Probabilistic Assessment of Monthly River Discharge using Copula and OSVR Approaches

In this study, two efficient approaches for bivariate simulation are presented, which include meteorological and hydrological variables. For this purpose, the applicability of support vector regression (SVR) model optimized by Ant colony and Copula-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) algorithms were investigated and compared in simulating the river discharge based on total monthly rainfall in Talezang Basin, Iran. Entropy theory was used to select a suitable meteorological station corresponding to a hydrometric station. The vector autoregressive model was also used as the base model in Copula-GARCH simulations. According to the 99% confidence intervals of the simulations, the accuracy of both models was confirmed. The simulation results showed that the Copula-GARCH model was more accurate than the optimized SVR (OSVR) model. Considering the 90% efficiency (NSE=0.90) of the Copula-GARCH approach, the results show a 36% improvement of RMSE statistics by the Copula-GARCH model compared to the OSVR model in simulating the river discharge on a monthly scale. The results also showed that by combining nonlinear ARCH models with the copula-based simulations, the reliability of the simulation results increases, which was also confirmed using the violin plot. The results also showed an increase in the accuracy of the Copula-GARCH model at the minimum and maximum values of the data.

     

Using Periodic Copula to Assess the Relationship Between Two Meteorological Cyclostationary Time Series Datasets

In environmental, hydrological, and meteorological research, one of the main aims is to study the relationship between some variables. This issue has an influential role in various fields such as predicting stochastic variables, reconstructing missing data (especially in studies related to assessing the changes in climate conditions and drought characteristics), etc. For this purpose, statisticians have proposed different parametric and non-parametric techniques. Most of the proposed methods are applicable for stationary and some special non-stationary time series datasets. This work was devoted to introducing and applying a novel copula-based approach, called the periodic copula model (in 5 methods, including Gaussian, t, Clayton, Gumbel, and Frank periodic copula models) to study the relationship between some cyclostationary processes. For assessing the performance of the introduced model, two numerical studies, including the first-order periodic autoregressive (PAR (1)) and the first-order periodic moving average (PMA (1)) time series were considered. Moreover, the comparison of the relationship between observed and simulated drought severities (based on the 3month standardized precipitation evapotranspiration index (SPEI)) using the periodic copula was used. To comput SPEI, data series of 10 stations over Iran during 1967–2019 (5 groups, each group includes two stations with a short spatial distance) were used. The ability and performance of the method was evaluated based on three indices, including Willmott’s index (WI), Nash-Sutcliff’s coefficient (NSC), and correlation of coefficient (r). The results of numerical studies verify the ability of the proposed technique. In all five copula models studied in 6month (with T?=?2) and 3month (with T?=?4) periods with n equal to 100, 200, 500, and 1000, the r, NSE, and WI indices in the periodic form of data series were more than the non-periodic form. The results of testing the performance of the proposed model based on actual data also verified the greater ability of periodic copula models compared to non-periodic copula models. So that in all chosen groups and all periods, including winter, spring, summer, and autumn, the R-Square between observed drought indices and predicted data using the periodic copula models was more than the R-Square between observed drought indices and predicted data using the non-periodic copula models.

     

Analysis and synthesis of seasonality and variability of daily potential evapotranspiration

Daily meteorological data over a period of nine years from Helliniko Station (Athens, Greece) are used to produce daily potential evapotranspiration (PET) series. A multipliative model describes the periodic and stochastic components of the series where Fourier aproximations of two- or three-harmonic periodic components and a first-order autoregressive one are employed, respectively. The assessment of adequacy of the model is based on the properties of (a) the correlogram and (b) the spectrum of a white noise process. The model is further used to generate daily PET values. Generated and historical data show remarkable affinity in average arithmetic mean, coefficient of variation, skewness coefficient and their standard deviations.     

Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality,Copula Selection and Correlation Coefficient

The outputs of Rainfall-runoff models are inherently uncertain and quantifying the associated uncertainty is crucial for water resources management activities. This study presents the uncertainty quantification of rainfall-runoff simulations using the copula-based Bayesian processor (CBP) in Danjiangkou Reservoir basin, China. The seasonality of uncertainty in rainfall-runoff modeling is explored, and impacts of copula selection and correlation coefficient on uncertainty quantification results are investigated. Results show that the overall performance of the CBP is satisfactory, which provides a useful tool for estimating the uncertainty of rainfall-runoff simulations. It is also demonstrated that the dry season has higher reliability and greater resolution compared with wet season, which illustrates that the CBP captures the actual uncertainty of rainfall-runoff simulations more accurately in dry season. Moreover, the performance the CBP highly depends on the selected Copula function and considered Kendall tau correlation coefficient. As a result, great attention should be paid to selecting the appropriate Copula function and effectively capturing the actual dependence between observed and simulated flows in the CBP-based uncertainty quantification of rainfall-runoff simulations practice.

     

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.

     

Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model

Most of the commonly used hydrological models do not account for the actual evapotranspiration (ETa) as a key contributor to water loss in semi-arid/arid regions. In this study, the HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) model was calibrated, modified, and its performance in simulating runoff resulting from short-duration rainfall events was evaluated. The model modifications included integrating spatially distributed ETa, calculated using the surface energy balance system (SEBS), into the model. Evaluating the model’s performance in simulating runoff showed that the default HEC-HMS model underestimated the runoff with root mean squared error (RMSE) of 0.14 m³/s (R²?=?0.92) while incorporating SEBS ETa into the model reduced RMSE to 0.01 m³/s (R²?=?0.99). The integration of HECHMS and SEBS resulted in smaller and more realistic latent heat flux estimates translated into a lower water loss rate and a higher magnitude of runoff simulated by the HECHMS model. The difference between runoff simulations using the default and modified model translated into an average of 95,000 m³ runoff per rainfall event (equal to seasonal water requirement of ten-hectare winter wheat) that could be planned and triggered for agricultural purposes, flood harvesting, and groundwater recharge in the region. The effect of ETa on the simulated runoff volume is expected to be more pronounced during high evaporative demand periods, longer rainfall events, and larger catchments. The outcome of this study signifies the importance of implementing accurate estimates of evapotranspiration into a hydrological model.

     

Application of SEBAL and Markov Models for Future Stream Flow Simulation Through Remote Sensing

Watershed hydrology, including the volumes of stream flow is widely considered to be influenced by global climate change. Traditional studies using the (GWLF) model to estimate stream flows have relied on evapotranspiration cover coefficient (Kc) obtained from published references. Other factors, such as future land-use status and evapotranspiration (ET) change, are usually not considered. This study aims to improve on traditional studies by including remote sensing techniques to estimate the Kc, as well as integrating the SEBAL model, the CGCM1 model, and the Markov model to predict land-use and ET changes. The chosen study area was in the north of Taiwan. The processes include land-use classification using hybrid approach and Landsat-5 TM images, a comparison of stream flow simulations using the GWLF model with two Kc values derived from remote sensing and traditional methods, and finally the prediction of future land-use and Kc parameters for assessing the effect of land-use change and ET change. The results indicated that the study area was classified into seven land-use types with 89.09% classification accuracy. The stream flows simulated by two estimated Kcs were different, and the simulated stream flows using the remote sensing approach presented more accurate hydrological characteristics than a traditional approach. In addition, the consideration of land-use change and ET change indeed affected the predicted stream flows under climate change conditions. These results imply that the integration of remote sensing, the SEBAL model, the CGCM1 model, and the Markov model is a feasible scheme to predict future land-use, ET change, and stream flow. Therefore, these models will improve future studies of predictions in water resource management and global environmental change.     

Separation of the Impact of Landuse/Landcover Change and Climate Change on Runoff in the Upstream Area of the Yangtze River,China

Landuse/landcover change (LULCC) and climate change (CC) impacts on streamflow in high elevated catchments are very important for sustainable management of water resources and ecological developments. In this research, a statistical technique was used in combination with the Soil and Water Assessment Tool (SWAT) to the Upstream Area of the Yangtze River (UAYR). Different performance criteria (e.g., R², NSE, and PBIAS) were used to evaluate the acceptability of the model simulation results. The model provided satisfactory results for monthly simulations in the calibration (R²; 0.80, NSE; 0.78 and PBIAS; 22.3%) and the validation period (R²; 0.89, NSE; 0.75 and PBIAS; 19.1%). Major landuse/landcover transformations from 1990 to 2005 have occurred from low grassland to medium grassland (2%) and wetlands (0.9%), bare land to medium grassland (0.2%), glaciers to wetland (16.8%), and high grassland to medium grassland (5.8%). The results show that there is an increase in average annual runoff at the Zhimenda station in UAYR by 15 mm of, which approximately 98% is caused by climate change and only 2% by landuse/landcover change. The changes evapotranspiration are larger due to climate change as compared to landuse/landcover change, particularly from August to October. Precipitation and temperature have increased during these months. On the contrary, there has been a decrease in evapotranspiration and runoff from October to March which depicts the intra-annual variations in the vegetation in the study area.

     

Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020

Many attempts have been made in the recent past to model and forecast streamflow using various techniques with the use of time series techniques proving to be the most common. Time series analysis plays an important role in hydrological research. Traditionally, the class of autoregressive moving average techniques models has been the statistical method most widely used for modelling water discharge, but it has been shown to be deficient in representing nonlinear dynamics inherent in the transformation of runoff data. In contrast, the relatively newly improved and efficient soft computing technique artificial neural networks has the capability to approximate virtually any continuous function up to an arbitrary degree of accuracy, which is not otherwise true of other conventional hydrological techniques. This technique corresponds to human neurological system, which consists of a series of basic computing elements called neurons, which are interconnected together to form networks. The aim of the study is to compare the artificial neural network and autoregressive integrated moving average to model River Opeki discharge (1982–2010) and to use the best predictor to forecast the discharge of the river from 2010 to 2020. The performance of the two models was subjected to statistical test based on correlation coefficient (r) and the root‐mean‐square error. The result showed that autoregressive integrated moving average performs better considering the level of root‐mean‐square error and higher correlation coefficient.     

植被覆盖的土质边坡土壤水分平衡模型及应用

土壤水分平衡模型在洪水预测、土壤湿度计算、灌溉设计管理以及全球气候变化影响的仿真分析上十分重要.以英格兰南部Newbury一个高速公路旁的边坡为例,介绍了土壤水分平衡模型的建立及应用.潜在蒸散量根据每日的气象观测资料,用FAO Penman-Monteith公式进行计算.在此基础上,建立了水分平衡模型并进行了校核,计算出的土壤湿度变化与时域反射仪(TDR)探头测量的数据一致.还利用建立的模型,模拟了2080年的气候条件变化对潜在蒸散量和土壤水分含量的影响.结果表明,该地区的日平均潜在蒸散量将增加10.7%,土壤水分消耗量将增加16.8 mm.     

Are Weather Generators Robust Tools to Study Daily Reference Evapotranspiration and Irrigation Requirement?

Reference evapotranspiration (ETo) is one of the driving forces in crop simulation models and is very important to be estimated accurately. Moreover, weather generator (WG) models are widely used in combination with these crop models. As the quality of model output is related to the quality of weather data used as input, the evaluation of the sensitivity of model outputs to the quality of generated weather data is essential. In this study, eight different weather generator models were assessed and their outputs were used to estimate daily reference evapotranspiration and irrigation requirement. Two daily weather generator algorithms were combined with a monthly weather generator and/or an adjustment algorithm for low-frequency variances. Precipitation occurrence series was generated by an independent semi-empirical distribution. The daily weather generators outperformed the monthly models in reproducing daily statistics, while the monthly models performed better in simulating the monthly and yearly variations. After analyzing the model performances in simulating climatic variables, more assessments were carried out on ETo and irrigation requirement. The results depicted the strength of all the models in simulating daily ETo and irrigation requirement. Although all the studied models have comparable performances in simulating these two daily variables on daily and monthly scales, the monthly WGs outperform the daily models on yearly time scales and have better performances in simulating standard deviation values of yearly mean ETo and irrigation requirement. It can be concluded that WG models are robust tools for estimating these two daily variables if they can at least reproduce daily statistics (i.e. mean and standard deviation) well. But it must be taken in considerations that each WG model (including the one studied here) has different weaknesses and strengths and the best choice must be done according to the requirements.     

Application of the Sebs Water Balance Model in Estimating Daily Evapotranspiration and Evaporative Fraction from Remote Sensing Data Over the Nile Delta

Estimation of evapotranspiration is always a major component in water resources management. The reliable estimation of daily evapotranspiration supports decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are used in conjunction with Surface Energy Balance (SEB) models to overcome difficulties in obtaining daily evapotranspiration measurements on a regional scale. In this study the SEB System (SEBS) is used to estimate daily evapotranspiration and evaporative fraction over the Nile Delta along with data acquired by the Advance Along Track Scanning Radiometer (AATSR) and the Medium Spectral Resolution Imaging Spectrometer (MERIS), and six in situ meteorological stations. The simulated daily evapotranspiration values are compared against actual ground-truth data taken from 92 points uniformly distributed all over the study area. The derived maps and the following correlation analysis show strong agreement, demonstrating SEBS’ applicability and accuracy in the estimation of daily evapotranspiration over agricultural areas.     

Forecasting of Remotely Sensed Daily Evapotranspiration Data Over Nile Delta Region, Egypt

Daily evapotranspiration is a major component in crops water consumption management plans. Consequently, forecasting of daily evapotranspiration is the keystone of any effective water resources management plans in fragile environment similar to the Nile Delta region. The estimation of daily evapotranspiration was carried out using Surface Energy Balance System (SEBS), while the forecasting of the daily evapotranspiration was carried out using Auto Regressive Integrated Moving Average (ARIMA) and its derivative Seasonal ARIMA. Remote sensing data were downloaded from European Space Agency (ESA) and used to estimate daily evapotranspiration values. Remote sensing data collected from August 2005 till December 2009 on a monthly basis for daily evapotranspiration estimation. The application of the most adequate ARIMA (2,1,2) to the evapotranspiration data set failed to sustain the forecasting accuracy over a long period of time. Although, time series analysis of daily evapotranspiration data set showed a seasonality behavior and thus, using seasonal ARIMA [(2,1,2) (1,1,2)6] was the optimum to forecast the daily evapotranspiration over the study area and sustain the forecasting accuracy. A linear regression model was established to test the correlation between the forecasted daily evapotranspiration values using S-ARIMA model and the actual values. The forecasting model indicates an increase of the daily evapotranspiration values with about 1.3 mm per day.     

Generating Synthetic Rainfall Total Using Multivariate Skew-<Emphasis Type="Italic">t</Emphasis> and Checkerboard Copula of Maximum Entropy

This study aims to test the appropriateness of multivariate skew-t copula and checkerboard copula of maximum entropy in generating monthly rainfall total data. The generation of synthetic data is important, as it provides hypothetical data in areas for which data availability remains limited. Three selected meteorological stations in Kelantan, Malaysia, Stesen Pertanian Melor, Rumah Pam Salor, and Ladang Lepan Kabu, are considered in this study. Monthly rainfall total data for the driest and wettest months in the year are tested in this study. For these three stations, the identified month with the least total of rainfall received (driest) is May, while the month with the highest total of rainfall received (wettest) is November. The data is fitted to gamma distribution with the corresponding parameters estimated. The observed data will be transformed to be in unit uniform using the gamma marginal. The resulting data is compared to simulated uniform data generated using multivariate skew-t copula and checkerboard copula of maximum entropy models based on the correlation values of the observed and simulated data. Next, the Kolmogorov-Smirnov test is used to assess the fit between the observed and generated data. The results show that the values of simulated correlation coefficients do not differ much for gamma distribution, multivariate skew-t, and maximum entropy approaches. This implies that the multivariate skew-t and maximum entropy may be used to generate monthly rainfall total for cases in which actual data is unavailable.     

Entropy-Based Flow and Sediment Routing in Data Deficit River Networks

The reliable estimate of the sediment load and streamflow is essential for water resources and flood management. In this study, the entropy-based technique and HEC-RAS are used for flow routing followed by sediment routing in HEC-RAS. The paper’s novelty is its application to data-deficit river networks, where observed sediment load and flow on tributaries are absent. The proposed method accommodates the flow and sediment contribution from the tributaries to the downstream station on a reach, despite unavailable observed data on it. The adopted flow routing techniques are applied to predict downstream flow on three different reaches (on the Mahanadi and the Godavari River). The prediction accuracy is evaluated using three statistical indices ? Nash–Sutcliffe efficiency (NSE), relative error (RE), and Coefficient of determination (R²). Both flow routing techniques showed good performance for all three reaches (with or without tributaries), having NSE, R²?>?0.8, and RE?<?13%. Despite the comparable performance, the entropy-based routing is suggested for natural rivers with or without tributary as it avoids the iterative calibration process to determine the roughness coefficient. Further, the sediment routing is performed on the data-deficit reach of the Mahanadi River to obtain the best-suited sediment transport function. The simulated sediment load using the Yang transport function matched satisfactorily with the observed data with NSE, R²?>?0.85, and RE?<?–27%. Subsequently, the Yang transport function and entropy-based flow routing are utilized for the sediment and flow estimation at an ungauged station on the Mahanadi river.

     

Distributed Hydrological Modeling and Sensitivity Analysis in Torysa Watershed, Slovakia

The spatially distributed hydrologic model WetSpa is applied to the Torysa river basin (1,297 km²) located in Slovakia. Daily hydrometeorological data from 1991 to 2000 are used as input to the model. The spatial characteristic of the basin are described by three base maps, i.e. DEM, landuse and soil type, in GIS form using 100 m cell size. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. The model predicts the daily discharge values with a good accuracy, i.e. about 73% according to the Nash–Sutcliff criterion. Sensitivity analysis of the model parameters is performed using a model-independent parameter estimator, PEST. It is found that the correction factor for calculating the actual evapotranspiration from potential evaporation has the highest relative sensitivity. Parameter K _gm which controls the amount of evapotranspiration from the groundwater has the least relative sensitivity.     

黏弹性人工边界地震动输入方法及实现

本文对实现黏弹性边界的常用方法进行了总结,对相应于黏弹性边界的地震动输入公式进行了详细推导,把自由场应力的求解也转化为自由场速度的求解,简化了地震动输入公式,并给出了地震动输入的简化方法。基于ABAQUS软件,进行算例分析并和理论解进行对比,验证了各种黏弹性边界实现方式及本文地震动输入方法的合理性和正确性。最后,对大朝山重力坝典型挡水坝段进行地震响应分析,通过施加黏弹性边界并输入相应地震动,评价了无限地基辐射阻尼的影响,并与无质量地基模型的计算结果进行了比较。结果表明,考虑辐射阻尼效应后坝体地震响应明显降低,故在实际工程抗震分析时对其影响应予以适当考虑。     

Evaluating and Calibrating Reference Evapotranspiration Models Using Water Balance under Hyper-Arid Environment

This research investigates five reference evapotranspiration models (one combined model, one temperature-based model, and three radiation-based models) under hyper-arid environmental conditions at the operational field level. These models were evaluated and calibrated using the weekly water balance of alfalfa by EnviroSCAN to calculate crop evapotranspiration (ET_c). Calibration models were evaluated and validated using wheat and potatoes, respectively, on the basis of weekly water balance. Based on the results and discussion, the FAO-56 Penman-Monteith model proved to be superior in estimating ET_c with a slight underestimation of 2 %. Meanwhile, the Hargreaves-Samani (HS) model (temperature-based) underestimated ET_c by 20 % and the Priestley-Taylor (PT) and Makkink (MK) models (radiation-based) had similar performances underestimating by up to 35 % of the measured ET_c. The Turc (TR) model had the lowest performance compared with other models, demonstrating values underestimated by up to 60 % of the measured ET_c. Local calibration based on alfalfa evapotranspiration measurements was used to rectify these underestimations. The surprisingly good performance of the calibrated simple HS model, with a new coefficient 0.0029, demonstrated its favorable potential to improve irrigation scheduling. The MK and PT models were in third and fourth rank, respectively, reflecting minor differences between one another. The new coefficients obtained for the MK and PT models were 1.99 and 0.963, respectively. One important observation was that the calibrated TR model performed poorly, with an increase in its coefficient from 0.013 to 0.034 to account for hyper-arid environmental conditions; moreover, it required additional seasonal calibration to adequately improve its performance.     

基于1D CNN-LSTM模型的红旗水库水位序列模拟

为了提升水库水位模拟的精度,通过1D CNN-LSTM模型与五种常用的机器学习模型对安徽省红旗水库历史水位数据和降雨量数据实现未来7天的水位模拟并进行对比验证。CNN和LSTM能够表现出比较好的模拟性能,结合两种模型的优势能够更加显著的提升模型的模拟效果;1D CNN-LSTM具有较高鲁棒性,对于未来3天以内水位模拟都有较好的预测效果和精度,虽然3天以后的模拟效果有明显下降,但对未来第7天的模拟NSE和KGE依然能够达到0.8以上,在不发生极端天气的情况下,模型对于水位趋势的模拟依然具有相当的参考价值。1D CNN-LSTM模型对于红旗水库的水位模拟优于其他五种传统的机器学习模型,并具有相当高的精度。     

水文序列相依变异识别的RIC定阶准则——以自回归模型为例

水文过程相依性是水文变异的主要表现形式之一,应用自回归模型对其进行拟合时合理确定模型阶数是一个难点问题。本文在分析AIC和BIC准则的基础上,提出了一种以原序列与其相依成分的相关系数作为拟合度指标,同时借用信息熵形式的函数式,作为模型不确定性度量指标的自回归模型定阶准则(简称RIC准则)。以AR(1)、AR(2)、AR(3)和AR(4)模型为例进行统计试验,将不同序列长度下该准则的定阶准确率与其他定阶准则进行比较,试验结果表明,RIC准则对于上述模型均具有较好的适应性,且定阶准确率远高于AIC准则,其中对于前三阶模型RIC准则优于BIC准则,但四阶模型略低于BIC准则。RIC准则的优势是可以同时满足模型定阶、相依程度分级与模型检验的需求,将其应用于实测水文序列分析,结果显示,该准则能较准确地识别自回归模型的阶数,且符合提出的"相依有变异而残差无变异的最小阶数"的检验标准。