Water Level Variations Research Articles

Assessment of daily altimeter-based open ocean water level with hindcast and forecast efficiency

Satellite altimetry water level measurements are valuable in episodic and climate change related hydrodynamic impact studies, despite their sparse temporal distribution over the global ocean. This study presents the spatiotemporal characteristics of the open-ocean satellite derived water level measurements globally for the period 31/12/1992-15/10/2019 and evaluates their efficacy to represent the water level even during intense atmospheric conditions. Water level measurements from 23 different satellite missions are compared with tide gauge records and hydrodynamic simulations. The satellite measurements reproduce the water-level variations with good to excellent skill for ~60% of the areas considered. Additionally, satellite measurements and local atmospheric conditions are utilized in order to examine whether statistical data driven models can contribute to decreasing the temporal sparseness of the water level data over the global ocean. The suitability of this low computational-cost method is demonstrated by deriving a 63-year hindcast of the daily maximum water level for the global ocean, and for a medium-term 15-day ensemble forecast. The publicly available long-term water-level hindcast and the parameters of the data-driven statistical model derived can serve as a tool for designing and facilitating local and global coastal risk-assessment studies.

Hydrological-driven changes in the phytoplankton community structure under nutrient stress in island river ecosystems

While the spatiotemporal dynamics of phytoplankton community structures in river ecosystems have been extensively studied, their primary driving factors remain elusive, particularly at the watershed scale. This research examined the phytoplankton community structure across three watersheds of an island and concurrently analyzed its response to water level fluctuations (WLFs). The phytoplankton density was notably higher at elevated water levels, registering an increase of 1.29 times in Nandu River, 1.34 times in Changhua River, and 1.28 times in Wanquan River. Notably, the middle reaches of the Changhua River recorded the highest phytoplankton density (4.46 × 108 cells/L). Broadly speaking, Cyanophyta, Chlorophyta, and Bacillariophyta remained the predominant phytoplankton across varied water levels. Dominant phytoplankton species varied with water levels; however, the most prevalent species consistently belonged to the Cyanophyta group, primarily Microcystis or Merismopedia, with dominance ranging between 0.17 to 0.38 (low water level) and 0.18 to 0.32 (high water level). Enhanced phytoplankton diversity and richness were observed at higher water levels, correlating with increased concentrations of NH4+-N, NO3−-N, and PO43− in the river water. Consequently, nutrient fluctuations driven by hydrodynamics significantly influence the phytoplankton community structure in island river ecosystems.

From aquatic to terrestrial: An examination of plant diversity and ecological shifts

Abstract Our study focuses on plant diversity in the Allal El Fassi dam, a semi-arid continental bioclimate, to understand human-impacted aquatic ecosystems. We analyzed plant, soil, and water samples from 40 stations using various indices. We identified 55 plant species across 35 families, with Poaceae, Asteraceae, Asparagaceae, and Rosaceae being dominant. The transition zone (formerly Zone 2) is characterized by dense vegetation of hydrophytes, hemicryptophytes, and therophytes. The transformed (formerly Zone 1) and terrestrial zones (formerly Zone 3) have less diverse vegetation, dominated by phanerophytes, geophytes, and chamerophites. Phanerophytes, due to their developed root systems, are suited to dam soil types. Predominant species like Tamarix gallica L., Nerium oleander L., Juncus acutus L., and Arundo donax L. indicate the dam’s ecological transformation into a terrestrial ecosystem isolated from the river by sedimentary deposits following floods. These species are opportunistic, and adapted to water level variations.

Assessment of Avifaunal Status, Diversity, and Conservation Implications in and around the Wetlands of Dharwad, Karnataka, India

The study shows an overall avifaunal diversity and status of selected wetlands in Dharwad district and conveys the presence/absence of bird species on habitat changes caused due to varying water levels (exposure/submergence of mudflats) and destruction of riparian vegetation such as the reed species. These wetlands have attracted bird species from local to migratory to forage, roost and breed in the area. The avifaunal diversity was found to fairly vary across the different wetlands. The data portrayed here are from the surveys during wintering months of 2017-18 and 2021-22. Line transects, point count and total count methods were used for surveying. Diversity indices (Beta, Shannon, Simpson, Evenness) and similarity matrices (Bray-Curtis cluster) were calculated using Past 4 package. The Pearson correlation, beta diversity and Bray-Curtis’s clustering were used to compare the wetlands for the similarity/dissimilarities in species composition. Overall, Shannon and Simpson diversity of the wetlands were H’=4.67 & D= 0.98 with total of 177 species, which portrays high degree of diversity. There were around 46 species of migrant birds and 133 resident species. The work also reports a few rare and coastal birds. The study suggests the management plans for the conservation of avifauna in and around the wetlands to prevent human disturbances and also considering the importance of faunal habitats before adopting lake developmental plans.

Exploring the influence of refuges and additional foods on predator–prey interactions amidst environmental stochasticity and water level fluctuations

By considering that the predators have some additional foods apart from the focal prey and the prey take refuge, a predator–prey model with the effect of seasonality in a fluctuating environment is investigated in this study. Gaussian white noises, created by random fluctuation of water level, are introduced on the prey’s growth rate and predator’s mortality rate. Both the deterministic and stochastic systems are analyzed mathematically as well as numerically. Our analytical findings show that the intensity of environmental noise, additional foods and prey refuge play significant roles in survival as well as extinction of both prey and predator populations in the aquatic system. Our numerical results show that the effectual level of additional foods, depending on water level, has positive as well as negative effects on the predator and prey populations. We find that both species strongly persist at certain water level and for the low intensity of noise. However, for an increased value of noise intensity, they persist weakly and then go to extinction. Overall, our findings show that the variations of water level together with additional food and white noise plays crucial roles in persistence and extinction of species in the ecosystem.

Variable target water level control method in front of sluice in flood storage area based on storage balance model

Abstract To prevent the increase in flood disaster frequency, scale, and impact degree, a flood control engineering system with flood storage and detention areas, embankments, and reservoirs as the main body has been formed. Flood storage area is the key to the flood control engineering system, and its water level control performance is directly related to the success of flood control. According to the actual situation, the flood evolution simulation model in the flood storage area is constructed to obtain the evolution information of any unit of flood in the flood storage area. The dynamic control judgment coefficient of the water level is calculated, and the dynamic control program of the variable target water level in front of the sluice is formulated. Water level control can be realized by executing the established procedures. The above experimental data show that the proposed method can effectively control the water level in the flood storage area within the range of 26.03m ∼ 29.12m, and the minimum value of the corresponding risk rate is 3%, which fully proves that the proposed method has better dynamic control effect on the variable target water level in front of the sluice.

Investigating the impacts of different degrees of deficit irrigation and nitrogen interactions on assimilate translocation, yield, and resource use efficiencies in winter wheat

Water and nitrogen (N) are recognized as the primary determinants influencing the development and output of winter wheat. They affect the growth of winter wheat not only through their own changes, but also through their interaction. The translocation and accumulation of pre- and post-anthesis assimilates (including dry matter and N) are important physiological processes in winter wheat, which are closely related to the resource use efficiency and yield. However, a dearth of research exists that examines the complex interplay between varying water levels, especially severe water deficit, and N deficiency levels on the growth dynamics of winter wheat. The purpose of this study was to quantify and compare the effects of different degrees of water deficit and N interaction on assimilate translocation, water and N use efficiency and yield of winter wheat. A four-year long-term field experiment conducted under the rain-out shelter including four irrigation and two N levels was launched during 2019–2023. This method avoided the impact of precipitation on water treatment and helped to achieve serious water deficit treatment. The findings indicated that the higher N application improved the wheat's ability by 49.6 % - 362.3 % to utilize the available soil water. The pre- and post-anthesis translocation and accumulation of assimilates (including dry matter and N) in winter wheat increase alongside elevated levels of water and N application, except under severe water deficit treatment. Plants under mild to moderate water deficit were better able to translocate the pre-anthesis assimilates. However, the severe water deficit hindered the re-translocation of assimilates before anthesis. Providing additional N during periods of severe water scarcity leads to a 13.4 % - 44.7 % reduction in water use efficiency (WUE). Furthermore, both the irrigation water use efficiency and WUE diminished by 10.3 % - 60.5 % and 4.5 % - 41.4 % with higher levels of irrigation, while improved by 24.4 % - 48.2 % and 12.6 % - 39.4 % with higher N application rates. Conversely, N partial factor productivity and N use efficiency followed the opposite trend. Similar to WUE, increasing N application under severe water deficit would result in a yield reduction of 38.7 %. The reduction in crop yield resulting from severe water stress was primarily ascribed to the decline in the kernels number per spike (up to 74.9 %), with the reduction in spike number per unit area following closely behind (up to 29.1 %). This integrated approach, combining resource use efficiency with a detailed assessment of assimilate dynamics, provides a holistic view of how water and N management strategies impact winter wheat performance. The findings will offer precise insights for developing targeted irrigation and N scheduling strategies for sustaining both winter wheat production and environmental sustainability.

Spatiotemporal variation of water level in wetlands based on multi-source remote sensing data and responses to changing environments

Under changing environmental conditions, water level is a crucial indicator for assessing the wetland hydrological cycle. However, due to some wetlands being located in remote and widely dispersed areas, acquiring data on wetland water level changes presents significant challenges, making wetland water level monitoring exceptionally difficult. Wetlands are extensively distributed in western Jilin Province, China, and are experiencing significant degradation due to various factors including natural conditions, agricultural activities, and social development. To address this challenge, this study proposes a monitoring method that combines Sentinel-3 radar altimetry satellites with optical remote sensing images to obtain wetland water level data. Additionally, the study takes into account the Chinese government's Interconnected River System Network Project (IRSNP), classifying wetlands in western Jilin Province into three different water recharge scenarios: direct recharge through main and branch canals, indirect recharge through ditches, and no recharge to isolated wetlands. This study analyses the relationship between wetland water level changes and climatic factors, and assesses how IRSNP can mitigate the negative impacts of climate factors on wetland water levels across different recharge scenarios. The results show that: (1) the wetland water level monitoring method, has high accuracy and feasibility. The average difference between the in-situ measured and satellite-monitored water levels was 0.254 m. (2) There was an overall increasing trend in wetland water levels directly influenced by IRSNP, an insignificant decreasing trend in wetland water levels indirectly influenced by IRSNP. (3) Increased precipitation and decreased evaporation are the predominant climatic factors contributing to rising wetland water levels. Conversely, lower relative humidity and higher temperatures primarily lead to declining water levels. The construction of IRSNP can mitigate the impact of climate change on water levels. Thus, under changing environmental conditions, the implementation of IRSNP has positively impacted wetland protection and provides valuable insights for understanding wetland water level changes and managing water resources effectively.

Research on the hydraulic response characteristics during the water conveyance process of water diversion projects

Abstract During the initial trial period, the Hanjiang-to-Weihe River Valley Water Diversion Project (HWRVWD) sourced water from the Sanhekou water conservancy junction. It then transferred water through the Qinling water conveyance tunnel and distributed it at the Huangchigou water distribution hub, ultimately transporting it to the Guanzhong area. The hydraulic simulation and real-time control of the water conveyance system play a crucial role in ensuring the safe and efficient operation of this engineering project. To maintain water transfer safety and reasonable scheduling during the water supply process, the hydraulic characteristics of the transient process were calculated and analyzed in this paper. The one-dimension (1D) hydrodynamic mathematical model and advection-dispersion model were established to calculate the hydraulic factors of the 81.78 km Qinling water conveyance tunnel and the Huangchigou water distribution hub within the HWRVWD. Validation results showed that the root mean squared error tended toward zero and the coefficient of determination exceeded 0.97 between the measured values and the simulated values. The opening of gates and upstream inflow directly affected water level variations and storage. Smaller gate openings led to increased water level amplitudes before the gate and faster reaching of maximum water levels, but it also led to opposite trends in flow rates behind the gate compared to upstream water levels. The impact of incoming water flow rates on flow variations at different channel sections was significant. The time for water heads to reach each section and the rate of flow variation positively correlated with incoming water flow rates. Gradual increases in upstream inflow resulted in progressively larger water level amplitudes both before and behind the gate, with an increased maximum value, but more water might be discarded. When the water demand changes, the primary focus is on controlling the upstream inflow, with gate opening adjustments as a secondary measure, to achieve the goal of minimizing or eliminating water wastage.

Environmental flows in a future climate: Balancing hydropower production and ecosystem rehabilitation in the Ume River system, Sweden

Hydropower is central to renewable electricity systems, but degrades ecosystems, calling for environmental flow schemes to enhance the ecological status of river systems. Environmental flow assessments need to account for climate change, since climate-driven changes in runoff affect both hydropower operation and riverine ecosystems. Here, we quantify expected changes in hydropower production and environmental benefits of introducing environmental flows in a large regulated river system in northern Sweden in a future climate. Compared with the hydrology of 1981–2010, runoff is projected to increase with climatic conditions projected for 2040, leading to a 2.2 % increase in hydropower production with present rules for turbine and reservoir operation. Implementing environmental flows will result in lower hydropower production losses in with the 2040 climate than at present: Introducing restrictions against zero flow events, discharge to technical fishways and bypassed reaches throughout the year (with seasonal flow variation), as well as having more natural water-level variation in all run-of-river impoundments, would reduce annual hydropower production with 3.5 % with present conditions, and by 3.3 % in 2040. At the same time, the net effect of higher runoff and introducing environmental flows means that the annual hydropower production in the 2040 climate would be only 0.8 % lower compared to 1981–2010. In all scenarios, reservoir filling degree in 2040 was projected to increase compared to scenarios for 1981–2010, and flow requirements were met for both environmental flows and hydropower production over an 83-year scenario-based time series. This study demonstrates the feasibility of introducing environmental flow actions in Sweden, and other regions where increases in runoff are projected, with sustained hydropower production, having large benefits for riverine biodiversity and enhancing resilience of riverine ecosystems to climate change. For this to be successful, collaboration among stakeholders in riverine management is needed.

Machine Learning for the Sustainable Management of Depth Prediction and Load Optimization in River Convoys: An Amazon Basin Case Study

The seasonal fluctuation of river depths is a critical factor in designing cargo capacity for river convoys and logistics processes used for grain transportation in northern Brazil. Water level variations directly impact the load capacities of pusher convoys navigating the Amazon rivers. This paper presents a machine learning model based on a multilayer perceptron artificial neural network developed with the aim of estimating the cargo capacities of river convoys one year in advance, which is essential for determining load capacities during dry periods. The prediction model was applied to the Tapajós River in the Amazon Basin, Brazil, where grain transportation is significant and relies on inland waterways. Navigability conditions were evaluated in terms of depth and geometric parameters. The results of this case study were satisfactory, validating the computational tool and enabling the assessment of capacity losses during dry periods and the identification of navigation bottlenecks. The main contributions of this work include optimizing river logistics, reducing costs, minimizing environmental impacts, and promoting the sustainable management of water resources in the Amazon. Conclusions drawn from the study indicate that the developed model is highly effective, with an R2 of 0.954 and RMSE of 0.095, demonstrating its potential to significantly enhance river convoy operations and support sustainable development in the region.

Seasonal wet–dry transition and denitrifying communities contributed to nitrous oxide emissions in the water-level fluctuating zone of the largest freshwater lake in China

The water-level fluctuating zone (WLFZ) is the alternating dry/wet zone in freshwater lakes, and plays a crucial role in nitrogen (N) biogeochemical cycling and nitrous oxide (N2O) emissions. However, N2O emission fluxes, the underlying microbial mechanisms, and their feedbacks to global warming in the extensive WLFZ of the freshwater lakes with dramatic water-level variations remain unclear. Here, we sampled through both wet and dry seasons and compared the N removal rates, N2O emission fluxes, composition and co-occurrence network properties of the denitrifying microbial communities of the WLFZ, continuously flooded, and non-flooded zones covered with the dominant plant (Carex. cinerascens) in Poyang Lake, the largest freshwater lake of China. We observed higher potential denitrification (1.129–5.657 nmol N g-1h−1) and anammox (0.047–0.756 nmol N g-1h−1) rates in the WLFZ for both wet and dry seasons than those in the other two zones. A large seasonal N2O sink-source transition (−2.297 and 0.297 μg m-2h−1 for wet and dry season, respectively) was observed in WLFZ. Moreover, the composition of nirK and nirS communities significantly varied in WLFZ between the wet and dry seasons. Especially, several keystone taxa (e.g., Mesorhizobium and Rhodanobacter) were enriched in the WLFZ and were responsible for denitrification and N2O emissions under drought stress in the dry season. Variations in denitrification rates and N2O fluxes were driven by pH, C and N substrates, as well as the co-occurrence network properties, including natural connectivity and small-world coefficients of the nirK and nirS communities. Our results suggested that WLFZ in freshwater lakes under future climate change scenarios would be a substantial N2O source and aggravate climate, which would result in the positive feedback.

Influence Mechanism of Water Level Variation on Deformation of Steep and Toppling Bedding Rock Slope

The construction of major hydropower projects globally is challenged by slope deformation in reservoir areas. The deformation and failure mechanisms of large rock slopes are complex and poorly understood, making prevention and management extremely challenging. In order to explore the influence mechanism of the water level variation on the deformation of steep toppling bedding rock slopes, this paper takes the right bank slope near the dam area of the Longtou Hydropower Station as an example, and field investigations, deformation monitoring, physical simulation tests and numerical analyses are carried out. It is found that the slope deformation response is obvious under the influence of the reservoir water level variation, which is mainly reflected in the change in the slope groundwater level, rock mechanical parameters and seepage field in the slope body. The toe of the slope produces plastic deformation and maximum displacement. With the increase in the reservoir water level, the plastic zone expands and the displacement increases, which leads to the intensification of the slope deformation. This paper puts forward that the deformation and failure modes of the steep and toppling bedding rock slope caused by water level variation are due to shear dislocation, bending deformation and toppling fracture. This study reveals the influence mechanism of the water level variation on the deformation of steep and toppling bedding rock slopes, which can provide theoretical support for the construction of major hydropower projects.

The effect of ship-induced wave trains on periphytic algal communities in the littoral zone of a large regulated river (River Danube, Austria)

Riverine ecosystems are profoundly influenced by hydrological dynamics and natural flow regimes, which dictate the temporal variability of water levels and the amplitude of fluctuations. Human activities, particularly navigation and hydropower generation, have significantly altered these natural patterns, leading to detrimental impacts on the physical, chemical, and biological integrity of river ecosystems. The littoral zone, in particular, is highly susceptible to anthropogenic disturbances, experiencing disruptions in biological activity and biogeochemical processes. This study evaluates the effects of ship-induced wave trains on the structural and functional properties of periphytic algal communities in a regulated river environment. Using data from the Austrian Danube River, periphytic algae's immediate and long-term responses to wave events generated by different types of ships were investigated. Immediate reactions of periphytic algae to wave trains were characterized by reductions in the effective quantum yield of PS II, indicating stress-induced down-regulation of photosystem II photochemistry. Abrasion and remobilization of periphytic algae due to wave action led to increased resuspension of chlorophyll-a into the water column. Furthermore, ship-induced wave trains influenced the pigment composition of periphyton, with photoprotective mechanisms being activated in response to fluctuating light conditions. Long-term effects of wave impact on periphytic algae biomass varied depending on water depth and exposure to aerial stress. While wave action mitigated desiccation stress in shallow areas, it resulted in biomass reduction and alterations in community composition in deeper zones. Notably, the occurrence of diatoms decreased in wave-impacted areas, potentially shifting the community towards Chlorophyceae dominance. Overall, this study underscores the complexity of ship-induced wave impacts on riverine ecosystems and highlights the importance of considering both immediate and long-term responses of periphytic algal communities. Understanding these dynamics is crucial for informing sustainable management strategies aimed at mitigating the adverse effects of navigation activities on riverine biodiversity and ecosystem functioning.

Estimation of the water level variations in the 2022 Tonga tsunami event based on multiple machine learning models

In this study, four machine learning models are applied to estimate the water level variations recorded at three buoy stations during the 2022 Tonga tsunami. A new model performance evaluation metric, the lag degree, is introduced to compensate for the limitations of the conventional evaluation metrics (such as RMSE and R2 values), which could specify the lag extent, thus lag failure, between the estimated and original time series data. The long short-term memory (LSTM) and gated recurrent unit (GRU) models can accurately estimate the water level variations with less lag failure, superior to the multi-layer perceptron (MLP) and random forest (RF) models. Model estimation at the volcano-near area is less satisfactory (R2 < 0.5 after 3 time steps) than that at the volcano-far area (R2 > 0.5 within 8 time steps for LSTM and GRU) or the tsunami-shadowed area (R2 > 0.85 within 8 time steps). This is because, at the volcano-near area, both the low- and high-frequency components co-exist, resulting in sophisticated local water level fluctuations, whereas at the volcano-far area, only the low-frequency component prevails. For the tsunami-shadowed area, only specified frequency components could reach, thus relatively easy for model estimation.

Prediction of groundwater level using artificial neural network as an alternative approach: a comparison assessment with numerical groundwater flow model

ABSTRACT Over-exploitation of groundwater in arid and semi-arid regions requires an understanding of dynamics for effective management strategies. This study uses an artificial neural network (ANN) to predict groundwater levels using initial water level, pumping, and hydrological and meteorological input variables. A sensitivity analysis assessed predictors’ impact on accurate groundwater level prediction. The accuracy of predictions by the ANN model was examined through different performance evaluation criteria and the result achieved is satisfactory as compared to reasonable statistical indicator values. A comparison of results simulated by the ANN model and numerical groundwater flow model (MODFLOW) was carried out and it was found that the prediction of the ANN model was consistently superior to that of the numerical model. Therefore, data-driven models such as ANN can provide better predictions of groundwater level that will aid in groundwater resource management.

Analysis of the influence of the daily regulation of power stations on navigable flow conditions at river confluences using the LSTM model

The daily operations of large hydropower stations on rivers induce frequent variations in downstream water levels and flow velocities, resulting in unsteady and complex hydraulic characteristics at the confluence of the main stream and its tributaries, which adversely affect navigation safety. The continuity and momentum equations, along with the RNG k-ϵ turbulence model and the VOF model, were employed to simulate the river flow process at the confluence under unsteady flow conditions. The simulated results for water levels and velocity vector fields are in good agreement with experimental data. Variations in hydraulic characteristics, including water level, flow velocity, and longitudinal gradient at the confluence of the main stream and its tributaries, were analysed. The results indicated that the water level at the confluence decreases when the tributary merges with the main stream. As the confluence ratio increases, fluctuations in the water level at the confluence decrease. However, an increase in the staggered period results in greater fluctuations in the water level within this region. Moreover, a crescent-shaped high-speed flow region is formed at the confluence of the tributary and the main stream. As the unsteady flow discharge of the main stream increases, the relative area of this region correspondingly enlarges, reaching its maximum at peak discharge, and subsequently gradually diminishes as the discharge decreases. Based on these simulation data, a Long Short-Term Memory (LSTM) model was developed to effectively predict water levels and flow velocities, providing a more convenient and accurate method for obtaining real-time information on confluence areas than traditional mathematical and statistical approaches. This study provides novel insights into predicting flow characteristics in confluence areas, thereby offering a basis for formulating navigation safety plans.

Attribution of hydrological droughts in large river-connected lakes: Insights from an explainable machine learning model

Large lakes play an important role in water resource supply, regional climate regulation, and ecosystem support, but they face threats from frequent extreme drought events, necessitating an understanding of the mechanisms behind these events. In this study, we developed an explainable machine learning (ML) model that combines the Bayesian optimized (BO) long short-term memory (LSTM) model and the integrated gradients (IG) interpretation method to simulate and explain lake water level variations. In addition, the hydrological drought trends and extreme drought events in Poyang Lake from 1960 to 2022 were identified using the standardized water level index (SWI) and run theory. The analysis revealed that the frequency of hydrological droughts in Poyang Lake increased from 1960 to 2022, especially in the autumn after 2003. By selecting the flows of the catchment and the Yangtze River as the input features, the BO-LSTM model accurately predicted the water level of Poyang Lake. The IG method was then used to interpret the prediction results from three aspects: the importance ranking of the input features, their roles in the seasonal drought trends, and their roles in extreme drought events. The results indicate that (1) the most influential factor affecting the water level of Poyang Lake was the inflow of the Ganjiang River in the catchment. (2) The increase in the lake outflow caused by the Yangtze River's draining effect was the reason for the intensification of the autumn drought in Poyang Lake. (3) The extreme hydrological drought events were primarily caused by low catchment inflows. Overall, this research provides a new approach that balances prediction accuracy with interpretability for predicting and understanding the hydrological processes in large river-connected lakes. Moreover, this method was also applied to the attribution analysis of hydrological drought in Poyang Lake, providing theoretical support for regional water resource management.

Does the upstream gate control scheme threaten the safety of extra-long pressurized water diversion tunnel: Gas–liquid evolution characteristics of the filling process

With the promotion of China's “National Water Networks” strategy, extra-long pressurized water diversion tunnels are increasingly implemented in trans-regional and trans-basin water diversion projects. Existing projects commonly employ middle or downstream gate control schemes, but setting the control gate at the upstream offers a new approach to mitigate the adverse effects of flow pattern changes and hydraulic inertia caused by gate operations. However, there is no precedent for a 200 km-extra-long pressurized water diversion tunnel worldwide, it is not clear whether deviating from established norms will create new problems, and how to illustrate the hydraulic evolution characteristics under this scheme is the primary challenge. Therefore, this study takes a follow-up project for China's South-to-North Water Diversion Project as the research object: (1) Modeling: establish a mathematical model of an extra-long pressurized water diversion tunnel based on the movement of gas–liquid interface; and (2) Simulating: analyze the transient process of two arrangement schemes (single-slope and variable-slope) under various operating conditions. The study reveals the characteristics of pressure distribution, flow rates, and water level variations along the tunnel, conducting a comparative analysis of different arrangement schemes. The findings demonstrate that, even under the most unfavorable assumption, the key indicators during the water filling process remain within the acceptable range specified by engineering design. Therefore, the adoption of the upstream gate control scheme for the extra-long water diversion tunnel is considered feasible. This research provides specific theoretical basis and technical support for the construction and operation of water diversion projects.

Numerical Analysis of Flood Invasion Path and Mass Flow Rate in Subway Stations under Heavy Rainfall Conditions

The occurrence of extreme weather, such as heavy rainfall and sudden increases in precipitation, has led to a notable rise in the frequency of flooding in subway stations. By conducting numerical simulations of flood disasters in subway stations under heavy rainfall conditions and gaining insights into the patterns of flood invasion inside the stations, it is possible to develop practical and feasible drainage designs for the stations. This paper employs the computational fluid dynamics (CFD) method, utilising the volume of fluid function (VOF) method and the renormalization k-ε group model within the vortex viscosity model. The complete process of flood invasion into subway stations with varying water levels (1500 mm, 2000 mm, and 2450 mm) is modelled, and the distribution of floods at different times under varying operational conditions is analysed to identify the evolutionary patterns of station flood history. The simulation calculations yielded the mass flow rate time history curve at the tunnel entrance and exit, which was then subjected to an analysis of its development trend over time. The total accumulated water in the subway station is calculated by integrating the difference in mass flow rate between the entrance and the tunnel exit, using the mass flow rate curve. In conclusion, the paper proposes drainage measures that provide valuable insights into pumping strategies when floodwaters infiltrate subway stations. The results indicate that the speed of flood spreading in subway stations increases with higher groundwater levels, and that the mass flow rate of floodwater entering the tunnels increases over time, eventually reaching a stable state. It was observed that, at certain times, the mass flow rate of floodwater into the tunnels exhibited a linear relationship with time.