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Against the background of global warming, the losses caused by hydrological extreme events are becoming more serious. Understanding how to quantitatively attribute the trends of river discharge and extreme events will play an important role in climate change adaptation. The main objective of this study is to analyze recent trends in river discharge and annual maximum daily runoff in Chinese catchments and attribute them to the ongoing climate change, where possible. For that, 20 catchments in China, which are not influenced or influenced only slightly by water management, are chosen as study areas. Then, we use the long-term observational climate dataset GSWP3-W5E5 from ISIMIP3a as factual climate and a detrended climate dataset based on it as counterfactual climate to drive the hydrological model for quantification of climate change contribution to trends in mean and extreme runoff. Our analysis shows that the trends in annual discharge over the period 1961–2019 in eight catchments (all located in western China) represented by five stations in the Upper Yellow and Upper Yangtze, Kaqun station in the Tarim-Yeerqiang, Changdu station in the Lancangjiang and Xindi station in the Heihe can be attributed to climate change. As well, it is shown that climate change enhanced annual maximum daily runoff in the Upper Yellow and Upper Yangtze River basins. The results provide a new understanding of the degree to which observed changes in mean and extreme runoff were induced by the observed changes in climate, which may improve adaptation to climate change in China.

Based on hourly rainfall observational data from 442 stations during 1960–2014, a regional frequency analysis of the annual maxima (AM) sub-daily rainfall series (1-, 2-, 3-, 6-, 12-, and 24-h rainfall, using a moving window approach) for eastern China was conducted. Eastern China was divided into 13 homogeneous regions: Northeast (NE1, NE2), Central (C), Central North (CN1, CN2), Central East (CE1, CE2, CE3), Southeast (SE1, SE2, SE3, SE4), and Southwest (SW). The generalized extreme value performed best for the AM series in regions NE, C, CN2, CE1, CE2, SE2, and SW, and the generalized logistic distribution was appropriate in the other regions. Maximum return levels were in the SE4 region, with value ranges of 80–270 mm (1-h to 24-h rainfall) and 108–390 mm (1-h to 24-h rainfall) for 20- and 100 yr, respectively. Minimum return levels were in the CN1 and NE1 regions, with values of 37–104 mm and 53–140 mm for 20 and 100 yr, respectively. Comparing return levels using the optimal and commonly used Pearson-III distribution, the mean return-level differences in eastern China for 1–24-h rainfall varied from −3–4 mm to −23–11 mm (−10%–10%) for 20-yr events, reaching −6–26 mm (−10%–30%) and −10–133 mm (−10%–90%) for 100-yr events. In view of the large differences in estimated return levels, more attention should be given to frequency analysis of sub-daily rainfall over China, for improved water management and disaster reduction.

The initial Sequential Monte Carlo cardinality balanced multi-target multi-Bernoulli (SMC-CBMeMBer) filter assumes that the target birth intensity is known as a priori, but in fact the new target may appear anywhere in the detected area. And SMC-CBMeMBer does not extract track label information and cannot distinguish the target. Therefore, this paper proposes a labeled SMC-CBMeMBer filter with adaptive track initiation (SMC-CBMeMBer-D-L-ATI). Firstly, the filter introduces the track label information in the prediction process, and selects the measurement that may correspond to the newborn target, obtaining the initial state of the newborn target according to the converted measurement. Then, the label information in the prediction process is inherited during the update process, and the target states are updated sequentially through the converted position measurement and Doppler measurement. The simulation proves that the proposed filter can effectively perform adaptive track initiation, with good tracking performance, and can provide the target track information.

In the background of detecting Foreign Object Debris (FOD) on the airport runway with conventional radar systems or optical systems, to solve the problems that the current detection technology of conventional radar is high frequency and high cost, and the optical system is vulnerable to environmental factors, this paper uses linear frequency modulated continuous wave (LFM-CW) signal in Ka band to detect FOD based on the Synthetic Aperture Radar (SAR) airport runway FOD detection system. Firstly, it is proved that the operating frequency of 36GHz is more suitable. Then demonstration that the use of LFM-CW can effectively reduce the system power. Finally, through the demonstration of the main technical indicators of the system, it is proved that the airport runway FOD detection system based on vehicle SAR can effectively detect objects with a height of 3cm and a cross-sectional area of 0.00051m2 in a distance of 210 meters when the transmitting power is 25dBm and the running speed is 61.2m/s. At this time, the detection probability is 0.9999 and the false alarm rate is 10[?]12.