1975－2015年洪泽湖水沙变化趋势及成因分析

淮河流域洪涝灾害频繁,洪泽湖对其防洪除涝起关键性作用。掌握洪泽湖水沙变化趋势及突变点对流域水资源管理、水沙调节有重要的现实意义。利用入、出洪泽湖各支流代表水文站1975-2015年实测年径流量和年输沙量数据,分析入、出洪泽湖水量和沙量分布特征。通过Mann-Kendall(M-K)秩相关检验法和Pettitt突变点识别法研究入湖、出湖水沙量年际变化趋势和突变点。在此基础上,从流域降雨、水资源开发利用和水库滞沙三个方面分析了洪泽湖水沙变化的主要影响因素。研究表明:洪泽湖入湖、出湖水量年际变化趋势一致,无明显减小趋势,且无显著突变点。入湖沙量有小幅减小趋势,出湖沙量M-K统计值超过95%显著性水平,有明显减小趋势。入湖、出湖沙量发生突变的年份为1991年。对影响因素的分析得到:降雨量变化是水量变化的重要影响因素。1993-2015年,入湖水量呈不明显减小趋势则与流域用水量明显增加、水资源开发利用程度不断提高有关。上游水库建设是导致洪泽湖沙量有明显减小趋势的主要原因,1991年治淮工程的实施,水库复建和水土保持等措施是沙量突变的主要原因。     

Reconciling Hydropower and Environmental Water Uses in the Leishui River Basin

Today's water systems require integrated water resource management to improve the water supply for conflicting water uses. This research explores alternative policies to improve the water supply for two conflicting uses, hydropower and environmental, using the Leishui River basin and Dongjiang reservoir as a case study. First, the natural flow regime prior to reservoir construction (pre‐1992) was estimated by performing a statistical analysis of 41 years of daily streamflow data (March 1952–February 1993). This natural flow regime was used as a template for proposing environmental flow (e‐flow) requirements. The post‐reservoir flow regime (post‐1992) (March 1993–February 2011) was analysed to estimate the streamflow alteration. Results show that the natural flow regime has been completely transformed; post‐1992 winter normal flows are greater, and summer flows are smaller than pre‐1992 conditions. Also, the occurrence of natural floods has been prevented. Second, a planning model was built of the current operation of the Dongjiang reservoir and used for comparison of four alternative water management policies that considered e‐flow releases from the Dongjiang reservoir. The scenarios that considered combinations of the current operational policy and e‐flow releases performed better in terms of hydropower generation than the current operation. Different volumes of e‐flow requirements were tested, and an annual e‐flow volume of 75% of the pre‐1992 hydrograph was determined to generate the most hydropower while providing for environmental water needs. Trade‐offs are essential to balance these two water management objectives, and compromises have to be made for both water uses to obtain benefits. Copyright © 2013 John Wiley & Sons, Ltd.     

Analysis of Flow Behavior as Influenced by Reservoir with Flow Regularization

Understanding the behavior of reservoirs with flow regularization formed by hydroelectric power plants is essential for assessing water availability. The operationalization of reservoirs can be influenced both by climatic characteristics and by the consequences resulting from human actions in the basin. The objective of this study was to evaluate the existing relationships between the inflows and outflows of a reservoir, as well as with the conventional streamflow gauge stations downstream of the dam. Also evaluated were trends in the behavior of minimum, average and maximum flows, in the post-operation period, considering the characteristics of rainfall and irrigation in the region. The results indicated that reservoir operationalization is strongly related to the behavior of inflows. Moreover, a reduction was also verified in all the variables analyzed related to inflows and outflows, as well as in the stations downstream of the dam, except for the maximum flow in the station farthest from the reservoir, which showed a stationary behavior. The reductions in the flows may be related to the almost three-fold increase in the area irrigated by the center pivot in the basin; however, the same cannot be said in relation to the annual rainfall regime of the region, since it showed a stationary behavior for most of the stations evaluated. The work demonstrates the importance of trend analysis of flows over the years in order to identify possible factors responsible for their variability and assist in decision making regarding measures for the recovery and preservation of water resources.

     

Estimating the Ungauged Natural Flow Regimes for Environmental Flow Management

Regime-based approach recently becomes an important strategy while considering aquatic ecosystems in environmental flow management. The key element for supporting this strategy is long streamflow data which is usually not available for determining natural flow regimes. This study uses a back-propagation network to estimate ungauged natural flow regimes. A set of the upper reaches of Taiwan’s 42 flow stations with non-human control streamflow and at least 20 years daily flow data is used to quantify the natural flow regimes using 31 Indicators of Hydrologic Alteration (IHA). Watershed geomorphologic characteristic factors and rainfall parameters are used to classify homogeneous flow regime areas. The results show that there are three types of flow regimes from the flow stations, and each group of indicators in the IHA has different correlations with different geomorphologic characteristic factors and rainfall parameters. The results of using an artificial neural network model to estimate IHA show that the group average percent error fell from 21 % to 8 % and the average correlation coefficient was over 0.7, indicating that the model presented in this study is able to accurately estimate the natural flow regime in ungauged stations. Instead of predicting daily streamflow, this study estimates indicator values for ease of ecological water resources management.     

Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

An estimation of daily suspended sediment concentration (SSC) is required for water resource and environmental management. The traditional methods for simulating daily SSC focus on modeling the SSCs themselves, whereas the cross-correlation structure between SSC and streamflow has received only minor attention. To address this issue, we propose a stochastic method to generate long-term daily SSC using multivariate copula functions that account for temporal and cross dependences in daily SSCs. We use the conditional copula method to construct daily multivariate distributions to alleviate the complications and workload of parameter estimations using high-dimensional copulas. The observed daily streamflow and SSC data are normalized using the normal quantile transform method to relax the computationally intensive model of building daily marginal distributions. Daily SSCs can thus be simulated through the multivariate conditional distribution using previous daily SSC and concurrent daily streamflow values. The proposed method is rigorously examined by application to a case study at the Pingshan station in the Jinsha River Basin, China, and compared with the bivariate copula method. The results show that the proposed method has a high degree of accuracy, in preserving the statistics and temporal correlation of daily SSC observations, and better preserves the lag-0 cross correlation compared with the bivariate copula method. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resource and environmental management, which play a critical role in accurately estimating the frequency and magnitude of extreme SSC events.

     

The effects of extremes and temporal scale on multifractal properties of river flow time series

For accurate forecasting of extreme events in rivers, streamflow time series with sub‐daily temporal resolution (1–6 hour) are preferable, but discharge time series for long rivers are usually available at daily or monthly resolution. In this study, the scaling properties of hourly and daily streamflow time series were measured. As an innovation, the effects of extreme values on the multifractal behavior of these series were evaluated. Interestingly, both hourly and daily discharge records led to nearly identical scaling trends and identical crossover times. Daily and hourly discharge time series appeared to be non‐stationary when the timescale ranged from 75 to 366 days. Otherwise, the signals may be considered stationary time series. In addition, the results indicated that the extreme values strongly contribute to the multifractality of the series. The width of singularity spectra decreased considerably when the extreme events were removed from both hourly and daily discharge records.     

A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating

Streamflow estimation is highly significant for water resource management. In this work, we improve the accuracy and stability of streamflow estimation through a novel hybrid decompose-ensemble model that employs variational mode decomposition (VMD) and back-propagation neural networks (BPNN). First, the latest decomposition algorithm, namely, VMD, was used to extract multiscale features that were subsequently learned and ensembled by the BPNN model to obtain the final estimate streamflow results. The historical daily streamflow series of Laoyukou and Wushan hydrological stations in China were analysed by VMD-BPNN, by a single GBRT and BPNN model, ensemble empirical mode decomposition (EEMD) models. The results confirmed that the VMD outperformed a single-estimation model without any decomposition and EEMD-based models; moreover, ensemble estimations using the BPNN model development technique were consistently better than a general summation method. The VMD-BPNN model’s estimation performance was superior to that of five other models at the Wushan station (GBRT, BPNN, EEMD-BPNN-SUM, VMD-BPNN-SUM, and EEMD-BPNN) using evaluation criteria of the root-mean-square error (RMSE?=?2.62 m³/s), the Nash–Sutcliffe efficiency coefficient (NSE?=?0. 9792) and the mean absolute error (MAE?=?1.38 m³/s). The proposed model also had a better performance in estimating higher-magnitude flows with a low criterion for MAE. Therefore, the hybrid VMD-BPNN model could be applied as a promising approach for short-term streamflow estimating.

     

Development of integrated watershed management schemes for an intensively urbanized region in Korea

A systematic, seven-step approach to integrated watershed management for sustainability was proposed and applied to upstream watershed of the Anyangcheon in Korea, which experiences streamflow depletion, frequent flood damages, and poor water quality due to rapid urbanization. To understand watershed components and processes, static and dynamic data were collected and synthetic hydrologic cycles generated by HSPF (Hydrologic Simulation Program – FORTRAN) were simulated (STEP 1). To identify and quantify problems within the watershed, three indices (following the pressure–state–response model) were employed: Potential Flood Damage (PFD), Potential Streamflow Depletion (PSD), and Potential Water Quality Deterioration (PWQD). Composite programming, a method of multi-criteria decision-making, was employed to estimate all indices and analytic hierarchy process are introduced to quantify the weighting values of all indicators (STEP 2). The primary goal of managers is to maintain certain minimum levels of water for instreamflow requirement and total maximum daily load (TMDL). Therefore target water quality and, instreamflow requirements (including low flow and fish flow) were specifically set (STEP 3). All possible management alternatives were listed (STEP 4) and a few specific management options which are technically, economically, and environmentally feasible, were selected (STEP 5). The ability of each feasible option to achieve the desired water quantity and quality criteria was analyzed and quantified using the HSPF (STEP 6). Finally, an evaluation index was calculated using each of the proposed alternatives in order to rank the sustainability and priority of alternatives (STEP 7).     

Streamflow Forecast and Reservoir Operation Performance Assessment Under Climate Change

This study attempts to investigate potential impacts of future climate change on streamflow and reservoir operation performance in a Northern American Prairie watershed. System Dynamics is employed as an effective methodology to organize and integrate existing information available on climate change scenarios, watershed hydrologic processes, reservoir operation and water resource assessment system. The second version of the Canadian Centre for Climate Modelling and Analysis Coupled Global Climate Model is selected to generate the climate change scenarios with daily climatic data series for hydrologic modeling. Watershed-based hydrologic and reservoir water dynamics modeling focuses on dynamic processes of both streamflow generation driven by climatic conditions, and the reservoir water dynamics based on reservoir operation rules. The reliability measure describes the effectiveness of present reservoir operation rules to meet various demands which are assumed to remain constant for the next 100 years in order to focus the study on the understanding of the structure and the behaviour of the water supply. Simulation results demonstrate that future climate variation and change may bring more high-peak-streamflow occurrences and more abundant water resources. Current reservoir operation rules can provide a high reliability in drought protection and flood control.     

Investigation of stage-discharge model performance for streamflow estimating: A case study of the Gono River,Japan

In water resource studies, long-term measurements of river streamflow are essential. They allow us to observe trends and natural cycles and are prerequisites for hydraulic and hydrology models. This paper presents a new application of the stage-discharge rating curve model introduced by Maghrebi et al. (2016) to estimate continuous streamflow along the Gono River, Japan. The proposed method, named single stage-discharge (SSD) method, needs only one observed data to estimate the continuous streamflow. However, other similar methods require more than one observational data to fit the curve. The results of the discharge estimation by the SSD are compared with the improved fluvial acoustic tomography system (FATS), conventional rating curve (RC), and flow-area rating curve (FARC). Some statistical indicators, such as the coefficient of determination (R²), root mean square error (RMSE), percent bias (PBAIS), mean absolute error (MAE), and Kling-Gupta efficiency (KGE), are used to assess the performance of the proposed model. ADCP data are used as a benchmark for comparing four studied models. As a result of the comparison, the SSD method outperformed of FATS method. Also, the three studied RC methods were highly accurate at estimating streamflow if all observed data were used in calibration. However, if the observed data in calibration was reduced, the SSD method by R² = 0.99, RMSE = 2.83 (m³/s), PBIAS = 0.715(%), MAE = 2.30 (m³/s), and KGE = 0.972 showed the best performance compared to other methods. It can be summarized that the SSD method is the feasible method in the data-scarce region and delivers a strong potential for streamflow estimation.     

Short-term River streamflow modeling using Ensemble-based additive learner approach

Accurate streamflow (Q_t) prediction can provide critical information for urban hydrological management strategies such as flood mitigation, long-term water resources management, land use planning and agricultural and irrigation operations. Since the mid-20th century, Artificial Intelligence (AI) models have been used in a wide range of engineering and scientific fields, and their application has increased in the last few years. In this study, the predictive capabilities of the reduced error pruning tree (REPT) model, used both as a standalone model and within five ensemble-approaches, were evaluated to predict streamflow in the Kurkursar basin in Iran. The ensemble-approaches combined the REPT model with the bootstrap aggregation (BA), random committee (RC), random subspace (RS), additive regression (AR) and disjoint aggregating (DA) (i.e. BA-REPT, RC-REPT, RS-REPT, AR-REPT and DA-REPT). The models were developed using 15 years of daily rainfall and streamflow data for the period 23 September 1997 to 22 September 2012. A set of eight different input scenarios was constructed using different combinations of the input variables to find the most effective scenario based on the linear correlation coefficient. A comprehensive suite of graphical (time-variation graph, scatter-plot, violin plot and Taylor diagram) and quantitative metrics (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliff efficiency (NSE), Percent of BIAS (PBIAS) and the ratio of RMSE to the standard deviation of observation (RSR)) was applied to evaluate the prediction accuracy of the six models developed. The outcomes indicated that all models performed well but the AR-REPT outperformed all the other models by rendering lower errors and higher precision across a number of statistical measures. The use of the BA, RC, RS, AR and DA models enhanced the performance of the standalone REPT model by about 26.82%, 18.91%, 7.69%, 28.99% and 28.05% respectively.     

Ensemble Based Forecasting and Optimization Framework to Optimize Releases from Water Supply Reservoirs for Flood Control

Water supply reservoir management is based on long-term management policies which depend on customer demands and seasonal hydrologic changes. However, increasing frequency and intensity of precipitation events is necessitating the short-term management of such reservoirs to reduce downstream flooding. Operational management of reservoirs at hourly/daily timescales is challenging due to the uncertainty associated with the inflow forecasts and the volumes in the reservoir. We present an ensemble-based streamflow prediction and optimization framework consisting of a regional scale hydrologic model forced with ensemble precipitation inputs to obtain probabilistic inflows to the reservoir. A multi-objective dynamic programming model was used to obtain optimized release strategies accounting for the inflow uncertainties. The proposed framework was evaluated at a water supply reservoir in the Hackensack River basin in New Jersey during Hurricanes Irene and Sandy. Hurricane Irene resulted in the overtopping of the dam despite releases made in anticipation of the event and resulted in severe downstream flooding. Hurricane Sandy was characterized by low rainfall, however, raised significant concerns of flooding given the nature of the event. The improvement in NSE for the Hurricane Irene inflows from 0.5 to 0.76 and reduction of the spread of PBIAS with decreasing lead times resulted in improvements in the forecast informed releases. This study provides perspectives on the benefits of the proposed forecasting and optimization framework in reducing the decision making burden on the operator by providing the uncertainties associated with the inflows, releases and the water levels in the reservoir.

     

超越概率贝叶斯判别分析方法及其在中长期径流预报中的应用

中长期径流预报是水文预报中的经典难题之一,其在防洪、水库调度及水资源管理中起着十分重要的作用。由于缺乏相应预见期的可靠气象预报资料,中长期径流预报一般采用统计方法。超越概率贝叶斯判别分析方法是一种数据驱动的非参数贝叶斯经验统计方法,通过设置不同的流量等级反复进行贝叶斯判别分析,对未来径流超过某一流量等级的概率 （超越概率） 进行预报。本文运用该方法对长江宜昌站、大通站的月、季径流预报进行了研究,其结果表明,超越概率贝叶斯判别分析方法能够有效实现宜昌站和大通站非汛期径流预报;对于汛期径流预报,采用厄尔尼诺和南方涛动等气象水文指标变量作为预报因子,是提高预报精度的可行途径。     

Climate Change and Resource Management in the Columbia River Basin

Abstract

Scenarios of global climate change were examined to see what impacts they might have on transboundary water management in the Columbia River basin. Scenario changes in natural streamflow were estimated using a basin hydrology model. These scenarios tended to show earlier seasonal peaks, with possible reductions in total annual flow and lower minimum flows. Impacts and adaptation responses to the natural streamflow scenarios were determined through two exercises: (a) estimations of system reliability using a reservoir model with performance measures and (b) interviews with water managers and other stakeholders in the Canadian portion of the basin. Results from the two exercises were similar, suggesting a tendency towards reduced reliability to meet objectives for power production, fisheries, and agriculture. Reliability to meet flood control objectives would be relatively unchanged in some scenarios but reduced in others. This exercise suggests that despite the high level of development and management in the Columbia, vulnerabilities would still exist, and impacts could still occur in scenarios of natural streamflow changes caused by global climate change. Many of these would be indirect, reflecting the complex relationship between the region and its climate.     

An opportunity loss model for estimating the value of streamflow data for reservoir planning

Streamflow data have economic value because they are used for making decisions in water resources. By quantifying this value, hydrologists should find it much easier to overcome traditional obstacles to investment in streamflow data collection programmes. In this paper, an opportunity loss model is described which enables this to be done. The study has focused on evaluating typical benefits which may arise from more abundant data records for a specific purpose and, through this, provide further evidence which justifies investment in streamflow data collection activities. The specific purpose investigated is the development of reservoir capacity-reliability-yield relationship. Finally, it is argued that, while it is not uncommon for a streamgauging station to be project-specific, the data would invariably be used for other purposes and through these generate additional values. Considerations of such secondary values should make the whole idea of investment in streamflow data collection even more attractive economically.     

Cause-driven Streamflow Forecasting Framework Based on Linear Correlation Reconstruction and Long Short-term Memory

Reasonable runoff forecasting is the foundation of water resource management. However, the impact of environmental change on streamflow was not fully revealed due to the lack of enough streamflow features in many previous studies. In contrast, too many features also could lead cause undesired problems, including unstable model, interpretation difficulty, overfitting, high computational complexity, and high memory complexity. To address the above problems, this study proposes a cause-driven runoff forecasting framework based on linear-correlated reconstruction and machine learning model and refers to this framework as CSLM. We use variance inflation factor (VIF), pairwise linear correlation (PLC) reconstruction, and long short-term memory (LSTM) to realize this framework, referred to as VIF-PLC-LSTM. Four experiments were conducted to demonstrate the accuracy and efficiency of the proposed framework and its VIF-PLC-LSTM realization. Four experiments compare 1) different filter thresholds of driving factors, 2) different combination prediction features, 3) different reconstruction methods of linear-correlated features, and 4) different CSLM models. Experimental results on daily streamflow data from the Tangnaihai station at the Yellow River source and the Yangxian station at the Han River show that 1) data filtering has the risk of feature information loss, 2) when the streamflow, ERA5L, and meteorology data are used as inputs at the same time, the performance of the model is superior to the combination of other prediction features; the prediction effect of different prediction features, 3) the reconstruction of linear-correlated features is not only better than dimension reduction but also can improve the forecasting performance for streamflow prediction, and 4) among different CSLM models, LSTM is superior to other models.

     

Basin/Reservoir System Integration for Real Time Reservoir Operation

This paper demonstrates the basin/reservoir system integration as a decision support system for short term operation policy of a multipurpose dam. It is desired to re-evaluate and improve the current operational regulation of the reservoir with respect to water supply and flood control especially for real time operation. The most innovative part of this paper is the development of a decision support system (DSS) by the integration of a hydrological (HEC-HMS) and reservoir simulation model (HEC-ResSim) to guide the professional practitioners during the real time operation of a reservoir to meet water elevation and flood protection objectives. In this context, a hybrid operating strategy to retain maximum water elevation is built by shifting between daily and hourly decisions depending on real time runoff forecasts. First, a daily hydro-meteorological rule based reservoir simulation model (HRM) is developed for both water supply and flood control risk. Then, for the possibility of a flood occurrence, hourly flood control rule based reservoir simulation model (FRM) is used. The DSS is applied on Yuvac?k Dam Basin which has a flood potential due to its steep topography, snow potential, mild and rainy climate in Turkey. Numerical weather prediction based runoff forecasts computed by a hydrological model together with developed reservoir operation policy are put into actual practice for real time operation of the reservoir for March – June, 2012. According to the evaluations, proposed DSS is found to be practical and valuable to overcome subjective decisions about reservoir storage.     

Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

Inflow prediction of reservoirs is of considerable importance due to its application in water resources management related to downstream water release planning and flood protection. Therefore, in this research, different new input patterns for predicting inflow to Zayandehroud dam reservoir is proposed employing artificial neural network (ANN) and support vector machine (SVM) models. Nine different models with different patterns of input data such as inflow to the dam reservoir considering time duration lags, time index, and monthly rainfall of Ghaleh-Shahrokh station have been proposed to predict the inflow to the dam reservoir. Comparison of the results indicates that the ninth proposed model has the least error for inflow prediction in which the results of SVM model outperform those of ANN model. That is, the least error has been obtained using the ninth SVM (ANN) model with correlation coefficient (R) values of 0.8962 (0.89296), 0.9303 (0.92983) and 0.9622 (0.95333) and root mean squared error (RMSE) values of 47.9346 (48.5441), 42.69093 (43.748) and 23.56193 (28.5125) for training, validation and test data, respectively.

     

Middle- and Long-Term Streamflow Forecasting and Uncertainty Analysis Using Lasso-DBN-Bootstrap Model

Middle-term and long-term streamflow forecasting is of great significance for water resources planning and management, cascade reservoirs optimal operation, agriculture and hydro-power generation. In this work, a framework was proposed which integrates least absolute shrinkage and selection operator (lasso), DBN and bootstrap to improve the performance and the stability of streamflow forecasting with the lead-time of one month. Lasso helps to screen the appropriate predictors for the DBN model, and the DBN model simulates the complex relationship between the selection predictors and streamflow, and then bootstrap with the DBN model contributes to evaluate the uncertainty. The Three-River Headwaters Region (TRHR) was taken as a case study. The results indicated that lasso-DBN-bootstrap model produced significantly more accurate forecasting results than the other three models and provides reliable information on the forecasting uncertainty, which will be valuable for water resources management and planning.

     

GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application

The generalised likelihood uncertainty estimation (GLUE) approach was applied to assess the performance of a distributed catchment model and to estimate prediction limits after conditioning based on observed catchment-wide streamflow. Prediction limits were derived not only for daily streamflow but also for piezometric levels and for extreme events. The latter analysis was carried out considering independent partial duration time series (PDS) obtained from the observed daily streamflow hydrograph. Important data uncertainties were identified. For streamflow the stage-discharge data analysis led to estimate an average data uncertainty of about 3 m³ s^− 1. For piezometric levels, data errors were estimated to be in the order of 5 m in average and 10 m at most. The GLUE analysis showed that most of the inspected parameters are insensitive to model performance, except the horizontal and vertical components of the hydraulic conductivity of one of the geological layers that have the most influence on the streamflow model performance in the application catchment. The study revealed a considerable uncertainty attached to the simulation of both high flows and low flows (i.e., in average terms 5 m³ s^− 1 before the Bayesian updating of the prediction limits). Similarly, wide prediction intervals were obtained for the piezometric levels in relevant wells, in the order of 3.3 and 1.5 m before and after the Bayesian updating of the prediction limits, respectively. Consequently, the results suggest that, in average terms, the model of the catchment predicts overall outputs within the limitations of the errors in the input variables.