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In the field of water depth inversion using imagery, the commonly used methods are based on water reflectance and wave extraction. Among these methods, the Optical Bathymetry Method (OBM) is significantly influenced by bottom sediment and climate, while the wave method requires a specific study area. This study introduces a method combining the FFT and spatial profile measurement to invert the wavelength of the wave bathymetry method (WBM), which enhances accuracy and reduces workload. The method was applied to remote sensing images of Sanya Bay in China, obtained from the Worldview satellite. The average error of the inverted depth results after applying the wavelength inversion technique was 15.9%, demonstrating consistency with the depth measurements obtained through the OBM in clear water of the bay. The WBM has notable advantages over the OBM, as it is unaffected by water quality. In addition, the influence of wave period on the accuracy of water depth retrieval was theoretically evaluated, revealing that a larger wave period leads to a better depth measurement. The depth measurement from two images with different wave periods aligned with the theoretical analysis. These results showcase the applicability and potential of the WBM for accurately estimating water depth in various coastal environments. [ABSTRACT FROM AUTHOR]

Water vapor plays an important role in climate change; thus, studying the spatial distribution and temporal variation of precipitable water vapor (PWV) in the coastal regions of China would help researchers to understand the climate characteristics of those regions. In this paper, 6-year 1-h interval PWV were derived from 27 Global Positioning System stations observations of Chinese coastal GPS observation network, surface meteorological data and European Center for Medium-Range Weather Forecasts (ERA-Interim) reanalysis products. The present study provides the use of these data to investigate the spatial-temporal variability of water vapor throughout the coastal regions of China. Latitude is the main factor affecting the spatial distribution of GPS-derived PWV; that is, PWV decreased by about 1.5 mm for each 1° increase of latitude. For regions at the same latitude, a region that is relatively close to the ocean will have a higher content of PWV. The PWV in the southeastern and southwestern coastal regions of China is significantly higher in summer; this may be influenced by the southeastern and southwestern water vapor inflow corridors. The PWV obviously varies monthly, reaching a minimum in January; however, the timing of the maximum varied but usually appeared in June, July or August and was affected by the monsoons. The PWV varies largely between summer and winter with a larger gradient of change in PWV with latitude in winter than in summer. The positive correlation coefficient between PWV and the surface temperature varied in different seasons; this is related to the changes of temperature and the horizontal motion of water vapor. Use of the Fast Fourier Transform method showed that the PWV time series data have multi-scale characteristics. The amplitude and phase of the PWV time series in annual, semiannual, four month and seasonal cycles were extracted through harmonic wave analysis. The amplitude of four month and seasonal cycles did not pass significance testing. The maximum amplitude of the annual PWV cycle is located in mid-latitudes with 22.09 mm, which gradually decreases towards high latitude and equator area. The peak time of annual PWV cycle occurs in July, which does not agree with the timing of the maximum monthly average PWV. The semiannual cycle amplitudes is between 0.42 and 6.32 mm, with no significant correlation between their distribution and latitude. The peak time of semiannual PWV cycles is during about January–March and July–September, and the peak time gradually becomes late from north to south. [ABSTRACT FROM AUTHOR]

Dust events affect human health in both drylands and downwind environments. In this study, we used county-level data during the period of 1965–2005 to assess the impact of dust events on measles incidence in Gansu province in Western China. We used Fast Fourier Transform (FFT) to set up the cyclical regression model; in particular, we set the model to downwind direction for the typical cities in the Hexi Corridor as well as the capital city Lanzhou. The results showed that Spring measles incidence was the highest in the Hexi Corridor, where dust events occur the most frequently over Gansu province. Measles incidence declined on the pathway of dust storms from west to east due to the weakening of both intensity and duration in dust storms. Measles incidence was positively correlated with monthly wind speed and negatively correlated with rainfall amount, relative humidity, and air pressure. Measles incidence was significantly ( p ≤ 0.01) positively correlated with daily coarse particles, e.g., TSP and PM 10 . According to the cyclical regression model, average monthly excess measles that is related to dust events was 39.1 (ranging from 17.3 to 87.6), 149.9 (ranging from 7.1 to 413.4), and 31.3 (ranging from 20.6 to 63.5) in Zhangye, Lanzhou, and Jiuquan, respectively. [ABSTRACT FROM AUTHOR]

• BEMD was applied to high-resolution SOM map in northwestern China. • Scale- and region-specific variations in SOM were unraveled. • The variance of SOM was partitioned by climate, topography and land surface. • Main factors that influencing SOM were different in different scales. Soil organic matter (SOM) significantly affects soil quality, food security, and the global carbon cycle. Variations in the SOM content are controlled by many environmental factors and differ depending on the scale and region. The Mountain–Oasis–Desert System (MODS) in arid areas is limited by climate conditions and water and soil resources. Therefore, the effects of environmental factors on the SOM content may differ. This study aimed to characterize spatial patterns of the SOM content in a MODS in northwestern China and reveal the SOM variations at different scales and regions. First, we employed the random forest model to predict the SOM content and the uncertainty at the nodes of a 90 m grid. We then extracted scale- and region-dependent variations in the SOM content using bivariate empirical mode decomposition (BEMD). Data analysis, geostatistics, and a two-dimensional fast Fourier transform were applied to analyze the SOM pattern and its correlations with environmental covariates at different scales. The results suggest that the variations in the SOM content were mainly concentrated at the large scale. The correlations between environmental factors and the SOM were scale- and region-specific, indicating complex interrelationships among topography, land use, salinization, soil erosion, and fertilization. At the intermediate-large spatial scale, topography characteristics and surface features were the major factors controlling the SOM content, whereas the climate had a larger effect on SOM than other factors at the large scale. Based on our results, BEMD has great potential to reveal SOM variations in the scale–region domain. BEMD can be used in environmental modeling in the future to gain insights into the variations in soil properties depending on environmental factors and anthropogenic activities. [ABSTRACT FROM AUTHOR]

The access tunnel in the main powerhouse of the Shuangjiangkou hydropower station in China has complex geological conditions with high in-situ stress. Rockbursts pose serious threats to the safety of personnel and equipment in the tunnel. Three-dimensional microseismic (MS) monitoring technology was used to explore MS activities inside the tunnel surrounding rock. In consideration of various kinds of signals, the Fast Fourier Transform (FFT) method was employed to obtain the amplitude-frequency spectra of signal waveforms, and then, some waveform characteristics (amplitude, duration, dominant frequency distribution range, peak frequency, main frequency value, etc.) were analyzed to recognize MS signals. It provided a guarantee for further analysis of signals, and also provided significant guidance for MS signal recognition in other tunnels. Based on MS activity frequency and released energy time-series curves, the rockburst sequence type in the tunnel can be determined as foreshock-mainshock-aftershock. Studying spatiotemporal distribution characteristics of microcracks inside the surrounding rock, dominant active areas inside the surrounding rock of the tunnel have been delimited. In addition, a more efficient signal analysis technique (wavelet packet transform) was used to frequency-decompose complex MS waveforms and the dominant information of signals was retained; furthermore, a time-frequency model was first established to analyze the activity characteristics inside the surrounding rock; by using the model we can more intuitively analyze and accurately judge rockburst precursor information. The results indicated that a downward shift phenomenon of frequency band energy distribution can be used as an early warning indicator of rockbursts. [ABSTRACT FROM AUTHOR]