Search Results (1,002)

Satellite-based precipitation estimates (SPE) provide essential spatial coverage and near real-time availability for hydrological applications but often exhibit systematic biases in regions characterized by complex terrain and strong climatic variability, limiting their reliability for flood-related studies. To address these limitations, this study proposes an Adaptive Regularization framework integrated within a Long Short-Term Memory (LSTM) model to enhance satellite–gauge rainfall fusion beyond conventional optimization strategies. The framework dynamically adjusts learning rate and weight decay during training based on validation performance and overfitting indicators, improving training stability, data efficiency, and model generalization across diverse precipitation regimes. The proposed approach was applied to refine Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-Final) daily rainfall estimates over the flood-prone east coast of Peninsular Malaysia. Model performance was assessed against ten optimization algorithms using correlation coefficient (CC), mean absolute error (MAE), normalized root mean squared error (NRMSE), percentage bias (PBias), and Kling–Gupta efficiency (KGE). Results show that the Adaptive Regularization framework consistently outperforms all benchmark optimizers, achieving an MAE of 6.87, CC of 0.68, NRMSE of 1.84, and KGE of 0.56. Overall, the proposed framework enhances spatial consistency and robustness across monsoon seasons, offering a scalable solution for improving SPE in flood-prone regions. Full article

Winter precipitation plays an important role in the Himalayan region. However, its reliable assessment is difficult due to sparse ground precipitation measurements, limited ability to capture heterogeneity, and snowfall undercatch. Recent advances in satellite-based winter precipitation products (SPPs) enable comprehensive, consistent spatial data in this region; however, despite rapid improvements and the increased availability of SPPs, their accuracy is still uncertain. This calls for rigorous evaluation across several regions. This study presents a new SPP evaluation method that extends existing frameworks by adding two additional indicators—spatial correlation and the water balance consistency ratio (WBCR) to create a unified multi-criteria matrix for selecting spatially and hydrologically consistent products from among 11 latest and earlier SPPs from the global satellite mapping of precipitation (GSMaP) and The integrated multi-satellite retrievals for the global precipitation measurement Mission (IMERG) in the Kabul, Dudhkoshi, and Chamkharchu River basins. The results show that the latest non-calibrated product performed significantly better than earlier releases, demonstrating improved ability to capture precipitation events, spatial heterogeneity, and WBCR across all three basins. However, the performance of those SPPs varies substantially across regions. GSMaP gauge-calibrated product performance was more consistent across conventional multi-criteria assessment and WBCR, but their inability to capture spatial heterogeneity limits their applicability for sub-catchment water resource management. On the other hand, IMERG Final V07 (gauge-calibrated) performed exceptionally well across all regions, although its 3.5 month latency limits near-real-time applications. Therefore, GSMaP NRT V08 is suitable for real-time applications, given its short ~4 h latency and relatively good performance across all three basins. Future studies using the selected products will provide reliable information for policymakers and will support water hazard risk reduction. Full article

Climate change leads to less water in forest ecosystems and higher evapotranspiration during the growing season, increasing the risk of drought. This study evaluates microclimate and soil hydrology at two different sites in the Dobroč Primeval Forest (National Nature Reserve, NATURA 2000): a near-natural fir–beech buffer zone and a managed Norway spruce monoculture. Measurements cover two hydrological years with very different climatic conditions. The Climatic Water Balance (CWB) was used to assess precipitation deficit, and soil moisture dynamics were simulated with the GLOBAL mathematical model. In 2021, precipitation was 223.7 mm below the long-term average, and the cumulative CWB deficit from March to September was 224 mm. Drought risk peaked in summer 2021. The spruce stand’s A/B horizon was 197 days below the point of decreased availability (PDA), compared to 179 days in the beech buffer zone. Drought moved through the soil profile with a 3–4-day lag between horizons at both sites. Results confirm that Norway spruce monocultures are more drought-vulnerable than near-natural beech stands under identical conditions, supporting active forest conversion in Central European mountain regions. Full article

Propagating easterly waves (EW) are analyzed here, within the dynamical environment of the Caribbean Low-Level Jet (CLLJ) using radiosondes from the Organization of Tropical East Pacific Convection (OTREC) field campaign, ERA5 reanalysis, and lightning from the World Wide Lightning Location Network (WWLLN) over $5^{\circ }$–${20}^{\circ }$ N, ${60}^{\circ }$–${100}^{\circ }$ W during 21 August–30 September 2019. Radiosondes resolve the vertical structure of the waves at San Andrés (Colombia), Limón and Santa Cruz–Guanacaste (Costa Rica), while ERA5 provides spatial–temporal continuity and vertically integrated diagnostics—namely, the vertically integrated moisture flux divergence (VIMFD) and the vertically integrated geopotential flux divergence (VIGFD). Lightning from WWLLN and precipitation from ERA5 and the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement mission (GPM IMERG) offer independent convective proxies to track disturbances. Mean profiles from radiosondes and ERA5 show strong agreement at Limón and Guanacaste and some differences at San Andrés, yet all datasets capture coherent, phase-locked anomalies in zonal wind, meridional wind, temperature, humidity, vertical velocity and vorticity used to diagnose EW–CLLJ interactions. VIMFD, VIGFD, lightning and precipitation exhibit westward-propagating cores that align with the above anomalies, indicating that organized convection is coupled to the disturbances, whereas the mean state preconditions the environment to enable wave-induced upward motion. A robust vertical adjustment of the CLLJ is documented: the core shifts from near 925 hPa over the Caribbean Sea to about 700 hPa over the Eastern Tropical Pacific ($\Delta p\sim 150$ hPa). This feature is reproduced by a 30-year ERA5 climatology, consistent with jet-exit forcing and enhanced boundary-layer coupling over land. Conditions favorable for barotropic instability using the Rayleigh–Kuo criterion, were present over most of the period. A qualitative barotropic conversion proxy, computed from the eddy momentum covariance $⟨u^{\prime }{v}^{\prime }⟩$, shows positive values in the lower troposphere at Guanacaste and in the layer 850–700 hPa at San Andrés, suggesting mean-to-eddy momentum transfer, whereas the signal at Limón is weaker. Together, these results provide a physically consistent view of EW–CLLJ interactions across the IAS; therefore, a schematic of those mechanisms is proposed here. The results highlight the need for high-resolution modeling and full energy-budget analyses. Full article

Atmospheric moisture availability plays a central role in regulating the occurrence and persistence of extreme precipitation, but its temporal linkage with large-scale moisture transport remains insufficiently quantified at the global scale. This study examines the lagged relationship between moisture transport across the land–ocean interface and extreme precipitation using ERA5 reanalysis data for the period 1979–2020. Extreme precipitation is characterized using the R95pTOT index, which measures the total precipitation accumulated during very wet periods. Vertically integrated moisture fluxes are projected onto coastal boundaries to quantify inflow, outflow, and netflow components of moisture transport. Lagged Pearson correlations as well as the p-value between these components and R95pTOT are evaluated globally and for four representative regions: the Asian Monsoon, the Amazon Basin, the Gulf of Mexico and North America, and West Africa. The results show that moisture inflow is positively associated with extreme precipitation across most regions, indicating that enhanced ocean-to-land moisture transport supports increased atmospheric moisture availability during extreme events. The strongest and most persistent relationships are found in tropical regions, where significant inflow–precipitation correlations persist for approximately 5–10 days. In contrast, mid-latitude coastal regions exhibit weaker and more transient relationships, consistent with the influence of rapidly evolving synoptic systems. Netflow correlations generally display weaker and more regionally dependent associations with extreme precipitation, with outflow showing weak or negative relationships in some regions, particularly in West Africa. Overall, the findings demonstrate that both the direction and temporal persistence of moisture transport play an important role in shaping regional differences in extreme precipitation. Full article

Cryphalus dilutus is an emerging invasive pest of tropical and subtropical regions, with Mangifera indica and Ficus carica being its primary host plants. Larval damage caused by this insect can lead to severe tree wilting, posing a direct threat to agricultural production and ecological security. Native to South Asia, C. dilutus has established introduced populations in the Near East, Mexico, and other areas. In recent years, it has invaded multiple regions, including southern China and southern Italy. Given the widespread global distribution of host plants and the intensification of climate change, their distribution ranges are expected to expand. However, research assessing the potential global geographical distribution of this pest under climate change is lacking. In this study, we used the Random Forest model to predict the potential distribution range of C. dilutus. Under historical climatic conditions between 1970 and 2000, suitable climatic regions for C. dilutus were primarily distributed across southern China, southeastern Brazil, southeastern Mexico, the Congo Basin periphery, and the Iberian Peninsula, with a total area of 12,192.42 × 10⁴ km². The Temperature Annual Range and Precipitation of Warmest Quarter were identified as key environmental determinants that shaped its distribution. Under the future RCP4.5 climate scenario projected for the 2050s, the total suitable area for C. dilutus is projected to contract. Specifically, high-, medium-, and low-suitability areas are projected to decline by 52.77%, 62.39%, and 24.02%, respectively. While the total area of the very low zones is expected to increase, the total area of the suitable region has been reduced to 11,891.17 ×10⁴ km². Future climate change is expected to drive the distribution northward to high-altitude areas and inland areas. Model projections indicate a poleward expansion of the fundamental climatic niche, with climatic suitability increasing in high-latitude and high-altitude regions, such as Northern Europe and western North America. Conversely, current core tropical habitats in the Indian subcontinent and the Amazon Basin are projected to face significant habitat degradation due to thermal stress. Agricultural regions previously considered relatively safe due to climatic constraints, such as northern China, the midwestern United States, and Eastern Europe, may face new challenges from pest infestation. These findings underscore the importance of proactive monitoring and implementation of preventive measures. This provides crucial decision support for countries and regions to formulate precise pest control strategies and offers a theoretical basis for early monitoring and prevention of cross-border invasions on a global scale. Full article

Empoasca fabae (Harris) is a destructive migratory pest threatening potato cultivation globally. As climate change may facilitate its invasion into China, the world’s largest potato producer, projecting its potential range is critical for early warning. To account for its migratory biology, this study utilized an optimized MaxEnt model tuned via ENMeval to approximate climatic suitability for potential establishment (year-round persistence) under current and future scenarios (CMIP6: BCC-CSM2-MR; SSP1-2.6, SSP2-4.5, SSP5-8.5). The main model achieved high accuracy (AUC = 0.912), identifying Precipitation Seasonality (Bio15) and Annual Precipitation (Bio12) as critical drivers. While current suitable habitats for permanent establishment are concentrated in East, Central, and Southwest China, future warming is projected to cause a sharp contraction (73.65–80.42%) and a northwestward centroid shift due to thermal stress. However, a sensitivity analysis excluding the winter temperature constraint (Bio6) revealed a critical spatiotemporal decoupling: while year-round establishment contracts, potential seasonal exposure during the growing season is projected to expand into higher latitudes. These findings provide a hierarchical scientific basis for targeting monitoring and quarantine measures against both permanent establishment and seasonal summer invasions in shifting high-exposure zones. Full article

Populations in areas with limited hydrological data face ongoing challenges related to water supply and management, with climate change increasing the risks of floods and droughts. New remote sensing and modeling tools can improve land and water management in these regions, especially when combined with limited ground measurements and local knowledge of extreme events. This study examined hydrological extremes and land cover change impacts in the Grande Rivière du Nord watershed, Haiti, using satellite and model-based data. Precipitation extremes were obtained from the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG; 2000–2025), and streamflow data were sourced from the Group on Earth Observation Global Water Sustainability (GEOGLOWS) system and bias-corrected with a small historical hydrologic database. Annual maximum series were created and fitted with Gumbel, Lognormal, and Generalized Extreme Value (GEV) distributions using the L-moment method. Goodness-of-fit tests identified the best models, and precipitation amounts for return periods of 2–100 years were estimated. The precipitation maxima aligned with locally reported extreme events, and GEV provided the best overall fit. Using the bias-corrected streamflow, a hydrologic model was calibrated and validated and then applied to land cover change scenarios. Simulations suggest that moderate land-use change can increase peak flows beyond channel capacity, raising flood risk and informing adaptation planning in northern Haiti, which has limited data. Full article

Intercomparison studies between satellite-based and ground-based radar systems are essential for advancing radar technologies and improving precipitation retrieval algorithms. This study conducted a systematic literature review of Global Precipitation Measurement Mission (GPM) Dual-Frequency Precipitation Radar (DPR) and ground-based radar intercomparison studies using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method, focusing on peer-reviewed literature published between 2014 and 2024. The review synthesizes current knowledge of DPR precipitation detection and estimation, including the application of DPR in ground-based radar calibration, and discussions on retrieval methods and attenuation correction algorithms. Most studies used a volume-matching method to compare observations between datasets and examine S- and C-band radars from national networks. Most analyses occurred over the Northern Hemisphere, and individual ground-based radars were more frequently compared to DPR rather than examining mosaics. Beyond summarizing existing studies, this review identifies systematic, geographic, methodological, and algorithmic gaps that constrain comprehensive validation of DPR products. Recurrent bias patterns—such as precipitation-type-dependent errors and attenuation-related uncertainties—highlight priority areas for algorithm refinement and targeted validation campaigns. By synthesizing validation strategies and recurring performance limitations, this work provides a structured reference for future intercomparison studies, supports more standardized validation practices, and informs the development of improved precipitation retrieval algorithms, ground-based radar calibration practices, and next-generation satellite radar missions. Full article

Coniferous forests account for 78% of the western US forests and store a substantial amount of carbon. Wildfires significantly alter vegetation structure and associated forest carbon stocks. This study evaluates postfire biomass recovery trajectories (1984–2017) and total biomass accumulation in conifer forests that historically experienced low-severity, high-frequency fire regimes in the western US using recently launched Global Ecosystem Dynamic Investigations (GEDI) mission lidar data. All three ecoregions studied, including the Pacific Northwest (PNW), Southern Rockies (SR), and Northern Rockies (NR), show site-specific biomass recovery trajectories shaped by fire severity. The recovery trajectories were characterized by an initial decline and a variable gain with time since fire across the three ecoregions. Regions with low burn severity recovered to the unburned background state within the first three decades, while regions with higher burn severity only recovered in the Northern Rockies after five decades without fire. Moderate- and high-severity burned areas in both SR and PNW exhibited slow declines or sustained low biomass periods following fires, implying potential ecosystem transformation or an arrested state of lower biomass. Time since fire and fire severity were identified as the most significant drivers of postfire biomass recovery, likely because they reflect both reduced seed availability and the process of seedling establishment and regeneration. In addition, distance to unburned area, drought (measured using the Standardized Precipitation Evapotranspiration Index (SPEI)), elevation, and fire size were important drivers of biomass recovery. Our results demonstrate that all three ecoregions experienced a loss of overall biomass (15–23% (+/−40%)), with the largest losses occurring in the areas with high-severity burns (59% (+/−23%)) in the Southern Rockies compared to unburned forests within the first three decades. This study thus confirms GEDI’s ability to assess disturbance-driven vegetation biomass dynamics and provides an open-science methodology that could be utilized for other regions. In conclusion, our study indicates that an increase in fire severity within low-severity, high-frequency fire regimes, beyond historically observed levels, results in greater carbon losses. It is therefore important to consider the effects of increases in fire severity on vegetation recovery trajectories to infer the future carbon potential in these ecosystems. Full article

In light of the dual challenges of global climate change and the pressure on agricultural resources, increasing crop yields and resource utilization efficiency has become the key to ensuring food security and sustainable agricultural development. This study takes environmental factors and cultivation measures as input and crop yield as output; systematically compares five ensemble learning models: RF, LightGBM, GBDT, XGBoost, and CatBoost; and then screens out the CatBoost algorithm with the best performance. The CatBoost-Nondominated Sorting Genetic Algorithm II (NSGA-II) hybrid model was constructed. This model provides data-driven solutions and strategies for cultivating rice and maize through precise yield prediction and multi-objective optimization. To enhance the interpretability of the model, we used the SHAP method to parse the predicted behavior to ensure that the results conform to common agricultural knowledge. Based on this, we constructed a constrained multi-objective optimization problem and solved it using the NSGA-II algorithm to obtain a Pareto frontier that strikes a balance among yield, resource consumption and growth cycle. Case studies showed that CatBoost performs best in the selected datasets. SHAP identified precipitation, fertilization/irrigation intensity and temperature as the main influencing factors; NSGA-II generated a well-distributed Pareto solution set, allowing for the flexible selection of representative cultivation schemes based on different management objectives. This modeling paradigm showed good generalization ability and can be extended to other crop cultivation strategy optimization scenarios based on tabular data. Full article

Detecting precipitation patterns remains a central challenge in hydrological sciences due to the non-linear nature of atmospheric dynamics and the growing influence of climatic variability. This study investigates the evolution of a 64-year daily precipitation series (1961–2024) at the Tulcea meteorological station (Dobrogea, Romania) and introduces a novel adaptation of the Most Probable Precipitation Method (AMPPM), shifting its application from a regional spatial framework to a temporal one. Shannon Entropy is used as a measure of “climatic disorder.” Model evaluation incorporates Mean Error (ME), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), which here measure structural divergence rather than predictive accuracy. Results demonstrate that the Synthetic Representative Series (SRS) isolates the stable climatic signal, reducing the global coefficient of variation (cv (%)) to 70.96% and mitigating extreme skewness typical of coastal convective activity. Seasonal entropy analysis reveals divergence: winter entropy decreases through signal stabilization (minimum 2.00 bits in March), whereas July–October entropy increases, highlighting previously hidden high-frequency daily oscillations. The aggregated Tot_64 series achieves a final entropy of 2.75 bits, confirming a complex, multi-state daily precipitation process. MAE and RMSE values for the SRS (e.g., October: MAE = 1.20, RMSE = 4.53; Tot_64: MAE = 1.40, RMSE = 4.58) indicate that the SRS captures dominant precipitation patterns with minimal deviation, comparable to or better than the moving average approaches. Full article

The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of SWV precipitation remains insufficiently quantified. Using Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM/DPR) observations from 2014 to 2022, this study investigates the vertical structure and macro- and microphysical characteristics of SWV precipitation, and quantifies their differences across life-cycle stages and precipitation types. The mature stage is characterized by higher echo tops, stronger radar reflectivity, higher strong-echo altitudes, and larger near-surface rainfall, together with a clearer melting-layer bright band and a stronger post-melting shift toward larger drops and lower number concentrations. The developing stage is weakest and shows the largest fraction of coalescence–breakup balance signatures, whereas the dissipating stage features enhanced evaporation- and breakup-related signals. Among precipitation types, deep strong convection exhibits the greatest vertical extent with enhanced ice/mixed-phase growth; stratiform precipitation produces stronger radar echoes and higher rainfall rates than deep weak convection despite similar echo-top heights; and shallow precipitation is characterized by smaller drops, higher concentrations, and active warm-rain spectral evolution. These findings provide satellite-based constraints for microphysics parameterization evaluation and improved numerical prediction of SWV-related rainfall over complex terrain. Full article

This study applies a qualitative Geographic Information Systems model that integrates satellite-derived wind and rainfall data to predict potential debris-flow locations in Puerto Rico triggered by Hurricane Maria (2017). A key innovation of the model is the use of wind-driven rainfall (WDR), calculated at multiple elevation levels using satellite wind data and Global Precipitation Measurement (GPM) precipitation at three time steps. WDR replaces the conventional use of total rainfall commonly applied in landslide modeling. A second innovation is the use of WDR slope exposure to hurricane direction in place of a standard aspect parameters. The model assumes that WDR was the primary trigger of debris flows during the hurricane. Predicted debris-flow locations were compared with mapped debris-flow inventories using threshold distances of 1000, 500, and 250 m. Prediction rates ranged from 30 to 100%, and success ratios from 10 to 90%, depending on elevation and distance thresholds, with the best performance at 500 and 1000 m ranges. Model performance could be enhanced through higher-resolution satellite observations of wind, soil moisture, and precipitation, supporting potential real-time hazard applications. Model limitations include its empirical nature, qualitative structure, and current applicability to equatorial or sub-equatorial regions affected by hurricanes or typhoons. Further testing and regional calibration are recommended. Full article

As a pivotal component of the global carbon cycle, the spatial variation in soil respiration (Rs) is crucial for forecasting climate change trajectories. Despite extensive research on the spatial patterns of total Rs, the distinct drivers of its two components, heterotrophic respiration (Rh) and autotrophic respiration (Ra), are still not well defined. We compiled a global dataset from studies published between 2007 and 2023 to investigate the drivers of spatial variations in Rs, Ra, and Rh. This dataset comprises 308 annual flux measurements from 172 sites. The results showed that Rh contributed 63% and 60% to Rs in forest and grassland ecosystems, respectively. Further analyses using structural equation modelling (SEM) showed that the spatial variation in Rh and Ra exhibited divergent responses to climatic factors and plant community structure (mostly driven by gross primary production, GPP). Rh was more affected by mean annual temperature (MAT) than by mean annual precipitation (MAP), with standardized total effects of 0.17 (forests) and 0.57 (grasslands) for MAT versus 0.10 and 0.07 for MAP, respectively. In contrast, Ra exhibited greater sensitivity to MAP (0.08 and 0.18) than to MAT (−0.01 and 0.04). GPP exerted biome-specific effects: in forests, high GPP enhanced Rh (0.18) more substantially than Ra (0.08), while in grasslands, elevated GPP significantly increased Ra (0.34) but suppressed Rh (−0.30). Moreover, these variables incorporated into the SEMs accounted for a greater proportion of the variation in Rh and Ra in grasslands (R² = 0.73 for Rh, 0.48 for Ra) as compared to forests (R² = 0.21 for Rh, 0.22 for Ra), suggesting the greater complexity in forest soil C dynamics. By using the whole yearly measured soil respiration data around the world, this study highlights the differential environmental regulation of Rh and Ra, providing critical insights into the mechanisms governing Rs variations under climate change. Full article