As a result of urbanization and climate change, urban areas are increasingly vulnerable to flooding, which can have devastating effects on the loss of life and property. Remote sensing technology can provide practical help for urban flood disaster management. This research presents a review of urban flood-related remote sensing to identify research trends and gaps, and reveal new research opportunities. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the systematic literature search resulted in 347 documents classified as geography, disaster management application, and remote sensing data utilization. The main results include 1. most of the studies are located in high-income countries and territories and inland areas; 2. remote sensing for observing the environment was more popular than observing the building; 3. the most often applied disaster management activities were vulnerability assessment and risk modeling (mitigation) and rapid damage assessment (response); 4. DEM is often applied to simulate urban floods as software inputs. We suggest that future research directions include 1. coastal urban study areas in non-high-income countries/territories to help vulnerable populations; 2. understudied disaster management activities, which often need to observe the buildings in more urban areas; 3. data standardization will facilitate integration with international standard methods for assessing urban floods.

Droughts have emerged as a global problem in contemporary societies. China suffers from different degrees of drought almost every year, with increasing drought severity each year. Droughts in China are seasonal and can severely impact crops. This study used spatiotemporal trend and characteristics analysis of drought disaster data from 1991 to 2018 in Chinese provinces, in addition to the Mann–Kendall test and wavelet analysis. The drought disaster data included the crop damage area, drought-affected area of the crops, and crop failure area. The outputs of the crops decreased by 10%, 30%, and 80%, respectively. The population with reduced drinking water caused by drought, and the domestic animals with reduced drinking water caused by drought, were numbered in the tens of thousands. The results of the study show that the crop damage areas owing to drought disasters, drought-affected areas of crops, and crop failure areas in China were mainly distributed in the northern, eastern, northeaster, and southwestern regions. The number of people and domestic animals with reduced drinking water owing to drought in China were mainly concentrated in the northern and southwestern regions. These indicators showed a general increasing trend. Tibet, Fujian, Shandong, Jiangsu, Anhui, and Henan provinces and autonomous regions also showed a slightly increasing trend. In particular, the number of domestic animals with reduced drinking water caused by drought in the Inner Mongolia Autonomous Region showed a clear increasing trend with a significant Z-value of 2.2629. The results of this research can be used to provide scientific evidence for predicting future trends in drought and for practising the best management of drought prevention and resistance.

Identifying the spatial and temporal heterogeneity of water-related ecosystem services and the mechanisms influencing them is essential for optimizing ecosystem governance and maintaining watershed sustainable development. However, the complex and undiscovered interplay between human activities and natural factors underpins the solutions to the water scarcity and flooding challenges faced by climate transition zone basins. This study used a multiple spatial-scale analysis to: (i) quantify the spatial and temporal variations of the water yield ecosystem service (WYs) of the Wei River Basin (WRB) from 2000 to 2020 using the InVEST model and remote sensing data; and (ii) look at how human activities, climate, topography, and vegetation affect the WYs at the climate transition zone sub-catchment scale using the geographical detector model and multi-scale geographically weighted regression (MGWR). The conclusive research reveals that there would be a gradual increase in WYs between the years 2000 and 2020, as well as a distinct and very different spatial aggregation along the climatic divide. The average yearly precipitation was shown to be particularly linked to the water yield of the WRB. The interplay of human, climatic, plant, and terrain variables has a substantially higher influence than most single factors on the geographical differentiation of WYs. Bivariate enhancement and non-linear enhancement are the most common types of factor interactions. This shows that there are significant interactions between natural and human variables. Our study shows that precipitation and temperature are the main factors that cause WYs in the semi-arid zone. In the semi-humid zone, precipitation and vegetation are the key controlling factors that cause WYs. We provide new perspectives for understanding and optimizing ecosystem management by comparing the drivers of WYS in sub-basins with different climatic conditions. Based on the findings, we recommend that particular attention should be paid to ecosystem restoration practices in watersheds in climatic transition zones.

Wu et al . (Reports, 5 August 2016, p. 579) reported an enormous flood in the upper Yellow River that destroyed the Lajia Ruins. However, published research shows that the Ruins were destroyed at 3950 years before the present (B.P.) by earthquakes accompanied with mudflows, whereas the landslide-dammed lake terminated about 5650 years B.P. Various kinds of sediments with different ages were taken as evidence to verify an outburst flood.

Continuously global warming and landscape change have aggravated the damage of flood disasters to ecological safety and sustainable development. If the risk of flood disasters under climate and land-use changes can be predicted and evaluated, it will be conducive to flood control, disaster reduction, and global sustainable development. This study uses bias correction and spatial downscaling (BCSD), patch-generating land-use simulation (PLUS) coupled with multi-objective optimization (MOP), and entropy weighting to construct a 1 km resolution flood risk assessment framework for the Guanzhong Plain under multiple future scenarios. The results of this study show that BCSD can process the 6th Climate Model Intercomparison Project (CMIP6) data well, with a correlation coefficient of up to 0.98, and that the Kappa coefficient is 0.85. Under the SSP126 scenario, the change in land use from cultivated land to forest land, urban land, and water bodies remained unchanged. In 2030, the proportion of high-risk and medium-risk flood disasters in Guanzhong Plain will be 41.5% and 43.5% respectively. From 2030 to 2040, the largest changes in risk areas were in medium- and high-risk areas. The medium-risk area decreased by 1256.448 km² (6.4%), and the high-risk area increased by 1197.552 km² (6.1%). The increase mainly came from the transition from the medium-risk area to the high-risk area. The most significant change in the risk area from 2040 to 2050 is the higher-risk area, which increased by 337 km² (5.7%), while the medium- and high-risk areas decreased by 726.384 km² (3.7%) and 667.488 km² (3.4%), respectively. Under the SSP245 scenario, land use changes from other land use to urban land use; the spatial distribution of the overall flood risk and the overall flood risk of the SSP126 and SSP245 scenarios are similar. The central and western regions of the Guanzhong Plain are prone to future floods, and the high-wind areas are mainly distributed along the Weihe River. In general, the flood risk in the Guanzhong Plain increases, and the research results have guiding significance for flood control in Guanzhong and global plain areas.