Search Results (2,981)

Evapotranspiration (ET) is a fundamental process within the water cycle and the agricultural water balance, optimizing resource allocation, maintaining soil health, and enhancing ecosystem resilience to climate change. Because ET represents a primary consumptive use of irrigation on agricultural lands, enhancing water-use efficiency and sustainable water management requires accurate estimation of evapotranspiration to support long-term sustainability and productivity. This study offers an effective means to visualize spatial and temporal patterns of reference evapotranspiration (ETo) across various vegetation management practices. This study examined the impacts of agroforestry buffers (ABs), grass buffers (GBs), biofuel crops in an agroforestry watershed (BCa), and biofuel crops in a grass buffer watershed (BCg) on ETo, compared to a corn (Zea mays L.)–soybean (Glycine max L.) rotation (RC) for claypan soil in Northern Missouri, USA. The experimental watersheds were located at the Greenley Memorial Research Center, Missouri, USA. Campbell Scientific sensors and Photosynthetically Active Radiation (PAR) smart sensors were installed to measure net radiation, anemometers, humidity, and air temperature. All instruments were mounted on masts at a height of 2 m above ground level in crop, tree, grass, and biofuel areas. Measured meteorological data were recorded hourly from April to October during 2017 and 2018. Daily ETo predictions were calculated using the Penman–Monteith model. These ETo predictions were displayed across the landscape using Python-based GIS for selected dates (each Saturday) for the watersheds. The methodology was implemented using the software programs of Python 2.7.10 and ArcGIS 10.3.1. The results indicated that ETo increased by 11%, 17%, 18%, and 25% in 2017, and by 7%, 9%, 14%, and 20% in 2018 for AB, BCa, BCg, and GB, respectively, compared to RC management. This process may improve soil water recharge in perennial management systems. Accurate estimation of ET in agricultural regions is critical for understanding water balance, hydrological and ecosystem processes, and climate variability. Given that agriculture constitutes the majority of global water consumption, precise ET estimation is particularly significant for sustainable water management, especially in regions experiencing water scarcity. These outcomes may support effective planning and management of agricultural water resources by enabling optimized irrigation and agricultural production. Full article

Existing flood risk models have improved predictive accuracy, but they prioritize natural and hydrological factors while giving limited attention to fine-grained urban morphology. This study develops an interpretable deep learning framework to examine how high-resolution, three-dimensional urban form shapes two dimensions of flood risk: inundation risk, measured by grid-level inundated area, and infrastructure risk, measured by flood-related disruptions, including water supply interruption, power outage, road blockage, and collapse-related damage. Using Zhengzhou, China, as a case study, we combine multi-source spatial data, convolutional neural networks, ablation analysis, SHAP interpretation, and Gaussian Mixture Model classification to examine how fine-grained urban morphology affects these two risk dimensions. Incorporating urban morphology improved inundation risk prediction, reducing MSE from 0.0431 to 0.0371. The improvement was greater for infrastructure risk, with accuracy increasing from 0.7327 to 0.8218, and ROC-AUC from 0.83 to 0.95. SHAP results show that inundation risk is associated with vegetation, elevation, hydrological proximity, and localized spatial disorder, whereas infrastructure risk is amplified by vertical intensity, imperviousness, building concentration, porosity, and shape. Spatially, very high infrastructure-risk areas accounted for only 2.30% of the city but 12.88% of the central districts, while 74.62% of very high infrastructure-risk zones were concentrated in dense mid- to high-rise morphology. These findings suggest that flood-resilient planning should move beyond hydrology-sensitive flood management toward morphology-sensitive planning. Full article

Climate change and urbanization have increased the precipitation variability and extreme hydrological events, highlighting the need for accurate river water level prediction. This study proposes a two-step sequential prediction framework based on a Long Short-Term Memory (LSTM) model and evaluates the impact of hydrological connectivity among observation stations on predictive performance. In Step 1, water levels at upstream and downstream stations are predicted. In Step 2, these predictions are incorporated as additional inputs for forecasting water levels at a target station. Input variables are selected using information gain (IG), and multicollinearity is assessed with the variance inflation factor (VIF). Results show that at Pojin Bridge, where short-term fluctuations are significant, incorporating predicted upstream and downstream water levels improves the coefficient of determination (R²) by approximately 3.9% to 9.24% as lead time increases. In contrast, at Andong Bridge, where hydrological responses are relatively stable, the additional inputs reduce model performance. These findings indicate that the effectiveness of incorporating hydrological connectivity depends on station-specific characteristics. The study provides practical guidance for designing data-driven river forecasting models under varying hydrological conditions. Full article

Many small reservoirs lack adequate monitoring, making flood risk management a major challenge. This study aims to develop a comprehensive machine learning framework for short-term water level prediction, comprising feature selection, deep learning algorithms, a model transfer strategy, and an error correction technique, and applies it to four representative small reservoirs in Jiangsu Province, including two treated as ungauged scenarios. The results show that historical water levels are the dominant predictors for short-term reservoir water level forecasting, whereas precipitation becomes increasingly important during flood events. Based on the selected predictor combinations, both LSTM and GRU achieved high accuracy in routine water level prediction at the four small reservoirs. Under the scheme without precipitation input, both models performed well, with LSTM showing a relative advantage in flood event simulation. After incorporating the precipitation factor, GRU showed greater overall robustness and generally outperformed LSTM, particularly under complex flood conditions. In addition, comparing multisource precipitation products indicates that GMCP-driven simulations were generally more accurate than those based on ERA5-Land. Specifically, the LSTM–GMCP scheme performed best for the Yuwa Reservoir, whereas the GRU–GMCP scheme performed best for the Wushanqian Reservoir. Overall, the proposed framework demonstrates strong potential for short-term water level forecasting and flood-process simulation in small reservoirs, and provides useful support for hydrological prediction in data-scarce regions. Full article

Smart building services are increasingly realised through combinations of sensors, actuators, communication infrastructures, software platforms, analytics, and artificial intelligence-based functions. These configurations enable adaptive control, real-time monitoring, contextual automation, predictive support, user interaction, and cross-domain coordination across heating, ventilation, air conditioning, lighting, energy management, security and access control, water management, and user-centric comfort services. At the same time, the European Union Smart Readiness Indicator provides a formal basis for assessing building smartness through technical domains, service functionalities, and multidimensional impact criteria. A systematic basis for translating real-world descriptions of smart building services and their enabling technology stacks into Smart Readiness Indicator-aligned assessment inputs remains underdeveloped. A PRISMA ScR informed review was conducted to identify principal smart building service domains, synthesise their core functionalities, and reconstruct the digital technologies through which these functionalities are realised. The synthesis shows that heating, ventilation, and air conditioning and lighting provide comparatively direct translation pathways to formal Smart Readiness Indicator domains, while energy management operates mainly as a supervisory and cross-domain layer. Security and access control, water management, and several user-centric services contribute meaningfully to building smartness but often show partial or extended formal correspondence. Monitoring and control emerge as a central cross-cutting layer because many higher-order smart building capabilities are expressed through visibility, supervision, orchestration, and digital representation. Building on this review, a methodological framework is established for translating smart building services into Smart Readiness Indicator-aligned assessments. The procedure uses the smart building service instance as the unit of analysis and links service identification, functionality formulation, technology stack reconstruction, formal domain correspondence, impact profiling, maturity classification, and building-level aggregation. This enables heterogeneous service descriptions to be converted into structured readiness profiles while preserving the distinction between operational functionality, enabling technology, formal assessment correspondence, and multidimensional impact contribution. Application of the framework to the IoT Building Cloud platform shows that a substantial share of smart building capability may derive from supervisory digital infrastructure rather than from isolated end-use control alone. The resulting readiness profile is characterised by strong representation in monitoring and control, information to occupants and operators, and maintenance awareness, together with more selective contributions to indoor environmental control and limited flexibility-related capability. The proposed framework supports Smart Readiness Indicator-aligned pre-assessment, comparative analysis, design stage reasoning, and digital tool development by providing a transparent bridge between smart building service descriptions and formal assessment-oriented interpretation. Full article

This study investigates the spatiotemporal dynamics and driving mechanisms of Water Supply Ecosystem Service Value (ESV) in the North China Plain from 2002 to 2022. Addressing the critical challenges of water scarcity and ecological degradation in this densely populated and agriculturally intensive region, the research develops an integrated framework to quantify the relative contributions of multi-dimensional drivers to the water supply service (quantified by biophysical supply, W). A Particle Swarm Optimization (PSO) algorithm was employed to automate hyperparameter tuning for XGBoost and Random Forest models, with model interpretability enhanced via SHAP (SHapley Additive exPlanations) to elucidate non-linear feature importance and directional impacts. Results demonstrate that the PSO-XGBoost model outperforms PSO-Random Forest in predictive performance (R² = 0.8013 vs. 0.7443). The total water supply exhibited a significant annual decline of 1.98 billion m³ (p < 0.05), with 53.4% of the study area showing significant pixel-level temporal trends. The supply structure is dominated by soil moisture (80–90%), while externally transferred water, despite increasing rapidly, exhibits high interannual variability. SHAP analysis identifies vegetation cover (NDVI), clay content, GDP, and population density as the predominant drivers. Notably, GDP shows a strong negative correlation with water supply, reflecting a trade-off where intensive socio-economic expansion increases water consumption at the expense of ecosystem supply capacity. Methodologically, the PSO-XGBoost-SHAP framework enables both high predictive accuracy and detailed attribution of driving factors. These findings highlight the strategic importance of soil water (“Green Water”) conservation and offer actionable insights for adaptive water resource management, providing a replicable analytical approach for other regions facing similar hydrological challenges. Full article

Transitioning to a circular economy (CE) is now a strategic priority for countries to decouple economic growth from environmental degradation. However, in developing contexts, the readiness of environmental resource sectors to adopt CE principles is unknown due to a lack of data and uneven institutional capacity. This study presents the first regional baseline assessment of circularity readiness in Vietnam’s environmental resource sectors, focusing on land, mining, water and waste. A five-dimensional readiness framework (policy, resource management, innovation, business, awareness) was developed and applied across Vietnam’s six ecological–economic regions. A Delphi process with 12 experts was conducted in three rounds to capture and refine expert judgments, supplemented by triangulated proxy indicators (e.g., plastic recycling rates, wastewater treatment coverage). Readiness scores were aggregated at dimension and regional levels and analyzed using radar charts, heatmaps and hierarchical clustering. Results showed significant regional disparities. The Southeast (SE) and Red River Delta (RRD) have high readiness due to clearer policy frameworks, stronger institutions and more dynamic business ecosystems. The Northern Midlands and Mountains (NMM) and Central Highlands (CH) have low readiness due to infrastructural gaps, weak innovation and limited public engagement. The Mekong Delta (MD) and North Central Coast (NCC) have medium readiness, reflecting partial progress but uneven implementation. The study made three contributions: (1) a new context-specific framework for CE readiness in environmental resource sectors; (2) the value of expert-based, proxy-informed methods in data-scarce contexts; and (3) a policy roadmap for different regional readiness levels. Findings suggest that the CE should be integrated into resource planning, regional observatories should be established and CE-related research and development (R&D) should receive investment. Future research should move towards standardized quantitative indicators and predictive models to track how readiness changes under policy interventions. Full article

Ultraviolet (UV) disinfection is widely used in water treatment; however, its effectiveness strongly depends on water optical quality (e.g., turbidity, total dissolved solids, and UV transmittance, UVT). This study investigates an integrated RO–UV scheme in which reverse osmosis (RO) pretreatment improves UVT and thereby increases the effective UV dose available for microbial inactivation. First, UV-only reactor performance is characterized using literature data to fit an intensity-specific dose response relationship. The RO contribution is then incorporated at the process level through a UVT based coupling and evaluated using deterministic low/central/high scenarios (p05/p50/p95) constructed from assumed input ranges. Finally, a multi-objective optimization solved with the Grey Wolf Optimizer (GWO) is used to identify operating conditions that maximize predicted bacterial log-inactivation while limiting a UV-equivalent energy proxy based on nominal UV dose. Across the investigated flow-rate and intensity ranges, RO pretreatment yields a systematic increase in effective dose (median gain $\approx 6.8\%$) and a corresponding improvement in predicted inactivation, with the marginal benefit depending on the dose response regime. Full article

Groundwater storage is vital for managing water resources, especially as global water scarcity intensifies. Estimating groundwater levels regionally is challenging due to natural heterogeneity. We employed a large groundwater observation sample, along with Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiments (GRACE) datasets, to develop a random forest model for predicting groundwater levels in China’s Yellow River Basin. The model showed robustness, achieving an R² of 0.95 in calibration and an R² of 0.91 ± 0.009 in 10-fold cross-validation with 100 repetitions. Temporal predictability was lower, with an R² of 0.72 for April–May 2023; however, the temporal prediction is preliminary and limited by the short validation period (April–May 2023), which should be interpreted with caution. Spatial maps revealed significant seasonal declines in fall and winter, particularly in the middle and lower reaches. This study highlights the potential of machine learning with extensive observations to estimate regional groundwater levels and supports groundwater analysis with robust data. Full article

Water scarcity and water quality degradation in river basins are critical issues addressed by water resources management authorities. Grey relational analysis is adopted to rank key factors affecting water resources in the Beijing-Tianjin-Hebei region. Bankruptcy theory is combined with an improved Nash bargaining game model, and spatiotemporal constraints of cross-regional water resources are incorporated to analyze water allocation under multiple water supply scenarios. Results indicate that the GM (1,1) model achieves Level II (good) prediction accuracy, with relative errors below 6% in most years. The cooperative game model (CGM) yields the highest correlation coefficient of 0.996, indicating the optimal allocation performance. The water demand satisfaction rate in Beijing is the highest among the three regions. An economic compensation range indicator (e) is established for water resource trading games. As the trading water volume increases from 0.01 to 20 billion m³, the feasible compensation range expands from 463.57 to 1,757,045.78 ten thousand yuan. These results provide a scientific basis for rational, stable, and sustainable water resources allocation in the Beijing-Tianjin-Hebei region. Full article

Hypoxia episodes in receiving waters near estuarine outlets pose persistent challenges to water-environment management because operational early warning is often hindered by noisy observations, class imbalance, and inter-annual distribution shifts. This study proposes an externally validated Event-Window early-warning framework that bridges high-frequency monitoring data and management-oriented decision support. An explainable gated recurrent unit model with temporal attention (GRU-Attn) was developed and evaluated using a strict External-Year test in 2025. To better reflect operational needs, model performance was assessed not only at the daily classification level but also at the event-window level. The model achieved a PR-AUC of 0.9138 for day-level prediction, while Event-Window aggregation further increased PR-AUC to 0.9723, reduced false alarms by 59% (from 22 to 9), and provided a median lead time of 2.0 days for severe events. To improve deployment transparency, a governance diagnostic layer integrating population stability index analysis, threshold reliability assessment, and attention-based temporal attribution was further introduced. The results show that combining External-Year validation with event-scale evaluation and transparent diagnostics can substantially improve the robustness and practical interpretability of hypoxia early warning under real-world distribution shifts. Full article

Numerical simulations quantify the transient impacts of implementing green infrastructure (GI) grass swales on unconfined aquifer storage and groundwater-surface water interactions around the Red Butte Creek (RBC) of Utah, USA. The Red Butte Creek Watershed (RBCW) transitions from undeveloped mountainous National Forest land to downstream urbanized areas within Salt Lake Valley (SLV). This reconnaissance-level study demonstrates that increasing stormwater infiltration in urbanized areas during the rainy months (April-June) can, until at least the subsequent March, (a) enhance aquifer recharge and support sustainable groundwater yields; and (b) improve surface water availability. Simulations predict hydrologic impacts of aquifer recharge resulting from hypothetical grass-swale implementation within a 704-acre area located around RBC. The employed model, HyperRBC, is an adaptation of a United States Geological Survey (USGS) transient numerical flow, MODFLOW, model implementation for SLV. Adaptations involved (a) uniformly refined horizontal discretization of seven aquifer layers within a sub-area encompassing parts of RBCW and an adjacent watershed; (b) updated input data; and (c) MODFLOW’s Streamflow-Routing (SFR) package to simulate RBC flow and aquifer-stream seepage. Model predictions indicated that by the end of next March: (a) about 3% of the GI-induced recharge would remain within the unconfined aquifer in the HyperRBC area; (b) 66.6% of the recharge would flow northward into the downgradient continuation of the unconfined aquifer; and (c) 30.3% would discharge to nearby stream and river. In summary, predicted hydrologic changes due to the short-term GI-induced recharge highlight increased groundwater availability within and outside the study area for at least the subsequent 12 months, including high-water-demand summer. These findings show the importance of GI in interim environmental management and in enhancing the effective use of water resources. Full article

Accurate multi-horizon forecasting of reservoir water levels is essential for effective water resource management and flood risk mitigation. While deep learning models have demonstrated strong predictive capabilities, they often struggle to adequately represent uncertainty and extreme hydrological events, particularly at longer forecast horizons. This study proposes four variants of a Conv1D–LSTM–Temporal Attention (CLTA) architecture for probabilistic multi-horizon forecasting, differing exclusively in loss function design. The models incorporate non-crossing constraints, horizon-aware weighting, and event-aware weighting to address key limitations of standard quantile regression approaches. All models are trained on hourly water level data from May 2021 to October 2022 and evaluated on a fully unseen dataset spanning December 2022 to May 2023. The results demonstrate that horizon-aware weighting achieves the lowest average RMSE (0.0149) and the most stable performance across forecast horizons on unseen data, while event-aware weighting improves representation of extreme hydrological events and achieves the highest coefficient of determination ($R^2=0.9961$). However, a controlled experiment further reveals that model performance is strongly influenced by the data partitioning strategy, even when architecture and loss formulation are held constant. Overall, the findings indicate that loss function design, in interaction with data partitioning strategy, is a key contributing factor to model performance in deep learning-based hydrological forecasting. A Multi-Criteria Decision Analysis (MCDA) framework identifies the horizon-weighted model as the most robust general-purpose solution, while the event-aware model is preferable for applications focused on extreme event representation. These results highlight the importance of integrating domain knowledge into both model design and evaluation strategy, offering a scalable and computationally efficient alternative to increasing architectural complexity. Full article

To address the low mechanization level, high labor intensity, and severe substrate disturbance in intertidal shellfish harvesting, a vibratory harvesting method based on local vibration-induced substrate fluidization was proposed, and a vibratory harvesting device for Mactra veneriformis was developed. Bench and intertidal field tests were conducted to systematically investigate the effects of vibration frequency, vibration pressure, and vibration amplitude on substrate fluidization, clam uplift, and harvesting performance. The single-factor results showed that all three parameters significantly affected the pore water pressure ratio, substrate viscosity, uplift distance, and harvesting rate, with better fluidization obtained at 8 Hz, 30 kPa, and 25 mm. A Box–Behnken response surface design was further used to establish quadratic regression models for these responses, and all models were highly significant with a non-significant lack of fit. The optimized parameter combination was 10 Hz, 35 kPa, and 25 mm, under which the predicted pore water pressure ratio and uplift distance were 101.3% and 97.2 mm, respectively, and the substrate viscosity was 1364 Pa·s. Field tests showed that the pore water pressure ratio remained above 85.3%, viscosity decreased to 1331–2639 Pa·s, shear strength decreased by 57.2–64.9%, and the average uplift distance at 100 mm burial depth reached 80–92 mm. The results indicate that vibratory harvesting can effectively promote substrate fluidization and reduce clam uplift resistance, providing a reference for the development of low-disturbance mechanized harvesting equipment for intertidal shellfish. Full article

Plant diversity is a key indicator of ecosystem structure, function, and restoration status, yet its rapid assessment remains challenging in sandy ecosystems where vegetation is sparse, spatially heterogeneous, and strongly affected by exposed soil backgrounds. In such environments, conventional greenness-based spectral indices may not adequately capture species-level variation because plant communities are controlled not only by photosynthetic biomass but also by soil moisture, micro-topography, and dune-related habitat heterogeneity. This study evaluated the potential of Sentinel-2-derived spectral indices for estimating plant α-diversity in the Hunshandak Sandland, northern China. Based on field observations from 888 plots collected during 2017–2024, four α-diversity metrics—species richness, Shannon–Wiener index, Simpson index, and Pielou evenness index—were calculated and compared with 21 spectral indices using correlation analysis, partial least squares regression (PLSR), and random forest (RF) models. The results showed that model performance varied substantially among diversity metrics. Species richness was estimated with the highest accuracy, whereas Shannon–Wiener, Simpson, and Pielou indices showed weaker predictability, indicating that remotely sensed spectral indices were more sensitive to species number than to abundance distribution and evenness. Moisture- and soil-background-sensitive indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BSI/BRI), and Chlorophyll Absorption Ratio Index (CARI), showed relatively stable relationships with plant diversity across different vegetation gradients. Although the overall explanatory power was moderate rather than high, the results demonstrate the practical value of Sentinel-2 spectral indices for regional screening of plant diversity patterns in sandy ecosystems. This study provides empirical evidence for biodiversity monitoring and ecological restoration assessment in semi-arid sandy landscapes and highlights the need to integrate environmental covariates, multi-source remote sensing, and phenological information in future studies. Full article