Search

Evaluation of changes in dry-wet climate is crucial in the context of global climate change to ensure regional water resources, ecosystem stability, and socio-economic development. Long-term daily meteorological data, including temperature, precipitation, relative humidity, wind speed, sunshine duration, and air pressure data from 1680 stations across mainland China from 1971 to 2019, were collected to investigate the temporal and spatial variations in aridity index (AI), precipitation (P), reference evapotranspiration (ET0), and the underlying driving climatic factors. Results indicated that the Northwest, Northeast, and Huang-Huai regions were undergoing significant wetting processes, while the Southwest and Southeast China were undergoing significant drying processes. The changing AI was mainly decided by the changing trends of ET0. For most regions, ET0 has undergone significant increases. The average increasing rate over mainland China was 3.76 mm/10a. Stations with decreasing trends were mainly located in the Tibet Plateau, Huang-Huai, and northern Northeast China. Trends in ET0 were negatively affected by the increasing changes in relative humidity and positively affected by the decreasing changes in wind speed and sunshine duration and the increasing changes in air temperature. Wind speed and relative humidity were found to be the main dominant factors driving the changes in ET0, and their contribution varied with regions. Huang-Huai and northern Northeast China showed a significant downward trend in ET0, mainly driven by the decrease in wind speed, while the increase in relative humidity was the primary contributor to the significant upward trends in ET0 across all other regions in China. [ABSTRACT FROM AUTHOR]

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change's negative effects and maintain water resources' sustainability in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Aboveground net primary productivity (ANPP) and rain-use efficiency (RUE) are important indicators in assessing the response of ecosystems to climate change. In this paper, the Changtang Plateau in the Tibetan Autonomous Region was selected as the study area to analyze the spatial and temporal changes of ANPP and RUE in grassland communities and their response to climate change. The results showed the following:(1) The spatial pattern of ANPP was closely related to rainfall on the Changtang Plateau. The average ANPP over the past 15 years increased gradually from the arid west to the humid east. A consistent pattern was exhibited in different grassland types and climate zones. (2) The RUE was higher at the east and west edges of the Changtang Plateau, especially in the arid west, but was lower in the center. From the perspective of different climatic zones, the average RUE in the southern Tibetan semiarid climate zone and the Ngari arid climate zone was significantly higher than that in other climate zones. However, the average RUE in different grassland types only varied from 0.07 to 0.09 g·m−2·mm−1. The spatial variation in RUE was more distinct in different climatic zones than in different grassland types. (3) Climate change influenced the interannual variation of ANPP and RUE, but the response of ANPP to rainfall showed a significant lag. The interannual change in RUE was negatively correlated with changes in precipitation. (4) In general, a greater area showed a significant increase rather than a decrease in ANPP on the Changtang Plateau, which meant that the grassland condition is improving. The temporal variation patterns of ANPP and RUE in different climate zones were consistent with the overall patterns on the Changtang Plateau, while the variation was not significant in different grassland types. [ABSTRACT FROM AUTHOR]

Land evaporation (LET) is an important variable in climate change, water cycle and water resources management. Mastering the projected changes in LET is significant for crop water requirements and the energy cycle. The global climate model (GCM) is a vital tool for future climate change research. However, the GCMs have low spatial resolution and relatively high errors. We use a deep learning (DL)-based model to deal with this problem. The DL approach can downscale the model data and merge simultaneously. We applied the DL approach to a suit of models from the Coupled Model Intercomparison Project 6th edition (CMIP6) LET data. From the result of all the evaluation metrics, the DL merged data greatly improved in both spatial and time dimensions. The mean RMSE is 5.85 mm and the correlation is 0.95 between the DL merged data and reference data (historical reliable evaporation data). The future LET evidently increases in four scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), and the upward intensity rises from the low to high emission scenarios. The highest increasing regions are in the Tibet Plateau and the south of China and the trend is larger than 10 mm/decade in the high scenarios. From the seasonal point of view, the increasing trend in spring and summer is far larger than for autumn and winter. The Tibet Plateau and the northeast of China have the largest upward trend in the spring of SSP5–8.5, higher than 1.6 mm/decade. [ABSTRACT FROM AUTHOR]