Search Results (219)

Accurate short-lead river discharge forecasting is critical for effective flood risk mitigation in small mountainous basins, where rapid hydrological responses pose significant challenges. In this study, we focus on the Fuhu Stream in Emeishan City, China, and utilize high-resolution 5-min time series of upstream precipitation, stage, and discharge to predict downstream flow. We benchmark three data-driven models—SARIMAX, XGBoost, and LSTM—using a dataset spanning from 7 June 2024 to 25 October 2024. The data were split chronologically, with observations from October 2024 reserved exclusively for testing to ensure rigorous out-of-sample evaluation. Lagged correlation analysis was employed to estimate travel times from upstream to the basin outlet and to inform the selection of time-lagged input features for model training. Results during the test period demonstrate that the LSTM model significantly outperformed both XGBoost and SARIMAX across all regression metrics: it achieved the highest coefficient of determination (R² = 0.994) and the lowest prediction errors (RMSE = 0.016, MAE = 0.011). XGBoost exhibited moderate performance, while SARIMAX showed a tendency toward mean reversion and failed to capture low-flow variability. Accuracy evaluation reveals that LSTM accurately reproduced both baseflow conditions and multiple flood peaks, whereas XGBoost and SARIMAX failed. These results highlight the advantage of sequence-learning architectures in modeling nonlinear hydrological propagation and memory effects in short-term discharge dynamics. Feature importance analysis indicates that the LSTM model was highly effective for real-time forecasting and that the WSQ/LY sites served as critical monitoring nodes for achieving reliable predictions. This research contributes to the operationalization of early warning systems and provides support for decision-making regarding downstream flood disaster prevention. Full article

Human gait exhibits stable contralateral coupling, making healthy-side motion a viable predictor for affected-limb kinematics. Leveraging this property, this study develops FusionTCN–Attention, a causality-preserving temporal model designed to forecast contralateral hip and knee trajectories from unilateral IMU measurements. The model integrates dilated temporal convolutions with a lightweight attention mechanism to enhance feature representation while maintaining strict real-time causality. Evaluated on twenty-one subjects, the method achieves hip and knee RMSEs of 5.71° and 7.43°, correlation coefficients over 0.9, and a deterministic phase lag of 14.56 ms, consistently outperforming conventional sequence models including Seq2Seq and causal Transformers. These results demonstrate that unilateral IMU sensing supports low-latency, stable prediction, thereby establishing a control-oriented methodological basis for unilateral prediction as a necessary engineering prerequisite for future hemiparetic exoskeleton applications. Full article

This study compares and evaluates the performance of a statistical model, Multiple Linear Regression (MLR), and a deep learning model, Long Short-Term Memory (LSTM), for predicting monthly mean temperature in the Han River Basin, South Korea. Predictor variables were dynamically selected based on lagged correlation analysis between climate indices and temperature over the past 40 years, identifying the top ten variables with the highest correlations for lag times ranging from 1 to 18 months. The MLR model was developed through stepwise regression with cross-validation, while the LSTM model was constructed using an 18-month input sequence to capture temporal dependencies in the data. Model performance was evaluated using percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficient (r), and tercile-based probability metrics. Both models reproduced the seasonal variability of monthly temperature with high accuracy (NSE > 0.97, r > 0.98). The LSTM model showed slightly higher predictive skill in several periods but also exhibited larger prediction variance, reflecting the sensitivity of nonlinear architectures to variations in predictor–response relationships. In contrast, the MLR model demonstrated more stable predictive behavior with narrower uncertainty bounds, particularly under low signal-to-noise conditions, owing to its structural simplicity. These findings indicate that the two approaches are complementary; the LSTM model better captures nonlinear temporal dynamics, while the MLR model provides interpretability and robustness. Future work will explore advanced hybrid architectures such as CNN–LSTM and Transformer-based models, as well as multi-model ensemble methods, to further enhance the accuracy and reliability of medium-range temperature prediction. Full article

The ongoing global decline in groundwater levels poses significant challenges for sustainable water management. Satellite gravity missions, such as the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO), provide valuable estimates of groundwater storage changes at regional scales. However, the relatively coarse spatial resolution of these satellite data limits their direct applicability to local groundwater management. In this study, we address this limitation for China by analyzing groundwater monitoring data from 108 cities with shallow groundwater use and 37 cities with deep groundwater use from the period 2019–2022, integrating in situ groundwater level records, official monitoring reports, monthly dynamic data, and GRACE-FO-derived groundwater storage estimates. Our findings reveal rapid groundwater depletion in northern China, especially in Xinjiang and Hebei Provinces. Fluctuations in shallow groundwater levels in Beijing and Jiangsu are closely related to precipitation variability. For deep aquifer regions, GRACE-FO-derived groundwater storage changes show a moderate Pearson correlation coefficient of 0.45 and groundwater level variations. Regional analysis for 2019–2021 in the Northeast Plain and the Huang–Huai–Hai Basin indicates better agreement between satellite-derived storage and groundwater levels, with a Pearson correlation coefficient of 0.58 in the Huang–Huai–Hai Basin. Groundwater level dynamics are strongly influenced by both precipitation and pumping, with an approximate three-month lag between precipitation events and groundwater storage responses. Overall, satellite gravity data are suitable for use in regional groundwater assessment and could serve as valuable indicators in areas with intensive deep groundwater exploitation. To enable fine-scale groundwater management, future work should focus on improving the spatial resolution through downscaling and other advanced techniques. Full article

The increasingly severe phenomenon of groundwater drought poses a dual threat to the development and construction of a region, as well as its ecological environment. Traditional groundwater drought monitoring methods rely on observation wells, which makes it difficult to obtain dynamic drought information in areas with limited measurement data. Based on Gravity Recovery and Climate Experiment (GRACE) satellite technology and data, the suitability of the standardized groundwater index (GRACE_SGI) was explored for drought characterization in the Mu Us Sandy Land. Multiscale and seasonal trend changes in groundwater drought in the study area from 2002 to 2021 were comprehensively identified. Subsequently, the characteristics of hysteresis time between the GRACE_SGI and the standardized precipitation index (SPI) were clarified. The results show that (1) different fitting functions impact the parameterized GRACE_SGI fitting results. The Anderson–Darling method was used to find the best-fitting function for groundwater data in the study area: the Pearson III distribution. (2) The gain and loss characteristics of the GRACE_SGI are similar, showing downward trends at different time scales, including seasonal scales. (3) The absolute values based on the maximum correlation coefficients between the SPI and the GRACE_SGI at different time scales were 0.1296, 0.2483, 0.2427, and 0.5224, with time lags of 0, 0, 12, and 11 months, respectively. The vulnerability of semiarid ecosystems to hydroclimatic changes is highlighted by these findings, and a satellite-based framework for monitoring groundwater drought in data-scarce regions is provided. Full article

The 27-day oscillation in total electron content (TEC) is analysed by means of world maps of TEC. The TEC maps are derived from measurements of the ground receiver network of the Global Navigation Satellite System (GNSS) and are provided by the International GNSS Service (IGS). The observed 27-day oscillation in TEC is mainly due to the 27-day solar rotation period, which induces a 27-day oscillation in extreme ultraviolet radiation (EUV) of the Sun. Analysing the time interval from 2003 to 2020, cross-correlation of the 27-day oscillation of the solar MgII-index of the Solar Radiation and Climate Experiment (SORCE) and the 27-day oscillation in TEC shows an average time delay of about 1.1 days for the ionospheric response with respect to the solar EUV variation. The average correlation coefficient of the solar and the ionospheric variation is 0.85. The cross-correlation of the 27-day oscillation in solar radio flux F10.7 and the 27-day oscillation in TEC gives a time lag of about 1.3 days and an average correlation coefficient of 0.78. The world maps of the amplitude of the 27-day oscillation in TEC are discussed for the TEC data from 1998 to 2024. Finally, TEC composites are derived for F10.7 enhancement events and geomagnetic storms. Full article

To improve preparedness and minimise losses, this paper presents the development of artificial intelligence (AI)-based forecasting models using a large-scale climate index for Victoria (Australia), which is known to be one of the most fire-prone areas in the country. Using an Artificial Neural Network (ANN) approach, this study investigates the nonlinear relationships between SOI and the Forest Fire Danger Index (FFDI) to develop a robust predictive model. Levenberg–Marquardt optimisation through the backpropagation method was employed to train the ANN models. Based on local climate data, FFDI values were calculated for eight locations within southeast Australia, and SOI values of earlier months were correlated with the FFDI values of the later months. A total of 55 years (1965–2019) of monthly SOI and FFDI values were used to train, validate, and test the developed ANN models. The findings show that the developed models can predict future FFDI values, having correlation coefficients ranging 0.71~0.96, 0.70~0.95, and 0.75~0.93 for 1-month, 2-month, and 3-month lagged periods, respectively. As is obvious, one-month-ahead predictions were more accurate than two/three-month-ahead predictions. In general, the stations located in the eastern parts are attributed to higher prediction accuracy than stations located in the western regions, possibly due to their closer proximity to the location from where SOI originates (i.e., southern Pacific). These variations between the stations located in the eastern and western parts may partly exhibit the applicability of FFDI to different vegetation types. However, the outcomes hold potential for informing stakeholders, improving resource allocation for fire preparedness, and mitigating the devastating impacts of bushfires on communities and ecosystems. Full article

The Tibetan Plateau is a globally critical climate-sensitive and ecologically fragile region. Vegetation phenology serves as a key indicator of ecosystem responses to climate change and simultaneously influences regional carbon cycling, water regulation, and ecological security. However, systematic quantitative assessments of phenological responses under the combined effects of multiple climate factors remain limited. This study integrates multi-source remote sensing data (MODIS MCD12Q2) and ERA5-Land meteorological data from 2001 to 2023, leveraging the Google Earth Engine (GEE) cloud platform to extract key phenological metrics, including the start (SOS) and end (EOS) of the growing season, and growing season length (GSL). Sen’s slope estimation, Mann–Kendall trend tests, and partial correlation analyses were applied to quantify the independent effects and spatial heterogeneity of temperature, precipitation, solar radiation, and evapotranspiration (ET) on GSL. Results indicate that: (1) GSL on the Tibetan Plateau has significantly increased, averaging 0.24 days per year (Sen’s slope +0.183 days/yr, Z = 3.21, p < 0.001; linear regression +0.253 days/yr, decadal trend 2.53 days, p = 0.0007), primarily driven by earlier spring onset (SOS: Sen’s slope −0.183 days/yr, Z = −3.85, p < 0.001), while autumn dormancy (EOS) showed limited delay (Sen’s slope +0.051 days/yr, Z = 0.78, p = 0.435). (2) GSL changes exhibit pronounced spatial heterogeneity and ecosystem-specific responses: southeastern warm–wet regions display the strongest responses, with temperature as the dominant driver (mean partial correlation coefficient 0.62); in high–cold arid regions, warming substantially extends GSL (Z = 3.8, p < 0.001), whereas in warm–wet regions, growth may be constrained by water stress (Z = −2.3, p < 0.05). Grasslands (Z = 3.6, p < 0.001) and urban areas (Z = 3.2, p < 0.01) show the largest GSL extension, while evergreen forests and wetlands remain relatively stable, reflecting both the “climate sentinel” role of sensitive ecosystems and the carbon sequestration value of stable ecosystems. (3) Multi-factor interactions are complex and nonlinear; temperature, precipitation, radiation, and ET interact significantly, and extreme climate events may induce lagged effects, with clear thresholds and spatial dependence. (4) The use of GEE enables large-scale, multi-year, pixel-level GSL analysis, providing high-precision evidence for phenological quantification and critical parameters for carbon cycle modeling, ecosystem service assessment, and adaptive management. Overall, this study systematically reveals the lengthening and asymmetric patterns of GSL on the Tibetan Plateau, elucidates diverse land cover and climate responses, advances understanding of high-altitude ecosystem adaptability and climate resilience, and provides scientific guidance for regional ecological protection, sustainable management, and future phenology prediction. Full article

Digital skills represent a key dimension of digital transformation, shaping the innovation potential, competitiveness, and long-term sustainability of the European economy. The aim of this paper is to compare the development of digital skills in EU-27 countries from 2018 to 2024 and identify the strengths and weaknesses within the European context. The analysis is based on secondary data from the Digital Economy and Society Index (DESI). From the total of 36 indicators included in DESI, 12 variables were selected, with an emphasis on 3 core digital-skills metrics: Internet use, ICT specialists, and ICT graduates. To assess their interrelationships and linkages with overall digital transformation performance, non-parametric correlation analyses (Kendall’s Tau and Spearman’s rank correlation) were applied. Furthermore, across-year nonparametric tests (Friedman ANOVA with Kendall’s coefficient of concordance, W) were used to evaluate year-to-year differences and the stability of country rankings over 2018–2024. The empirical results confirmed that higher levels of digital skills are associated with stronger digital transformation performance among EU member states, while significant cross-country disparities persist. Germany and the Nordic economies (Finland, Sweden, and Denmark) achieved the best results, while Southern and Eastern European countries such as Bulgaria, Portugal, and Greece lagged behind. These findings highlight the strategic role of digital education, ICT specialization, and lifelong learning initiatives in promoting sustainable digital transformation and competitiveness across Europe. In addition, panel regression analysis confirmed that digital infrastructure, particularly FTTP coverage and Very High Capacity Networks, is a key driver of digital skills development, whereas the effects of business digitalization appear indirect or delayed. The outcomes provide relevant implications for broadband deployment and user-centric digital public services to support the objectives of the EU Digital Decade 2030. The study contributes to a deeper understanding of the determinants of digital skills and digital transformation performance, providing evidence-based guidance for targeted digital policies aimed at reducing the digital divide and strengthening digital transformation performance within the European Union. Full article

Empirical research has revealed conflicting associations between dependent and independent variables, with few studies tackling the dynamics in developing economies. This study investigates the effects of carbon dioxide emissions, renewable energy consumption (REC), foreign direct investment (FDI), government green capital spending (GGCS), and economic growth (EG) in Morocco, employing the Keynesian framework of economic growth. An autoregressive distributed lag (ARDL) methodology was applied to assess both short- and long-term relationships among the model’s variables, using annual data from the World Development Indicators (WDI) database for the period 1993–2020. All ARDL variables were transformed into first differences to ensure stationarity. The bounds test confirmed a long-term equilibrium relationship between the dependent and independent variables. Diagnostic tests, including the White test, indicated no evidence of heteroscedasticity, and the Shapiro–Wilk test confirmed that residuals followed a normal distribution, validating model robustness. The model demonstrated overall stability across the study period with no structural breaks. The empirical findings suggest that both carbon dioxide emissions and renewable energy consumption exhibit positive trends, whereas GGCS demonstrates a significant short-run negative correlation with economic growth. However, the long-term coefficients were found to be statistically insignificant, suggesting that sustained policy effects may be attenuated by macroeconomic structural factors. Full article

The large lakes and reservoirs of the northeastern Tibetan Plateau play a key role in regional water resources, yet their influence on terrestrial water storage (TWS) changes at different spatial scales remains unclear. This study employed the constrained forward modeling (CFM) method to correct leakage errors in level-2 spherical harmonic (SH) coefficients from the Gravity Recovery and Climate Experiment and its follow-on missions (GRACE/GRACE-FO) at three spatial scales: two circular regions covering 90,000 km² and 200,000 km², respectively, and a 220,000 km² region based on the shape of mass concentration (Mascon). TWS changes derived from SH solutions after leakage correction through CFM were compared with level-3 Mascon solutions. Individual water storage components, including lake and reservoir water storage (LRWS), groundwater storage (GWS), and soil moisture storage (SMS), were quantified, and their relationships with precipitation were assessed. From 2003 to 2022, the CFM method effectively mitigated signal leakage, revealing an overall upward trend in TWS at all spatial scales. Signals from Qinghai Lake and Longyangxia Reservoir dominated the long-term trend and amplitude variations of LRWS, respectively. LRWS explained more than 47% of the TWS changes, and together with GWS, accounted for over 85% of the changes. Both CFM-based and Mascon-based TWS changes indicated a consistent upward trend from January 2003 to September 2012, followed by declines from November 2012 to May 2017 and October 2018 to December 2022. During the decline phases, GWS contributions increased, while LRWS contributions and component exchange intensity decreased. LRWS, SMS, and TWS changes were significantly correlated with precipitation, with varying time lags. These findings underscore the value of GRACE/GRACE-FO data for monitoring multiscale TWS dynamics and their climatic drivers in lake- and reservoir-dominated regions. Full article

This research examines the relationship between capital structure, intellectual capital, and financial performance among insurance companies in Sub-Saharan Africa (SSA). Anchored in a positivist paradigm, the study employed descriptive and quantitative methodologies, leveraging secondary panel data spanning from 2010 to 2022 across 122 insurance firms sampled from a population of 178 companies across 46 SSA countries. Utilizing a Panel Vector Error Correction Model (P-VECM), the analysis explored long-term equilibrium relationships and dynamic interactions among variables, including return on assets (ROAs), debt-to-equity ratio (DER), long-term debt (LTD), short-term debt (STD), Value-Added Intellectual Coefficient (VAIC™), and firm size (SIZE). Optimal lag lengths were determined through robust statistical criteria, ensuring model precision. The impulse response analysis revealed significant findings: variations in ROA negatively impacted intellectual capital (VAIC), leverage indicators (DER, LTD, and STD), and positively influenced firm size over a ten-period horizon. Specifically, decreases in ROA were consistently associated with reduced intellectual capital effectiveness and adverse financial liquidity conditions, while increased firm size correlated positively with improved financial performance. Full article

As a region with functions such as energy production and as an ecological barrier, the Loess Plateau plays a vital role in China. This study examines the spatiotemporal changes in water storage and vegetation cover and their correlations. The changes in water storage were calculated using GRACE data and the GLDAS-NOAH model, while vegetation changes were derived from MODIS data. The results showed that the groundwater inventory decreased by 7.80 mm/a and the land inventory decreased by 9.72 mm/a. Surface water storage capacity increased by 1.92 mm/a. From west to east, terrestrial and groundwater storage decrease, reflecting overall losses, but surface water storage remains positive. By analyzing the FVC, it can be observed that since 2006, vegetation coverage has shown an overall increasing trend, with the highest value occurring in 2018. There has been a remarkably increase in vegetation coverage in most areas, while there was a decrease in vegetation coverage along the borders of Qinghai Province and northern Shaanxi Province. By conducting a correlation analysis, it can be found that the correlation coefficients between terrestrial water storage, surface water storage, and groundwater storage changes and vegetation coverage are −0.85, 0.60, and −0.93, respectively, indicating that increased vegetation coverage leads to reduced groundwater and terrestrial water storage. The results also indicate that there are significant spatial differences in the monthly correlations and maximum lag months between water storage and vegetation coverage. In addition, through discussing the driving factors of water storage changes in the Loess Plateau, we consider that the Grain for Green Project and mining activities may be the two major drivers of these changes. This study is highly important and valuable to the study of changes in water reserves in the Loess Plateau, as well as ecological protection and environmental assessment in the Loess Plateau. Full article

Ecological drought in terrestrial systems is a vegetation-functional degradation phenomenon triggered by the long-term imbalance between ecosystem water supply and demand. This process involves nonlinear coupling of multiple climatic factors, ultimately forming a compound ecological stress mechanism characterized by spatiotemporal heterogeneity. Based on meteorological and remote sensing datasets from 1982 to 2022, this study identified the spatial distribution and temporal variability of ecological drought in China, elucidated the dynamic evolution and return periods of typical drought events, unveiled the scale-dependent effects of climatic factors under both univariate dominance and multivariate coupling, as well as deciphered the response mechanisms of ecological drought to meteorological drought. The results demonstrated that (1) terrestrial ecological drought in China exhibited a pronounced intensification trend during the study period, with the standardized ecological water deficit index (SEWDI) reaching its minimum value of −1.21 in February 2020. Notably, the Alpine Vegetation Region (AVR) displayed the most significant deterioration in ecological drought severity (−0.032/10a). (2) A seasonal abrupt change in SEWDI was detected in January 2003 (probability: 99.42%), while the trend component revealed two mutation points in January 2003 (probability: 96.35%) and November 2017 (probability: 43.67%). (3) The drought event with the maximum severity (6.28) occurred from September 2019 to April 2020, exhibiting a return period exceeding the 10-year return level. (4) The mean values of gridded trend eigenvalues ranged from −1.06 in winter to 0.19 in summer; 87.01% of the area exhibited aggravated ecological drought in winter, with the peak period (88.51%) occurring in January. (5) Evapotranspiration (ET) was identified as the dominant univariate driver, contributing a percentage of significant power (POSP) of 18.75%. Under multivariate driving factors, the synergistic effects of ET, soil moisture (SM), and air humidity (AH) exhibited the strongest explanatory power (POSP = 19.21%). (6) The response of ecological drought to meteorological drought exhibited regional asynchrony, with the maximum correlation coefficient averaging 0.48 and lag times spanning 1–6 months. Through systematic analysis of ecological drought dynamics and driving mechanisms, a dynamic assessment framework was constructed. These outcomes strengthen the scientific basis for regional drought risk early-warning systems and spatially tailored adaptive management strategies. Full article

The Poincaré plot was introduced as a tool to analyze heart rate variations caused by arrhythmias. Later, it was applied to time series with normal beats. The plot shows the relationship between the inter-beat interval (IBI) of one beat to the next. Several parameters were developed to characterize this relationship. The short and long axis of the fitting ellipse, SD1 and SD2, respectively, their ratio, and their product are used. The difference between the IBI of a beat and m beats later are also studied, SD1(m) and SD2(m). We studied the mathematical relations between heart rate variability measures and the Poincaré measures in the time (standard deviation of IBI, SDNN, root mean square of successive differences, RMSSD) and frequency domain (power in low and high frequency band, and their ratio). We concluded that SD1 and SD2 do not provide new information compared to SDNN and RMSSD. Only the correlation coefficient r(m) provides new information for m > 1. Novel findings are that ln(SD2(m)/SD1(m)) = tanh⁻¹(r(m)), which is an approximately normal distributed transformation of r(m), and that SD1(m) and SD2(m) can be calculated by multiplying the power spectrum by a weighing function that depends on m, revealing the relationship with spectral measures, but also the relationship between SD1(m) and SD2(m). Both lagged parameters are extremely difficult to interpret compared to low and high frequency power, which are more closely related to the functioning of the autonomic nervous system. Full article