Water Level Process Research Articles

Real-time predictive control assessment of low-water head hydropower station considering power generation and flood discharge

In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.

Risk-benefit comparative analysis of different control methods for reservoirs under the water to electricity mode

Abstract For some small hydropower stations that are not under the control of power dispatching department, there are many ways to implement the reservoir operation scheme formulated under the premise of determining power by water. This paper respectively selects the output, flow and water level process as the control conditions, explores the risks and benefits of reservoir operation with different control variables, and selects the optimal operation scheme through comprehensive comparative analysis. Simulation results of a hydropower station in the Han River Basin show that when the output is taken as the control condition, the waste water is small, the power generation benefit is high, and the risk of the water level outside boundary is within the acceptable range. Considering the risk and benefit of reservoir operation and taking output as the control condition, it has significant advantages over water level and flow.

Aquatic Insights: A Cutting-Edge Water Quality Monitoring System

This abstract presents a web-based application, "Digital Technologies/MIS for Monitoring Water Quality Management in the Water Supply Network at the District Level," designed to efficiently manage water processes. The project's primary objectives are to incorporate hydrostatic details, hydrostatic method information, and district-specific data. The system consists of two key modules: Administrator and User. The Administrator module offers administrative functionalities such as adding hydrostatic details and reviewing information, which aids in controlling water levels and capacity, ensuring efficient hydrostatic allocation for users, and overseeing water level processes. Meanwhile, the User module enables users to view hydrostatic details, access hydrostatic method information, and post reviews. This project contributes to improved water quality management at the district level through digital technologies, streamlining processes and enhancing transparency.

River Flood Characteristics Along the Canal Area Effected By Hanzhuang Canal Jacking

In this paper, flood characteristics along the canal area effected by Hanzhuang Canal jacking are studied. By coupling the rainfall runoff (NAM) model with the hydrodynamic (HD) model, a 1-D hydrologic-hydrodynamic coupling model was built. On the basis of parameter calibration and model validation, different flood simulation schemes are drawn up, and the influence range of Hanzhuang Canal jacking and the flood characteristics of tributaries affected by Hanzhuang Canal jacking are analyzed respectively. The results show that with the increasing discharge of Hanzhuang Canal, the water level of Hanzhuang canal rises gradually, the main stream flood flows upstream along the tributaries, the water level of the tributaries rises gradually, and the risk of flood disasters increases. Among the tributaries, Taogou River is most affected by Hanzhuang Canal jacking and has the highest risk of flood disaster, followed by Dasha River, Dasha River Floodway and Sizhi Ditch. The results also show that the backwater of Hanzhuang Canal on the tributaries is mainly reflected in the influence on the upstream flood water level process, but has little or no influence on the flood discharge process, which can provide a reference for regional flood control and scheduling department.

Variations in Aquatic Vegetation Diversity Responses to Water Level Sequences during Drought in Lakes under Uncertain Conditions

Water level variability and temporal change are critical for shaping the structure of aquatic vegetation. Much research has examined the response of aquatic vegetation to hydrological metrics. However, the hydrological sequence is a fundamental driver of aquatic ecosystem structure and function. Given the aleatory uncertainty of future water levels under an unstable climate, how aquatic vegetation responds to changing dynamics in hydrological processes, especially shifting water level sequences, remains insufficiently explored. In this paper, we establish an evaluation framework to study the response of vegetation diversity to variation in water level sequences during a drought event. To do this, the uncertainty and variability of water level processes are both considered. Altering water level processes was achieved using two types of scenarios in order to explore the effects of differing water level sequences (i.e., changing the order of high vs. low water levels) on the probability distribution of four indexes of aquatic plant diversity (e.g., Margalef’s, Simpson’s, Shannon’s, and Pielou’s evenness index). Our results show that altering the order of water level state can lead to differences in the diversity of aquatic vegetation, with a pronounced impact on vegetation complexity. This suggests that the specific sequence of water level events is critical for shaping aquatic vegetation structure. In addition, we found that a uniform distribution of water level state is beneficial for enhancing a species’ dominance in aquatic vegetation. Our findings provide guidance for improving the future development of freshwater ecosystem protection and lake management.

Accurate water level predictions in a tidal reach: Integration of Physics-based and Machine learning approaches

Accurate water level prediction is very important for coastal construction and flood prevention in an estuarine area. However, it is challenging to represent the water level processes accurately in a spatial domain due to the dual influences of river discharge from the upstream tidal reach (upstream discharge) and astronomical tides. In this study, a new method is proposed to predict water levels in tidal reaches by developing a riverine-estuary Hydrologic-Hydrodynamic Coupling model (H2C) and integrating the outputs (Upstream discharge and water levels, tidal levels) with long-short term memory. Then, the relative contribution of upstream discharge to water level predictions is assessed by extreme gradient boosting. The coupling model (H2C-XL) is driven by basin-scale Satellite-derived Meteorology Estimate datasets and TPXO9 tidal data. H2C-XL is tested in a tidal reach (Tianhe-Zhuyin reach) and found to be effective in enhancing the accuracy of water level predictions dramatically. The Nash coefficient and Kling-Gupta Efficiency values of predicted upstream discharge are 0.866 and 0.922, respectively. Accurate upstream discharge prediction is crucial for water level predictions in tidal reaches. This is reflected in that the Nash coefficient and Kling-Gupta Efficiency values are up to 0.781 and 0.806 for predicting water levels at upper hydrologic stations (Jiangmen and Daao stations), which are improved by 12.34% − 40.46% and 16.98% − 32.34% than those of above models. The relative contribution of upstream discharge to water level predictions in Tianhe-Zhuyin reach ranges from 0.37 to 0.55. The values vary at the same site depending on the hydrologic conditions each year and are relatively low near the coastline, but nonlinearly increase with the distance from the coastline.

A SVM-based implicit stochastic joint scheduling method for ‘wind-photovoltaic-cascaded hydropower stations’ systems

With the gradual expansion of the development scale of wind power and photovoltaic (PV) power plants, the multi-energy complementary power generation system, typically represented by hydro-PV/hydro-wind/hydro-wind-PV, has become an important part of modern power systems. Aiming at the joint operation of the cascaded hydropower stations after wind-PV grid connection, a medium- and long-term implicit stochastic joint dispatching function model for wind-PV-cascaded hydropower stations based on the SVM(support vector machine) method is developed in this paper, which selects the final water levels of the reservoirs as the dependent variables, and the initial water levels of the reservoirs, the reservoir inflow, the interval inflow as well as the wind and PV output are independent variables. First, the optimization of main parameters C (Penalty coefficient), g (Kernel function parameter) and p (Insensitive loss coefficient) of the model are achieved by particle swarm algorithm. The Gaussian radial basis function is then used to fit the scheduling function proposed in this paper. Finally, the rolling simulation calculation and correction of the obtained scheduling function are realized by C# programming language of VS2017 platform. The results show that the proposed scheduling function is an effective method for scheduling decision-making, and the revised water level process, output process as well as annual electricity production of the scheduling model are not significantly different from the optimal scheduling results. Moreover, the simulation results conform to the existing scheduling rules, which has shown it can be used to inform the operation of cascaded hydropower stations under the multi-energy complementary system.

Mixing processes in a reservoir corresponding to different water level operations caused spatial differences during two phytoplankton bloom events

Reservoir operation may impact algal blooms by altering flow conditions, but relevant studies and a theoretical understanding of this subject are limited. In this study, two phytoplankton bloom events with different spatial coverage patterns in a reservoir were investigated. By examining environmental variables, a suitable nutrient content and water temperature were considered responsible for bloom initiation during both events, but the different water level operations could possibly explain the observed spatial difference. A two-dimensional hydrodynamic numerical model was then used to simulate the mixing processes within the reservoir during these two bloom events. The simulation results indicated that the rising water level during the bloom event with a larger coverage area caused closed circulation flow conditions above the intrusion layer, displacing surface water in front of the dam upstream until reaching inflowing river water, which then intruded to support water offtake and water level lifting operations, while the other event did not exhibit a similar flow pattern. The water age was sensitive to water level operation, which suggested that phytoplankton dispersal could be promoted by rising water level processes. Inflow densimetric Froude number analysis indicated that reservoir impoundment facilitated a conversion of the inflow kinetic energy into potential energy, thus promoting algal dispersal via circulation. This study provides insights into the impact of reservoir operation on phytoplankton communities, offering a reference for operational design to mitigate algal blooms in reservoirs.

Delayed feedback between adaptive reservoir operation and environmental awareness within water supply-hydropower generation-environment nexus

In order to ensure sustainable coevolution of water supply, hydropower generation and environment (WHE), the impact of the delayed feedback between adaptive reservoir operation and environmental awareness on WHE nexus is explored in this paper through comprehensive analysis incorporating the policy, social, economic, and environmental factors. The WHE nexus states referring to water storage in reservoir, population, water use, hydropower generation, environmental awareness, and behavior response of community, have been mimicked using the WHE model. The delaying time caused by the delayed feedback can be estimated from the state processes through two indexes (i.e., response delay and recovery delay). Progressive Optimality Algorithm-Particle Swarm Optimization (POA-PSO) hybrid algorithm is adopted to search for an optimal water level process and water transfer. Moreover, the model parameter sensitivity analysis is conducted to determine the dominant model parameters affecting the time delays. Taking Jiayan reservoir as a case study, the Nash-Sutcliffe Efficiency coefficients of the WHE nexus model are above 0.5 in both calibration and validation period. The delay time between environmental awareness and decision preferences ranges from 0 to 8 years under parameter variations. The decision preference has a quicker response to the rise of the water shortage awareness (0.4 years) than the ecological awareness (5.3 years). The most sensitive parameters affecting the delay times are the response rate, decay rate, and threshold of environmental awareness. The proposed model helps us to understand the feedback among adaptive reservoir operation, environmental awareness and decision preferences, and further gives a new access to make effective and scientific long-term adaptive reservoir operation for the human society-environment coupled system.

Three-Parameter Regulation Rules for the Long-Term Optimal Scheduling of Multiyear Regulating Storage Reservoirs

The perennial storage water level (PL), the water level at the end of wet season (WL), and the water level at the end of dry season (DL) are three critical water levels for multiyear regulating storage (MRS) reservoirs. Nevertheless, the three critical water levels have not been paid enough attention, and there is no general method that calculates them in light of developing regulating rules for MRS reservoirs. In order to address the issue, three-parameter regulation (TPR) rules based on the coordination between the intra- and interannual regulation effects of MRS reservoirs are presented. Specifically, a long-term optimal scheduling (LTOS) model is built for maximizing the multiyear average hydropower output (MAHO) of a multireservoir system. The TPR rules are a linear form of rule with three regulation parameters (annual, storage, and release regulation parameters), and use the cuckoo search (CS) algorithm to solve the LTOS model with three regulation parameters as the decision variables. The approach of utilizing the CS algorithm to solve the LTOS model with the WL and DL as the decision variables is abbreviated as the OPT approach. Moreover, the multiple linear regression (MLR) rules and the artificial neural network (ANN) rules are derived from the OPT approach-based water-level processes. The multireservoir system at the upstream of Yellow River (UYR) with two MRS reservoirs, Longyangxia (Long) and Liujiaxia (Liu) reservoirs, is taken as a case study, where the TPR rules are compared with the OPT approach, the MLR rules, and the ANN rules. The results show that for the UYR multireservoir system, (1) the TPR rules-based MAHO is about 0.3% (0.93 × 108 kW∙h) more than the OPT approach-based MAHO under the historical inflow condition, and the elapsed time of the TPR rules is only half of that of the OPT approach; (2) the TPR rules-based MAHO is about 0.79 × 108 kW∙h more than the MLR/ANN rules-based MAHO under the historical inflow condition, and the TPR rules can realize 0.1–0.4% MAHO more than the MLR and ANN rules when the reservoir inflow increases or reduces by 10%. According to the annual regulation parameter, the PLs of Long and Liu reservoirs are 2572.3 m and 1695.2 m, respectively. Therefore, the TPR rules are an easy-to-obtain and adaptable LTOS rule, which could reasonably and efficiently to determine the three critical water levels for MRS reservoirs.

Biogeochemical and Hydrological Variables Synergistically Influence Nitrate Variability in Coastal Deltaic Wetlands

AbstractCoastal river deltas are centers of surface water nitrate processing, yet the mechanisms controlling spatio‐temporal patterns in nutrient variability are still little understood. Nitrate fluctuations in these systems are controlled by complex interactions between hydrological and biogeochemical drivers, which act together to transport and transform inorganic nutrients. Distinguishing the contributions of these drivers and identifying wetland zones where nitrate processing is occurring can be difficult, yet is critical to make assessments of nutrient removal capacity in deltaic wetlands. To address these issues, we analyze relationships among regional “external” (river discharge, tides, wind) and local “internal” (water level, temperature, turbidity, and nitrate) variables in a deltaic wetland in coastal Louisiana by coupling a process connectivity framework with information theory measures. We classify variable interactions according to whether they work uniquely, redundantly, or synergistically to influence nitrate dynamics and identify timescales of interaction. We find that external drivers work together to influence nitrate transport. Patterns of hydrological and sediment connectivity change over time due to tidal flushing and discharge variation. This connectivity influences the emergence of functional zones where local nitrate fluctuations and temperature and water level process couplings are strong controls on nitrate variability. High vegetation density decreases hydrological process connectivity, even during periods of high river discharge, but it also increases biogeochemical process connections, due to the lengthening of the hydraulic residence time. Based on these results we make recommendations for monitoring nitrate in a wetland.

Local scour around a bridge pier under ice-jammed flow condition – an experimental study

Abstract The appearance of an ice jam in a river crucially distorts local hydrodynamic conditions including water level, flow velocity, riverbed form and local scour processes. Laboratory experiments are used for the first time here to study ice-induced scour processes near a bridge pier. Results show that with an ice sheet cover the scour hole depth around a bridge is increased by about 10% compared to under equivalent open flow conditions. More dramatically, ice-jammed flows induce both greater scour depths and scour variability, with the maximum scour depth under an ice-jammed flow as much as 200% greater than under equivalent open flow conditions. Under an ice-jammed condition, both the maximum depth and length of scour holes around a bridge pier increase with the flow velocity while the maximum scour hole depth increases with ice-jam thickness. Also, quite naturally, the height of the resulting deposition dune downstream of a scour hole responds to flow velocity and ice jam thickness. Using the laboratory data under ice-jammed conditions, predictive relationships are derived between the flow’s Froude number and both the dimensionless maximum scour depth and the dimensionless maximum scour length.

A framework for determining lowest navigable water levels with nonstationary characteristics

The Lowest navigable water level (LNWL) is an important indicator used for navigation design to balance the relationship between navigation safety and economic benefits of a waterway. However, it is a challenge of accurately estimating LNWLs due to the nonstationary characteristics of observed water level data series. In this study, a comprehensive framework was developed for handling this issue. In this framework, inter-annual variabilities in both the mean and variance of water level series were described by decomposing original series and were eliminated by composing new series. Intra-annual variability was determined by detecting indicators describing intra-annual water level distributions. Considerations of inter- and intra-annual variabilities were combined by designing annual water level processes for the past and current environments. Shipping risks during both annual and multi-annual periods were considered in the framework as well. The framework was demonstrated in estimating LNWLs at the Gaodao and Shijiao stations in the North River basin, southern China. The recommended LNWLs at the Gaodao station were 22.32 m for 95% guaranteed rate and 21.84 m for 98% guaranteed rate; LNWLs at the Shijiao station were 0.27 m for 95% guaranteed rate and 0.15 m for 98% guaranteed rate. The impact of variance variability on estimations of LNWLs was also evaluated. Results indicated that the recommended LNWLs would have errors of 0.11 ~ 0.48 m at the Gaodao station and 0.03 ~ 0.04 m at the Shijiao station if the variance variability was not considered. The proposed framework was then compared with Nonstationary synthetic duration curve (NSDC) method, and results illustrated that the duration curves plotted by NSDC method were unreasonable, leading to inaccurate design values. Overall, the developed framework is more reasonable and suitable for designing LNWLs of waterways where the variabilities of the water levels at different time scales are different or where the historical water level data contain various variations .

Tuning Generalized Predictive PI controllers for process control applications

Predictive PI (PPI) controllers have demonstrated to exceed traditional PID controllers when they are applied to systems with long delays. This work proposes a new controller structure and tuning that we call Generalized Predictive PI (GPPI) controller which provides greater design flexibility than PI and PPI strategies. To realize a fair comparison, the design and tuning rules for discrete PI and PPI controllers were developed using optimal arguments based on the root-locus, for critically damped response before a step change in the reference. Experimental results, using industrial equipment, have illustrated the tuning methodology and the performance of the proposed controller under real conditions. Flow and water level process in a laboratory flume were considered. For these processes, First Order Plus Time Delay (FOPTD) models are used. The GPPI control results are encouraging, reducing the settling time plus a very small overshoot before step change in the reference regarding the PI and PPI strategies, up to 41.03% for the flow control loop and up to 54.21% for the level control loop. The discrete analysis of the strategies in the Z plane was performed, allowing for a direct translation to recursive equations that can then be programmed into a Programmable Logic Controller (PLC), other industrial controllers such as Distributed Control Systems (DSC), or microcontrollers, such as Arduino, Raspberry or FPGA. This is an important result, since it demonstrates that the increased complexity of the proposed controller does not hamper its implementation in industrial controller systems. In this work, we used a Rockwell ControlLogix \\protect \\relax \\special {t4ht=®} PLC with Structured Text programming language.

Quantifying suitable dynamic water levels in marsh wetlands based on hydrodynamic modelling

AbstractThe water level of marsh wetlands is a dominant force controlling the wetland ecosystem function, especially for aquatic habitat. For different species, water level requirements vary in time and space, and therefore ensuring suitable water levels in different periods is crucial for the maintenance of biodiversity in marsh wetlands. Based on hydrodynamic modelling and habitat suitability assessment, we determined suitable dynamic water levels considering aquatic habitat service at different periods in marsh wetlands. The two‐dimensional hydrodynamic model was used to simulate the temporal and spatial variation of water level. The habitat suitability for target species at various water levels was evaluated to obtain the fitting curves between Weighted Usable Area (WUA) and water levels. And then suitable water levels throughout the year were proposed according to the fitting curves. Using the Zhalong Wetland (located in northeastern China) as a case study, we confirmed that the proposed MIKE 21 model can successfully be used to simulate the water level process in the wetland. Suitable water levels were identified as being from 143.9–144.2 m for April to May, 144.1–144.3 m for June to September, and 144.3–144.4 m for October to November (before the freezing season). Furthermore, proposed water diversion schemes have been identified which can effectively sustain the proposed dynamic water levels. This study is expected to provide appropriate guidance for the determination of environmental flows and water management strategies in marsh wetlands.

Hydrological changes along the Jing River and its causal analysis

The hydrological response of rivers to upstream reservoir regulation is one of the increasingly important research issues of watershed management. In this study, a hydrodynamic coupling model was used to simulate water level and flow processes of the Jing river and diversion flow of three outlets before and after the operation of TGP (Three Gorges Projects). The results indicated that water level and flow of three diversion channels both decreased in flood and dry seasons: the compensation regulation of the TGP was significant to maintain the water level and flow of three diversion outlets in the Jing river, but it also increased zero-flow days in the diversion channels of the Yangtze River. The riverbed erosion of channels in the upper reaches as well as lower reaches caused hydraulic gradient varying so much that it can’t be ignored in the water exchange process between the Yangtze and Jing river. This mechanism cooperates with the operation of TGP to change the natural process of the Yangtze-Jing river’s hydrologic process. This work provides essential information for future water resources and environmental management of the river-lake system and facilitates the effective flow regulation of dams, which has significant implications not only for the Dongting river-lake system but also the similar lakes and rivers elsewhere.

Light attenuation as a control for microbiogeomorphic features: Implications for coastal cave speleogenesis

San Salvador (Bahamas) is a carbonate island with dozens of flank margin caves formed in the phreatic zone by fresh seawater mixing within the freshwater lens. These caves have no direct connection with the sea, and form at or close to the tidally influenced fluctuating water table. After sea-level fall, in their subaerial parts caves are enlarged mainly by rock dissolution and by erosion close to the water level, condensation-corrosion and breakdown processes. For understanding the geomorphological features observed in these caves and how they are related to light attenuation, we investigated three sampling sites in the tidally influenced zone of Lighthouse Cave, which has been re-invaded by seawater during the Holocene sea-level highstand. A freshwater lens no longer exists within or adjacent to the cave. Rock samples were collected above and below the internal lake shores close to the entrance, and in the twilight and dark zones of this cave. Light and electron microscopy examinations were conducted for detecting microbial cells, as well as bioconstruction and bioweathering features. In addition, a high precision laser scanner was used for characterising sample microtopography. Our data showed that the microtopography and geomorphology of the lake shore samples (cave entrance) are dominated by bioweathering, whereas the samples of the twilight and dark zones are controlled by a combination of both bioweathering and bioconstructive processes depending on light availability. Bioconstructive structures, such as semi-planar lamination, at the fluctuating water level of the Lighthouse Cave show that dissolution due to water mixing of sea and freshwater in the Holocene is no longer the most important speleogenetic process. We propose that the geomorphological evolution is strongly influenced by the degree of rock diagenesis more than the initial mechanism of speleogenesis.

Water level prediction of Lake Poyang based on long short-term memory neural network

湖泊水位是维持其生态系统结构、功能和完整性的基础.鄱阳湖受流域五河和长江来水双重影响，水位变化复杂.为了准确预测鄱阳湖水位变化，采用长短时记忆神经网络方法（LSTM）构建了鄱阳湖水位预测模型.该模型以赣江、抚河、信江、饶河和修水五河入湖流量和长江干流流量作为输入条件，预测鄱阳湖湖区不同代表站（湖口、星子、都昌、吴城和康山）的水位过程.研究以1956-1980年的水文时间序列数据作为训练集，1981-2000年作为验证集，探讨了LSTM模型输入时间窗、隐藏神经元数目、初始学习率等模型参数对预测精度的影响，并确定了鄱阳湖水位预测模型的最优参数.结果表明，采用LSTM神经网络方法可基于流域五河和长江来水量历时数据合理预测鄱阳湖不同湖区的水位过程，五站水位预测的均方根误差为0.41~0.50 m，纳什效率系数和决定系数达0.96~0.98.为考察模型训练数据集对鄱阳湖水位预测结果的影响，进一步选取了随机5年（1956-1960年）的资料和5个典型水文年（1954年、1973年、1974年、1977年和1978年）的日均流量资料来训练模型.结果显示随机5年资料作为训练数据的预测精度要差于典型年水文资料训练得到的模型，尤其是洪、枯水位的预测；由于典型水文年数据量仍远低于20年的资料，故其总体预测精度要略低于采用20年资料训练的模型.建议应用这类基于数据驱动的模型时，应该尽可能多选取具有代表性的资料来训练.;Lake water level is the basis for maintaining the structure, function, and integrity of its ecosystem. The water level change of Lake Poyang is complicated as it was affected by five rivers within the basin and the Yangtze River. To accurately predict the water level change of Lake Poyang, the long short-term memory (LSTM) is used to construct the water level prediction model of Lake Poyang. The model uses the flows of the Ganjiang River, Fuhe River, Xinjiang River, Raohe River, Xiushui River and the mainstream of the Yangtze River as input conditions to predict the water level process of different representative stations in the Lake Poyang area (Hukou, Xingzi, Duchang, Wucheng and Kangshan). The hydrological time series data from 1956 to 1980 is used as the training set, and data from 1981 to 2000 was used as the verification set. The influence of model parameters such as input time window, hidden neuron nodes and initial learning rate on prediction accuracy is discussed. The optimal parameters of the Lake Poyang water level prediction model are determined. The results show that the LSTM can accurately predict the water level at different parts of Lake Poyang based on the water flow from the five rivers and the Yangtze River. The RMSE value of the five stations is 0.41-0.50 m, and the NSE and R² are 0.96-0.98. In order to investigate the impact of the model training set on the water level prediction results of Lake Poyang, the study further selects data from 5 random years (1956-1960) and 5 typical hydrological years (1954, 1973, 1974, 1977 and 1978) daily average flow data to train the model. The results show that the prediction accuracy of random 5 years data as training set is worse than that of typical annual hydrological data training, especially the prediction of flood and dry water level; since the typical hydrological data volume is still much lower than 20 years of data, the overall prediction accuracy is slightly lower than the model with 20 years of data training. Therefore, representative data should be selected as much as possible for training, when applying such a data-driven LSTM neural network model.

Risks Analysis of Discarding Water in Power Generation Process of Hydropower Station

Hydropower, as a kind of clean energy, has the characteristics of rapid response to load changes, and is often used to undertake peak regulation and frequency regulation tasks. However, due to the uncertainties of reservoir inflow and runoff, the relative lag of power grid outgoing channels, and the interconnection of intermittent new energy, such as wind power and solar energy, the hydropower system is facing enormous peak regulation pressure and the continuous prominent problem of discarding water. In the study, aiming at the problem of risk control of discarding water in power generation process of hydropower station, a practical calculation method of risk of discarding water in power generation is proposed based on the analysis of frequency of runoff. Taking the Ertan hydropower station as an example, the corresponding water level processes of different discarding water risks under the control of pre-flood falling water level, flood limit water level and storage water level after flood season are deduced, and in the routine dispatching process, the water level operation control guidance corresponding to different discarding risk is introduced. The simulation results show that the proposed discarding water risk control method is practical and effective, and can provide guidance for discarding water risk control in the actual dispatching process of hydropower station.

Water level characteristics of Lake Caizi, lower reaches of the Yangtze River during wintering period based on long-term hydrological alteration

湖泊生态水位过程对维持湖泊生态系统结构、过程和功能的完整性具有重要意义，也是当今湖泊科学领域面临的重要科学问题.基于菜子湖湖区水位站（车富岭水位站）1956-2018年日水位资料，采用pettitt突变检验法分析水位的突变性特征.结合年保证率法得到菜子湖车富岭水位站低水位值，并在此基础上分析了低水位发生时间及历时、候鸟越冬期水位变化速率及其生态水位的区间阈值.主要结论有：菜子湖车富岭水位站1956-2018年年均水位无显著突变.菜子湖车富岭水位站低水位发生时间均值为年内的344 d，年际标准差为27 d，低水位的年均历时为69 d，标准差为49 d，有6年（1978、1997、2015-2018年）未发生低水位事件.菜子湖候鸟越冬期水位变化速率的均值为-0.009 m/d，年均值为-0.034~0.009 m/d，日均值为-0.051~0.016 m/d.菜子湖低水位发生时间的区间阈值为332~351 d，历时的区间阈值为33~98 d，变化速率的区间阈值为-0.070~0.020 m/d.加强菜子湖候鸟越冬期湿地生境保护适应性调度试验研究及生态环境监测，为菜子湖输水水位优化控制和菜子湖湿地生态保护提供科学依据.;The ecological water level process of lakes is of great significance for maintaining the integrity of the lake ecosystem structures, processes and functions, and is also an important scientific issue of lake science field nowadays. Based on the daily water level data, pettitt test was used to analyze the abrupt change characteristics of water level of Lake Caizi at Chefuling Station from 1956-2018. The low water level of Lake Caizi at Chefuling Station was obtained using the annual guaranteed frequency method. The starting time and duration of low water level, the water level change rate during the overwintering period of migratory birds and the interval threshold of ecological water level were analyzed accordingly. The main conclusions are as follows:there is no significant abrupt change point in the average annual water level of Lake Caizi at Chefuling Station from 1956 to 2018. The average occurrence time of low water level of Lake Caizi at Chefuling Stations is day 344, with the inter-annual standard deviation 27 days. The average duration time of annual low water level is 69 days with the standard deviation 49 days. Among the calculated 63 years, there are 6 years (1978, 1997, 2015-2018) without low water level event. The average rate of water level change during the overwintering period of migratory birds is -0.009 m/d, the annual average rate is -0.034-0.009 m/d, and the daily average rate is -0.051-0.016 m/d. The interval threshold of the occurrence of low water level of Lake Caizi is 332-351 d, the interval threshold of the duration time is 33-98 d, and the interval threshold of water level change rate is -0.070-0.020 m/d. Strengthening experimental study on habitat adaptation and ecological environment monitoring during the overwintering period of migratory birds to provide scientific basis for water level regulation and wetland protection of the Lake Caizi.