AI Article Synopsis
- - This study developed a new method called spatiotemporal geographic autocorrelation weighted regression analysis (SGAWRA) to examine how vegetation changes affect runoff in the Weihe River Basin, accounting for various factors like climate change and human activities.
- - The findings revealed that over 50% of the areas in the basin show an increasing trend in vegetation, indicated by the Normalized Difference Vegetation Index (NDVI), which correlates positively with factors like precipitation and temperature, affecting runoff patterns.
- - The study concluded that changes in vegetation significantly contribute to runoff variability, and the results provide valuable insights for improving water management strategies in the Weihe River Basin.
Article Abstract
In order to investigate the effects of vegetation changes on runoff and to obtain recommendations for improving runoff in the Weihe River Basin (. In this study, a spatiotemporal geographic autocorrelation weighted regression analysis (SGAWRA) approach was newly developed based on previous studies. This approach investigates spatial non-stationarity of the dynamic response from vegetation variations to climatic change and human activity. Implications of spatial non-stationarity related to runoff variability were also discussed, which in turn yield the effect that vegetation changes have on runoff. The method systematically analysed the spatial non-stationarity of vegetation variations and its associated effects on runoff. Therefore, more closely related results with less error were produced at each step, and results with more accuracy were obtained. These results indicated that the average trend rates of NDVI in the annual average, each season, and the growing season (Growing season refers to April to September) exceeded 0. Areas where NDVI show a growing trend cover more than 50%, which is greater than the area with a decreasing trend. The GWR regression parameters of precipitation, average temperature, and NDVI are all greater than 0. The GWR regression parameters of human activities and NDVI also have more than 50% of the area greater than 0. Based on the visual analysis of the calculation results, it can be seen that there are obvious spatial trends in the data, and the spatial data are significantly different between different regions. Therefore, WRB can be regarded as spatio-temporally non-stationary. In the WRB, the underlying surface change with vegetation change as the prominent feature is the leading cause (about 60%) of the runoff attenuation. The results showed that WRB has spatial and temporal non-stationarity. The spatial non-stationarity of vegetation has a greater effect on runoff changes. The results of this study support recommendations for improving runoff in the WRB.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jenvman.2024.121908 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A practical approach to the spatial-domain calculation of nonprewhitening model observers in computed tomography.
Med Phys
January 2025
Department of Nuclear Medicine and Medical Physics, Karolinska University Hospital, Stockholm, Sweden.
Background: Modern reconstruction algorithms for computed tomography (CT) can exhibit nonlinear properties, including non-stationarity of noise and contrast dependence of both noise and spatial resolution. Model observers have been recommended as a tool for the task-based assessment of image quality (Samei E et al., Med Phys.
View Article and Find Full Text PDFSpatial distribution and driving factors of soil selenium on the Leizhou Peninsula, southern China.
Environ Geochem Health
January 2025
Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, No. 84, Jinguang Road, Langfang, 065000, China.
Selenium (Se) is an essential element for humans, playing a critical role in the functioning of the immune system. The global prevalence of dietary Se deficiency is a significant public health concern, largely attributed to the low levels of Se present in crops. The sufficient Se in plants and humans is determined by the presence of stable Se sources in the soil.
View Article and Find Full Text PDFSpatial analysis of socioeconomic data and its relationship with illicit crops in Nariño-Colombia.
PLoS One
January 2025
Faculty of Economic and Administrative Sciences, University of Medellin, Medellin, Colombia.
The Sustainable Development Goals (SDGs) aim to eradicate poverty and promote sustainable development; however, socioeconomic disparities persist globally, particularly in Colombia. With a Gini index of 0.556 in 2022, Colombia ranks among the most unequal countries in Latin America, with its southwest region of Nariño facing severe socioeconomic challenges.
View Article and Find Full Text PDFMultiple Long-Term, Landscape-Scale Data Sets Reveal Intraspecific Spatial Variation in Temporal Trends for Bird Species.
Ecol Lett
December 2024
Fenner School of Environment and Society, The Australian National University, Canberra, ACT, Australia.
Quantifying temporal changes in species occurrence has been a key part of ecology since its inception. We quantified multidecadal site occupancy trajectories for 18 bird species in four independent long-term, large-scale studies (571 sites, ~1000 km latitude) in Australia. We found evidence of a year × long-term study interaction in the best-fitting models for 14 of the 18 species analysed, with differences in the temporal trajectories of the same species in multiple studies consistent with non-stationarity.
View Article and Find Full Text PDFHand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture.
Sci Rep
December 2024
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!