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Flash drought (FD) has attracted much attention due to its severe stress on vegetation ecosystems. Yet to date, the impacts of FD on vegetation ecosystems in different regions have not been fully evaluated and explored, especially for ecologically fragile areas. In this study, we identified the FD events in the Loess Plateau from 2000 to 2023 based on the attenuation rate in soil moisture percentile over time. The evolution process of FD, the driving roles of meteorological conditions and the responses of different vegetation types to FD were explored by vegetation indicators such as solar-induced chlorophyll fluorescence (SIF), SIFyield, SIF-RCI, etc. The results showed that FD events were predominantly concentrated in wetter areas with dense vegetation, with the highest frequency being 29. Meteorological factors contributed differently to the occurrence and development of FD. The responses of vegetation to FD were not only related to vegetation types (cropland was more sensitive to FD than forest and grassland) but were also significantly influenced by background climate. The SIFyield anomaly of vegetation was more sensitive than SIF anomaly and SIF-RCI. The results advance our understanding of the formation mechanisms of FD and facilitate the exploration of vegetative photosynthetic responses to FD.

Periodic inundation of floodplains and wetlands is critical for the well being of ecosystems. This study proposes a simple but efficient model that integrates time series daily flow data and the Landsat-derived Water Observation from Space (WOfS) product to model the spatio-temporal flood inundation dynamics of the Murray-Darling Basin. A zone-gauge framework is adopted in order to reduce the hydrologic complexity of the large river basin. Under this framework, flood frequency analysis was conducted at each gauge station to identify historical peak flows and their annual exceedance probabilities. The results were then linked with the WOfS dataset through date to model the inundation probability in each zone. Inundation frequency was derived by simply overlaying the yearly inundation extent from 1988 to 2015 and counting the inundation times. Both the resultant inundation frequency map and inundation probability map are of ecological significance for the survival and prosperity of riparian ecosystems. The assumptions of the model were validated carefully to enhance its theoretical basis. The WOfS dataset was also compared with another independent water observation dataset to cross-validate its reliability. It is hoped that with the development of more and more global high-resolution surface water datasets, this study could inspire more studies that integrate surface water datasets with hydrological observations for flood inundation modeling.

Sunflower broomrape is a noxious parasitic weed which has caused severe damage to crop ecosystems. Trap crops can release a mixture of allelochemicals to induce the germination of sunflower broomrape. We studied the allelopathic effects of soybean on sunflower broomrape. Fourteen common soybean cultivars were grown in pots. Samples were collected from soybean plants and rhizosphere soil at five growth stages (V1, V3, V5, R2, and R4). The allelopathic effects of soybean reached highest at the V3 stage. Methanolic extracts of soybean roots induced higher broomrape germination than methanolic extracts of stems or leaves. The germination rates induced by root extracts (10-fold dilution) were positively correlated with germination rates induced by stem (10-fold dilution) and leaf extracts (10-fold dilution). The broomrape germination rates induced by root extracts were also positively correlated with soybean nodule diameter and dry weight. The results indicated that soybeans could induce sunflower broomrape germination. We conclude that soybean has the potential to be used as a trap crop for sunflower broomrape.