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Flash droughts are characterized by rapid onset and intensification, with severe impacts on agriculture and ecosystems. They often begin as meteorological droughts and, if conditions worsen, evolve into agricultural droughts. While precipitation deficit is often the primary driver, atmospheric and hydrological anomalies can exacerbate flash drought development. This study characterizes flash droughts across the Contiguous United States using remote sensing data from 2003 to 2020. A combination of satellite‐derived meteorological, agricultural, and ecological variables are used to investigate large‐scale flash drought development. Events are defined using root‐zone soil moisture. We used the Aridity Index to assess how background aridity influences agricultural and ecological impacts. Cross‐correlation and Cross Wavelet analyses are applied to examine the propagation of flash droughts from meteorological to agricultural and ecological stages. Results show that flash drought characteristics—including frequency, duration, and onset/recovery rates—are significantly influenced by landscape aridity characteristics. Precipitation is identified as the main driver across all climate regimes while Relative Humidity (RH) and Vapor Pressure Deficit (VPD) also indicate early signals. Time lags between meteorological variables and Soil Moisture (SM), as well as between soil moisture and ecological variables, vary across climates. Generally, results show that ecosystems respond to flash drought after soil moisture. Solar Induced Fluorescence (SIF), a measure of ecological stress, detects flash drought onset earlier than SM, highlighting its potential for early detection and monitoring. Plain Language Summary Flash droughts develop quickly and can cause sudden stress to crops and ecosystems. They usually start with a drop in rainfall but are also influenced by other factors like high temperatures and dry air. This study uses satellite data across the continental United States from 2003 to 2020 to track the propagation of flash drought from meteorological drivers to soil moisture and subsequent effects on vegetation. Our results show that the onset of flash drought in soil moisture is characterized by dry spells in combination with low humidity. Vegetation shows a delayed negative response the rapid onset of soil moisture drought. However, satellite observations of Solar Induced Fluorescence (SIF), which track plant activity, responds earlier than soil moisture highlighting SIFs potential for flash drought detection before rapid declines in soil moisture. These findings can help improve early warning and management of flash droughts. Key Points Flash drought frequency, duration, and onset vary with regional aridity, with humid regions being notably vulnerable Precipitation triggers flash droughts, while higher temperatures and vapor pressure deficits accelerate their intensification across the US Solar‐induced fluorescence provides early flash drought warnings in humid climates, while leaf area index shows a delayed response

Parkinson’s disease (PD) is a brain disorder, that affects a person’s body movement causing stiffness, shaking and imbalance. Earlier detection of PD is a challenging task for researchers. In this paper, earlier detection of PD is performed using the Cross-Non-Decimated Wavelet Transform (CNDWT) and Bayesian Optimized Multiple Linear Regression (BOMLR) algorithm. The PD voice data is amplitude-sliced, augmented and processed with CNDWT. The CNDWT decomposes amplitude-sliced augmented PD voice data with the Haar transform and reconstruction is performed using Daubechies wavelet of order 3 (DB3) transformations. Preprocessed data is correlated for identification of highly influential data attributes of PD. The Bayesian Optimized Multiple Linear Regression (BOMLR) method is applied to the highly correlated data attributes for earlier PD prediction. A voice data set is created in this study, which consists of 31 voice recordings of which 23 are from individuals affected by PD. The proposed CNDWT method is compared with existing methods. The results show that the proposed CNDWT method outperforms other traditional algorithms with an accuracy of 99% in predicting Parkinson’s disease.

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m 2 /year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Synthetic aperture radar interferometry (InSAR) has proven to be a powerful tool for monitoring landslide movements with a wide spatial and temporal coverage. Interpreting landslide displacement time-series derived from InSAR techniques is a major challenge for understanding relationships between triggering factors and slope displacements. In this study, we propose the use of various wavelet tools, namely, continuous wavelet transform (CWT), cross wavelet transform (XWT) and wavelet coherence (WTC) for interpreting InSAR time-series information for a landslide. CWT enables time-series records to be analysed in time-frequency space, with the aim of identifying localized intermittent periodicities. Similarly, XWT and WTC help identify the common power and relative phase between two time-series records in time-frequency space, respectively. Statistically significant coherence and confidence levels against red noise (also known as brown noise or random walk noise) can be calculated. Taking the Huangtupo landslide (China) as an example, we demonstrate the capabilities of these tools for interpreting InSAR time-series information. The results show the Huangtupo slope is affected by an annual displacement periodicity controlled by rainfall and reservoir water level. Reservoir water level, which is completely regulated by the dam activity, is mainly in ‘anti-phase’ with natural rainfall, due to flood control in the Three Gorges Project. The seasonal displacements of the Huangtupo landslide is found to be ‘in-phase’ with respect to reservoir water level and the rainfall towards the front edge of the slope and to rainfall at the higher rear of the slope away from the reservoir.

The advent of climate change has induced frequent occurrence of droughts in the past few decades. Identification of hydrological droughts require computation of drought indices by probabilistic standardization procedures. The existing hydrological drought indices could not answer the zero monthly streamflow condition for the ephemeral streams. This issue was resolved by developing a modified Standardized Streamflow Index to characterize the hydrological drought of Upper Kangsabati River Basin, West Bengal, India. 45 hydrological droughts were extracted for the basin and the most severe drought occurred in the year 2015–2016. The basin experienced the most severe drought of 10.67 severity and longest drought duration of 13 months. A bivariate analysis of drought characteristics was carried out using copula technique to determine different design drought events. The bivariate distribution which showed the basin experienced most severe drought of 16 years and longest duration drought of 15 years ‘OR’ return period. Propagation time of the drought hazard from the meteorological to the hydrological drought is extensively studied in this research using both correlation and Cross Wavelet Transform (XWT) methods. XWT was mostly used for qualitative comparison of hydrological and meteorological signal in drought propagation studies in the past. In this research, a novel quantitative approach of using XWT and the phase angles obtained between the hydrological and meteorological signals is proposed to determine the drought propagation times. It was concluded that the basin had in general a drought propagation time of 2 months. However, there were seasonal variability in the drought propagation times showing prompt response in the summer season which increased to 2 months for monsoons and stretching far to 5 months for the late winter.

The People’s Victory Canal irrigation area is an important agricultural irrigation region in the North China Plain, where groundwater resources play a crucial role in both agricultural production and the ecological environment. However, in recent years, the increasing depth of the groundwater, influenced by climate change and human activities, has posed significant challenges to the sustainable use of water resources in the region. Therefore, exploring the lag effect and its trends between precipitation and groundwater depth is essential for the scientific management of groundwater resources and optimizing water allocation. This study is based on the monthly average precipitation and groundwater depth data from the Xiazhuang in the People’s Victory Canal irrigation area from 1993 to 2021. It uses methods such as continuous wavelet transform, cross-wavelet transform, and cross-correlation analysis to systematically analyze the lag effect and its changing patterns of groundwater table depth in response to precipitation at different time scales. The study finds that during the research period, precipitation generally showed a downward trend, while the groundwater table depth continuously increased and experienced a sudden change in 1999, after which it rose significantly. Before 2000, there was a strong correlation between precipitation and groundwater depth, with a noticeable response of groundwater depth to changes in precipitation, and the lag time was about 58.97 days. However, after 2000, this relationship gradually weakened, especially in years other than those with abundant rainfall, where the influence of precipitation on groundwater depth decreased significantly, and the lag time increased to 6 to 8 months. The study shows that before 2000, abundant precipitation led to a shallower groundwater, and the groundwater was more significantly influenced by precipitation. After 2000, reduced precipitation and the increased depth of the groundwater table weakened the response of groundwater to precipitation, thus enhancing the lag effect. This trend reflects a change in the mechanism of precipitation recharge to groundwater, which is likely closely related to intensified human activities, changes in the irrigation water extraction methods, and over-extraction of groundwater. The findings of this study can provide a scientific basis for water resource management in the irrigation area, helping to formulate appropriate groundwater regulation measures and ensure the sustainable use of regional water resources.

This study presents a wavelet-based framework for mapping inter-well connectivity (IWC) between multiple injectors and producers to support waterflood optimization. The method applies Cross-Wavelet Transform Coherence (CrWTC) with a complex Morlet wavelet to injection and production rate data, enabling the time-localized and frequency-dependent identification of dynamic injector–producer communication. The novelty of this work lies in continuous coherence mapping, the use of the complex Morlet wavelet for improved sensitivity to nonstationary responses, continuous updating as new data become available, and benchmarking on both the Volve and COSTA datasets. Validation using reservoir simulation and field data showed strong qualitative agreement with expected connectivity behavior and demonstrated clearer tracking of connectivity evolution and waterfront movement than the Capacitance Resistance Method (CRM). The proposed approach improves the reliability and interpretability of IWC assessment and offers a practical tool for reservoir surveillance and waterflood management.

The rise in shallow groundwater levels is typically triggered by precipitation recharge, exhibiting a certain lag relative to precipitation changes. Therefore, identifying the response mechanism of shallow groundwater levels to precipitation is crucial for clarifying the interaction between precipitation and groundwater. However, the response mechanism of groundwater levels to precipitation is complex and variable, influenced by various hydrogeological and geographical conditions, and often exhibits significant nonlinear characteristics. To address this issue, this study employs methods such as continuous wavelet transform, cross wavelet transform, and wavelet coherence to analyze the response patterns of groundwater levels to precipitation at different wavelet scales in the Datong Basin from 2013 to 2022: (i) At short wavelet scales (10.33~61.96 d), the groundwater level dynamics respond almost instantaneously to extreme rainfall; (ii) At medium wavelet scales(61.96~247.83 d), the precipitation-groundwater recharge process shows characteristics of either rapid recovery or significant delay; (iii) At long wavelet scales (247.83~495.67 d), three potential groundwater processes were identified in the Datong Basin, exhibiting long-term lag responses throughout this study period, with lag times of 11.18 days, 148.75 days, and 151.49 days, respectively. Furthermore, the results indicate that the lag response time of shallow groundwater levels to precipitation is not only related to the wavelet scale but also to the identified depth conditions of different groundwater regions, groundwater extraction intensity, precipitation intensity, and aquifer lithology. This study distinguishes the temporal and spatial response mechanisms of shallow groundwater to precipitation at different wavelet scales, and this information may further aid in understanding the interaction between precipitation and groundwater levels.

At the Baihetan Hydropower Station, the world’s second largest hydroelectric project, reservoir filling began on April 6, 2021. This resulted in a 165-m rise in the reservoir level, leading to potential landslide instability. In this study, we focus on the Shenjiagou landslide, which was exacerbated by the secondary process of water storage at the Baihetan Hydropower Station. We analyzed the deformational features of the landslide before and after impoundment via various data sources, including optical images, field investigations, borehole data, television surveys, interferometric synthetic aperture radar (InSAR), and monitoring data. A cross-wavelet transform was used to analyze the relationship between the reservoir level time series and landslide displacement. The results indicated that the Shenjiagou landslide was an old translational landslide that exhibited continuous deformation prior to the secondary process of water storage. While the reservoir was being filled, the buoyancy effect on the resistant portion decreased the landslide stability, leading to increased deformation, resulting in surface cracks, bank collapses, road damage, and tilted trees. The cross-wavelet transform revealed an in-phase link between the reservoir level time series and landslide displacement. The methods and findings are valuable for understanding the deformational features and processes of landslides in the presence of significant variations in the reservoir level.

The intensity and frequency of droughts in Poyang Lake Basin have been increasing due to global warming. To properly manage water resources and mitigate drought disasters, it is important to understand the long-term characteristics of drought and its possible link with large-scale climate indices. Based on the monthly meteorological data of 41 meteorological stations in Poyang Lake Basin from 1958 to 2017, the spatiotemporal variations of drought were investigated using the standardized precipitation evapotranspiration index (SPEI). Ensemble empirical mode decomposition (EEMD) methods and the modified Mann–Kendall (MMK) trend test were used to explore the spatiotemporal characteristics and trends of drought. Furthermore, to reveal possible links between drought variations and large-scale climate indices in Poyang Lake Basin, the relationships between SPEI and large-scale climate indices, such as North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Arctic Oscillation (AO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO) were examined using cross-wavelet transform. The results showed that the SPEI in Poyang Lake Basin exhibited relatively stable quasi-periodic oscillation, with approximate quasi-3-year and quasi-6-year periods at the inter-annual scale and quasi-15-year and quasi-30-year periods at the inter-decadal scale from 1958 to 2017. Moreover, the Poyang Lake Basin experienced an insignificantly wetter trend as a whole at the annual and seasonal scales during the period of 1958–2017, except for spring, which had a drought trend. The special characteristics of the trend variations were markedly different in the basin. The areas in which drought was most likely to occur were mainly located in the Poyang Lake region, northwest and south of the basin, respectively. Furthermore, relationships between the drought and six climate indices showed that the drought exhibited a significant temporal correlation with five climate indices at restricted intervals, except for IOD. The dominant influences of the large-scale climate indices on the drought evolutions shifted in the Poyang Lake Basin during 1958–2017, from the NAO, Niño 3.4, and the Southern Oscillation Index (SOI) before the late 1960s and early 1970s, to the AO and PDO during the 1980s, then to the NAO, AO and SOI after the early 2000s. The NAO, AO and SOI exerted a significant influence on the drought events in the basin. The results of this study will benefit regional water resource management, agriculture production, and ecosystem protection in the Poyang Lake Basin.