Based on the daily meteorological data from 72 weather stations from 1961-2003, a quantitative analysis was conducted on the spatiotemporal changes of the potential evapotranspiration in the Plain. The Penman-Monteith model was applied to calculate the potential evapotranspiration; the Mann-Kendall test, accumulative departure curve, and climatic change rate were adopted to analyze the change trend of the evapotranspiration; and the spatial analysis function of ArcGIS was used to detect the spatial distribution of the evapotranspiration. In 1961-2003, the mean annual potential evapotranspiration in the Plain was 330 - 860 mm, and presented an overall decreasing trend, with the high value appeared in southwest region, low value in surrounding areas of southwest region, and a ring-belt increasing southwestward. The climatic change rate of the annual potential evapotranspiration was -0.21 mm x a(-1). The annual potential evapotranspiration was the highest in 1982, the lowest in 1995, and increased thereafter. Seasonally, the climatic change rate of the potential evapotranspiration in spring, summer, autumn, and winter was -0.19, 0.01, -0.05, and 0.03 mm x a(-1), respectively, suggesting that the potential evapotranspiration had a weak increase in winter and summer and a slight decrease in spring and autumn.
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Sensors (Basel)
December 2024
North Willamette Research and Extension Center, Oregon State University, Aurora, OR 97002, USA.
Incorporating data-driven technologies into agriculture presents a promising approach to optimizing crop production, especially in regions dependent on irrigation, where escalating heat waves and droughts driven by climate change pose increasing challenges. Recent advancements in sensor technology have introduced diverse methods for assessing irrigation needs, including meteorological sensors for calculating reference evapotranspiration, belowground sensors for measuring plant available water, and plant sensors for direct water status measurements. Among these, infrared thermometry stands out as a non-destructive remote sensing method for monitoring transpiration, with significant potential for integration into drone- or satellite-based models.
View Article and Find Full Text PDFSci Total Environ
December 2024
College of Water Resources and Architecture Engineering, Northwest A&F University, Yangling 712100, Shanxi Province, China.
Terrestrial evapotranspiration (ET) is a key variable in the global water cycle, notably affected by climate change and vegetation greening. However, its intrinsic driving modes and the ways through which driving factors influence it remain largely unexplored. Here, we quantified the internal and external drivers behind the spatiotemporal variability of ET across global drylands at seasonal and annual temporal scales and component levels based on pixel-by-pixel partial correlation and ridge regression analyses.
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November 2024
Environmental Science and Engineering (EnSE) Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Introduction: It is desirable to rehabilitate desert ecosystems with a selection of native plant species that render ecosystem services and yield natural products for creating a high-value industry, e.g., pharmaceuticals or cosmetics.
View Article and Find Full Text PDFZootaxa
April 2024
Sección Entomología; Facultad de Ciencias; Universidad de la República; Iguá 4225; PC 11400; Montevideo; Uruguay.
Heliyon
October 2024
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China.
The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982-2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M - K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982-2022, with a growth rate of 0.
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