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Hydrological connectivity is crucial for understanding water-ecosystem dynamics, as it serves as a key link between different landscape units. However, the variability of hydrological connectivity in Central Asia remains unexplored, which poses challenges to a comprehensive understanding of ecohydrological processes. This study investigates the spatiotemporal patterns and driving mechanisms of hydrological connectivity in the Tarim River Basin (TRB), Central Asia, from 1990 to 2020, employing a novel approach that integrates remote sensing and reanalysis data. The results indicate an increasing trend in the hydrological connectivity index (HCI), with approximately 60% of the TRB exhibiting significant increases. Climate change exerts the greatest direct (0.59) and total (0.64) effects on HCI, with potential evapotranspiration (19.2%) and temperature (12.6%) being the dominant factors. In mountainous regions, climate change (0.65) is the primary driver, while human activities have a greater impact in the plains (−0.27). These findings offer a new framework for studying ecohydrological processes in arid regions.Hydrological connectivity plays a distinctive and crucial role in biodiversity conservation, geochemical cycling, and human social development 1 . It facilitates the movement and exchange of water, as well as water-mediated organisms, sediments, organic matter, nutrients, and energy in water patches 2 , thereby regulating the processes from water source to sink. This regulatory effect is particularly important in arid regions, where hydrological connectivity establishes and sustains dryland ecosystems by controlling the distribution of water resources between bare land and vegetation patches 3 . Consequently, hydrological connectivity is widely recognized as a key driver of both structural and functional variations in freshwater ecosystems 4 .The study of hydrological connectivity has emerged as a prominent research focus in hydrology, geomorphology, and geography 2,5,6 . Much of the research has concentrated on how to conceptually and empirically utilize hydrological connectivity to understand complex environmental systems 5,7,8 , significantly advancing the hydrological connectivity-related knowledge base. Studies have demonstrated that hydrological connectivity is an effective framework for interpreting water redistribution processes, which dominate the exchange of material energy in the water medium, which controls hydrological processes between sparsely vegetated landscapes and dryland 9 , and which affects the growth of vegetation 10,11 . Thus, hydrological connectivity can serve as an important indicator for detecting transitions and degradation thresholds in dryland systems 12 . In addition, the effects of hydrologic connectivity on river water quality 4,13 , runoff 7,8 , and biodiversity 14,15 have been widely scrutinized by researchers. It has been found that hydrological connectivity can drive extremes and high variability in vegetation productivity and water quality in arid and semi-arid ecosyste...
Hydrological connectivity is crucial for understanding water-ecosystem dynamics, as it serves as a key link between different landscape units. However, the variability of hydrological connectivity in Central Asia remains unexplored, which poses challenges to a comprehensive understanding of ecohydrological processes. This study investigates the spatiotemporal patterns and driving mechanisms of hydrological connectivity in the Tarim River Basin (TRB), Central Asia, from 1990 to 2020, employing a novel approach that integrates remote sensing and reanalysis data. The results indicate an increasing trend in the hydrological connectivity index (HCI), with approximately 60% of the TRB exhibiting significant increases. Climate change exerts the greatest direct (0.59) and total (0.64) effects on HCI, with potential evapotranspiration (19.2%) and temperature (12.6%) being the dominant factors. In mountainous regions, climate change (0.65) is the primary driver, while human activities have a greater impact in the plains (−0.27). These findings offer a new framework for studying ecohydrological processes in arid regions.Hydrological connectivity plays a distinctive and crucial role in biodiversity conservation, geochemical cycling, and human social development 1 . It facilitates the movement and exchange of water, as well as water-mediated organisms, sediments, organic matter, nutrients, and energy in water patches 2 , thereby regulating the processes from water source to sink. This regulatory effect is particularly important in arid regions, where hydrological connectivity establishes and sustains dryland ecosystems by controlling the distribution of water resources between bare land and vegetation patches 3 . Consequently, hydrological connectivity is widely recognized as a key driver of both structural and functional variations in freshwater ecosystems 4 .The study of hydrological connectivity has emerged as a prominent research focus in hydrology, geomorphology, and geography 2,5,6 . Much of the research has concentrated on how to conceptually and empirically utilize hydrological connectivity to understand complex environmental systems 5,7,8 , significantly advancing the hydrological connectivity-related knowledge base. Studies have demonstrated that hydrological connectivity is an effective framework for interpreting water redistribution processes, which dominate the exchange of material energy in the water medium, which controls hydrological processes between sparsely vegetated landscapes and dryland 9 , and which affects the growth of vegetation 10,11 . Thus, hydrological connectivity can serve as an important indicator for detecting transitions and degradation thresholds in dryland systems 12 . In addition, the effects of hydrologic connectivity on river water quality 4,13 , runoff 7,8 , and biodiversity 14,15 have been widely scrutinized by researchers. It has been found that hydrological connectivity can drive extremes and high variability in vegetation productivity and water quality in arid and semi-arid ecosyste...
Surface water fraction mapping is an essential preprocessing step for the subpixel mapping (SPM) of surface water, providing valuable prior knowledge about surface water distribution at the subpixel level. In recent years, spectral mixture analysis (SMA) has been extensively applied to estimate surface water fractions in multispectral images by decomposing each mixed pixel into endmembers and their corresponding fractions using linear or nonlinear spectral mixture models. However, challenges emerge when introducing existing surface water fraction mapping methods to hyperspectral images (HSIs) due to insufficient exploration of spectral information. Additionally, inaccurate extraction of endmembers can result in unsatisfactory water fraction estimations. To address these issues, this paper proposes an adaptive unmixing method based on iterative multi-objective optimization for surface water fraction mapping (IMOSWFM) using Zhuhai-1 HSIs. In IMOSWFM, a modified normalized difference water fraction index (MNDWFI) was developed to fully exploit the spectral information. Furthermore, an iterative unmixing framework was adopted to dynamically extract high-quality endmembers and estimate their corresponding water fractions. Experimental results on the Zhuhai-1 HSIs from three test sites around Nanyi Lake indicate that water fraction maps obtained by IMOSWFM are closest to the reference maps compared with the other three SMA-based surface water fraction estimation methods, with the highest overall accuracy (OA) of 91.74%, 93.12%, and 89.73% in terms of pure water extraction and the lowest root-mean-square errors (RMSE) of 0.2506, 0.2403, and 0.2265 in terms of water fraction estimation. This research provides a reference for adapting existing surface water fraction mapping methods to HSIs.
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