Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated the relationship between vegetation productivity and dry/wet conditions, the capability of different drought indices of assessing the influence of water deficit is not well understood. Moreover, few studies consider the effects of drought on vegetation with a focus on periods of drought. Here, we investigated the spatial-temporal patterns of GPP, the standardized precipitation evapotranspiration index (SPEI), and the vapor pressure deficit (VPD) in China from 2001 to 2020 and examined the relationship between GPP and water deficit/drought for different vegetation types. The results revealed that SPEI and GPP were positively correlated over approximately 70.7% of the total area, and VPD was negatively correlated with GPP over about 66.2% of the domain. Furthermore, vegetation productivity was more negatively affected by water deficit in summer and autumn. During periods of drought, the greatest negative impact was on deciduous forests and croplands, and woody savannas were the least impacted. This research provides a scientific reference for developing mitigation and adaptation measures to lessen the impact of drought disasters under a changing climate.

Droughts are serious natural disasters that adversely affect water resources, agriculture, the economy, and the environment. Reconstructing historical drought records is necessary to assess the impact of droughts and their evolution and has become a top priority to support and improve sustainable water management decisions. In this study, we used Shanxi Province as the research area, and meteorological data from the early years of Guangxu in the Qing Dynasty were reconstructed using historical rain and snow records. The Variable Infiltration Capacity (VIC) model is driven by the reconstruction of historical meteorological data. The study area’s monthly runoff and soil water sequence from 1875 to 1879 were simulated, and the hydrology and soil of the ancient historical period were reproduced in the absence of data. The results show the following: (1) The idea of reconstructing hydrological parameters using historical data is feasible and the VIC model can be used to study drought characteristics under specific scenarios. (2) The proportions of areas with runoff depths less than 10 mm throughout Shanxi from 1875 to 1879 were 55%, 48%, 58%, 19%, and 30%. The annual runoff depth in each region from 1875 to 1877 was less than 60 mm. The hydrological drought from 1875 to 1877 was very serious, and the area covered by the drought was relatively large. (3) The annual average soil water content of various regions was stable between 150 and 510 mm from 1875 to 1879. The soil water content had no apparent interannual variation. The area with soil water content less than 180 mm accounted for ratios as high as 31%. This research provides new ideas for ancient drought research and a scientific basis for regional drought prevention, mitigation, and water resources management, and ensures the orderly progress of agricultural production activities.

The coordinated relationship between ecology, economy and transportation is essential for regional sustainable development. Does the high-quality ecological environment mean the lagging development of economy and transportation, or does the rapid growth of the economy and transportation lead to the deterioration of the ecological environment? To shed new light on the complicated relationship between ecology, economy and transportation, our study aims to construct three comprehensive indicators, including an ecological index (EI), economic development level (EC) and transport superiority degree (TR), to reflect the systems mentioned above, and to measure the coordination of the three indicators’ development and evolution using a model of the coordination degree (CD). Specifically, and by applying methods for the indicators’ normalization, including superposition analysis and principal component analysis, the three indicators’ values are reasonably evaluated for measuring their coordination relationship. The above three indicators use data from 58 counties in Fujian province from 2000 to 2018 in our study. All three indicators show differences in the west and east of Fujian province; the EI is relatively low in the eastern coastal areas and relatively high in the western mountainous areas, the EC shows a relatively discrete and irregular distribution and the distribution pattern of the TR is almost the opposite of the EI. The CD shows a relationship among the three indicators, with the EI and EC coordinated in most counties and the EI and TR coordinated in most counties, while the highly coordinated counties are mainly distributed in the northwest and east coastal regions of Fujian province in 2000, and the northwest, south and northeast of Fujian province in 2018. More than 50% of the county EC and TR values are kept in a coordinated state, and are mainly distributed in the eastern coast and central part of Fujian province. Over 50% of counties’ CD between EI and EC, EI and TR and EC and TR are in a coordinated state. The CD of the EI and EC and TR, in most counties, are in a coordinated state, mainly distributed in the eastern coast and central areas of Fujian province. In other words, the findings show that the coordinated state of ecology, economy and transportation can be achieved at the county level of Fujian province. These conclusions have significant reference value for understanding regional sustainable development.

Water use efficiency (WUE) reflects the quantitative relationship between vegetation gross primary productivity (GPP) and surface evapotranspiration (ET), serving as a crucial indicator for assessing the coupling of carbon and water cycles in ecosystems. As a sensitive region to climate change, the Qinghai Tibetan Plateau’s WUE dynamics are of significant scientific interest for understanding carbon water interactions and forecasting future climate trends. However, due to the scarcity of observational data and the unique environmental conditions of the plateau, existing studies show substantial errors in GPP simulation accuracy and considerable discrepancies in ET outputs from different models, leading to uncertainties in current WUE estimates. This study addresses these gaps by first employing a machine learning approach (random forest) to integrate observed GPP flux data with multi-source environmental information, developing a predictive model capable of accurately simulating GPP in the Qinghai Tibetan Plateau (QTP). The accuracy of the random forest simulation results, RF_GPP (R² = 0.611, RMSE = 69.162 gC·m⁻²·month⁻¹), is higher than that of the multiple linear regression model, regGPP (R² = 0.429, RMSE = 86.578 gC·m⁻²·month⁻¹), and significantly better than the accuracy of the GLASS product, GLASS_GPP (R² = 0.360, RMSE = 91.764 gC·m⁻²·month⁻¹). Subsequently, based on observed ET flux data, we quantitatively evaluate ET products from various models and construct a multiple regression model that integrates these products. The accuracy of REG_ET, obtained by integrating five ET products using a multiple linear regression model (R² = 0.601, RMSE = 21.04 mm·month⁻¹), is higher than that of the product derived through mean processing, MEAN_ET (R² = 0.591, RMSE = 25.641 mm·month⁻¹). Finally, using the optimized GPP and ET data, we calculate the WUE during the growing season from 1982 to 2018 and analyze its spatiotemporal evolution. In this study, GPP and ET were optimized based on flux observation data, thereby enhancing the estimation accuracy of WUE. On this basis, the interannual variation of WUE was analyzed, providing a data foundation for studying carbon water coupling in QTP ecosystems and supporting the formulation of policies for ecological construction and water resource management in the future.