% during the last 3 decades. The world's largest habitat for Populus euphratica, which is in the Tarim River basin, significantly shrank. To protect and restore the deteriorated ecosystems along the Tarim River and its associated wetlands, China's government initiated a multimillion dollar river restoration project to release water from upper dams to the dried-up lower reaches of the Tarim River starting in 2000. We monitored the responses of groundwater and vegetation to water recharge in the lower reaches of the river from 2000 to 2006 by establishing nine 1000-m-long transects perpendicular to the river at intervals of 20-45 km along the 320-km river course below the Daxihaizi Reservoir, the source of water conveyance, to Lake Taitema, the terminus of the Tarim River. Water recharges from the Daxihaizi Reservoir to the lower reaches of the Tarim River significantly increased groundwater levels and vegetation coverage at all monitoring sites along the river. The mean canopy size of the endangered plant species P. euphratica doubled after 6 years of water recharge. Some rare migrating birds returned to rest on the restored wetlands in summer along the lower reaches of the Tarim River. The biggest challenge facing decision makers, however, is to balance water allocation and water rights between agricultural and natural ecosystems in a sustainable way. A large number of inhabitants in the Tarim Basin depend on these limited water resources for a living. At the same time, the endangered ecosystems need to be protected. Given the ecological, socioeconomic, and sociopolitical realities in the Tarim Basin, adaptive water policies and strategies are needed for water allocation in these areas of limited water resources.