Search Results (26)

Water and nitrogen supply are key factors limiting the establishment of alpine plant seedlings and the efficiency of ecological restoration on the Tibetan Plateau. As an endemic shrub to Tibet, the morphological and physiological response mechanisms of Piptanthus nepalensis (Hook.) D. Don to coupled water and nitrogen stress remain poorly understood. This study employed a pot experiment with a completely randomized two-factor design, incorporating five water gradients (0–100% field capacity, FC) and five nitrogen levels (0–4 g·plant⁻¹ urea). The aim was to elucidate the regulatory mechanisms of water/nitrogen coupling on Piptanthus nepalensis growth, physiology, and morphogenesis. The results indicated the following: (1) A significant water/nitrogen coupling effect was observed, with optimal water/nitrogen combinations producing pronounced synergistic effects. Principal component analysis (PCA) revealed that the first two axes cumulatively explained 99.32% of the morphological variation. The W3N3 treatment (40–60% FC water + 2 g·plant⁻¹ nitrogen) exhibited optimal growth traits and maximum leaf elongation, establishing the optimal water and fertilizer management threshold for this species. (2) Confronted with two starkly contrasting stresses—drought (W4, W5) and waterlogging (W1)—plants adopted convergent “conservative” morphological adaptation strategies (significantly reduced leaf length and width) to lower metabolic expenditure. (3) Photosynthetic physiological analysis revealed that under extreme water deficiency (W5) or waterlogging (W1) stress, intercellular CO₂ concentration (Ci) paradoxically increased, indicating a shift in photosynthetic suppression mechanisms from stomatal limitation to non-stomatal limitation (metabolic injury). (4) The Mantel Test confirmed that photosynthetic physiological traits significantly drove morphological trait variation (p < 0.001), establishing a close feedback loop between “physiological function and morphological structure”. Conclusions: Moderate water deficit (40–60% FC) combined with moderate nitrogen fertilization (2 g·plant⁻¹) effectively alleviates non-stomatal limitation and releases morphological constraints, thereby promoting rapid growth in Piptanthus nepalensis. This study reveals the phenotypic plasticity and convergent adaptation mechanisms of Piptanthus nepalensis under water/nitrogen co-stress, providing precise water and fertilizer management guidelines for vegetation restoration in degraded ecosystems of Tibet. Full article

Demand for irrigation water varies substantially between upstream and downstream reaches of river basins due to spatial variability in rainfall, agro-climatic situations, and management practices. Upstream areas often experience over-irrigation and waterlogging, while downstream regions are challenged with water scarcity, timing mismatches, and allocation conflicts. This study proposes a novel SWAT–AquaCrop–optimization nexus framework to minimize both the frequency (DDF) and severity (DDS) of irrigation demand deficit under hydro-climatic uncertainty. To enhance numerical stability and a realistic representation of system stress, deficit frequency is formulated using a smooth, differentiable exceedance function instead of conventional binary thresholds. The framework integrates SWAT-based hydrological projections with AquaCrop simulations of crop yield and evapotranspiration-driven water demand, simultaneously evaluating three interlinked objectives: allocation-disparity deficit (equity), yield deficit (productivity), and irrigation-efficiency deficit (operational performance). Hydro-climatic uncertainty is represented through a quantile-based classification, with favorable (S₁), normal (S₂), and extreme (S₃) scenarios defined by the 33rd and 66th percentiles of the time-varying deficit ratio. The results indicate that stage-specific irrigation timing adjustments (advanced by 2–5 days) better align water applications with peak crop water requirements during flowering and grain-filling stages. This enhances downstream reliability, mitigates upstream over-irrigation, and substantially reduces both demand deficit frequency and severity. Full article

Waterlogging stress severely limits crop photosynthesis and energy supplies, resulting in significant yield reductions. However, the critical duration of waterlogging stress during the maize jointing stage remains unclear, and the physiological and molecular mechanisms underlying its effects on photosynthetic efficiency and energy synthesis in maize require further investigation. In this study, we systematically analyzed the responses of physiological traits, transcriptomic profiles, and the yield formation in maize (Zea mays L.) to varying waterlogging durations imposed during the jointing stage, including 0 days (CK), 2 days (F2), 4 days (F4), 6 days (F6), 8 days (F8), and 10 days (F10). Our results indicate that the (1) grain weight (GW) showed no significant difference between F2 and CK. However, the GW in F4, F6, F8, and F10 decreased significantly by 17.49%, 26.45%, 60.24%, and 100.00%, respectively, compared to the CK. (2) Compared with the CK, the malondialdehyde content progressively increased from F4 to F10, while antioxidant enzyme activity gradually decreased. The chlorophyll content declined by 29.93% to 57.38%, and net photosynthetic efficiency decreased by 13.82% to 38.93%. Although the leaf sucrose content in from F4 to F10 gradually decreased, the leaf starch content remained stable in F4 and F6. In contrast, the starch content in F8 and F10 leaves was significantly reduced by 37.55% and 47.60%, respectively, compared with CK. (3) A transcriptomic analysis revealed that during from F2 to F4, genes encoding photosystem I subunit protein, such as PSAD, and the cytochrome b6f complex proteingene PETC were downregulated. At F6, these key genes encoding photosynthetic proteins were upregulated. However, at F8 and F10, their expression was significantly downregulated. Concurrently, genes related to ATP synthesis (e.g., ATPD) as well as starch and sucrose metabolism (e.g., SPP2, SS1) were also downregulated. In summary, when waterlogging stress persists for no longer than 6 days, plants can maintain their starch content to supply energy for growth, thereby ensuring basic developmental needs. When waterlogging persists for more than 6 days, energy synthesis is impaired, and the nutrient transport to the grains is significantly inhibited, ultimately resulting in a substantial reduction in yield. Therefore, 6 days of waterlogging can be considered the critical threshold for significant yield loss in maize during the jointing stage. Full article

Intensifying urbanization in Central Europe is increasingly pushing flood retention areas onto private farmland, yet the agronomic and socio-economic trade-offs remain poorly quantified. We conducted a narrative review of published field data and post-event assessments from 2014–2023 along the transboundary Sava River. Information was collected from research articles, case studies, and environmental monitoring reports, and synthesized in relation to national and EU regulatory thresholds to evaluate how floods altered soil functions and agricultural viability. Water erosion during floods stripped up to 30 cm of topsoil in torrential reaches, while stagnant inundation deposited 5–50 cm of sediments enriched with potentially toxic elements, occasionally causing food crops to exceed EU contaminant limits due to uptake from the soil. Flood sediments also introduced persistent organic pollutants: 13 modern pesticides were detected post-flood in soils, with several exceeding sediment quality guidelines. Waterlogging reduced maize, pumpkin, and forage yields by half where soil remained submerged for more than three days, with farm income falling by approximately 50% in the most affected areas. These impacts contrast with limited public awareness of long-term soil degradation, raising questions about the appropriateness of placing additional dry retention reservoirs—an example of nature-based solutions—on agricultural land. We argue that equitable flood-risk governance in the Sava River Basin requires: (i) a trans-boundary soil quality monitoring network linking agronomic, hydrological, and contaminant datasets; (ii) compensation schemes for agricultural landowners that account for both immediate crop losses and delayed remediation costs; and (iii) integration of strict farmland protection clauses into spatial planning, favoring compact, greener cities over lateral river expansion. Such measures would balance societal flood-safety gains with the long-term productivity and food security functions of agricultural land. Full article

The factors influencing urban pluvial flooding in cities with complex topography, such as hill–basin systems, are highly nonlinear and spatially heterogeneous due to the interplay between rugged terrain and intensive human activities. However, previous research has predominantly focused on plain, mountainous, and coastal cities. As a result, the waterlogging mechanisms in hill–basin areas remain notably understudied. In this study, we developed a geographically explainable artificial intelligence (GeoXAI) framework integrating Geographical Machine Learning Regression (GeoMLR) and Geographical Shapley (GeoShapley) values to analyze nonlinear impacts of flooding factors in Changsha, a typical hill–basin city. The XGBoost model was employed to predict flooding risk (validation AUC = 0.8597, R² = 0.8973), while the GeoMLR model verified stable nonlinear driving relationships between factors and flooding susceptibility (test set R² = 0.7546)—both supporting the proposal of targeted zonal regulation strategies. Results indicated that impervious surface density (ISD), normalized difference vegetation index (NDVI), and slope are the dominant drivers of flooding, with each exhibiting distinct nonlinear threshold effects (ISD > 0.35, NDVI < 0.70, Slope < 5°) that differ significantly from those identified in plain, mountainous, or coastal regions. Spatial analysis further revealed that topography regulates flooding by controlling convergence pathways and flow velocity, while vegetation mitigates flooding through enhanced interception and infiltration, showing complementary effects across zones. Based on these findings, we proposed tailored zonal management strategies. This study not only advances the mechanistic understanding of urban waterlogging in hill–basin regions but also provides a transferable GeoXAI framework offering a robust methodological foundation for flood resilience planning in topographically complex cities. Full article

An optical remote sensing approach was developed to identify areas with high and low salinity within the mangrove swamp rice system in West Africa. Conducted between 2019 and 2024 in Guinea-Bissau, this study examined two contrasting rice-growing environments, tidal mangrove (TM) and associated mangrove (AM), to assess changes in vegetation dynamics, soil salinity concentration, and soil chemical properties. Field sampling was conducted during the dry season to avoid waterlogging, and soil analyses included texture, cation exchange capacity, micronutrients, and electrical conductivity (EC_e). Meteorological stations recorded rainfall and environmental conditions over the period. Moreover, orthorectified and atmospherically corrected surface reflectance satellite imagery from PlanetScope and Sentinel-2 was selected due to their high spatial resolution and revisit frequency. From this data, vegetation dynamics were monitored using the Normalized Difference Vegetation Index (NDVI), with change detection calculated as the difference in NDVI between sequential images (ΔNDVI). Thresholds of 0.15 ≤ NDVI ≤ 0.5 and ΔNDVI > 0.1 were tested to identify significant vegetation growth, with smaller polygons (<1000 m²) removed to reduce noise. In this process, at least three temporal images per season were analyzed, and multi-year intersections were done to enhance accuracy. Our parameter optimization tests found that a locally calibrated NDVI threshold of 0.26 improved site classification. Thus, this integrated field–remote sensing approach proved to be a reproducible and cost-effective tool for detecting AM and TM environments and assessing vegetation responses to seasonal changes, contributing to improved land and water management in the salinity-affected mangrove swamp rice system. Full article

The increasing frequency and intensity of extreme weather events pose significant challenges to mountain communities, particularly in terms of flash flood risks. This study presents a framework for stress testing and evaluating flood resilience in mountain communities through the integration of high-resolution InfoWorks ICM two-dimensional hydrodynamic modeling and systematic resilience assessment. The framework makes three key innovations: (1) multi-scale temporal stress scenarios combining short-duration extreme events (1–2 h) with long-duration persistent events (24 h) and historical extremes; (2) integrated infrastructure–drainage stress analysis that explicitly models roads’ dual role as critical infrastructure and emergency drainage channels; and (3) dynamic resilience quantification under multiple stressors across 15 systematically designed stress conditions. Using Western Beijing as a case study, the model is validated, achieving Nash–Sutcliffe efficiency values exceeding 0.9, demonstrating its robust capability in simulating complex mountainous terrain flood processes. Through systematic analysis of fifteen rainfall scenarios designed based on Chicago rainfall patterns and historical events (including the July 2023 Haihe River basin flood), encompassing various intensities (30–200 mm/h), durations (1 h, 2 h, 24 h), and return periods (10, 50, 100 years), the key findings include the following: (1) A rainfall intensity of 60 mm/h represents a crucial threshold for system performance, beyond which significant impacts on community infrastructure emerge, with built-up areas experiencing inundation depths of 0.27–0.4 m that exceed safe passage limits. (2) Road networks become primary drainage channels during intense precipitation, with velocities exceeding 5 m/s in village roads and exceeding 5 m/s in country road sections, creating significant hazard potential. (3) Four major risk spots were identified with distinct waterlogging patterns, characterized by maximum depths ranging from 0.8 to 2.0 m and recovery periods varying from 2 to 12 hours depending on the topographic confluence effects and drainage efficiency. (4) The system demonstrates strong recovery capability, achieving >90% recovery within 3–6 hours for short-duration events, while showing vulnerability to extreme scenarios, with performance declining to 0.75–0.80, highlighting the coupling effects between water depth and flow velocity in steep terrain. This research provides quantitative insights for flood risk management and for enhancing community resilience in mountainous regions, offering valuable guidance for infrastructure improvement, emergency response optimization, and sustainable community development. This study primarily focuses on physical resilience aspects, with socioeconomic and institutional dimensions representing important directions for future research. Full article

In response to the increasing frequency of urban rainstorms, this study focuses on investigating the friction coefficient related to pedestrian instability under urban road flooding conditions. The objective is to conduct an in-depth analysis of the friction coefficient between pedestrians and the ground in actual flood scenarios and its variations, providing practical data to support future pedestrian safety assessments under flood conditions. Wet friction coefficient experiments were conducted under waterlogged conditions, with real human subjects tested across various operational scenarios. A buoyancy calculation formula was introduced to explore the impact of pressure changes caused by buoyancy on the human body in water, influencing the friction coefficient. An exponential relationship between pressure and the friction coefficient was established. Furthermore, by considering factors such as outsole hardness, ground type, and pressure variations with water depth, a dynamic method for selecting the friction coefficient was proposed, offering a scientific basis for determining friction coefficient thresholds associated with pedestrian instability risks. Experimental results indicate that, in the combination of hydrophilic materials with experimental asphalt and cement pavements, the friction coefficient under waterlogged conditions is generally higher than under dry conditions. However, as pressure increases, the friction coefficient of rubber materials decreases. This study concludes that the selection of the friction coefficient in pedestrian instability analysis should be treated as a dynamic process, and relying on a fixed friction coefficient for force analysis of pedestrian instability may lead to significant inaccuracies. Full article

Time-series anomaly detection is a critical task in various domains, including industrial control systems, where the early detection of unusual patterns can prevent system failures and ensure operational reliability. This paper introduces ChaMTeC (CHAnnel Mixing and TEmporal Convolution Network), a novel deep learning framework designed for time-series anomaly detection. ChaMTeC integrates an inverted embedding strategy, multi-layer temporal encoding, and a Mean Squared Error (MSE)-based feedback mechanism with dynamic thresholding to enhance anomaly detection performance. The framework is particularly tailored for industrial environments, where anomalies are rare and often subtle, making detection challenging. We evaluate ChaMTeC on six publicly available datasets and a newly introduced dataset, WaterLog, which is specifically designed to reflect real-world industrial control system scenarios with reduced anomaly rates. The experimental results demonstrate that ChaMTeC outperforms state-of-the-art models, achieving superior performance in terms of F1-CPA (Coverage-based Point-Adjusted F1) scores. The WaterLog dataset, which has been made publicly available, provides a more realistic benchmark for evaluating anomaly detection systems in industrial settings, addressing the limitations of existing datasets that often contain frequent and densely packed anomalies. Our findings highlight the effectiveness of combining channel-mixing techniques with temporal convolutional networks and dynamic thresholding for detecting anomalies in complex industrial environments. The proposed framework offers a robust solution for real-time anomaly detection, contributing to the reliability and sustainability of critical infrastructure systems. Full article

The Tidal River Management (TRM) approach plays a significant role in enhancing diversified services of the ecosystem in the ecosystem of rivers affected by tides and their floodplains and reducing coastal hazards in southwest Bangladesh. The main aim of this investigation was to complete the development of the Sustainability Index for Tidal River Management (SITRM) and to assess the sustainability of TRM in coastal regions. In the first stage, the key components along with indicators of the Sustainability Index of TRM were identified to address problems of the coast. In the second stage of this study, a five-point Likert scale was applied to gather responses from key informants. In addition, it includes direct field observations and consultation meetings to collect information concerning the SITRM indicators. The results showed that the framework of SITRM included several important indicators to solve coastal problems, including drainage congestion, waterlogging, rising sea levels, new land formation, compensation, alternative livelihoods, and terrestrial biodiversity as indicators. It also established standard tidal flow thresholds for the Hari–Teka River at 600 m³/s (maximum) and 250 m³/s (minimum) for high tide and 550 m³/s (maximum) and 200 m³/s (minimum) for low tide. Moreover, the results showed that the Canadian Water Sustainability Index (CWSI), West Java Water Sustainability Index (WJWSI), and Water Poverty Index (WPI) are suitable for overcoming coastal problems and climate change issues. Full article

Due to climate change, cotton production is extensively restricted by waterlogging, especially under accompanying high temperatures. Yield production functions are powerful tools in agricultural water management, but there is a lack of consideration for crop dynamic growth and the impact of accompanied high temperatures during waterlogging. In this work, to simulate cotton yields under waterlogging regarding accompanying high temperatures, a comprehensive stress index was proposed, and a dynamic yield production function model was accordingly developed. The model was calibrated and evaluated by using multi-year and multi-site experimental data in the Hubei Province of China, and, then, it was applied under various waterlogging scenarios. The results showed that including the impact of accompanying high temperatures can effectively improve model performance, and the temperature threshold for triggering this additional impact was 30 °C. The dynamic model exhibited satisfactory performance during both calibration and evaluation, with low relative mean absolute error values (RMAE = 12.12% and 21.51%) and low coefficient of residual mass values (CRM = -0.028 and 0.063). According to model simulations, even under the same amount of excessive water, yield losses can vary from 3.90% to 33.93% due to different waterlogging timings and air temperature conditions. In summary, the present model is a convenient and powerful tool for crop drainage schedules and sustainable agriculture under global climate change. Full article

The rice waterlogging stress test was conducted at the experimental base of the College of Agriculture, Yangtze University, using Yangxian You 418 as the test subject, in order to investigate the impact of waterlogging on rice growth during the period from July to August each year. Six waterlogging stress tests with different waterlogging depth (1/4 plant height (1/4PH), 2/4 PH, and 3/4 PH) and duration (5 d and 7 d) were set up at the jointing–booting stage of rice (T1: 1/4 PH, 7 d; T2: 2/4 PH, 7 d; T3: 3/4 PH, 7 d; T4: 1/4 PH, 5 d; T5: 2/4 PH, 5 d; T6: 3/4 PH, 5 d;) with shallow water irrigation (CK) as control. The plant height, population leaf area, above-ground dry matter, and the yield of rice were measured. The correlation between the waterlogging depth and rice yield reduction was analyzed, and the flood disaster threshold index of rice was established. The results showed that at the end of stress, the plant height of all waterlogged treatments exceeded CK, and the plant height of T3 and T6 treatments significantly increased by 31.90% and 15.93%, respectively. The leaf area of rice treated with T1, T3, T4, and T5 was higher than CK (p < 0.05), and the above-ground dry matter of rice treated with T2, T3, T4, T5, and T6 was higher than CK (p < 0.05). When normal irrigation was restored to the maturity stage, the plant height of all rice treated with waterlogging was still higher than CK (p < 0.05). However, as the degree of waterlogging increased, rice yield decreased significantly, with a notable reduction of 31.68% observed in the T3 treatment compared to CK. Assuming a drainage index based on a 20% decrease in rice yield, it is imperative that the ratio of flooded depth to plant height remains below 37% when waterlogging persists for 7 days in rice cultivation. These research findings offer crucial scientific insights for implementing effective drainage management measures during flood disasters in rice paddies. Full article

Strawberry plants have shallow roots and large leaves, which are highly sensitive to variations in water levels. To explore the physicochemical and molecular mechanisms of strawberry response to water stress, and provide new ideas for strawberry scientific irrigation, we measured the transpiration rate, fresh weight, biomass gain, and other indicators of potted “Zhangji” strawberry plants under drought and waterlogging treatments using a Plantarray system. Transcriptomic and metabolomic analyses of strawberry leaves following mild drought, moderate drought, severe drought, and rehydration treatments were performed to identify key genes and metabolites involved in the response to drought stress. Below a certain threshold, the transpiration rate of strawberry plants was significantly lower after the deficit irrigation treatment than the conventional water treatment. Transcriptome analysis revealed that genes involved in oxidoreductase activity and in sulfur and nitrogen metabolism were up-regulated, as well as starch and sucrose. Strawberry plants secrete various endogenous growth hormones to maintain their normal growth under drought stress. The syntheses of salicylic acid (SA) and abscisic acid (ABA) were up-regulated in the mild and moderate drought treatments. However, the syntheses of 1-aminocyclopropanecarboxylic acid (ACC) and indole-3-acetic acid (IAA) were down-regulated in severe drought treatment and up-regulated in rehydration after severe drought treatment. Full article

With the frequency of extreme rainfall increasing, the risk of waterlogging is significantly exacerbated in subway systems. It is imperative to first identify the rainfall threshold for waterlogging risk for subway stations in order to develop effective waterlogging prevention and control plans. This study focuses on Line 11 of the Beijing Subway, using InfoWorks ICM to construct a model of the research area and simulate waterlogging at various subway stations under different rainfall scenarios. The results indicate that there is a risk of waterlogging at Jinanqiao station, Moshikou station, and Beixinan station on Line 11. The accumulated water may enter the subway station through exits A, B, C, and D of Jinanqiao Station. The inlet sequence of Jinanqiao Station always follows A(B), C, and D, and the difference in waterlogging time for each outlet does not exceed 10 min. We derived the rainfall threshold formula for waterlogging risk at Jinanqiao subway station. Among the three influencing factors of topographic features, step height, and drainage capacity of the pipeline network, step height has a significant effect on increasing the rainfall threshold for waterlogging risk. The conclusions obtained can provide reference for the refined management of waterlogging risks in subway stations. Full article

The development of an accurate root water-uptake model is pivotal for evaluating crop evapotranspiration; understanding the combined effect of drought and waterlogging stresses; and optimizing water use efficiency, namely, crop yield [kg/ha] per unit of ET [mm]. Existing models often lack quantitative approaches to depicting crop root water uptake in scenarios of concurrent drought and waterlogging moisture stresses. Addressing this as our objective; we modified the Feddes root water-uptake model by revising the soil water potential response threshold and by introducing a novel method to calculate root water-uptake rates under simultaneous drought and waterlogging stresses. Then, we incorporated a water stress lag effect coefficient, $\phi \left(W_s\right)$, that investigated the combined effect of historical drought and waterlogging stress events based on the assumption that the normalized influence weight of each past stress event decreases with an increase in the time interval before simulation as an exponential function of the decay rate. Further, we tested the model parameters and validated the results obtained with the modified model using data from three years (2016–2018) of rice (Oryza sativa, L) trails with pots in Bengbu, China. The modified Feddes model significantly improved precision by 9.6% on average when calculating relative transpiration rates, particularly post-stress recovery, and by 5.8% on average when simulating soil moisture fluctuations during drought periods. The root mean square error of relative transpiration was reduced by 60.8%, and soil water was reduced by 55.1%. By accounting for both the accumulated impact of past moisture stress and current moisture conditions in rice fields, the modified model will be useful in quantifying rice transpiration and rice water use efficiency in drought–waterlogging-prone areas in southern China. Full article