Understanding the effects of flash drought on vegetation photosynthesis and potential drivers over China.

Flash droughts characterized by rapid onset and intensification are expected to be a new normal under climate change and potentially affect vegetation photosynthesis and terrestrial carbon sink. However, the effects of flash drought on vegetation photosynthesis and their potential dominant driving factors remain uncertain. Here, we quantify the susceptibility and response magnitude of vegetation photosynthesis to flash drought across different ecosystems (i.e., forest, shrubland, grassland, and cropland) in China based on reanalysis and satellite observations. By employing the extreme gradient boosting model, we also identify the dominant factors that influence these flash drought-photosynthesis relationships. We show that over 51.46 % of ecosystems across China are susceptible to flash drought, and grasslands are substantially suppressed, as reflected in both sensitivity and response magnitude (with median gross primary productivity anomalies of -0.13). We further demonstrate that background climate differences (e.g., mean annual temperature and aridity) predominantly regulate the response variation in forest and shrubland, with hotter/colder or drier ecosystems being more severely suppressed by flash drought. However, in grasslands and croplands, the differential vegetation responses are attributed to the intensity of abnormal hydro-meteorological conditions during flash drought (e.g., vapor pressure deficit (VPD) and temperature anomalies). The effects of flash droughts intensify with increasing VPD and nonmonotonically relate to temperature, with colder or hotter temperatures leading to more severe vegetation loss. Our results identify the vulnerable ecological regions under flash drought and enable a better understanding of vegetation photosynthesis response to climate extremes, which may be useful for developing effective management strategies.

Identification and evaluation of soil moisture flash drought by a nonstationary framework considering climate and land cover changes.

Soil moisture flash drought can cause extensive damage to agriculture, ecosystems, and economies due to its sudden onset. Previous research identified soil moisture flash drought using stationary methods, in which, stationary probability distributions were employed to derive cumulative percentages (CPs) of given soil moisture values, and then based on the CPs sequence, the run theory was used to identify soil moisture flash drought events. However, because changes in climate or land cover can induce significant variations in the underlying probability distributions of soil moisture, the method's usual assumption of stationarity should be questioned. In this study, a nonstationary framework based on the nonstationary frequency analysis method and the run theory was developed for identifying soil moisture flash drought events. This framework was applied to the study of the pentad average root-zone soil moisture (PRZSM) of the Pearl River basin (PRB) in South China based on the ERA5-Land and the GLEAM datasets from 1981 to 2020. The results of the ERA5-Land were general consistent with those of the GLEAM, and the major findings include: (1) without considering the nonstationarity of soil moisture, the onset rate of flash droughts may be underestimated. The average onset rate of flash droughts in nonstationary conditions is slightly greater by 1 %/pentad -2 %/pentad than that in stationary conditions; (2) without considering the nonstationarity of soil moisture, the severity of flash droughts may be overestimated. The average severity of flash droughts in nonstationary conditions is smaller by 10% ⋅ pentads-20% ⋅ pentads than that in stationary conditions; and (3) the trends of the characteristics of soil moisture flash drought are consistent between the stationary and the nonstationary conditions. In conclusion, the above findings contribute to a better understanding of the implications of soil moisture nonstationarity on flash drought identification.