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Taking the megacity of Beijing as an example, a short‐time Fourier transform (STFT) method was employed to extract the multi‐timescale evolution pattern of the canopy urban heat island intensity (CUHII) during 2000–2020. The STFT of CUHII showed a close relationship between the evolution of the CUHII in Beijing and the background meteorological forcing at intra‐annual, weather and intra‐daily scales. The intra‐annual‐scale spectrum of CUHII exhibited an increasing trend with obvious seasonal variation of the canopy urban heat island (CUHI). The intra‐daily‐scale spectrum of CUHII showed an increasing trend with the nighttime CUHI developing faster. Increasing Western Pacific Subtropical High intensity can enhance the seasonal and diurnal fluctuations of CUHII. The weather‐scale spectrum of CUHII is controlled by weather system evolution, showing that the frequency of cold/heat waves (CWs/HWs) in Beijing was significantly negatively correlated with the weather‐scale spectral intensity of the CUHII. CWs and HWs can increase the CUHII for a long duration. Plain Language Summary The canopy urban heat island (CUHI) phenomenon can affect human health and the ecological environment, and its multi‐timescale variability brings great uncertainty to the study of urban climates worldwide. In this study, taking the megacity of Beijing as an example, a novel short‐time Fourier transform (STFT) method was used to extract the multi‐timescale pattern of the CUHI intensity (CUHII) during 2000–2020. The STFT of CUHII showed a close relationship between the CUHI and the background meteorological forcing at intra‐annual, weather and intra‐daily scales. The intra‐annual spectrum of CUHII showed an increasing trend with a V‐shaped mode. The local climatic backgrounds of different cities can lead to differences in the seasonal development of the CUHI. The intra‐daily spectrum of CUHII showed an increasing trend due to the asymmetry in the day/night development of the CUHI. Increasing Western Pacific Subtropical High Intensity can enhance the seasonal and diurnal fluctuations of CUHII. The weather‐scale spectrum of CUHII was mainly controlled by weather system evolution, showing that cold waves and heat waves can increase the CUHII over a long duration. Our findings indicate that the evolution of CUHII is a nonlinear and complex process that is directly related to multi‐timescale background climate forcing. Key Points Using a short‐time Fourier transform to study the multi‐timescale evolution of the canopy urban heat island intensity (CUHII) Close relationships existed between CUHII and the background meteorological forcing at intra‐annual, intra‐daily, and weather scales The frequency of cold/heat waves in Beijing showed a significant negative correlation with the weather‐scale spectral intensity of the CUHII

Deterministic earthquake prediction remains elusive, but time‐dependent probabilistic seismicity forecasting seems within reach thanks to the development of physics‐based models relating seismicity to stress changes. Difficulties include constraining the earthquake nucleation model and fault initial stress state. Here, we analyze induced earthquakes from the Groningen gas field, where production is strongly seasonal, and seismicity began 3 decades after production started. We use the seismicity response to stress variations to constrain the earthquake nucleation process and calibrate models for time‐dependent forecasting of induced earthquakes. Remarkable agreements of modeled and observed seismicity are obtained when we consider (a) the initial strength excess, (b) the finite duration of earthquake nucleation, and (c) the seasonal variations of gas production. We propose a novel metric to quantify the nucleation model's ability to capture the damped amplitude and the phase of the seismicity response to short‐timescale (seasonal) stress variations which allows further tightening the model's parameters. Plain Language Summary Earthquakes are difficult to predict with certainty, but progress in forecasting their likelihood using probabilistic models based on stress changes has been made. However, challenges remain in understanding how earthquakes start and the initial conditions of faults. Here, we analyzed induced earthquakes in the Groningen gas field, where production is seasonal and seismic activity began 34 years after gas production started. By studying how the earthquakes respond to rapid changes in stress, we could better understand how they start and develop models to forecast their temporal occurrence. By considering factors like the initial strength of the faults, the duration of earthquake initiation, and seasonal variations in gas production we could accurately match the observed seismic activity. We introduced a new measure to evaluate how well the models captured the dampened strength and timing of seismic activity in response to short‐term stress changes (such as seasonal variations), which helped refine the model's parameters. Key Points An improved reservoir, geomechanical, and seismicity modeling workflow is proposed for forecasting induced seismicity at various timescales Short‐timescale stress variations allow constraining the characteristics of the earthquake nucleation process using Groningen as case study Initial strength excess and finite duration of the nucleation process allow reproducing long‐and‐short timescale characteristics of seismicity

This research introduces a novel method for accurate and consistent hydrological forecasting at multiple timescales. Deep learning (DL) models are increasingly being used for hydrological forecasting across various timescales (hourly, daily, etc.). However, one of the main challenges with multi‐timescale DL‐based hydrological forecasting is the potential inconsistency (discrepancy) between forecasts across different timescales. Inconsistent multi‐timescale forecasts can be problematic especially when decision‐making relies on forecasts across different timescales. This paper introduces a hierarchical DL (HDL) model incorporating temporal hierarchical reconciliation (THR) with DL models for consistent multi‐timescale streamflow forecasting. HDL is developed, deployed, and tested for multi‐step (seven days ahead), multi‐timescale (daily and weekly) streamflow forecasting using over 400 catchments across the contiguous United States. HDL is based on long short‐term memory (LSTM) networks and implements THR through a differentiable output layer. HDL consistently improved the median Nash Sutcliffe efficiency (NSE) of daily streamflow forecasts (e.g., by 3.01% for lead time one) compared to a multi‐timescale LSTM benchmark and resulted in significantly more accurate forecasts in more than 65% of the catchments at the weekly scale than daily forecast aggregation. HDL performance is influenced by both the THR formulation and the accuracy of the forecasts at different timescales. For instance, at the weekly timescale, HDL yielded a notable median improvement of 9.45% in NSE for the lowest‐performing decile of catchments (NSE < 0.37), which are typically the most challenging to forecast. The proposed HDL framework offers a novel, generalizable, and promising approach for multi‐timescale forecasting across diverse water resources forecasting tasks. Plain Language Summary Accurately forecasting streamflow at different timescales, such as daily and weekly, is essential for effective water management. Current methods often produce forecasts that are inconsistent across these timescales. For example, a weekly forecast might fail to align with the sum of daily forecasts over the next 7 days. This inconsistency can undermine decision‐making. In this research, a deep learning pipeline that internally applies a technique called temporal hierarchical reconciliation to align forecasts across different timescales is developed. The proposed method ensures that forecasts at the daily and weekly levels are not only improved, but also consistent with one another, making them ideal for informed decision making in water resources management tasks, such as reservoir operations. Key Points Hierarchical deep learning model developed for 7 days and weekly streamflow forecasting across 400+ contiguous United States catchments Neural network‐based temporal hierarchical reconciliation ensures consistent forecasts without additional postprocessing Hierarchical deep learning outperformed benchmark long short‐term memory model at both daily and weekly timescales

The characteristics of vegetation and drought for different seasons between 1982 and 2015 in Inner Mongolia were studied based on the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). The response of vegetation to drought over various timescales for different seasons and vegetation types was investigated using the maximum Pearson correlation, allowing a discussion about the possible causes of any changes. The results indicate that the vegetation NDVI in Inner Mongolia showed an increasing trend in different seasons, with spring vegetation NDVI (April–May) having the largest significant increasing rate, followed by the growing season (April–October), autumn (September–October), and summer (June–August). Accordingly, the proportion of stations with decreasing SPEI was, in descending order, summer, growing season, autumn, and spring. Additionally, the magnitude of the SPEI decrease was greater in eastern Inner Mongolia. NDVI and SPEI were positively correlated in most regions of Inner Mongolia, indicating that changes in vegetation in most parts of this region were affected by the spatial and temporal characteristics of drought, the correlation being them being strongest in the growing season, followed by summer, then spring and autumn. Considering the different types of vegetation, forests were less affected by drought, with broadleaf forests more affected than coniferous forests. The meadow steppes and typical steppes were more affected by 12‐month droughts in the growing season and summer, 6‐month droughts in spring, and 3‐month droughts in autumn, with desert steppes mainly affected by 3‐month droughts. The shrubs, sandy vegetation, and cropland were mostly affected by droughts in summer, and show a greater response to 3‐month droughts in autumn. Finally, the water balance was found to be the most important factor affecting the response of vegetation to drought in Inner Mongolia.

As renewable energy increasingly penetrates into electricity-heat integrated energy system (IES), the severe challenges arise for system reliability under uncertain generations. A two-stage approach consisting of pre-scheduling and re-dispatching coordination is introduced for IES under wind power uncertainty. In pre-scheduling coordination framework, with the forecasted wind power, the robust and economic generations and reserves are optimized. In re-dispatching, the coordination of electric generators and combined heat and power (CHP) unit, constrained by the pre-scheduled results, are implemented to absorb the uncertain wind power prediction error. The dynamics of building and heat network is modeled to characterize their inherent thermal storage capability, being utilized in enhancing the flexibility and improving the economics of IES operation; accordingly, the multi-timescale of heating and electric networks is considered in pre-scheduling and re-dispatching coordination. In simulations, it is shown that the approach could improve the economics and robustness of IES under wind power uncertainty by taking advantage of thermal storage properties of building and heat network, and the reserves of electricity and heat are discussed when generators have different inertia constants and ramping rates.

Recent advancements in face forgery techniques have continuously evolved, leading to emergent security concerns in society. Existing detection methods have poor generalization ability due to the insufficient extraction of dynamic inconsistency cues on the one hand, and their inability to deal well with the gaps between forgery techniques on the other hand. To develop a new generalized framework that emphasizes extracting generalizable multi-timescale inconsistency cues. Firstly, we capture subtle dynamic inconsistency via magnifying the multipath dynamic inconsistency from the local-consecutive short-term temporal view. Secondly, the inter-group graph learning is conducted to establish the sufficient-interactive long-term temporal view for capturing dynamic inconsistency comprehensively. Finally, we design the domain alignment module to directly reduce the distribution gaps via simultaneously disarranging inter- and intra-domain feature distributions for obtaining a more generalized framework. Extensive experiments on six large-scale datasets and the designed generalization evaluation protocols show that our framework outperforms state-of-the-art deepfake video detection methods.

Clarifying the impact of driving forces on multi-temporal-scale (annual, quarterly and monthly) runoff changes is of great significance for watershed water resource planning. Based on monthly runoff data and meteorological data of the Jialing River (JLR) during 1982–2020, the Mann–Kendall tendency testing approach was first applied to analyze variation tendencies of multi-timescale runoff. Then, abrupt variation years of runoff were determined using Pettitt and cumulative anomaly mutation testing approaches. The ABCD model was employed for simulating hydrological change processes in the base period and variation period. Finally, influences of climatic and anthropic factors on multi-scalar runoff were computed using the multi-scalar Budyko formula. The following conclusions were drawn in this study: (1) The mutation year of discharge was 1993; (2) the monthly runoff in the JLR presented a “single peak” distribution, and the concentration degree and concentration period in the JLR both showed an insignificant reduction trend; (3) anthropic factors were the dominant factor for spring runoff variations; climatic factors were the dominant factor on annual, summer, fall and winter runoff variations; (4) except for November, climatic factors were the dominant factor causing runoff changes in the other 11 months. This study has important reference value for water resource allocation and flood control decisions in the JLR.

With the increasing penetration of converter-interfaced renewable energy, modern power systems are increasingly exposed to weak-grid conditions, where reduced short-circuit strength and low inertia significantly challenge the stability of grid-forming inverters. To address these issues, this paper proposes a multi-timescale energy-aware grid-forming (GFM) control strategy for PV–battery energy storage systems (PV–BES). The fast layer provides immediate stabilization by shaping the inverter output impedance and injecting virtual damping. The medium layer adaptively tunes the virtual inertia and damping gains according to the estimated DC-link energy and the online-evaluated grid strength, enabling the inverter to autonomously regulate its dynamic behavior under varying operating conditions. A slow layer regulates long-term energy trajectories to reduce battery cycling stress. In addition, a dedicated mode coordinator is introduced to ensure smooth transitions among MPPT, charging/discharging, and GFM operation, preventing discontinuities in DC-link energy and adaptive control gains. Numerical simulations demonstrate that the proposed strategy effectively enhances transient stability, suppresses oscillatory responses under weak-grid disturbances, and significantly mitigates battery degradation by reducing DC-link energy fluctuations. These results highlight the potential of incorporating energy-awareness into GFM control design for achieving both improved dynamic performance and extended battery lifetime.

Because it uses a set of model integrations to simulate the temporally varying background probability distribution function and implement Bayes' theorem, the ensemble Kalman filter (EnKF), which produces an optimal data assimilation solution that coherently combines model dynamics and observational information, has been widely used in weather and climate studies. However, in practice, the EnKF has two limitations: (1) the insufficient representation of error statistics of low‐frequency background flows due to its finite ensemble size and model integration over time and (2) the high demand of computational power for ensemble model integrations in high‐resolution coupled Earth system models. Given that background error statistics consist of stationary, slow‐varying, and fast‐varying parts, a multi‐timescale, high‐efficiency approximate EnKF (MSHea‐EnKF) is designed to increase the representation of low‐frequency background error statistics and enhance its computational efficiency. The MSHea‐EnKF is a combination of multi‐timescale filters implemented by regressions based on data sampled from the time series of a single‐model solution. Validation shows that with the improved representation of stationary and slow‐varying background statistics, the MSHea‐EnKF only requires a small fraction of computer resources and shows a comparable performance relative to a finite size EnKF. Our experiments also show that the result can be further improved by using a small set of MSHea‐EnKFs through second‐stage EnKF filtering if sufficient computer resources are available. This new algorithm makes it practical to assimilate multisource observations into any high‐resolution coupled Earth system model that is intractable with current computing power for weather‐climate analysis and predictions. Key Points A new multi‐timescale high‐efficiency approximate EnKF has been developed using only one single‐model solution With improved representation on slow‐varying background statistics, the new algorithm produces comparable results as a finite size EnKF The new algorithm makes it practical to assimilate multisource observations into any high‐resolution coupled Earth system model

Power systems with high renewable penetration impose multi-dimensional demands on energy storage (ES) regulation. Short-duration ES is required for power balance and frequency support, while medium- and long-duration ES is essential for daily, weekly, and seasonal peak shaving and energy time-shifting. Aiming at the challenge of multi-timescale configuration of hybrid energy storage (HES) in the initial planning stage of carbon-neutral transition, this paper proposes an optimal configuration strategy combining STL-PDM-VMD. First, the seasonal and trend decomposition using Loess (STL) is used to extract quarterly trends of annual net power for seasonal ES configuration. Then, the Past Decomposable Mixing (PDM) module in the time-mixer model is applied to decouple and mix multi-scale features of the detrended power curve for monthly and weekly configurations. Finally, an improved Variational Mode Decomposition (VMD) is adopted to decompose daily net power fluctuations and optimize intra-day energy storage schemes. Based on actual data from a carbon-neutral transition region, simulations are carried out and compared with the VMD method with decomposition layers optimized by Gurobi. The results show that the proposed STL-PDM-VMD multi-timescale hybrid energy storage configuration strategy can effectively capture the multi-timescale fluctuation characteristics of net load, significantly improve the Renewable Energy (RE) penetration rate, and ensure the power and energy balance of the new power system at multiple timescales. penetration, and maintain power and energy balance in the new-type power system.