RESUMEN
Interception loss (IL) is an important process in the hydrological cycle within semi-arid forest ecosystems, directly affecting the amount of effective rainfall. However, the factors influencing IL during individual rainfall events remain to be quantified. This study collected rainfall, vegetation, and interception data during the 2022 and 2023 growing seasons in a typical black locust forest within the Zhifanggou watershed. It employed the Random Forest Regression (RFR) and back-propagation neural network (BPNN) methods to quantitatively evaluate the contribution rates of various factors to the IL and interception loss percentage (ILP). The IL among the 48 effective rainfall events was 172.05 mm, accounting for 19.54% of the rainfall amount. IL and ILP increased as the distance from the trunk decreased. During all rainfall events, both IL and ILP were significantly negatively correlated with the leaf area index (LAI) and canopy cover (CC); IL is significantly positively correlated with total rainfall (TR) and rainfall intensity (RI), while ILP is significantly negatively correlated with TR, RI, and rainfall duration (RD). The BPNN and RFR results indicated that rainfall, canopy, and tree characteristics contributed 43.06%, 44.79%, and 12.15% to IL, respectively, and 57.27%, 34.09%, and 8.63% to ILP, respectively. TR, CC, and LAI represented the primary influencing factors. Rainfall and canopy characteristics were the main factors affecting IL (ILP). As rainfall event magnitude increases, canopy contributions to IL and ILP decrease. In semi-arid areas, managing forest canopies to control IL helps address water imbalances in ecosystems.
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Highly efficient and practical carbon-chalcogen (S, Se) and amide bonds formation methodologies for the synthesis of thio- and seleno-acetamides were developed, via the base-promoted one-pot two-step reactions of 2-amino(benzo)thiazoles and aryl acetyl chlorides with dichalcogenides. This cross-coupling reaction afforded the goal products that had been chalcogenated regioselectively in moderate to good yields. Further transformations of the new synthesized compounds, DFT calculations and preliminary mechanism studies are discussed as well.
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Vegetation is a crucial component of terrestrial ecosystems, and its changes are driven mainly by a combination of climate change and human activities. This paper aims to reveal the relationship between vegetation and climate change by using the normalized difference vegetation index (NDVI) and standardized precipitation evapotranspiration index (SPEI), and to find the cause of vegetation change by performing residual analysis on the Loess Plateau during the period from 2000 to 2016. The results showed that the NDVI on the Loess Plateau exhibited an increase of 0.086 per decade, and an increasing trend was observed across 94.86% of the total area. The relationship between the NDVI and SPEI was mainly positive, and the correlation increased as the time scale of the SPEI lengthened, indicating that long-term water availability was the major climate factor affecting vegetation growth. Residual analysis indicated that climate change was responsible for 45.78% of NDVI variation, while human activities were responsible for 54.22%. In areas with degraded vegetation, the relative roles of climate change and human activities were 28.11% and 72.89%, respectively. In addition, the relative role of climate change increased with an increase in the time scales, implying that the long-term NDVI trend was more sensitive to climate change then the short-term trend. The results of this study are expected to enhance our understanding of vegetation changes under climate change and human activities and provide a scientific basis for future ecological restoration in arid regions.
Asunto(s)
Cambio Climático , Ecosistema , China , Actividades Humanas , HumanosRESUMEN
Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human-environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970-2050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16 W/m2 in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.