Bivariate Simulation of Potential Evapotranspiration Using Copula-GARCH Model

Developing statistical period and simulating the required values in case of data shortage increases certainty and reliability of simulations and statistical analyses, which is very important in studies on hydrology and water resources. Therefore, in this study, for simulating values of potential evapotranspiration at Birjand Station located in eastern Iran, contemporaneous autoregressive moving average (CARMA), CARMA-generalized autoregressive conditional heteroskedasticity (GARCH), and Copula-GARCH models were used in statistical period of 1984–2019. The potential evapotranspiration and relative humidity time series were simulated using these three models. CARMA model has acceptable accuracy for simulating potential evapotranspiration values due to the effect of the second parameter on simulations. Nash–Sutcliffe efficiency (NSE) coefficient of CARMA model for simulating potential evapotranspiration values was estimated as 0.85. NSE coefficient of CARMA-GARCH model was obtained as 0.87 through extracting residuals of CARMA model and simulating variance of data using GARCH model. Comparing the CARMA and CARMA-GARCH models with each other, it was concluded that a combination of two linear and non-linear time series models increases simulation accuracy to some extent. Using Clayton copula (the selected copula from the studied copulas), the mentioned values were simulated by Copula-GARCH model. The results showed that among the three models used, Copula-GARCH model reduced root mean square error of bivariate simulation compared to CARMA and CARMA-GARCH models by 15 and 13%, respectively. The results also showed that the proposed model simulates the average, first, and third quarters and range of changes in the data by 5 and 95% better than the two CARMA and CARMA-GARCH models.

     

河谷局地气象场的WRF模式边界层方案敏感性分析

在河谷局地气象场对泄洪雾化的影响研究中,不同边界层方案的模拟效果并不明确,需要对边界层方案进行敏感性分析。使用WRF模式获取河谷地形的局地气象场,选择RMM5近地面层方案并搭配使用8种边界层方案进行对比分析。研究表明:风速受到河谷地形的影响,模拟效果不够理想,可耦合小尺度数值模式以提高其模拟效果;WRF能模拟河谷地形的狭管效应、风速增大以及风向逐渐偏向河谷方向,压强和温度的模拟效果较好。综合各气象要素的模拟结果,YSU和MYNN2边界层方案的模拟精度较高。该方法对泄洪雾化效应和水库环境水文气象效应研究具有参考价值。     

A Novel Hybrid Method for River Discharge Prediction

Accurate prediction of river discharge is essential for the planning and management of water resources. This study proposes a novel hybrid method named HD-SKA by integrating two decomposition techniques (termed as HD) with support vector regression (SVR), K-nearest neighbor (KNN) and ARIMA models (combined as SKA) respectively. Firstly, the proposed method utilizes local mean decomposition (LMD) to decompose the original river discharge series into sub-series. Next, ensemble empirical mode decomposition (EEMD) is employed to further decompose the LMD-based sub-series into intrinsic mode functions. Further, the EEMD decomposed components are used as inputs in three data-driven models to predict river discharge respectively. The prediction of all components is then aggregated to obtain the results of HD-SVR, HD-KNN and HD-ARIMA models. The final prediction is obtained by taking the average prediction of these models. The proposed method is illustrated using five rivers in Indus Basin System. In five case studies, six models were built to compare the performance of the proposed HD-SKA model. The data analysis results show that the HD-SKA model performs better than all other considered models. The Diebold-Mariano test confirms the superiority of the proposed HD-SKA model over ARIMA, SVR, KNN, EEMD-ARIMA, EEMD-KNN, and EEMD-SVR models.

     

基于多元变量组合的回归支持向量机集成模型及其应用

为进一步提高径流预测的精度和泛化能力,提出基于多元变量组合的回归支持向量机(SVR)集成年径流预测模型,以云南省龙潭站年均径流预测为例进行实例研究。首先,以实例1—10月月均流量作为预测因子,采用相关分析法确定预测因子与年均径流量的相关系数,按照相关系数大小顺序依次选取预测因子,构建2维输入变量~10维输入变量的9种SVR模型对实例后12年的年均径流量进行预测。最后,采用简单平均(SA)和加权平均(WA)两种集成方法对具有较高预测精度的7种SVR模型的预测结果进行综合集成。结果表明:①SVR模型的预测精度随着输入变量维数的增加明显提高。②SA-SVR和WA-SVR模型对实例后12年年均径流量预测的平均相对误差绝对值分别为1.73%和1.79%,最大相对误差绝对值分别为6.34%和6.47%,精度和泛化能力均优于各SVR模型。相对而言,由于采用多个SVR模型进行集成,SA-SVR模型预测效果略优于WASVR模型。     

河流健康评价的回归支持向量机模型及应用

建立河流健康评价指标体系、分级标准及回归支持向量机（ SVR ）河流健康评价模型,并以云南省文山州清水河健康评价为例进行研究。首先,利用层次分析法（ AHP ）从水文水资源、物理结构、水质、水生生物和社会服务功能5个方面遴选出13个评价指标,构建3个层次的河流健康评价指标体系和5个等级的分级标准;其次,基于SVR原理,利用随机生成和随机选取的方法,在等级标准阈值间构造5种不同容量大小的训练样本和检验样本,提出5种不同容量方案的SVR河流健康评价模型,设计合理的输出模式,并构建具有良好性能的RBF（radial basis function neural network ）回归模型作为对比模型,利用模型随机5次运行的平均相对误差绝对值、最大相对误差绝对值和运行时间对各方案模型性能进行评价;最后,利用达到期望精度的SVR模型对实例进行评价分析。结果表明：①无论是训练样本还是检验样本,5种方案的SVR模型的预测精度和泛化能力均优于 RBF模型。在相同参数设置条件下,SVR模型随着样本容量的增加其精度和泛化能力变化不大;而RBF模型随着样本容量的增加其精度和泛化能力均有提高。表明SVR模型具有较高的精度和泛化能力,可以用于河流健康评价,尤其在小样本情况下,SVR模型的精度和泛化能力是RBF模型不可比拟的。②5种方案的SVR模型对清水河2011-2012年3次调查的评价结果均为健康,但已接近于亚健康。     

基于PSO-SVR模型的供水系统余氯预测研究

支持向量机回归(SVR)模型在非线性预测方面具有优良性能,基于该模型对供水系统余氯变化过程进行预测,并采用二阶振荡粒子群优化算法(SOPSO)对SVR模型参数进行优化调整,以提高小样本状态下模型的模拟精度,增强模型的泛化性能。将优化后的SVR模型应用于某供水系统余氯预测,结果表明:在有限样本状态下,优化后的SVR模型的预测平均误差小,明显优于BP神经网络模型和ARX模型,并具有较强的稳健性。该预测模型能较好地解决传统模型在小样本状态下余氯预测精度不高、预测效果较差的问题,为研究供水系统余氯变化过程及动态预测提供了新的途径。     

气候变化对汉江上游径流特征影响预估

为了预测水文站逐月径流,对该流域水资源变化进行评估,运用小波神经网络建立汉江上游流域气象因子与径流过程模拟预测模型,并依据未来气候变化增量情景,对石泉水文站以上流域径流变化响应过程进行不同时间尺度分析。由已知汉江上游流域的月降水量和月平均温度,经小波神经网络自动“学习”训练获得石泉水文站精度较高的逐月径流数据。模拟计算结果表明:在不同未来气候变化设定情景下,该区域径流变化过程较为明显,年平均径流量最大变化范围为-34.7% ~ 21.4%。在降雨量不变、气温升高的情况下,年平均径流的响应变化范围为-5.1% ~ -13.3%。温度升高引起冬季径流增加较为明显,春季及秋季径流则存在减小趋势,秋季明显减少,而降雨量变化对夏季径流的影响最显著。     

基于粒子群算法与最小二乘支持向量机的ET0模拟

以月最高气温、月最低气温、月平均气温、平均风速、日照时数以及相对湿度6个气象因子的不同组合作为输入数据,以FAO Penman-Monteith公式计算结果作为标准值,构建基于粒子群优化算法与最小二乘支持向量机的ET_0预测模型(PSO-LSSVM)。选取新疆额尔齐斯河流域哈巴河气象站1986—2013年的气象数据进行模型训练与预测,并与其他常用ET_0计算公式进行对比研究。结果表明,PSO-LSSVM模型能够很好地反映ET_0同各气象因子之间的非线性关系,其中气温条件是影响ET_0模拟精度最重要的因素,同时随着气象因子输入的减少PSO-LSSVM模型模拟精度有所下降;当分别基于辐射条件、温度条件计算时,PSO-LSSVM模型模拟结果较Priestley-Taylor公式、Hargreaves-Samani公式计算结果要优。基于多因子量化指标的ET_0预测模型实现了精度和实用性的统一,可为缺资料地区ET_0研究预报提供科学参考。     

少资料条件河渠冬季冰情过程模拟研究

针对天然河道开展冰情过程数值模拟普遍存在的河道断面资料、水文和气象资料缺少的难题,本研究提出了时均气温和时均太阳辐射计算方法和支流流量的动态分配方法,即根据实测日最大气温、日最小气温和日净太阳辐射率定出关键影响参数,模拟出给定气象站的时均气温和时均太阳辐射的变化过程;通过上游流量向下游传播过程的特征分析,推演出缺少支流...     

月水量平衡模型在中国不同气候区的应用

概念性水文模型是目前评价环境变化对区域水文水资源影响的有力工具,大尺度水文模拟是气候变化影响评价中的关键技术.利用10个位于我国不同气候区的代表性流域的水文气象资料.验证了月水量平衡模型在不同气候区的应用效果.结果表明:月水量平衡模型能够适用于我国不同气候区的月流量过程模拟,其中,对湿润半湿润地区的模拟精度好于干旱半干旱地区的模拟效果;若流域内降水径流量关系密切,则水文模拟的效果也会较好.人类活动的影响,使得长序列水文模拟误差增大,但不同人类活动类型对流域水文模拟效果的影响是不同的.     

Hydrological Modeling of Snow Accumulation and Melting on River Basin Scale

Snowmelt is of importance for many aspects of hydrology, including water supply, erosion and flood control. In this study, snow accumulation and melt are modeled using a distributed hydrological model with two different snowmelt simulation modules. The model is applied for simulating river discharge in the Latyan dam watershed, in the southern part of central Alborz mountain range, Iran. The data consists of 3 years of observed daily precipitation, air temperature, potential evaporation, windspeed and discharge. The discharge data is used for model calibration. When using the temperature index method for snowmelt three parameters need to be calibrated, while for the energy balance approach all parameters are preset and not optimized. The model performance is satisfactory for both methods with efficiencies of more than 80%. In order to show the performance of the model, two interesting snow accumulation and melt periods are discussed in detail. This study shows that the model has great potentiality to simulate the impact of snow accumulation and melt on the hydrological behavior of a river basin.     

Operational River Discharge Forecasting with Support Vector Regression Technique Applied to Alpine Catchments: Results,Advantages, Limits and Lesson Learned

The main objective of this study is to derive a flexible approach based on machine learning techniques, i.e. Support Vector Regression (SVR), for monthly river discharge forecasting with 1-month lead time. The proposed approach has been tested over 300 alpine basins, in order to explore advantages and limits in an operational perspective. The main relevant input features in the forecast performances are the snow cover areas and the discharge behavior of the previous years. Forecasts obtained by training SVR machine on single gauging stations show better performances than the average of the previous 10 years, considered as benchmark, in 94% of the cases, with a mean improvement of about 48% in root mean square error. In case of poorly gauged basins, to increase the number of training sample, multiple basins have been considered to train the SVR machine. In this case, performances are still better than the benchmark, even if worse than those of SVR machine trained on single basins, with a decrease of the performances ranging from 13% to 54%.     

The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition

Based on wavelet analysis theory, a wavelet predictor-corrector model is developed for the simulation and prediction of monthly discharge time series. In this model, the non-stationary time series of monthly discharge is decomposed into an approximated time series and several stationary detail time series according to the principle of wavelet decomposition. Each one of the decomposed time series is predicted, respectively, through the ARMA model for stationary time series. Then the correction procedure is conducted for the sum of the prediction results. Taking the monthly discharge at Yichang station of Yangtse River as an example, the monthly discharge is simulated by using ARMA model, seasonal ARIMA model, BP artificial neural network model and the wavelet predictor-corrector model proposed in this article, respectively. And the effect of decomposition scale for the wavelet predictor-corrector model is also discussed. It is shown that the wavelet predictor-corrector model has higher prediction accuracy than the some other models and the decomposition scale has no obvious effect on the prediction for monthly discharge time series in the example.     

溃口近区二维数值模拟与溃坝洪水演进耦合

基于黏土心墙砂石坝的溃决过程,以及溃坝洪水传播和运动的特性,建立黑河金盆水库大坝溃口近区二维数值模型和下游地区溃坝洪水演进耦合数学模型。使用DAMBRK法计算逐渐溃坝,并应用其结果进行后续模拟。采用Abbott-Ionescu六点隐式有限差分格式求解一维模型,采用单元中心的有限体积法求解二维模型方程。采用侧向连接方式,将黑河两岸计算水位点与二维网格单元相连,实现一、二维模型的耦合。采用所建立的二维模型对溃口近区进行计算与模拟,得到计算区域某一时刻的水深及流速分布。应用所建耦合模型对黑河金盆水库万年一遇入库洪水漫顶致溃坝洪水进行数值模拟,得到一维河道内各断面的水位和流量变化过程,以及二维计算区域内不同时刻的水深分布图、流速矢量图和淹没范围变化过程。溃口的形成过程不仅包括漫顶水流的直接作用,同时包括溃口形成过程中两侧漩涡状水流的反冲刷作用。耦合模型可以同时兼顾河道内的水流变化以及河道外计算区域内的洪水演进过程,从而减少由于计算结果偏大或偏小所带来的防洪资源浪费和防洪措施不利等不良影响。     

一、二维耦合数学模型在感潮河网洪水风险图编制中的应用

为了解决感潮河网径流、潮汐交汇,动力复杂,溃决洪水难以用经验公式准确概化的问题,建立直接以溃口为耦合断面的河网一维、保护区二维侧向耦合模型,将感潮河网与保护区一体化,避免环境因素及经验参数的不确定性带来的溃口流量估算误差。典型算例和中顺大围溃决洪水情景模拟表明：洪水自溃口集中喷射出后分散流向围内,流态受围内下垫面影响显著,溃口水位、流量随外江潮位涨落而起伏变化,由溃口流量过程线计算所得溃口水量与根据淹没区各单元的面积和水深计算的围内总水量一致。模拟成果直接反映下垫面、水头差、潮位涨落,及溃口流态对溃决洪水的综合影响,反映出本耦合模型计算溃口流量及对溃决洪水模拟的合理性、可信性,具有较好的应用前景。     

Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods

In this paper, two novel methods, echo state networks (ESN) and multi-gene genetic programming (MGGP), are proposed for forecasting monthly rainfall. Support vector regression (SVR) was taken as a reference to compare with these methods. To improve the accuracy of predictions, data preprocessing methods were adopted to decompose the raw rainfall data into subseries. Here, wavelet transform (WT), singular spectrum analysis (SSA) and ensemble empirical mode decomposition (EEMD) were applied as data preprocessing methods, and the performances of these methods were compared. Predictive performance of the models was evaluated based on multiple criteria. The results indicate that ESN is the most favorable method among the three evaluated, which makes it a promising alternative method for forecasting monthly rainfall. Although the performances of MGGP and SVR are less favorable, they are nevertheless good forecasting methods. Furthermore, in most cases, MGGP is inferior to SVR in monthly rainfall forecasting. WT and SSA are both favorable data preprocessing methods. WT is preferable for short-term forecasting, whereas SSA is excellent for long-term forecasting. However, EEMD tends to show inferior performance in monthly rainfall forecasting.     

黄河上游河道一维河道水温模型和经验公式法对比

为对比不同方法在黄河上游水温模拟中的适用性,分别用一维河道水温模型和经验公式法(四次多项式、三次多项式和幂函数)对兰州以上的黄河上游干支流共8个主要水文站日平均水温和月平均水温进行模拟,并采用平均绝对误差、均方根误差和纳什系数对模拟的精度进行检验。结果表明:四次多项式在黄河干流(除黄河沿站)日平均水温模拟中精度最高,幂函数在支流湟水的日平均水温模拟中精度最高;四次多项式适合黄河干流水文站的月平均水温模拟,幂函数更适合于支流湟水的月平均水温模拟;经验公式法在黄河干流日平均水温模拟中精度高于一维河道水温模型,更适合黄河上游干流段的日平均水温模拟。     

回归支持向量机集成模型在年径流预测中的应用

为进一步提高径流预测精度和泛化能力,根据回归支持向量机(SVR)特性及基本原理,提出考虑不同影响因子(输入向量)的SVR集成预测模型,以云南省南盘江西桥站1961—2007年径流预测为例进行实例研究。首先,利用相关分析法选取年径流预测的若干影响因子,依次构建不同影响因子的SVR单一模型对研究实例进行预测,并构建对应的RBF模型作为对比预测模型;然后,采用加权平均和简单平均2种方法对具有较好预测精度和互补性的单一模型的预测结果进行综合集成。结果表明基于SVR的加权平均和简单平均2种集成模型径流预测的平均相对误差绝对值分别为1.27%和1.54%,最大相对误差绝对值分别为2.99%和2.74%,其精度和泛化能力均大幅优于各单一模型以及基于RBF的加权平均和简单平均集成模型,表明加权平均SVR和简单平均SVR集成模型具有较高的预测精度和泛化能力。相对而言,加权平均集成模型赋予了预测效果好的模型更大的权重,预测精度和泛化能力均优于简单平均集成模型。预测模型和方法可为相关预测研究提供参考和借鉴。     

Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags

The reference evapotranspiration (ET₀) plays a significant role especially in agricultural water management and water resources planning for irrigation. It can be calculated using different empirical equations and forecasted by applying various artificial intelligence techniques. The simulation result of a machine learning technique is a function of its structure and model inputs. The purpose of this study is to investigate the effect of using the optimum set of time lags for model inputs on the prediction accuracy of monthly ET₀ using an artificial neural network (ANN). For this, the weather data time-series i.e. minimum and maximum air temperatures, vapour pressure, sunshine hours, and wind speed were collected from six meteorological stations in Serbia for the period 1980–2010. Three ANN models were applied to monthly ET₀ time-series to study the impacts of using the optimum time lags for input time-series on the performance of ANN model. Achieved results of goodness–of–fit statistics approved the results obtained by scatterplots of testing sets - using more time lags that are selected based on their correlation to the dataset is more efficient for monthly ET₀ prediction. It was realized that all the developed models showed the best performances at Loznica and Vranje stations and the worst performances at Nis station. Simultaneous assessment of the impact of using a different number of time lags and the set of time lags that show a stronger correlation to the dataset for input time-series, on the performance of ANN model in monthly ET₀ prediction in Serbia is the novelty of this study.

     

Comparing Flow Duration Curves and Discharge Hydrographs to Assess Eco-flows

Although eco-flows (ecosurplus (ES) and ecodeficit (ED)) based on the flow duration curve (FDC) have been used to assess hydrologic alterations in recent years, they are defective, and their limitations have never been clarified. In this study, the causes of these limitations were analyzed, and the ES and ED on different time scales were redefined using the discharge hydrograph (DH). The monthly ES was redefined as the surplus monthly runoff exceeding the 75th percentile DH divided by its maximum ecological water requirement, and the monthly ED was redefined as the deficient monthly runoff below the 25th percentile DH divided by its minimum ecological water requirement. The seasonal eco-flows were the sum of three corresponding monthly eco-flows, and the annual eco-flows were the sum of twelve consecutive monthly eco-flows. The daily discharge time series from 1956 to 2015 at Xiaolangdi Station was used to test the new method. The results showed that the inconsistencies of eco-flows on different time scales based on FDCs were closely related to the loss of time-dependent information in FDCs. The correlation coefficients and Fréchet distance between Shannon Index (SI) and annual eco-flows showed that DHs performed better than FDCs in assessing eco-flows. Annual EDs calculated from DHs changed synchronously with SI in most years. This novel method for calculating eco-flows are useful for assessing river health in the future.