Search Results (23,823)

Water use strategies reflect the ability of plants to adapt to drought caused by climate change. However, how these strategies change with stand development and seasonal drought is not fully understood. This study used stable isotope techniques (δD, δ¹⁸O, and δ¹³C) combined with the MixSIAR model to quantify the seasonal changes in water use sources and water use efficiency (WUE) of Eucalyptus urophylla S.T.Blake × E. grandis (E. urophylla × E. grandis) at four stand ages (2-, 4-, 9- and 14-year-old) and to identify their influencing factors. Our results showed that the young (2-year-old) and middle-aged (4-year-old) stands primarily relied on shallow soil water throughout the growing season due to the limitations of a shallow root system. In contrast, the mature (9-year-old) and overmature (14-year-old) stands, influenced by the synergistic effects of larger and deeper root systems and relative extractable water (REW), exhibited more flexibility in water use, mainly relying on shallow soil water in wet months, but shifting to using middle and deep soil layer water in dry months, and quickly returning to mainly using shallow soil water in the episodic wet month of the dry season. The WUE of E. urophylla × E. grandis was affected by the combined effect of air temperature (T), vapor pressure deficit (VPD), and REW. WUE was consistent across the stand ages in the wet season but decreased significantly with stand age in the dry season. This suggests that mature and overmature stands depend more on shifting their water source, while young and middle-aged stands rely more on enhanced WUE to cope with seasonal drought stress, resulting in young and middle-aged stands being more vulnerable to drought stress. These findings offer valuable insights for managing water resources in eucalyptus plantations, particularly as drought frequency and intensity continue to rise. Full article

Tree root intrusion can cause failures of underground sewer pipes and thus represent a major water asset management issue. If tree root intrusion is not detected early, this may lead to the interruption of wastewater services and high costs of repair to the pipeline. The objectives of this review are to assess the existing maintenance strategies, explore suitable strategies for Australia and similar settings around the world, and identify possible factors and predictive tools. Maintenance strategies can be divided into two categories: reactive and proactive approaches. The current reactive approaches are (1) mechanical techniques to clean the root mass in pipe networks and (2) chemical techniques to remove the root mass and control future growth. The literature suggests that the reactive approaches often provide only partial solutions. The proactive approaches, guided by a predictive model of tree root intrusion and its related factors, showed the potential to improve maintenance and limit the risk of the damage from re-occurring. Predictive models could help to evaluate the risk of planting trees in different conditions and minimise the damage of tree root intrusion after further multifactor investigations. Full article

The integration of smart sensor networks and Internet of Things (IoT) technologies has emerged as a key strategy for enhancing productivity and sustainability in agriculture and aquaculture under increasing climate and resource pressures. This review consolidates empirical findings on the performance of sensor-driven systems in optimizing the management of water, nutrients, and energy. Studies have demonstrated that IoT-based irrigation systems can reduce water use by up to 50% without compromising yields, while precision nutrient monitoring enables a 20–40% reduction in fertilizer inputs. In aquaculture, real-time monitoring and automated interventions have improved feed conversion ratios, reduced mortality by up to 40%, and increased yields by 15–50%. The integration of artificial intelligence (AI) into IoT frameworks further enhances predictive capabilities and operational responsiveness. Despite these benefits, widespread adoption remains constrained by high infrastructure costs, limited sensor robustness, and fragmented policy support. This paper provides a comprehensive evaluation of current technologies, adoption barriers, and strategic directions for advancing scalable, sustainable, and data-driven food production systems. Full article

Plant roots and soil properties play crucial roles in regulating soil hydrological processes, particularly in determining soil water infiltration capacity. However, the infiltration patterns and underlying mechanisms across different forest types in subtropical regions remain poorly understood. In this study, we measured the infiltration characteristics of three typical stands (pure Phyllostachys edulis forest, mixed Phyllostachys edulis-Cunninghamia lanceolata forest, and pure Cunninghamia lanceolata forest) using a double-ring infiltrometer. Stepwise multiple regression and structural equation modeling (SEM) were employed to analyze the effects of root traits and soil physicochemical properties on soil infiltration capacity. The results revealed the following: (1) The initial infiltration rate (IIR), stable infiltration rate (SIR), and average infiltration rate (AIR) followed the order pure Phyllostachys edulis stand > mixed stand > pure Cunninghamia lanceolata stand. (2) Compared to the pure Cunninghamia lanceolata stand, the IIR, SIR, and AIR in the pure Phyllostachys edulis stand increased by 6.66%, 35.63%, and 28.51%, respectively, while those in the mixed stand increased by 28.79%, 28.82%, and 33.51%. (3) Fine root biomass, root length density, non-capillary porosity, and soil bulk density were identified as key factors influencing soil infiltration capacity. (4) Root biomass and root length density affected infiltration capacity through both direct pathways and indirect pathways mediated by alterations in non-capillary porosity and soil bulk density. These findings provide theoretical insights into soil responses to forest types and inform sustainable water–soil management practices in Phyllostachys edulis plantations. Full article

Hybrid striped bass (HSB), a cross between white bass (Morone chrysops) and striped bass (Morone saxatilis), has garnered attention in aquaculture due to its adaptability, rapid growth, and high market value. This study investigates the morphometric, nutritional, and blood characteristics of HSB reared in a recirculating aquaculture system (RAS) in Sicily, Italy, over a 22-month grow-out period. The fish were managed under standardized feeding and water quality protocols, with weekly monitoring of the physicochemical parameters. A total of 21 clinically healthy fish, averaging 571.33 ± 129.32 in body weight, were randomly sampled in the spring season from a commercial RAS facility equipped with biological filtration, UV sterilization, and seasonally regulated water parameters. The results revealed strong positive correlations between the morphometric parameters and blood indices, such as red blood cell (RBC) count, hemoglobin (Hb) levels, and hematocrit (Hct), highlighting their importance as health indicators. The proximate composition revealed an average moisture content of 75.55 ± 1.49, crude protein at 20.29 ± 0.26, total lipid at 4.25 ± 0.97, and ash content at 1.69 ± 0.17. Additionally, statistical analyses, including a principal component analysis (PCA), identified relationships between body size, nutritional content, and blood parameters, emphasizing the role of body size in influencing nutritional and health outcomes. The findings of this study are crucial for optimizing farming protocols and improving the health and productivity of HSB in RAS under Mediterranean conditions. Full article

Drought is a complex and interconnected natural phenomenon, involving multiple drought types that mutually influence each other. To capture this complexity, various composite drought indices have been developed using diverse methodologies. Traditionally, Principal Component Analysis (PCA) has served as the primary method for extracting index weights, predominantly capturing linear relationships among variables. This study proposes an innovative approach by employing Independent Component Analysis (ICA) to develop an ICA-based Composite Drought Index (ICDI), capable of addressing both linear and nonlinear interdependencies. Three drought indices—representing meteorological, hydrological, and agricultural droughts—were integrated. Specifically, the Standardized Precipitation Index (SPI) was adopted as the meteorological drought indicator, whereas the Standardized Reservoir Supply Index (SRSI) was utilized to represent both hydrological (SRSI(H)) and agricultural (SRSI(A)) droughts. The ICDI was derived by extracting optimal weights for each drought index through ICA, leveraging the optimization of non-Gaussianity. Furthermore, constraints (referred to as ICDI-C) were introduced to ensure all index weights were positive and normalized to unity. These constraints prevented negative weight assignments, thereby enhancing the physical interpretability and ensuring that no single drought index disproportionately dominated the composite. To rigorously assess the performance of ICDI, a PCA-based Composite Drought Index (PCDI) was developed for comparative analysis. The evaluation was carried out through three distinct performance metrics: difference, model, and alarm performance. The difference performance, calculated by subtracting composite index values from individual drought indices, indicated that PCDI and ICDI-C outperformed ICDI, exhibiting comparable overall performance. Notably, ICDI-C demonstrated a superior preservation of SRSI(H) values, yielding difference values closest to zero. Model performance metrics (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation) highlighted ICDI’s comparatively inferior performance, characterized by lower correlations and higher RMSE and MAE. Conversely, PCDI and ICDI-C exhibited similar performance across these metrics, though ICDI-C showed notably higher correlation with SRSI(H). Alarm performance evaluation (False Alarm Ratio (FAR), Probability of Detection (POD), and Accuracy (ACC)) further confirmed ICDI’s weakest reliability, with notably high FAR (up to 0.82), low POD (down to 0.13), and low ACC (down to 0.46). PCDI and ICDI-C demonstrated similar results, although PCDI slightly outperformed ICDI-C as meteorological and agricultural drought indicators, whereas ICDI-C excelled notably in hydrological drought detection (SRSI(H)). The results underscore that ICDI-C is particularly adept at capturing hydrological drought characteristics, rendering it especially valuable for water resource management—a critical consideration given the significance of hydrological indices such as SRSI(H) in reservoir management contexts. However, ICDI and ICDI-C exhibited limitations in accurately capturing meteorological (SPI(6)) and agricultural droughts (SRSI(A)) relative to PCDI. Thus, while the ICA-based composite drought index presents a promising alternative, further refinement and testing are recommended to broaden its applicability across diverse drought conditions and management contexts. Full article

Rapid suburbanization can alter catchment flow regime and increase stormwater runoff, posing threats to sensitive ecosystems. Applications of Nature-based Solutions (NbS) have increasingly been adopted as part of integrated water management efforts to tackle the hydrological impact of urbanization with co-benefits for improved urban resilience, sustainability, and community well-being. However, the implementation of NbS can be hindered by gaps in performance assessment. This paper introduces a physically based dynamic modeling approach to assess the performance of a nature-based storage facility designed to capture excess runoff from an urbanizing catchment (Armstrong Creek catchment) in Geelong, Australia. The study adopts a numerical modelling approach, supported by extensive field monitoring of water levels over a 2.5-year period. The model provides a decision support tool for Geelong local government in managing stormwater runoff to protect Lake Connewarre, a Ramsar-listed wetland under the Port Phillip Bay (Western Shoreline) and Bellarine Peninsula. Runoff is currently managed via a set of operating rules governing gate operations that prevents flows into the ecological sensitive downstream waterbody from December to April (drier periods in summer and most of autumn). Comparison with observed water level data at three monitoring stations for a continuous simulation period of May 2022 to October 2024 demonstrates satisfactory to excellent model performance (NSE: 0.55–0.79, R²: 0.80–0.89, ISE rating: excellent). Between 1670 × 10³ m³ and 2770 × 10³ m³ of runoff was intercepted by the nature-based storage facility, representing a 56–70% reduction in stormwater discharge into Lake Connewarre. Our model development underscores the importance of understanding and incorporating user interventions (gate operations and emergency pumping) from the standard operation plan to better manage catchment runoff. As revealed by the seasonal flow analysis for consecutive years, adaptive runoff management practices, capable of responding to rainfall variability, should be incorporated. Full article

Background: The need for innovative therapeutic strategies to enhance patient outcomes has increased due to the rise in bacterial co-infections associated with COVID-19. Methods: In this study, ten hybrid compounds were synthesized by combining two known pharmaceutical scaffolds to enhance antibacterial activity and overcome resistance mechanisms. The synthesized compounds were evaluated for their antibacterial activity against five Gram-negative and seven Gram-positive bacterial strains. In silico pharmacokinetic and drug-likeness properties of selected active compounds (12–16, 19, 21, and 23) were predicted using the SwissADME web tool. Results: Compounds 12–16, 19, 21, and 23 demonstrated significant antibacterial activity, with compound 16 (a ciprofloxacin-containing hybrid) exhibiting the most potent effect, showing a minimum inhibitory concentration (MIC) of 7.8125 µg/mL against all tested bacterial strains. The in silico analysis revealed favorable pharmacokinetic profiles, drug-likeness, lipophilicity, and water solubility of most hybrid compounds. Discussion: The synthesized hybrid compounds exhibited enhanced antibacterial activity and desirable pharmacokinetic properties, particularly compound 16. These findings suggest the potential of these molecules in combating bacterial pathogens, especially those implicated in co-infections in COVID-19 infections. Conclusions: The study presents promising hybrid antibacterial agents with potential application as adjunct therapies for treating COVID-19-associated bacterial co-infections. Further investigation is needed, which may lead to effective treatments for managing secondary bacterial infections in viral disease contexts. Full article

Streamflow in the Upper Williamson Basin of the Klamath Watershed is groundwater dominated with year-to-year fluctuations in both volume and duration, including multi-year periods with no streamflow. The relationship between precipitation, groundwater, and streamflow is difficult to characterize because of the limited number of monitoring wells, large data gaps, and a unique geologic structure that controls flow. To understand why surface flow has ceased entirely, we use the Groundwater Data Mapper Tool to impute gaps in the well data using machine learning and open-source Earth observation data and then compute changes in groundwater storage over time. Our research confirms that groundwater storage is correlated to streamflow and finds that there is a control groundwater storage below which flow does not occur. Furthermore, we find that groundwater storage is correlated to rainfall with a three- to four-year delay. This lag and the geologic structural control mean that even with several years of above-average precipitation, live flow may take years to resume. This insight allows water managers to understand and adjust for this highly irregular streamflow for better management decisions. Full article

Early detection of plant water status is crucial for efficient crop management. In this research, proximal sensing tools (i.e., hyperspectral imaging HSI and thermal IR camera) were used to monitor changes in spectral and thermal profiles of a citrus orchard in Sicily (Italy), managed under five irrigation systems. The irrigation systems differ in the amount of water distribution and allow four different strategies of deficit irrigation to be obtained. The physiological traits, stem water potential, net photosynthetic rate, stomatal conductance and the amount of leaf chlorophyll were measured over the crop’s growing season for each treatment. The proximal sensing data consisted of thermal and hyperspectral imagery acquired in June–September during the irrigation seasons 2023–2024 and 2024–2025. Significant variation in physiological traits was observed in relation to the different irrigation strategies, highlighting the highest plant water stress in July, in particular for the partial root-zone drying irrigation system. The water-use efficiency (WUE) values in subsurface drip irrigation were similar to the moderate deficit irrigation treatment and more efficient (up to 50%) as compared to control. Proximal sensing measures confirmed a different plant water status in relation to the five different irrigations strategies. Moreover, four spectral indices (Normalized Difference Vegetation Index NDVI; Water Index WI; Photochemical Reflectance Index PRI; Transformed Chlorophyll Absorption Ratio Index TCARI), calculated from HSI spectra, highlighted strong correlations with physiological traits, especially with stem water potential and the amount of leaf chlorophyll (coefficient of correlation ranged between −0.4 and −0.5). This study demonstrated the effectiveness of using proximal sensing tools in precision agriculture and ecosystem monitoring, helping to ensure optimal plant health and water use efficiency. Full article

The potato (Solanum tuberosum L.) is highly dependent on water availability, with physiological sensitivity varying throughout its phenological cycle. In the context of increasing water scarcity and greater climate variability, identifying critical periods where water stress negatively impacts productivity and tuber quality is essential. This study evaluated the physiological response of potatoes under different deficit irrigation strategies in field conditions, and aimed to determine the irrigation reduction thresholds that optimize water use efficiency without significantly compromising yield. Five irrigation regimes were applied: well-watered (T1; irrigation was applied when the volumetric soil moisture content was close to 35% of total water available), 130% of T1 (T2, 30% more than T1), 75% of T1 (T3), 50% of T1 (T4), and 30% of T1 (T5). Key physiological parameters were monitored, including gas exchange (net photosynthesis, stomatal conductance, and transpiration), chlorophyll fluorescence (Fv’/Fm’, ΦPSII, electron transport rate), and photosynthetic pigment content, at three critical phenological phases: tuberization, flowering, and fruit set. The results indicate that water stress during tuberization and flowering significantly reduced photosynthetic efficiency, with decreases in stomatal conductance (gs), effective quantum efficiency of PSII (ΦPSII), and electron transport rate (ETR). In contrast, moderate irrigation reduction (75%) lowered the seasonal application of water by ~25% (≈80 mm ha⁻¹) while maintaining commercial yield and tuber quality comparable to the fully irrigated control. Intrinsic water use efficiency increased by 18 ± 4% under this regime. These findings highlight the importance of irrigation management based on crop phenology, prioritizing water supply during the stages of higher physiological sensitivity and allowing irrigation reductions in less critical phases. In a scenario of increasing water limitations, this strategy enhances water use efficiency while ensuring the production of tubers with optimal commercial quality, promoting more sustainable agricultural management practices. Full article

Proximal plant-based monitoring provides high-resolution data about trees, leading to more precise orchard management and in-depth knowledge about tree physiology. The present work focuses on continuous real-time monitoring of olive cv. ‘Ascolana tenera’ over hourly intervals during the third stage of fruit growth (mesocarp cell expansion) under mild water stress conditions (ψStem above −2 MPa). This is achieved by mounting dendrometers on the root, trunk, branch, and fruit to assess and model the behavior of each organ. The diameter variation in each organ over different time intervals (daily, two-weeks, and throughout the entire experiment), as well as their hysteretic patterns relative to each other and vapor pressure deficit, are demonstrated. The results show different correlations between various organs, ranging from very weak to strongly positive. However, the trend of fruit versus root consistently shows a strong positive relationship throughout the entire experiment (R² = 0.83) and a good one across various two-week intervals (R² ranging from 0.54 to 0.93). Additionally, different time lags in dehydration and rehydration between organs were observed, suggesting that the branch is the most reactive organ, regulating dehydration and rehydration in the tree. Regarding the hysteretic pattern, different rotational patterns and characteristics (shape) were observed among the organs and in relation to vapor pressure deficit. This research provides valuable insight into flow dynamics within a tree, models plant water relations and time lags in terms of water storage and transport, and could be implemented for precise olive tree water status detection. Full article

Flood events have become more frequent as a result of seasonal changes, global warming, and changes in sea level. In terms of basin management, it is necessary to know the hydrodynamics of the basin in order to produce faster solutions in emergency action plans. The Filyos River is one of the two most important floodplains in the western Black Sea basin and has so far only been analyzed to a limited extent using modern hydrological and hydraulic models. In order to analyze the flood dynamics and determine the flood risks in the Filyos River. In this context, flood hydrographs, rainfall depths, peak flows, and excess water volumes were calculated for different return periods (2, 5, 10, 20, 50, and 100 years) using HEC-RAS, HEC-HMS, and Hyfran Plus software. The analyses showed that the rainfall depth and peak flow rate increased significantly as the return period increased. It was also observed that although the volume of precipitation increased, the amount of water converted into surface runoff remained limited due to infiltration and other losses. The results of the study contribute to the identification of high flood-risk areas in the Filyos River basin, the improvement of flood prevention infrastructure, and the development of sustainable water management policies. Analyses using modeling tools such as HEC-RAS and HEC-HMS provide a scientific basis to help local governments and decision makers strengthen flood prevention strategies, update risk maps, and make emergency response plans more effective while making flood scenarios more reliable. Full article

Remote sensing and AI models have been utilized for monitoring Chlorophyll-a (Chl-a), a primary indicator of eutrophication across broad water bodies. Previous studies have primarily relied on optical remote sensing data for assessing Chl-a’s spectral characteristics. Synthetic-aperture radar (SAR) data, which contain valuable information about surface algae containing Chl-a, remains underutilized despite its high potential for improving Chl-a retrieval accuracy. Therefore, this study aims to develop a Convolutional neural network (CNN) based Chl-a retrieval model utilizing both SAR data and optical data in Korean lakes. The model dataset was established by acquiring Chl-a concentration data and Sentinel-1/2 imagery from the Copernicus Open Access Hub. The CNN model trained on both optical and SAR data exhibited superior performance (R² = 0.7992, RMSE = 10.3282 mg/m³, RPD = 2.2315) compared with the model trained exclusively on optical data. Moreover, SAR data exhibited moderate variable importance among all variables, demonstrating their efficacy as input variables for Chl-a concentration estimation. Furthermore, the CNN model estimated Chl-a concentrations with a spatial distribution that matched the observed spatial heterogeneity of Chl-a concentrations. These results are expected to serve as a foundation for future research on remote monitoring of Chl-a using such data. Full article

Agroforestry (AF) effectively enhances ecological restoration and soil–water conservation (SWC), yet the relationship among soil and water conservation agroforestry (SWCAF) in karst soil, water loss (SWL) and rural eco-revitalization (RER) remains unclear, which may hinder the ecological restoration process around the world. This study aims to reveal whether SWCAF in karst areas improves RER through SWC benefits, ecosystem service (ES) enhancement and rural ecological environment quality (REEQ) improvement. We take Guizhou Province, the core area of the South China Karst (SCK), as the study area and 2010–2020 as the study period. By using the equivalent factor method, the remote sensing ecological index (RSEI) model, bivariate spatial autocorrelation and the panel vector autoregressive (PVAR) model, the study reveals SWCAF’s ecological benefits and its interaction mechanism with RER. Key findings reveal the following: (1) SWCAF reduced the area of SWL by 14.93% by converting cropland into forests. (2) The AF ecosystem service value (AFESV) increased by CNY 9.181 billion, and the forest-related AFESV increases represented 184% of the total AFESV, while REEQ showed an overall positive trend in the western SWC area. (3) The AFESV has an obvious synergistic effect with REEQ (r = 0.60) and obvious positive synergy with SWL (r = 0.69), and its spatial correlation increases over time. (4) The PVAR model verified that there is a bidirectional Granger causal relationship between the AFESV and RER, showing dynamic positive and negative alternating influences. This research study reveals that SWCAF drives RER through the dual path of SWL control and value-added ecological services, among which the forest ecosystem plays a core role. In the future, it is necessary to optimize the diversity of AF structures to avoid ecological service trade-offs. This research study provides a scientific basis for decision making and the ecological management of SWC in karst soils globally. Full article