Search Results (631)

Japanese modern irrigation management is considered a successful model of water governance worldwide. However, debates continue over whether this success is due to natural water abundance or to water management practices. This study evaluates pre-modern water scarcity risk for six irrigation schemes, developed during that period in the Kinu River Basin (1603–1868); a period without large reservoirs, canal systems, or modern regulatory technologies. As the methodology, pre-modern river flows were reconstructed by removing the effects of four modern dams from the present-day river discharge, adjusting the conveyance efficiency, changes in paddy field area, rainfall input, and return flows. Water demand was assessed using Japanese irrigation standards of 5 mm/d (minimum water demand corresponding to evapotranspiration) and 20 mm/d (easy management), and risk was evaluated under both the prior appropriation and Equal Water Distribution rules. Results show that modern flow in the dry season is approximately 25 m³/s, whereas reconstructed natural flow during drought years declines to 10–18 m³/s, and about 15 m³/s after rainfall adjustment. Under the 20 mm/d demand scenario, scarcity occurred in four schemes (2 of 17 years in the third scheme and 7 of 17 years for the sixth scheme), while no scarcity occurred under the minimum-demand scenario (5 mm/d), even during low-flow conditions. This indicates that the available water in these schemes was at a level where drought damage could occur under extensive irrigation management, but could be avoided by intensive irrigation management to supply the minimum necessary water to all paddy fields. Full article

Floods are among the most catastrophic natural disasters, causing severe impact on human lives and ecosystems. The proposed methodology integrates Geographic Information Systems, 2D hydraulic modeling, and deep learning techniques to develop a computational simulation approach for flood extent prediction and was implemented in the Enipeas River basin, located within the Thessalia River Basin District, Greece. Hydrological analysis was performed using the HEC-HMS software (version 4.12), while hydraulic simulations were conducted with HEC-RAS 2D. The hydraulic modeling produced synthetic flood scenarios for a 1000-year return period, generating spatially distributed outputs of flood extents. The deep learning algorithm was based on a U-Net (CNN) architecture. The model was trained using multi-channel raster tiles, including open access geospatial data such as Digital Elevation Model, slope, flow direction, stream centerline, land use, and simulated flood extents. Model validation was carried out in two independent domains (TS1 and TS2) located within the same river basin. Model outputs are adequately compared with both 2D hydraulic simulations and official Flood Risk Management Plan maps, and the comparison indicates close spatial and quantitative agreement, with flood extent area differences below 8%. Based on the results, the proposed methodology presents a potential and efficient tool for rapid flood risk mapping. Full article

High-resolution satellite precipitation products are key inputs for basin-scale rainfall estimation, but they still exhibit substantial biases in complex terrain and during heavy rainfall. Recent multi-source fusion studies have shown that simply stacking multiple same-type microwave satellite products yields only limited additional gains for high-quality precipitation estimates and may even introduce local degradation, suggesting that targeted correction of a single, widely validated high-quality microwave product (such as IMERG) is a more rational strategy. Focusing on the mountainous, gauge-sparse Lüshui River basin with pronounced relief and frequent heavy rainfall, we use GPM IMERG V07 as the primary microwave product and incorporate CHIRPS, ERA5 evaporation, and a digital elevation model as auxiliary inputs to build a daily attention-enhanced CNN–LSTM (A-CNN–LSTM) bias-correction framework. Under a unified IMERG-based setting, we compare three network architectures—LSTM, CNN–LSTM, and A-CNN–LSTM—and test three input configurations (single-source IMERG, single-source CHIRPS, and combined IMERG + CHIRPS) to jointly evaluate impacts on corrected precipitation and SWAT runoff simulations. The IMERG-driven A-CNN–LSTM markedly reduces daily root-mean-square error and improves the intensity and timing of 10–50 mm·d⁻¹ rainfall events; the single-source IMERG configuration also outperforms CHIRPS-including multi-source setups in terms of correlation, RMSE, and performance across rainfall-intensity classes. When the corrected IMERG product is used to force SWAT, daily Nash-Sutcliffe Efficiency increases from about 0.71/0.70 to 0.85/0.79 in the calibration/validation periods, and RMSE decreases from 87.92 to 60.98 m³ s⁻¹, while flood peaks and timing closely match simulations driven by gauge-interpolated precipitation. Overall, the results demonstrate that, in gauge-sparse mountainous basins, correcting a single high-quality, widely validated microwave product with a small set of heterogeneous covariates is more effective for improving precipitation inputs and their hydrological utility than simply aggregating multiple same-type satellite products. Full article

To unravel hydrological controls on dissolved organic matter (DOM)–microbe interactions in river ecosystems, this study integrated 3D excitation–emission matrix spectroscopy (3D-EEMs), parallel factor analysis (PARAFAC), and 16S rRNA sequencing to characterize seasonal DOM dynamics and microbial assembly in China’s Rushan River Basin. PARAFAC resolved contrasting DOM signatures between dry (four protein-like, two humic-like components) and wet seasons (three protein-like, three humic-like components). Dry-season DOM was dominated by tyrosine-like substances (58.03%), reflecting microbial degradation and phytoplankton activity, while wet-season DOM showed elevated tryptophan-like components (34.38%) and terrestrial fulvic acids (17.14%), which may be related to rain-driven external inputs. The α -diversity of the microbiota is relatively high in the wet season, mainly consisting of Proteobacteria (34.06–68.10%) and Actinobacteriota (9.15–20.76%). In the dry season community, there are Bacteroidota (14.71–38.45%) and Verrucomicrobiota (6.13–14.32%). The structural equation model (SEM) semi-quantified the comprehensive pathways by which microorganisms inhibit unstable proteins and enhance humification. These results reveal the synergistic regulatory role of hydrological seasonality on DOM and microorganisms, and provide a basis for adaptive water quality management. Full article

Background/Objectives: Glioblastoma (GBM) exhibits heterogeneous, nonlinear invasion patterns that challenge conventional modeling and radiomic prediction. Most deep learning approaches describe the morphology but rarely capture the dynamical stability of tumor evolution. We propose an AI framework that approximates a latent attractor landscape of GBM morphodynamics—stable basins in a continuous manifold that are consistent with reproducible morphologic regimes. Methods: Multimodal MRI scans from BraTS 2020 (n = 494) were standardized and embedded with a 3D autoencoder to obtain 128-D latent representations. Unsupervised clustering identified latent basins (“attractors”). A neural ordinary differential equation (neural-ODE) approximated latent dynamics. All dynamics were inferred from cross-sectional population variability rather than longitudinal follow-up, serving as a proof-of-concept approximation of morphologic continuity. Voxel-level perturbation quantified local morphodynamic sensitivity, and proof-of-concept control was explored by adding small inputs to the neural-ODE using both a deterministic controller and a reinforcement learning agent based on soft actor–critic (SAC). Survival analyses (Kaplan–Meier, log-rank, ridge-regularized Cox) assessed associations with outcomes. Results: The learned latent manifold was smooth and clinically organized. Three dominant attractor basins were identified with significant survival stratification (χ² = 31.8, p = 1.3 × 10⁻⁷) in the static model. Dynamic attractor basins derived from neural-ODE endpoints showed modest and non-significant survival differences, confirming that these dynamic labels primarily encode the morphodynamic structure rather than fixed prognostic strata. Dynamic basins inferred from neural-ODE flows were not independently prognostic, indicating that the inferred morphodynamic field captures geometric organization rather than additional clinical risk information. The latent stability index showed a weak but borderline significant negative association with survival (ρ = −0.13 [−0.26, −0.01]; p = 0.0499). In multivariable Cox models, age remained the dominant covariate (HR = 1.30 [1.16–1.45]; p = 5 × 10⁻⁶), with overall C-indices of 0.61–0.64. Voxel-level sensitivity maps highlighted enhancing rims and peri-necrotic interfaces as influential regions. In simulation, deterministic control redirected trajectories toward lower-risk basins (≈57% success; ≈96% terminal distance reduction), while a soft actor–critic (SAC) agent produced smoother trajectories and modest additional reductions in terminal distance, albeit without matching the deterministic controller’s success rate. The learned attractor classes were internally consistent and clinically distinct. Conclusions: Learning a latent attractor landscape links generative AI, dynamical systems theory, and clinical outcomes in GBM. Although limited by the cross-sectional nature of BraTS and modest prognostic gains beyond age, these results provide a mechanistic, controllable framework for tumor morphology in which inferred dynamic attractor-like flows describe latent organization rather than a clinically predictive temporal model, motivating prospective radiogenomic validation and adaptive therapy studies. Full article

The Eocene fourth member of the Shahejie formation (Es4x) in the Bonan Depression, Bohai Bay Basin, records syn-rift sedimentation under alternating arid and humid climates. It provides insight into how orbital-scale climatic fluctuations influenced tectonics, facies patterns, and reservoir distribution. This study integrates 406 m of core data, 92 thin sections, 450 km² of 3D seismic data, and multiple geochemical proxies, leading to the recognition of five facies associations (LFA): (1) alluvial fans, (2) braided rivers, (3) floodplain mudstones, (4) fan deltas, and (5) saline lacustrine evaporites. Three major depositional cycles are defined within the Es4x. Seismic reflections, well-log patterns, and thickness trends suggest that these cycles represent fourth-order lake-level fluctuations (0.8–1.1 Myr) rather than short 21-kyr precession rhythms. This implies long-term climate and tectonic modulation, likely linked to eccentricity-scale monsoon variability. Hyperarid phases are marked by Sr/Ba > 4, δ¹⁸O > +4‰, and thick evaporite accumulations. In contrast, Sr/Ba < 1 and δ¹⁸O < −8‰ reflect humid conditions with larger lakes and enhanced fluvial input. During wet periods, rivers produced sand bodies nearly 40 times thicker than in dry intervals. Reservoir quality is highest in braided-river sandstones (LFA 2) with 12%–19% porosity, preserved by chlorite coatings that limit quartz cement. Fan-delta sands (LFA 4) have <8% porosity due to calcite cementation, though fractures (10–50 mm) improve permeability. Floodplain mudstones (LFA 3) and evaporites (LFA 5) act as seals. This work presents a predictive depositional and reservoir model for arid–humid rift systems and highlights braided-river targets as promising exploration zones in climate-sensitive basins worldwide. Full article

For an alpine inland river basin affected by climate change, the interaction between groundwater (GW) and surface water (SW) within the watershed plays a crucial role in water resource management. To explore the bidirectional dynamic coupling of surface water and groundwater, this work adopted the extensively employed SWAT–MODFLOW model. Results indicate that statistical parameters including R² (0.81 for calibration periods and 0.79 for validation), NSE (0.79 for calibration periods and 0.75 for validation), RMSE (0.59~1.25 m), and PBIAS (15.21%) demonstrate the dependability of the SWAT–MODFLOW model in evaluating groundwater–surface water exchange processes within alpine inland river basins. Long-term monitoring data show that groundwater levels exhibited an upward trend, rising from 2895.35 m in 2005 to 2906.75 m in 2022. Notably, since 2018, groundwater levels have entered a period of being consistently above the long-term average. In terms of spatial distribution, the groundwater level patterns in 2005, 2010, and 2015 remained relatively consistent, marked by a west-to-east decreasing gradient. However, by 2020, this spatial distribution pattern shifted, marked by an east-to-west decreasing gradient. Meanwhile, our results reveal a pattern of upstream surface water recharge, bidirectional fluctuation in the middle reaches, and downstream groundwater-dominated recharge during the period of 2000~2023. During the 2000–2009 period, groundwater in sub5 received recharge from surface water, with the exchange rate ranging from −4987.75 to −374.82 m³/d. Conversely, during 2010–2023, groundwater in sub5 discharged into surface water, with the exchange rate ranging from 1136.75 to 56,646.56 m³/d. Moreover, there is seasonal variability in the SW–GW interchange relationship. In spring and summer, surface water primarily replenishes groundwater, whereas in autumn and winter, groundwater primarily replenishes surface water. This study provides a foundational method for assessing groundwater–surface water interactions in alpine inland river basins, which will contribute to the evaluation and management of local water resources. Full article

As the last critical hydrological control station on the Lancang River before it flows out of China, the daily runoff variations at the Yunjinghong Hydrological Station are directly linked to agricultural irrigation, hydropower development, and ecological security in downstream Mekong River riparian countries such as Laos, Myanmar, and Thailand. Aiming at the core issues of the runoff sequence in the Lancang–Mekong Basin, which is characterized by prominent nonlinearity, non-stationarity, and coupling of multi-scale features, this study proposes a synergistic prediction framework of “multi-scale decomposition-model improvement-parameter optimization”. Firstly, Regenerated Phase-Shifted Sine-Assisted Empirical Mode Decomposition (RPSEMD) is adopted to adaptively decompose the daily runoff data. On this basis, a Convolutional Sparse Attention Transformer (CSAT) model is constructed. A one-dimensional convolutional neural network (1D-CNN) module is embedded in the input layer to enhance local feature perception, making up for the deficiency of traditional Transformers in capturing detailed information. Meanwhile, the sparse attention mechanism replaces the multi-head attention, realizing efficient focusing on key time-step correlations and reducing computational costs. Additionally, an Improved Multi-Verse Optimizer (IMVO) is introduced, which optimizes the hyperparameters of CSAT through a spiral update mechanism, exponential Travel Distance Rate (T_DR), and adaptive compression factor, thereby improving the model’s accuracy in capturing short-term abrupt patterns such as flood peaks and drought transition points. Experiments are conducted using measured daily runoff data from 2010 to 2022, and the proposed model is compared with mainstream models such as LSTM, GRU, and standard Transformer. The results show that the RPSEMD-IMVO-CSAT model reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 15.3–28.7% and 18.6–32.4%, respectively, compared with the comparative models. Full article

Intensity–Frequency–Duration Curves (IDF curves) are a tool applied in hydraulic and hydrology engineering to design infrastructure for rainfall management. They express how precipitation, with a defined duration (D) and intensity (I), is frequent in a certain area. They are built from past recorded rainfall series, applying the extreme value statistics, and they are considered invariant in time. However, the current climate change projections are showing a detectable positive trend in temperatures, which, according to Clausius–Clapeyron, is expected to intensify extreme precipitation (higher temperatures bring more water vapour available for precipitation). According to the IPCC (Intergovernmental Panel on Climate Change) reports, rainfall events are projected to intensify their magnitude and frequency, becoming more extreme, especially across “climatic hot-spot” areas such as the Mediterranean basin. Therefore, a sensible modification of IDF curves is expected, posing some challenges for future hydraulic infrastructure design (i.e., sewage networks), which may experience damage and failure due to extreme intensification. In this paper, a methodology for reconstructing IDF curves by analysing the EURO-CORDEX climate model outputs is presented. The methodology consists of the analysis of climatic rainfall series (that cover a future period up to 2100) using GEV (Generalised Extreme Value) techniques. The future anomalies of rainfall height (H) and their return period (RP) have been evaluated and then compared to the currently adopted IDF curves. The study is applied in Lombardy (Italy), a region characterised by strong orographic precipitation gradients due to the influence of Alpine complex orography. The future anomalies of H evaluated in the study show an increase of 20–30 mm (2071–2100 ensemble median, RCP 8.5) in rainfall depth. Conversely, a significant reduction in the return period by 40–60% (i.e., the current 100-year event becomes a ≈40–60-year event by 2071–2100 under RCP 8.5) is reported, leading to an intensification of extreme events. The former have been considered to correct the currently adopted IDF curves, taking into account climate change drivers. A series of applications in the field of hydraulic infrastructure (a stormwater retention tank and a sewage pipe) have demonstrated how the influence of IDF curve modification may change their design. The latter have shown how future RP modification (i.e., reduction) of the design rainfall may lead to systematic under-design and increased flood risk if not addressed properly. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

In this study, we develop a 3D thermal model of the South China Sea (SCS) lithosphere through the joint analysis of heat flow, Curie-point depth derived from magnetic anomalies, and shear wave velocity. Results show the Moho temperature is below 250 °C in the oceanic basin but exceeds 350 °C in continental margins. We evaluate potential Moho drilling sites based on temperature, crustal thickness, water depth, and sediment thickness, identifying six favorable zones in the east sub-basin. The thermal lithosphere thickness correlates with tectonic settings in continental areas, while the oceanic lithosphere is thicker than predicted by theoretical models. Global analysis suggests that the slow spreading rate may have also contributed to the thickening of the oceanic lithosphere in the SCS. Full article

This study addresses the technical gaps in current flood simulation for regulated lakes, such as insufficient accuracy in simulating complex gate and dam operation processes and low computational efficiency that fails to meet practical engineering needs. By employing an improved two-dimensional (2D) hydrodynamic model, it systematically analyzes flood control strategies for large regulated lakes. Using the August 2018 flood event for model validation, the final simulation results indicate that the current flood control capacity meets standards for 50-year floods (Nanyang 36.79 m, Weishan 35.99 m) but fails for 100-year floods, exceeding limits by 0.23 m (Nanyang 37.22 m) and 0.15 m (Weishan 36.64 m). The designed conditions reduce 100-year flood levels to 36.98 m and 36.47 m, respectively, achieving the required flood defense standard for 100-year events. The findings provide a quantitative framework for evaluating flood control capacity across different planning scenarios, which advances flood risk management and offers implementable insights for achieving sustainable water resource management in regulated lake basins globally. This, in turn, contributes directly to two United Nations Sustainable Development Goals (SDGs): enhancing human community safety and resilience (SDG 11: Sustainable Cities and Communities) through improved flood control engineering and operations, and strengthening climate adaptation (SDG 13: Climate Action) by boosting basin-wide resilience to extreme rainfall and flooding. Full article

The noise produced by high dam flood discharge is prolonged and propagates over a great distance, significantly impacting the lives of nearby residents. However, accurately predicting and mitigating this noise remains challenging due to the complex nature of its sources and the lack of comprehensive models that are capable of deconstructing the overall sound field. This study systematically investigates the propagation characteristics and generation mechanisms of environmental noise from flood discharges at the Xiangjiaba Hydropower Station. A novel three-dimensional framework for classifying acoustic sources (point, line, and surface) is proposed. By integrating prototype observations with Strouhal-scaled hydraulic model tests, a multi-source sound field model was developed that employs a regression algorithm to quantify the power of individual sound sources based on holistic field measurements. The model achieves prediction accuracy within 1.5 dB when validated against prototype data. The results indicate that the rolling water surface in the stilling basin (surface source) is the dominant contributor to noise. A key quantitative finding is that, under identical discharge conditions, the noise intensity generated by surface spillways is three times greater than that produced by bottom spillways. Overall, this model serves as a critical tool for understanding acoustic characteristics and formulating noise-informed operational strategies. Full article

The Devonian–Permian succession of the Tasbulak Trough in the Shu–Sarysu Basin contains confirmed gas shows (wells 462, 1-P Izykyr, 1-P Sokyr-Tobe, and 1-P Kamenistaya) and a sedimentary cover exceeding 5500 m but still lacks a unified 3D structural interpretation capable of explaining the distribution of gas-prone intervals. This study addresses this gap by digitizing and integrating legacy well and 2D seismic datasets to construct horizon-consistent three-dimensional structural surfaces for eight target horizons. The resulting model reveals a low-deformation structural framework dominated by a previously undocumented element—the Central Tasbulak Ridge—which exerts first-order control on fault segmentation, trap geometry, and gas preservation. Structural surfaces were synthesized with stratigraphic intervals to define reservoir–seal–trap relationships, highlighting the late Visean–early Serpukhovian carbonate subformation as the primary target interval. Building on these relationships, a prospect evaluation matrix was developed to classify structural, stratigraphic (including intraformational), and combination trap types together with their corresponding sealing units. The results demonstrate long-term tectonic stability, multi-level evaporitic seals, and inheritance-guided trap evolution, providing a reference framework for assessing gas prospectivity in data-limited intracratonic basins and advancing understanding of petroleum-system architecture in stable continental settings. Full article

A combined Differential InSAR (D-InSAR) and Digital Elevation Model (DEM)-based analysis revealed that earthquake-triggered landslides significantly altered river morphology and intensified flooding in the Kota Belud catchment, Sabah, Malaysia. This 1386 km² catchment, home to about 120,000 people, has experienced a marked rise in flood events following the 4 June 2015 and 8 March 2018 earthquakes. Multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data and a 30 m Shuttle Radar Topography Mission (SRTM) DEM, complemented by river network information from HydroBASINS, were integrated to map sediment redistribution and model flood extent. Upstream zones exhibited extensive coseismic landslides and pronounced geomorphic disruption. Interferometric analysis showed that coherence was well preserved over stable terrain but rapidly degraded in vegetated and steep areas. Sediment aggradation, interpreted qualitatively from patterns of coherence loss and increased backscatter intensity, highlights slope failure initiation zones and depositional build-up along channels. Conversely, downstream, similar sedimentary adjustments were detected immediately upstream of areas with repeated flood incidents. Between 2015 and 2018, flood occurrences increased over fivefold, and after 2018, they increased by more than thirteenfold relative to pre-2015 conditions. DEM-based inundation simulations demonstrated that channel shallowing substantially reduced conveyance capacity and expanded flood extent. Collectively, these results confirm that earthquake-induced landslides have contributed to reshaping the geomorphology and amplified flooding in the area. Full article