Spatially explicit and multiscale ecosystem shift probabilities and risk severity assessments in the greater Mekong subregion over three decades

Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018

Drought, a complex phenomenon exacerbated by climate change, is influenced by various climate factors. The escalating global temperatures associated with climate change, impact precipitation patterns and water cycle processes, consequently intensifying the occurrence and severity of droughts. To effectively address and adapt to these challenges, it is crucial to identify the dominant climate factors driving drought events. In this study, we utilized the 1979-2018 Chinese meteorological forcing dataset to calculate the daily Standardized Precipitation Evapotranspiration Index (SPEI). The Theil-Sen and Mann-Kendall (M-K) tests were employed to analyze the spatial and temporal trends of drought severity and duration. Additionally, partial correlation analysis was conducted to examine the relationship between climate factors (precipitation and potential evapotranspiration (PET)) and drought characteristic (drought severity and duration). Through this comprehensive analysis, we aimed to identify the primary factors influencing drought severity and duration. The findings revealed the following key results: (1) Over the 40-year period from 1979 to 2018, drought trends in China and its seven climate divisions exhibited an increasing pattern. (2) During drought periods, most regions exhibited a positive correlation between PET and drought severity and duration, while precipitation demonstrated a negative correlation. However, certain areas experiencing severe drought displayed a negative correlation between PET and drought severity and duration, precipitation demonstrated a positive correlation with drought severity and duration. (3) PET emerged as the dominant climatic factor for meteorological drought in the majority of China. These findings contribute valuable insights for policymakers in the development of climate change adaptation and mitigation strategies. By understanding the dominant climate factors driving drought events, policymakers can implement effective measures to mitigate the adverse socioeconomic and environmental impacts associated with climate change.

Spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity in the Mekong subregion in Cambodia

Water resources in the Mekong subregion in Cambodia (MSC) have experienced dramatic changes in past decades, threatening regional ecosystem quality and sustainable development. Thus, it is important to explore the spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity. This study proposed an effective framework including spatiotemporal analysis of land use/cover change (LUCC) and ecological sensitivity assessment by combining remote sensing (RS) and geographic information system/science (GIS). An optimized feature space and a machine learning classification algorithm were constructed to extract four typical land cover types in the MSC from 1990 to 2020. An ecological sensitivity evaluation system, including four sub-sensitivities calculated by twelve indicators, was then constructed. The results suggest that severe shrinkage of water resources occurred before 2006, decreasing by 21.68%. The correlation between water resources and climate conditions displays a high to low level as human activity becomes involved. A significant spatiotemporal evolutionary pattern of ecological sensitivity was observed under the impact of external interference. Generally, the largest proportion of MSC belongs to the lightly sensitive level, which is mainly concentrated in the lower reaches, with an average of 33.93%. The highly sensitive area with a significant value in ecological protection has a slightly downward trend from 23.72 in 1990 to 22.55% in 2020.

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.