Search Results (1,359)

This study investigates the influence of rainfall variability on runoff generation in the Upper Ing River Basin and inflow to the Mae Tum Reservoir in northern Thailand using the physically based Soil and Water Assessment Tool (SWAT) version 2012. In small mountainous watersheds, water supply reliability for irrigation and domestic use—particularly for unmonitored royal initiated projects like the Mae Tum Reservoir—has become a critical concern due to shifting climatic extremes. A SWAT model was developed using detailed spatial data on topography, land use, and soil characteristics together with long-term daily climate and streamflow records. The model performance at Station I.17 was evaluated through calibration and validation using the R², Nash–Sutcliffe Efficiency (NSE), and percent bias indices. Rainfall regimes were classified into dry, normal, and wet years based on the mean and standard deviation of 25-year gauge records to drive scenario simulations. The calibrated model reproduced seasonal runoff patterns satisfactorily (monthly NSE up to 0.685 and R² up to 0.712). The simulations demonstrated the strong sensitivity of both the runoff at Station I.17 and reservoir inflow to interannual rainfall differences, with the annual runoff ranging from 71.5 to 379.7 million m³ and the annual inflow to Mae Tum Reservoir ranging from 28.84 to 48.33 million m³. These findings demonstrate that physically based spatial modeling can effectively replace traditional empirical operating rules, providing a highly transferable framework for runoff forecasting, reservoir inflow assessment, and climate responsive water resources planning in data-scarce tropical mountainous basins. Full article

This study proposes an innovative framework that combines hydroclimatic and agro-environmental predictors, including nitrate concentration and NDVI, with climatic factors such as Rainfall, temperature, and evapotranspiration to forecast piezometric level variations in the Saïss Basin, Morocco. Eight Machine Learning (ML) models were benchmarked, and feature importance was assessed using Shapley Additive exPlanations (SHAP) to ensure model transparency and interpretability. In parallel, the PELT algorithm was applied to detect structural change points, while Sen’s slope estimator and the Mann–Kendall test were used to quantify long-term trends. The Extra Trees (ET) model achieved the best performance (R² = 0.92), with Rainfall emerging as the most influential predictor, followed by nitrate concentration, confirming the added value of hydrochemical indicators for groundwater forecasting. Change-point analysis revealed significant declines during the 1980s and 1990s, followed by lower-amplitude fluctuations since the late 2000s. Projections toward 2050 suggest partial stabilization in the central part of the basin under favorable recharge conditions, whereas persistent declines are expected to continue in peripheral areas subjected to sustained groundwater abstraction pressure. These findings provide a robust and transferable decision-support tool for the sustainable management of groundwater resources in semi-arid agricultural area. Full article

Climate change is expected to alter the climatic suitability of crops. This study evaluated the climatic suitability of maize cultivation in the TR21 Thrace Region of Türkiye using the EcoCrop model. Climatic suitability was assessed for a reference period (1950–2000) and projected for the 2050s using three CMIP5 global climate models (MPI_ESM_LR, HADGEM2_ES, and CNRM_CM5) under the RCP4.5 and RCP8.5 scenarios. The EcoCrop model implemented in DIVA-GIS was used to evaluate climatic suitability. All climate models projected increases in average annual temperature of 1.7–3.8 °C, whereas projected changes in average annual precipitation ranged from −92 to +46 mm. Despite variations in temperature and rainfall forecasts, the projections indicate that the area suitable for maize cultivation will increase; the HADGEM2_ES model produced the highest proportion of suitable areas. The suitability rate across all projections ranged from 54.7% to 96.8%. Projected climate change is likely to improve maize climatic suitability in TR21 by the 2050s. Because EcoCrop evaluates climatic suitability based solely on temperature and precipitation thresholds, the projected changes should be interpreted as a climatic envelope for maize rather than as a direct increase in future yield or productivity. These findings inform agricultural adaptation strategies and regional land-use planning under climate change. Full article

The Xiashu loess in the middle and lower reaches of the Yangtze River with typical aeolian characteristics is widely distributed in the hilly areas and is the main kind of landslides in this area. The paper reveals the infiltration process of the Xiashu loess slope under different rainfall intensity conditions on the basis of on-site artificial rainfall simulation tests and laboratory experiments. The distribution of pore water pressure inside the Xiashu loess slope under different rainfall intensity conditions were compared and analyzed. The relationship between rainfall intensity and water content at different depths was clarified, and the relation function among rainfall intensity conditions, slopes, and the ultimate depth and critical rainfall intensity of the Xiashu soil slope landslide is proposed. The research results indicate that: (1) the infiltration rate and depth of the soil at the foot of the slope are greater than those at the top and middle of the slope; (2) an increase in the rainfall duration is found to cause an increase in slope infiltration depth; (3) an increase in the rainfall duration can lead to a more significant influence of the infiltration depth under the slope angle; (4) the infiltration depth of rainfall with low intensities and long durations is larger than that of high intensities and short durations. Finally, the ultimate rainfall infiltration depth under different slope angles and rainfall conditions was determined. The research results can be used to forecast the Xiashu loses soil landslide scale, providing theoretical basis for early warning of instability of Xiashu loess slope under different unfavorable conditions. Full article

Accurate and interpretable forecasting of meteorological variables is essential for environmental monitoring and for the development of reliable decision-support systems. This study proposes an explainable multi-step deep learning framework for forecasting daily mean air temperature in central Saudi Arabia. The dataset comprises daily observations collected from 2020 to 2024, including air temperature, atmospheric pressure, relative humidity, and rainfall. Historical measurements from a 30-day lookback window were used to generate direct forecasts for 7-day and 30-day horizons. Four forecasting approaches were evaluated: Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Network (CNN1D), and Transformer. Forecasting performance was assessed using Mean Absolute Error, Root Mean Square Error, and Mean Absolute Percentage Error, together with Taylor diagrams, residual distributions, and observed-versus-predicted analyses. The LSTM model achieved the highest predictive accuracy, obtaining MAE and RMSE values of 2.139 °C and 2.953 °C, respectively, for the 7-day horizon, and 2.345 °C and 3.134 °C for the 30-day horizon. To investigate model behavior, seven complementary explainable artificial intelligence methods were applied, including Integrated Gradients, Grad-CAM, SHAP, LIME, permutation importance, occlusion sensitivity, and saliency maps. These methods were selected to provide global feature-level, local prediction-level, and temporal explanations. The results show that recent air-temperature observations dominate short-term forecasts, whereas atmospheric pressure and relative humidity exhibit greater relative influence at the longer forecasting horizon. Rainfall contributes less consistently because of its sparse distribution within the study region. Overall, the proposed framework combines multi-horizon forecasting with comprehensive interpretability, providing a transparent approach for meteorological prediction and a methodological foundation for future environmental forecasting systems that integrate meteorological and pollutant observations. Full article

Forecasting individual cattle live weight in herd-managed grazing systems remains challenging because automated weighing observations are irregular and environmental conditions vary seasonally. Evidence remains limited for live-weight forecasting across multiple forecasting periods under commercial grazing conditions. This study developed a machine learning (ML) framework for forecasting the live weight of individual cattle using automated observations from a managed herd. The framework incorporated demographic variables, historical live weights, and climatic lag predictors. The framework compared monthly, weekly, and rolling-window aggregations across forecasting periods of 1, 2, and 3 months. Quality control retained 494 of 1140 cattle (43.3%), yielding 4069 monthly aggregated records. The resulting dataset was more suitable for modelling cattle with regular voluntary weighing records. The respective forecasting datasets contained 3048, 2558, and 2068 records for one-, two-, and three-month periods. Gradient Boosting achieved the strongest testing performance. The corresponding coefficients of determination (R²) were 0.950, 0.935, and 0.902. Monthly aggregation achieved higher entropy retention, greater variance preservation, and stronger forecasting performance than alternative aggregation approaches. Feature-importance analysis identified animal age and historical live weight as the most important predictors across all forecasting periods. Lagged rainfall and temperature variables provided complementary predictive information for medium-term forecasting. The findings demonstrate that automated livestock monitoring, climatic information, and ML can support accurate live-weight forecasting. The framework produced forecasts across multiple periods for individual cattle in herd-managed grazing systems. Full article

Accurate rainfall input is critical for reliable flood simulation, particularly in semi-arid watersheds with pronounced spatiotemporal precipitation heterogeneity. This study developed a radar–gauge rainfall fusion framework to improve HEC-HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model performance in the Liulin experimental watershed, Xingtai City, Hebei Province. Radar quantitative precipitation estimation (QPE) was generated via a dynamically optimized Z-I relationship, then fused with gauge observations using three methods—Geographical Differential Analysis (GDA), Conditional Merging (CM), and Random Forest (RF). The fused products drove a calibrated HEC-HMS model, evaluated over five representative flood events. All three methods corrected radar QPE underestimation. Under independent cross-validation, GDA and CM achieved comparable point-scale accuracy (CC ≈ 0.81, RMSE ≈ 5.7 mm), while RF showed lower generalization (CC ≈ 0.48, RMSE ≈ 8.7 mm) due to overfitting. In flood simulations, GDA performed most robustly, followed by RF and CM, all surpassing single-source inputs. Notably, CM’s higher statistical accuracy did not translate into better flood performance, indicating that optimal statistical fidelity does not guarantee optimal hydrological results. Peak discharge deviations persisted for short-duration intense storms and long-duration uneven rainfall events. This study confirms that radar–gauge fusion enhances rainfall input quality and provides a reliable approach for improving flood forecasting. Full article

High-resolution precipitation fields are essential for flash-flood forecasting and hydrological risk management, especially in small and medium-sized basins, yet single-source precipitation products often show limited accuracy over complex terrain. This study develops a two-stage machine-learning framework for 1 km/1 h multi-source precipitation fusion over Shaoxing, China, during the 2025 flood season. In the first stage, a machine-learning classifier identifies precipitation occurrence and reduces zero-inflated noise; in the second stage, an optimized tree-based residual-regression model corrects precipitation estimates for rainy samples. A 61-dimensional feature set was constructed by integrating satellite precipitation estimates, weather-radar precipitation estimates from the Zhejiang radar network, temporal-lag and accumulation statistics, neighborhood descriptors, cyclic time variables, and terrain-derived interaction features, with gauge observations used as the training target. After quality control, the dataset comprised 41,458 hourly station samples from 72 rain gauges. The stations were divided at the station level into a 57-station development set and a fixed 15-station held-out spatial test set containing 8637 hourly samples. Station-blocked fivefold cross-validation within the development set was used for model selection, hyperparameter tuning, and probability-threshold selection, whereas the held-out stations were used only for final performance evaluation. On the fixed held-out test set, the occurrence classifier achieved an overall accuracy of 0.947, with a probability of detection of 0.806, a false alarm ratio of 0.158, a critical success index of 0.700, and an F1 score of 0.823. For quantitative estimation, the two-stage fusion product reduced root mean square error from 2.342 mm for satellite precipitation estimates to 1.189 mm, corresponding to a 49.22% reduction, and decreased mean absolute error from 0.712 mm to 0.262 mm, while increasing the coefficient of determination to 0.685. The fused precipitation product also improved the detection of intense rainfall events, with probability of detection and critical success index reaching 0.511 and 0.442, respectively, for events exceeding 10 mm/h, while reducing false weak precipitation and showing closer agreement with observed station-level spatial variability. By separating precipitation-occurrence identification from rainfall-intensity correction, the framework reduces zero-inflated bias, improves heavy-rainfall representation, and demonstrates predictive skill at gauges excluded from model development during the 2025 flood season. Full article

The “spin-up” problem, in which convection-permitting models require hours to develop realistic clouds from large-scale initial fields, critically limits short-term severe weather forecasting. Cloud analysis can serve as a feasible approach to directly assimilate hydrometeor information from remote sensing retrievals. In this study, we leverage multi-source remote sensing data, including three-dimensional mosaic radar reflectivity, hourly averaged FY-2G satellite brightness temperature (black-body temperature, TBB), and FY-2G total cloud water products, within a stepwise cloud analysis initialization scheme. The scheme is implemented in a convective-scale ensemble forecasting system (CMA-Meso, 3 km resolution) for a heavy rainfall event. For each ensemble member, three-dimensional hydrometeor increments are independently generated from these remote sensing retrievals and gradually introduced over the first ten time steps, ensuring smooth coordination with the model’s dynamic thermal framework. Quantitatively, the scheme reduces near-surface Continuous Rank Probability Score (CRPS) errors, improves the overall predictive skill by 2.6–7.9% (maximum at the 12 h spin-up period), and increases ensemble spread by 2–5.8%, mitigating under-dispersion. Probabilistic precipitation forecasts show uniform area under the relative operating characteristic curve (AROC) improvements across all thresholds, 1.16–5.77% for light rain, 3.03–8.97% for moderate rain, and 6.00–12.07% for heavy rain, with these maxima consistently occurring at the 12 h spin-up time. Although Brier scores are marginally larger, these AROC gains confirm the enhanced discrimination of convective rainfall. At 500 hPa, CRPS reductions of 7.1–15.6% emerge after 24 h (largest 15.6% for geopotential height at 24 h), zonal wind CRPS is reduced by 2.2% at 12 h, and ensemble spread increases by 3.1–7.0% for all three variables. These improvements, particularly the pronounced benefits during the initial 12 h, demonstrate that the remote sensing-driven cloud analysis effectively shortens spin-up. Mechanistically, the gains arise from physically coordinated hydrometeor-latent heat perturbations and subsequent cloud radiation feedback that continuously regulate thermal-dynamic structures. This study establishes that assimilating diverse remote sensing data via cloud analysis is an effective approach for overcoming spin-up challenges in convective-scale ensembles. Full article

Accurate short-term rainfall forecasting is essential for disaster mitigation. Although numerical weather prediction models are widely used, their application to short lead times is constrained by computational demands. Data-driven approaches provide an efficient alternative. To better exploit atmospheric water vapor information, this study develops a dual-branch long short-term memory (LSTM) model that integrates Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) with surface meteorological parameters for rainfall forecasting. The model processes historical rainfall and meteorological variables through separate branches. Historical rainfall characterizes precipitation persistence, while PWV, PWV variation (ΔPWV), PWV rate of change (ΔtPWV), and air temperature describe atmospheric moisture evolution and thermodynamic conditions before rainfall. The model was evaluated using hourly observations from 18 GNSS-collocated meteorological stations in Taiwan collected during 2018–2019 and compared with a rainfall history-based LSTM baseline model. Results show that the proposed model achieved accuracies of 89–91% and recalls of 88–90% for 1–3 h forecasts. Its advantages became more evident for longer lead times, with Recall and Threat Score increasing by 6–11% and 4–8%, respectively, for 2–3 h forecasts. These findings demonstrate that integrating GNSS-derived PWV with surface meteorological parameters can improve short-term rainfall forecasting. Full article

Global Navigation Satellite Systems (GNSS) have emerged as a reliable and cost-effective tool for estimating atmospheric precipitable water vapor (PWV), particularly in regions with limited meteorological instrumentation. In this study, the performance of three tropospheric mapping functions—the Global Mapping Function (GMF), Niell Mapping Function (NMF), and Vienna Mapping Function 1 (VMF1)—was evaluated for PWV estimation using GPS observations collected during the 2009–2011 period in southwestern Mexico, a region characterized by high atmospheric variability and frequent extreme weather events. GPS data from three stations (TECO, COL2, and PENA) were processed using the GAMIT/GLOBK 10.71 software, and the resulting PWV estimates were validated against independent radiosonde observations and the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth-Generation Reanalysis (ERA5) data. The results show that GPS-derived PWV successfully captures the seasonal variability of atmospheric water vapor, with maximum values during the summer rainy season. High correlations were obtained with both radiosonde and ERA5 data, particularly at the TECO station (R = 0.95–0.99), where RMSE values ranged from 3.27 to 5.46 mm and BIAS values from $-2.73$ to $-1.47$ mm. In contrast, larger discrepancies were observed at COL2 and PENA, mainly due to horizontal separation and altitude differences relative to the radiosonde site, highlighting the importance of spatial representativeness during validation. Among the evaluated mapping functions, no single model consistently outperformed the others across all stations, years, and reference datasets. Nevertheless, GMF and NMF generally exhibited more stable and consistent performance, whereas VMF1 showed greater variability under the adopted processing strategy. Additionally, a clear relationship was identified between PWV and precipitation records, indicating that increases in PWV coincided with periods of intense rainfall and suggesting its potential as an indicator of atmospheric conditions favorable for precipitation events. Overall, this study shows that GPS-derived PWV can reproduce the seasonal variability of atmospheric water vapor under the adopted processing strategy and demonstrates the importance of mapping function selection and spatial representativeness for accurate PWV estimation. Full article

The Huaibei region is a critical grain and energy–chemical base in northern China, characterized by substantial water demand for industrial and agricultural production. Karst groundwater systems constitute the primary water supply source in this area. Under the superimposed impacts of intensive exploitation, climate change, and anthropogenic activities, karst aquifers have encountered a series of geo-environmental problems, including groundwater level decline and expansion of cones of depression. Most previous studies have predominantly focused on water quality assessment and groundwater resource quantification, yet systematic investigations into the multi-scale characteristics and driving mechanisms of karst groundwater level dynamics remain insufficient. In this study, based on long-term groundwater level and rainfall monitoring data (2014–2024) from three monitoring wells representing different types of karst aquifers, continuous wavelet transform (CWT) and wavelet coherence (WTC) approaches are introduced to identify the periodic patterns of karst groundwater levels and reveal the dominant controlling factors of groundwater level dynamics. The results demonstrate that groundwater levels in all types of karst aquifers exhibit distinct multi-scale periodic variations. The groundwater levels of HB01 and HB02 share dominant oscillation periods of 18~19 months and 9 months with regional rainfall, while the groundwater level at HB03 displays a more complex, multi-scale, periodic combination of 41 months, 18~19 months, and 9 months. Periodic variations in regional rainfall serve as the dominant controlling factor for the intra-annual and inter-annual periodic fluctuations of karst water levels, with a prominent resonance relationship identified between the two variables at dominant periodic scales. Distinct heterogeneity is observed in the response magnitude and lag time of different karst aquifer types to rainfall; specifically, the lag time of water level response to rainfall on the annual periodic scale ranges from 2.7 to 2.9 months. The correlation between annual average water level and pumping discharge is moderate for boreholes HB01 and HB03, whereas a strong correlation is detected for borehole HB02, implying that its water level regime is likely subjected to pronounced pumping disturbance. The degree of karst development, aquifer burial depth, and overlying stratum architecture are the key geological factors accounting for such heterogeneous response patterns. For the first time, this study utilizes long-term water level time series data from the karst water exploitation zone of the Huaibei Plain, complemented by synchronous precipitation and pumping records. Integrated with regional hydrogeological settings, wavelet analysis is employed to conduct an in-depth investigation into the dynamic variations in karst water levels in the Huaibei region from the perspective of groundwater recharge–discharge relationships. The results provide a scientific underpinning for the remediation of karst water over-exploitation and the optimal allocation of water resources. Specifically, pumping and artificial recharge schemes can be proactively adjusted based on periodicity forecasts. Zoned management strategies for water resources are put forward: artificial regulation and storage are recommended for zones with sensitive hydrological responses, while preventive protection is prioritized for zones with sluggish responses. By incorporating periodic characteristics and lag durations, targeted pumping strategies for dry and wet seasons can be developed, and a coupled water level–rainfall–pumping early warning system can be established to realize the long-term sustainable regulation of karst water resources. Full article

Accurate solar radiation forecasting is essential for photovoltaic energy generation, smart grid stability, and renewable energy management. This study proposes a lightweight hybrid deep learning framework that combines a transformer encoder and Gated Rrecurrent Uunit (GRU) network for short-term solar radiation forecasting in Makkah and Madinah, Saudi Arabia. Hourly meteorological data from the NASA POWER dataset (2020–2025) were utilized, including solar radiation intensity, temperature, humidity, wind speed, cloud amount, rainfall, surface pressure, and dew point temperature. A preprocessing pipeline consisting of missing value treatment, outlier removal, normalization, timestamp alignment, and data cleaning was applied to improve data quality. Feature engineering techniques were incorporated to capture temporal dependency, meteorological interactions, weather dynamics, and solar variability patterns. The transformer encoder was used to learn long-range temporal dependencies through multi-head self-attention, while the GRU layer modeled sequential temporal dynamics efficiently. Hyperparameter optimization was performed using Bayesian optimization with Optuna. The experimental results demonstrate that the proposed transformer GRU framework achieved a Mean Absolute Error (MAE) of 0.014, Root Mean Square Error (RMSE) of 0.0219, and a coefficient of determination (${\mathrm{R}}^2$) of 0.98. The proposed model outperformed ARIMA, LSTM, GRU, and XGBoost models while maintaining stable performance across varying weather conditions and forecasting horizons. Full article

Specific humidity profile retrievals from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard Fengyun-4B (FY-4B) are often degraded by cloud contamination, while strict quality control flags further limit data usability. To address these issues, an optimization framework for FY-4B GIIRS specific humidity profiles was developed using ERA5 reanalysis data spanning January 2023 to January 2024. Guangxi and the Beibu Gulf were selected as the study domain. Independent ERA5 datasets, not involved in model construction, were used as a benchmark to evaluate performance. The results indicate that the original FY-4B GIIRS specific humidity profiles tend to underestimate moisture relative to ERA5. For profiles with quality flags of 0 and 1, the Bias, Root Mean Square Error (RMSE), and Mean Relative Error (MRE) range of −3~0 g/kg, 0~4 g/kg, and 17~53%, respectively. After optimization, the bias is effectively reduced to 0 g/kg. RMSE shows an average reduction of 15% within the 700~300 hPa layer, while the most notable improvement in MRE occurs between 1000 and 920 hPa. For lower-quality data (Flags 2–3), the bias, RMSE, and MRE span −12~0 g/kg, 0~13 g/kg, and 48~140%, respectively. Following optimization, the bias range narrows to −6~0 g/kg. RMSE decreases by 20~40% from the near-surface layer up to 400 hPa, and MRE is reduced by 40% below 300 hPa. A case study of Typhoon “Peipah” further demonstrates the model’s effectiveness. At stations experiencing intense rainfall, the optimized specific humidity profiles show markedly improved accuracy, and the Mean Absolute Errors (MAEs) of derived forecast-related physical variables are substantially reduced. Overall, the proposed optimization model significantly enhances both the accuracy and practical usability of FY-4B GIIRS specific humidity profiles, providing more reliable data support for monitoring severe weather events such as typhoons and heavy rainfall. Full article

To address the challenges of information fusion and prediction for highly non-stationary, noisy, and lag-responsive heterogeneous time-series data from multi-source environmental sensing, this paper proposes a novel adaptive hybrid framework, SAPSO-VMD-GRU. First, the framework employs Variational Mode Decomposition (VMD) to decouple the target sequence into trend, periodic, and random components to reduce complexity and filter noise. Then, Lagged Cross-Correlation Analysis (LCCA) is introduced to quantify the time-lagged correlation between the target components and variables such as rainfall and multi-depth soil temperature and moisture, eliminating redundant features to achieve deep fusion of multi-source information. This paper designs an adaptive particle swarm optimization algorithm, SAPSO, by integrating improved Circle chaotic initialization, Sa-function-based nonlinear inertia weight, and a two-stage Cauchy mutation strategy. SAPSO is used to adaptively determine the VMD parameters and the key GRU hyperparameters in different modeling stages. Experiments based on the Bayintun landslide dataset show that, by utilizing the past 5 days of multi-source historical data, including GNSS displacement, rainfall, and lagged soil moisture and temperature, as inputs to forecast the next-day displacement, the proposed framework achieved $R^2$ values above 0.95 on the chronological hold-out validation subset at all three GNSS monitoring stations. These results indicate that the proposed framework can effectively capture lagged triggering effects and improve displacement prediction accuracy under complex, noisy, and non-stationary monitoring conditions. Full article