Inverse modeling of natural tracer transport in a granite massif with lumped-parameter and physically based models: case study of a tunnel in Czechia

This study deals with numerical modelling of hydraulic and transport phenomena in granite of the Bohemian massif in Bedrichov, Czechia (Czech Republic). Natural tracers represented by stable isotopes δ¹⁸O and δ²H were collected at the tunnel outflow points and nearby catchment and their concentrations were monitored for seven years. The study compared transport simulations by a two-dimensional (2D) physically based model (advection-dispersion) developed in Flow123d software and a simpler lumped-parameter model, calculated with FLOWPC. Both variants were calibrated with UCODE software, either fitting the concentration data alone, or including the tunnel inflow rates in the case of the 2D model calibration (either in separate steps or within a single optimization problem). Since each of the models describes the tracer transport with different parameters, the models were compared based on the mean transit time as a postprocessed quantity. Besides this, two different options for processing the recharge data (input for both models) were evaluated. Calibration and data interpretation were possible for three of the four observed places in the tunnel, thus determining the depth limit of applicability of the stable isotopes. The estimates for discharge sampling at 25–35 m depth based on inverse modelling provide reasonable values of mean transit time (20–40 months) for the lumped parameter models, little revising the results of previous studies at the site. The resulting transport parameters of the advection-dispersion model (porosity and dispersivity) are in accordance with the hydrogeological structures present at the sampling sites.

     

Optimization of the Garzan Hydropower System operations

Through the rapid development of the watersheds in Turkey with projects developed by incorporated companies, a problem has arisen of how to operate a cascade reservoir system composed of state- and private sector-owned reservoirs in terms of the volume and timing of water releases to meet downstream water demands. This study presents a catchment-based optimization model based on inflow forecast with frequent updating for the integrated operation of hydropower plants under various sales methods. The model is formulated in terms of nonlinear programming (NLP) on a monthly basis for a 1-year period to assess the production strategies of the system reservoirs for that year. This model provides the basic constraints on the reservoir volume for daily and hourly optimization procedures. Forecasted flows are generated using seasonal autoregressive integrated moving average (ARIMA) models based on historical flow values. The proposed model is tested on the Garzan Hydropower System using historical, mean, and forecasted flow values. The results show that the integrated operation plan and improvement in the accuracy of inflow forecasts yield economic benefits as a consequence of optimal reservoir operation.     

Rainfall-Runoff Modeling using Clustering and Regression Analysis for the River Brahmaputra Basin

In this research, k-means, agglomerative hierarchical clustering and regression analysis have been applied in hydrological real time series in the form of patterns and models, which gives the fruitful results of data analysis, pattern discovery and forecasting of hydrological runoff of the catchment. The present study compares with the actual field data, predicted value and validation of statistical yields obtained from cluster analysis, regression analysis with ARIMA model. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models is investigated for monthly runoff forecasting. The different parameters have been analyzed for the validation of results with casual effects. The comparison of model results obtained by K-means & AHC have very close similarities. Result of models is compared with casual effects in the same scenario and it is found that the developed model is more suitable for the runoff forecasting. The average value of R² determined is 0.92 for eight ARIMA models. This shows more accuracy of developed ARIMA model under these processes. The developed rainfall runoff models are highly useful for water resources planning and development.     

水库汛期限制水位控制理论与观念的更新探讨

传统的水库汛限水位的控制,只利用了洪水的统计信息,使水库在汛期要时刻预防设计与校核洪水事件的发生,致使一些水库在汛期不敢蓄水而汛后又无水可蓄,造成洪水资源的浪费。提出水库汛限水位动态控制的新理念及其综合推理模式,适应当前预报技术的发展水平,考虑降雨径流洪水预报与一定时间内的短期降雨预报,排除不可能发生的洪水事件,预报可能发生的洪水,实施水库汛限水位的动态控制。但预报不可避免地存在误差,当小概率预报误差事件发生时,仍可采取弥补措施以确保大坝的防洪安全。     

降雨输入不确定性对分布式流域水文模拟的影响研究——以武烈河流域为例

降雨输入对分布式流域水文模拟具有重要影响。针对流域降雨资料不完整的情况,以武烈河流域为例,基于反距离加权平均法对雨量站降雨资料进行插补延长,并结合SWAT模型研究了降雨输入不确定性对分布式流域水文模拟的影响。结果表明:不同降雨输入对流域平均降雨量的影响较小,但基于气象站资料的降雨数据在降雨空间差异显著的年份会明显低估面雨量,且在夏季汛期表现更为显著;不同降雨输入对分布式流域水文模拟的影响较大;在雨量站降雨资料不完整的情况下,通过对雨量站降雨数据进行插补延长,相对于直接利用气象站降雨资料,在一定程度上可以提高径流模拟精度,满足降雨资料欠缺流域分布式水文模拟的实际需求。     

Using the DPFT approach to improve flash flood forecasting models

DPFT is a lumped approach for operational flash flood forecasting, based on the unit hydrograph. Using a multi-event alternating iterative algorithm, it identifies a robust and stable average transfer function and a consistent set of effective rainfall series associated with each event at the same time. This key ingredient allows an objective calibration of different loss functions, relating gauged precipitation and effective rainfall. A case study based on an operational French basin (545 km²) is presented. Three lumped production functions have been calibrated and compared. The results show that more elaborate models of loss functions must be proposed, and some possible directions for this are pointed out.     

Adequacy of satellite derived rainfall data for stream flow modeling

Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated. G. Artan, J. L. Smith and K. Asante – work performed under USGS contract 03CRCN0001.     

Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

Uncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

     

分布式水文概念模型率定方案比较研究

本文在基于新安江模型的分布式水文概念模型的基础上,以伊河东湾流域为研究对象,分别采用集总式、半集总式、半分布式三种率定方案进行模型率定和径流模拟,并与传统的集总式水文模型进行成果比较。研究结果表明:总的来说,分布式模型的模拟结果略好于集总式模型;由于考虑了不同子流域参数变异性,半分布式方案要优于其他方案;但随着模型结构的复杂性增加,参数率定的难度增大,模拟结果的不确定性增多,分布式水文概念模型的模拟效率还有待于进一步的提高。     

基于非线性系统理论的分布式水文模型

提出了一种基于水文系统理论的分布式水文模拟方法,它将集总的水文非线性系统模型通过GIS平台,结合单元水文模拟,拓展到分布式流域水文模拟。它既具有分布式水文概念性模拟的特征,同时又具有水文系统分析适应能力强的优点。实例分析表明:在黄河板桥流域,通过考虑时变增益因子G的空间变异性,该模型吸纳了局部高强度降水信息,对分布式空间降水输入能够产生积极的响应,获得了比集总模型更好的峰值模拟效果;在华北潮白河流域,通过分析不同森林覆盖率下的G值变化曲线,初步揭示了潮白河流域土地利用变化的水文响应趋势,即在相同的土壤湿度条件下,随着森林覆盖率的增加,G值呈减小趋势,地表产流能力降低。     

Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN

The present article reports studies to develop a univariate model to forecast the summer monsoon (June–August) rainfall over India. Based on the data pertaining to the period 1871–1999, the trend and stationarity within the time series have been investigated. After revealing the randomness and non-stationarity within the time series, the autoregressive integrated moving average (ARIMA) models have been attempted and the ARIMA(0,1,1) has been identified as a suitable representative model. Consequently, an autoregressive neural network (ARNN) model has been attempted and the neural network has been trained as a multilayer perceptron with the extensive variable selection procedure. Sigmoid non-linearity has been used while training the network. Finally, a three-three-one architecture of the ARNN model has been obtained and after thorough statistical analysis the supremacy of ARNN has been established over ARIMA(0,1,1). The usefulness of ARIMA(0,1,1) has also been described.     

Using the DPFT approach to improve flash flood forecasting models

DPFT is a lumped approach for operational flash flood forecasting, based on the unit hydrograph. Using a multi-event alternating iterative algorithm, it identifies a robust and stable average transfer function and a consistent set of effective rainfall series associated with each event at the same time. This key ingredient allows an objective calibration of different loss functions, relating gauged precipitation and effective rainfall. A case study based on an operational French basin (545 km²) is presented. Three lumped production functions have been calibrated and compared. The results show that more elaborate models of loss functions must be proposed, and some possible directions for this are pointed out.Presently at the Institut de Ciences de la Terra Jaume Almera, Apartat 30102, 08080 Barcelona, Spain.Presently at EDF-DTG. Service de Ressources en Eau, BP 4348, 31029 Toulouse Cedex, France.     

生物滞留设施规模设计方法研究

研究提出了基于单场次降雨分析以及基于长系列降雨统计的生物滞留设施规模设计方法。针对国内市政排水设计一般采用设计重现期的概念,基于单场次降雨分析的规模设计方法用暴雨强度公式和雨型分配方法计算设计降雨过程,由设计降雨推算设施入流过程,用水量平衡法推算出流过程,用洪量削减率、洪峰削减率、洪峰延迟时间评估设计效果。基于长系列降雨统计的规模设计方法对长期日降雨量进行统计分析,得出不同降雨发生频率对应的设施入流量及处理量,用年径流总量削减率评估设计效果。     

时间尺度对入库径流基流分割影响的初步研究

以湖北省漳河灌区内非岩溶发育的杨树垱水库小流域和广西桂林市分布有岩溶发育的青狮潭水库流域为研究对象,利用数字滤波技术对杨树垱水库的日径流、旬径流和月径流量,以及青狮潭水库的月径流、季径流和年径流量进行基流分割。结果表明:随着时间尺度的增大,基流分割得出的基流指数呈逐渐增大的趋势。杨树垱水库小流域日径流的基流指数Fr比旬径流与月径流的Fr小,青狮潭水库月径流的基流指数Fr比季径流与年径流的Fr小。杨树垱水库入库径流的基流指数小于青狮潭水库。     

洪水预报模型输入与输出的误差分布及相关性分析

降雨预报信息作为洪水预报模型的输入,该信息的准确性直接影响洪水预报模型输出的准确性.为探究模型输入(降雨预报)误差与输出(洪水预报)误差之间的关系,以英那河流域为例,分析了不同雨量等级下,预报模型的输入误差与输出误差的分布规律,并定性分析了两种误差的相关关系.结果表明,降雨量等级若为无雨及小雨时,两种误差不相关;若为中...     

Rainfall flood hazard at nuclear power plants in India

Flood hazard evaluation is an important input for Nuclear Power Plants external events safety studies. In the present study, flood hazard at various nuclear sites in India due to rainfall has been evaluated. Hazard estimation is a statistical procedure by which rainfall intensity versus occurrence frequency is estimated from historical records of rainfall data and extrapolated with asymptotic extreme value distribution. Rainfall data needed for flood hazard assessment are daily annual maximum rainfall (24?h data). The observed data points have been fitted using Gumbel, power law and exponential distribution, and return period has been estimated. To study the stationarity of rainfall data, a moving window estimate of the parameters has been performed. The rainfall pattern is stationary in both coastal and inland regions over the period of observation. The coastal regions show intense rainfall and higher variability than inland regions. Based on the plant layout, catchment area and drainage capacity, the prototype fast breeder reactor (PFBR) site is unlikely to be flooded.     

Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA–GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years’ worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA–GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA–ANN models. The results indicate that the SARIMA–GEP model (R ²=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA–ANN (R ²=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA–GEP over the SARIMA–ANN model.     

1956—2008年鄱阳湖流域水沙输移趋势及成因分析

利用鄱阳湖流域代表性水文站1956—2008年实测年径流量及年输沙量资料序列,采用Mann-Kendall秩相关检验法,研究鄱阳湖流域水沙量年际变化趋势和可能发生突变的时间,并从流域降雨、用水、水库拦沙、水土流失及其治理等方面分析了鄱阳湖流域水沙变化的主要影响因素。研究结果表明:鄱阳湖地区来水量和出湖水量有小幅增大趋势;鄱阳湖流域来沙量呈异常显著减小趋势,发生突变的年份为1998—1999年;实测出湖沙量从2002年开始呈明显的减少趋势。通过对有关影响因素的研究,发现鄱阳湖流域气候变化降雨量减少对近10年鄱阳湖出入湖径流量的减少影响显著;水库滞沙是鄱阳湖入湖沙量减少的主要原因;出湖沙量变化主要受上游水库群的影响,还有近年湖区采砂的影响。     

GBHM模型原理及其在中尺度流域的应用

分布式水文模型核心之一是对流域的离散方法.基于地形分析和单位线概念,GBHM利用面积函数和宽度函数概化流域地形特性,将流域划分为一系列内部均一的流带和流条,形成均质坡面流单元,使模型可用一维水份运动方程描述降水产流过程.模型在永定河流域的应用表明,其原理和效率均较为满意.     

Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India

Spatial and temporal variability of rainfall over different seasons influence physical, social and economic parameters. Pre-monsoon (March, April and May – MAM) rainfall over the country is highly variable. Since heat lows and convective rainfall in MAM have an impact on the intensity of the ensuing monsoons, hence the pre-monsoon period was chosen for the study. The pre-whitened Mann Kendall test was used to explore presence of rainfall trend during MAM. The results indicate presence of significant (at 10% level) increasing trend in two stations (Ajmer, Bikaner). The practical significance of the change in rainfall was also explored as percentage changes over long term mean, using Theil and Sen's median slope estimator. Forecast using univariate ARIMA model for pre-monsoon months indicates that there is a significant rise in the pre-monsoon rainfall over the northwest part of the country.