Runoff modelling using radar data and flow measurements in a stochastic state space approach.

In urban drainage the estimation of runoff with the help of models is a complex task. This is in part due to the fact that rainfall, the most important input to urban drainage modelling, is highly uncertain. Added to the uncertainty of rainfall is the complexity of performing accurate flow measurements. In terms of deterministic modelling techniques these are needed for calibration and evaluation of the applied model. Therefore, the uncertainties of rainfall and flow measurements have a severe impact on the model parameters and results. To overcome these problems a new methodology has been developed which is based on simple rain plane and runoff models that are incorporated into a stochastic state space model approach. The state estimation is done by using the extended Kalman filter in combination with a maximum likelihood criterion and an off-line optimization routine. This paper presents the results of this new methodology with respect to the combined consideration of uncertainties in distributed rainfall derived from radar data and uncertainties in measured flows in an urban catchment within the Emscher river basin, Germany.     

Developing a framework for continuous use of models in daily management and operation of WWTPs: a life cycle approach.

We developed and evaluated a framework for the continuous use of dynamic models in daily management and operation of WWTPs. The overall aim is to generate knowledge and build in-house capacity for the reliable use of dynamic models in practice (within a regional water authority in The Netherlands). To this end, we have adopted a life cycle approach, where the plant model follows the different stages that make up the typical lifespan of a plant. Since this approach creates a framework in which models are continuously reused, it is more efficient in terms of resources and investment than the traditional approach where one always makes a new model for the plant whenever it is needed. The methodology was evaluated successfully at a 50,000 PE domestic EBPR plant (Haaren, The Netherlands). It is shown that the continuous use and update of models in a cyclic manner creates a learning cycle, which results in experience and knowledge generation about the plant's modelling that accumulates and translates into improvements into the modelling quality and efficiency. Moreover, a model is now always on-the-shelf for process optimization.     

Towards a synthesis of data-based and theory-based models of environmental systems.

A two-pronged approach to interpreting field data through the use of models is presented. This approach builds upon both data- and theory-based models and their associated methods of system identification. It seeks to overcome their respective limitations: that theory-based models are not unambiguously identifiable from the observations, while a well identified data-based model may not be capable of a satisfactory theoretical interpretation. The purpose of the approach is thereby to gain a deeper understanding of complex environmental systems. Recursive methods of time-series analysis are used to identify the data-based models and the modified recursive prediction error algorithm is employed for parameter estimation of the theory-based models. The results of these identification exercises for the two classes of models can be compared in terms of the macro-parameters of the studied system's time constant and steady-state gain. Two case studies are presented to illustrate the overall performance of the two-pronged approach. It is found that: (1) more is to be gained through the joint application of the two classes of models than the exclusive use of either; (2) to some extent, identifying the structure and estimating the parameters of one type of model can be improved by recourse to the corresponding results for the other; and (3) reconciliation of the results from identifying the two classes of model in the parameter space has significant advantages over the more familiar process of evaluating a model's performance in the terms of its (observed) state space features.     

Prediction of the future flood severity in plain river network region based on numerical model: A case study

Suzhou is one of China's most developed regions, located in the eastern part of the Yangtze Delta. Due to its location and river features, it may at a high risk of flood under the climate change background in the future. In order to investigate the flood response to the extreme scenario in this region, 1-D hydrodynamic model with real-time operations of sluices and pumps is established. The rain-runoff processes of the urban and rural areas are simulated by two lumped hydrologic models, respectively. Indicators for a quantitative assessment of the flood severity in this region are proposed. The results indicate that the existing flood control system could prevent the Suzhou Downtown from inundation in the future. The difficulty of draining the Taihu Lake floods should be given attention to avoid the flood hazard. The modelling approach based on the in-bank model and the evaluation parameters could be effective for the flood severity estimation in the plain river network catchment. The insights from this study of the possible future extreme flood events may assist the policy making and the flood control planning.     

Model-based evaluation of nitrogen removal in a tannery wastewater treatment plant.

Computer modelling has been used in the last 15 years as a powerful tool for understanding the behaviour of activated sludge wastewater treatment systems. However, computer models are mainly applied for domestic wastewater treatment plants (WWTPs). Application of these types of models to industrial wastewater treatment plants requires a different model structure and an accurate estimation of the kinetics and stoichiometry of the model parameters, which may be different from the ones used for domestic wastewater. Most of these parameters are strongly dependent on the wastewater composition. In this study a modified version of the activated sludge model No. 1 (ASM 1) was used to describe a tannery WWTP. Several biological tests and complementary physical-chemical analyses were performed to characterise the wastewater and sludge composition in the context of activated sludge modelling. The proposed model was calibrated under steady-state conditions and validated under dynamic flow conditions. The model was successfully used to obtain insight into the existing plant performance, possible extension and options for process optimisation. The model illustrated the potential capacity of the plant to achieve full denitrification and to handle a higher hydraulic load. Moreover, the use of a mathematical model as an effective tool in decision making was demonstrated.     

An Adaptive Metropolis-Hastings Optimization Algorithm of Bayesian Estimation in Non-Stationary Flood Frequency Analysis

Global climate changing and human activities have altered the assumption of stationarity, and intensified the variation of hydrological process in recent decades. It is essential to make progress in accommodating appropriate models to the changing environment where non-stationary models are taken into account. The developing adapted Bayesian inference offers an attractive framework to estimate non-stationary models, when compared with conventional maximum likelihood estimation (MLE). As the inseparable companions of Bayesian inference, an efficient MCMC sampler are expected to be built. However, proper tunings are needed for the sampler to improve the performance by integrating adaptive algorithm and optimization method. A Bayesian approach with the adaptive Metropolis-Hastings optimization (AM-HO) algorithm is adopted to estimate the parameters and quantify the uncertainty in a two-parameter non-stationary Lognormal distribution model. To verify the performance of the developed model, simulation experiments and practical applications are implemented to fit annual maximum flood series of two gauges in Hanjiang River basin. From the point view of parameters estimation, both Bayesian and MLE methods perform similarly. However, Bayesian method is more attractive and reliable than MLE on uncertainty quantification, which provides a relative narrow intervals to be beneficial for risk analysis and water resource management.     

Development and Application of a Watertable Model for the Assessment of Waterlogging in Irrigated Semi-arid Regions

Direct measurement of groundwater recharge is nearly impossible. So there are various direct and indirect methods and sophisticated models have been used for its estimation. However, the use of multiple approaches has been recommended to estimate the groundwater recharge since each individual approach is associated with some limitations. In many cases, different approaches complement each other and help refine the conceptual model of recharge processes. In this study, a simple watertable model, which is a combination of the groundwater budget and watertable fluctuation, was developed. The model is unique, simple, cost effective, and easy to apply. The model requires input parameters that are readily available or obtainable and which can be accurately measured. The model was applied to estimate the seasonal long?Cterm (30?years, e.g., from 1981 to 2011) groundwater recharge of a canal command located in Jhajjar district of Haryana State (India), where watertable is rising. The results were analysed to provide an overview of the process dynamics that led to watertable rise in the command area. The calculated watertable depths reasonably matched with the observed ones for all the seasons which were confirmed by the high R?Csquared value of 0.963. The mean error and root mean squared error were low at ?C0.0068 and 0.2548?m, respectively, while the model efficiency was 0.83. Different water management alternatives were studied to examine the effect of variation in model parameters on its output. Among the alternatives studied, increased tubewell draft had a higher impact on the overall water balance followed by reduced rice area and canal lining, respectively.     

Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks

The applicability of artificial neural networks (ANN) for modelling of daily river flows in a humid tropical river basin with seasonal rainfall pattern is investigated and the model performance assessed using the commonly adopted efficiency indices. Although the developed model showed satisfactory results for rainy period, the predicted hydrograph for the low flow period deviate from the observed data considerably. The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified. The alternate approach for modelling of river flows utilising the knowledge from SOM analysis has improved the model results. The results show that ANN models can be adopted for forecasting of river flows in the humid tropical river basins for the monsoon period. Input data exploration using SOM is found helpful for developing logically sound ANN models.     

Discrepancies in Flood Modelling Approaches in Transboundary River Systems: Legacy of the Past or Well-grounded Choices?

Flood modelling in transnational rivers requires efficient cross-border collaboration among the riparian countries. Currently, each country/region usually uses a different hydraulic modelling approach, which may hinder the modelling of the entire river. For the sake of accurate and consistent river modelling there is a necessity for the establishment of a framework that fosters international collaborations. This study investigates the current hydraulic modelling approach across the whole length of the River Meuse, the main course of which crosses three north-western European countries. The numerical models used by French, Belgian, and Dutch agencies and authorities were interconnected by exchanging boundary conditions at the borders. At the central part of the river, the Belgian hydraulic model assumed steady flow conditions, while the rest of the river was modelled in unsteady mode. Results for various flood scenarios revealed a distinctive pattern of water depths at the Belgian-Dutch border. To clarify whether this is a bias induced by the change in modelling approach at the border (steady vs. unsteady), we remodelled a stretch of the river across the Belgian-Dutch border using a consistent unsteady modelling approach. The steady and unsteady approaches led to similar patterns across the border, hence discarding the hypothesis of a bias resulting from a change in the employed model. Instead, the pattern in water depths was attributed to a change in the topography of the Meuse Valley, where there is a transition from a narrow steep corridor with limited water storing capacity in Ardennes massif to wide floodplains in the Dutch lowlands. The associated flood damping for the 100-year discharge is less than 1% in the Ardennes and exceeds 15% in the Dutch lowlands. It can be inferred that the current differences in regional hydraulic modelling approaches for the River Meuse are generally well-grounded and not just a legacy of the past.

     

Calibration via Multi-period State Estimation in Water Distribution Systems

Calibration of model parameters is of utmost importance to ensure the good performance of hydraulic simulation models. In this work, calibration is conceived within a joint multi-period parameter and state estimation approach, where model parameters (i.e. roughness coefficients) and hydraulic variables should be computed from available measurements at different times. The aim of this paper is twofold: (1) to present a novel methodology for the calibration of water networks via multi-period state estimation, and (2) to adapt observability analysis to this approach. The novelty of this work is that such a large-scale non-linear optimisation problem is here solved using mathematical programming decomposition techniques. On the other hand, observability analysis requires the construction of the multi-period measurement and parameter Jacobian matrix of the problem. The proposed approach enables computation of the observable roughness coefficients from available readings over time, making possible the periodic reassessment of roughness values based on recent online measurements. The potential of the method is illustrated by means of a case study, which shows how such a methodology would contribute to make the most of telemetry data for calibration purposes.     

基于数据同化校正参数的河流磷迁移估计研究

通过磷迁移数学模型合理估计磷在河流中的时空分布,对防治水体富营养化,抑制水华暴发具有重要的科学和工程意义。数据同化方法可以将模型和观测两种研究手段有机地结合起来,将观测数据融入模型,优化模型状态变量,校正模型参数,进而提高数学模型的模拟预报精度,并依托物联网技术将传统数学模型发展为实时数学模型。本文将粒子滤波数据同化算法引入到水动力-泥沙-磷迁移模型中,以实测的断面磷含量作为观测数据,在观测时刻优化磷含量估计结果,同时校正模型参数磷相平衡分配系数Kd,构建了水动力-泥沙-磷迁移模型同化系统。将其应用于长江上游寸滩至坝前河段的计算结果表明,所构建的同化系统在真实的河流中计算效果良好,可以有效地优化更新状态变量各相磷含量浓度,并反演出模型参数Kd随水沙水环境条件变化的动态变化过程,同化之后模型模拟预报磷输移过程的精度显著提升,为水质实时模型打下基础。     

Comparison of Process-Based and Temperature-Index Snowmelt Modeling in SWAT

Snowmelt hydrology is an important part of hydrological analyses where significant proportion of precipitation is expected to fall in a snow form. Many models have long been introduced to enable the simulation of snowmelt processes in the watershed ranging from simple temperature based equations to complex and sophisticated process-based equations. Usually, mixed results have been reported whether or not the difference between results achieved by incorporating data demanding models vis-à-vis simple temperature-index models is justifiable. In this study, we compared the performances of physically based energy budget and simpler temperature-index based snowmelt calculation approaches within the SWAT model at three sites in two different continents. The results indicate insignificant differences between the two approaches. The temperature-index based snowmelt computation method had the overall model efficiency coefficients ranging from 0.49 to 0.73 while the energy budget based approach had efficiency coefficients ranging from 0.33 to 0.59 only. The magnitude of the differences varied based on where the models were applied. However, comparison between two process-based snowmelt estimation procedures (with and without the inclusion of aspect and slope as factors dictating the incoming solar energy) indicate that accounting for ground surface slope and aspect in the snowmelt model slightly improved the results. We conclude that for most practical applications where net solar radiation, not turbulent heat flux, dominates the snowmelt dynamics, a simpler temperature-index snowmelt estimation model is sufficient.     

Activated sludge modelling: development and potential use of a practical applications database

This study aims at synthesizing experiences in the practical application of ASM type models. The information is made easily accessible to model users by creating a database of modelling projects. This database includes answers to a questionnaire that was sent out to model users in 2008 to provide inputs for a Scientific and Technical Report of the IWA Task Group on Good Modelling Practice - Guidelines for use of activated sludge models, and a literature review on published modelling projects. The database is analysed to determine which biokinetic model parameters are usually changed by modellers, in which ranges, and what values are typically used for seven selected activated sludge models. These results should help model users in the calibration step, by providing typical parameter values as a starting point and ranges as a guide. However, the proposed values should be used with great care since they are the result of averaging practical experience and not taking into account specific parameter correlations.     

An improved parameter estimation procedure in lake modelling

This paper proposes an alternative parameter estimation procedure. Usually the parameters that yield the highest sensitivity are calibrated by trial and error or by an automatic calibration procedure. If the number of parameters to be calibrated is more than 3-5, this procedure is very time consuming. The presented procedure is based on the assumption that the biological components in an ecosystem model attempt to develop such properties that they become best possible survivors, i.e. develop as much biomass as possible. It has previously been proposed that the survival of the entire ecosystem can be measured by use of the thermodynamic function exergy, which measures the distance from thermodynamic equilibrium and accounts for the biomass and its information content. If these assumptions are correct, it should be possible to determine the combination of parameters which gives the highest exergy. The paper presents the use of this idea in combination with a normal calibration. The parameters, which are less known from the literature and still yield a relatively high sensitivity, are determined by use of the above-mentioned exergy principle. The parameters that are known within relatively narrow ranges from the literature are calibrated by the normal procedure. The method has been used on a concrete lake modelling study and given good results. This combination method seems therefore to offer clear advantages, particularly for models with a relatively high number of sensitive parameters (> 5), which otherwise would require a very cumbersome calibration.     

Estimating Sediment Budget at a River Basin Scale Using a Process-Based Distributed Modelling Approach

The paper presents a process-based distributed modelling approach for estimating sediment budget at a river basin scale with partitions of suspended and bed loads by simulating sediment loads and their interactions. In this approach, a river basin is represented by hillslopes and a network of channels. Hillslopes are divided into an array of homogeneous grid cells for modelling surface runoff and suspended sediments. Channels are defined by incorporating flow hydraulic properties into the respective hillslope grids as sub-grid attributes for modelling both suspended and bed loads. Suspended sediment transport is modelled using one dimensional kinematic wave approximation of Saint-Venant’s principles of conservation of mass and momentum. Transport capacity of runoff or streamflow is used to set the limit of suspended sediment transport rate. Bed load in channels is estimated based on the instantaneous water and hydraulic parameters. Fractional interchange between suspended load and bed load is then back calculated. The performance of the model was evaluated through a case study application in a large river basin in Japan. The model satisfactorily calculated the sediment transport and total sediment budget in the basin. The simulated bed load was found to be reasonable and consistent with the water flow and suspended sediment flux. The results showed the bed load can be expressed as a linear function of the suspended load. The fractions of different sediment loads also exhibit linear relationships with water discharge for the rising and recession limbs of the flood hydrographs. The case study has demonstrated that the process-based distributed modelling approach can efficiently describe the basin-scale sediment budgets with due consideration of the suspended and bed loads and their interactions in the hillslopes and channels.     

The Development of Rating Curve Considering Variance Function Using Pseudo-likelihood Estimation Method

This paper employs a new estimation method for estimating stage–discharge rating curve parameters. In typical practical applications, the original non-linear rating curve is transformed into a simple linear regression model by log-transforming the measurement without examining the effect of heteroscedasticity of residuals. Therefore, the model with pseudo-likelihood estimation is developed in this study to deal with heteroscedasticity of residuals in the original non-linear model. The parameters of rating curves and variance functions of errors are simultaneously estimated by the pseudo-likelihood estimation (P-LE) method. Also simulated annealing, a sort of global optimization techniques, is adapted to minimize the log likelihood of the weighted residuals. At first, the developed P-LE model was applied to a hypothetical site where stage–discharge data were generated by incorporating various errors for statistical test. Also, the limit of stages for segmentation is estimated in the process of P-LE using the Heaviside function. For the validation of effects of the developed P-LE model, the results of the conventional log-transformed linear regression (LT-LR) model and the P-LE model are estimated and compared. After statistical simulation, the developed P-LE model is then applied to the real data sets from six gauge stations in the Geum River basin. It can be suggested that this new estimation method is applied to real river sites to successfully determine the weights taking into account error distributions from observed discharge data.     

Comparison of empirical and theoretical remote sensing based bathymetry models in river environments

Knowledge of underwater morphology is an essential component of many hydrological and environmental applications such as flood modelling and lotic habitat mapping. Remote sensing allows modelling of bathymetry at spatial scales that are impossible to achieve with traditional methods. However, the use of passive remote sensing for modelling water depth in fluvial environments remains a challenge. Different methods of computing bathymetry models based on remotely sensed imagery combined with ground measurements for calibration were investigated in order to produce a digital bathymetry model of a reach of the river Tana in Lapland. An empirical deep water correction model was evaluated together with theoretical hydraulically assisted bathymetry (HAB) models. The empirical model produced good results, correlating to known depths at 0.98 (R ² = 0.96) with a mean error of ±12.0 cm. It was demonstrated that usable levels of accuracy can be achieved with data that had previously been considered unsuitable for bathymetry modelling. Some issues related to channel substrate were addressed. The models based on hydraulic theory were tested for the first time outside the area they were developed in. Both models were found to be rather sensitive to certain assumptions, such as the channel friction parameter. The HAB models are able to produce relative depth estimates that can under certain conditions approach actual depths at accuracies similar to the empirical model. Extensive accuracy assessment was performed in order to evaluate the vertical as well as the spatial accuracy of the three models. Copyright © 2010 John Wiley & Sons, Ltd.     

Application of a leakage model to assess exfiltration from sewers.

The exfiltration of wastewater from sewer systems in urban areas causes a deterioration of soil and possibly groundwater quality. Beside the simulation of transport and degradation processes in the unsaturated zone and in the aquifer the analysis of the potential impact requires the estimation of quantity and temporal variation of wastewater exfiltration. Exfiltration can be assessed by the application of a leakage model. The hydrological approach was originally developed to simulate the interactions between the groundwater and surface water, it was adapted to allow for modelling of interactions between groundwater and sewer system. In order to approximate the exfiltration specific model parameters infiltration specific parameters were used as a basis. Scenario analysis of the exfiltration in the City of Dresden from 1997 to 1999 and during the flood event in August 2002 shows the variation and the extent of exfiltration rates.     

Efficient data assimilation method based on chaos theory and Kalman filter with an application in Singapore Regional Model

Data assimilation is a methodology that utilizes information from observations, and assimilates it into numerical models, with the intention of improving the quality and accuracy of the model outputs. This paper introduces a hybrid data assimilation scheme, which combines a temporal error prediction algorithm based on local model (LM) inspired by chaos theory and a spatial error distribution scheme through the propagation of error covariances derived from the Kalman filter (KF). Local model is only capable of predicting the model errors at the stations where the observations are available, while the effect of traditional Kalman filter is limited to a time horizon where the improved initial conditions are washed out. The hybrid scheme outlined in this paper is performed in two steps: (i) predicting the model errors at the measurement stations using the local model approach and (ii) distributing the predicted errors over the computational domain using the Kalman filter. Incorporating error distribution with error prediction assimilates limited information from the observed data into non-measurement stations. Therefore all stations of interest are able to be benefited. The proposed hybrid scheme has been implemented in the Singapore Regional Model (SRM) constructed by Delft3D modelling system, with the improvements in the assimilated characteristics discussed in detail.     

Green-Ampt Infiltration Models for Varied Field Conditions: A Revisit

The Green-Ampt (GA) infiltration model is a simplified version of the physically based full hydrodynamic model, known as the Richards equation. The simplicity and accuracy of this model facilitates for its use in many field problems, such as, infiltration computation in rainfall-runoff modelling, effluent transport in groundwater modelling studies, irrigation management studies including drainage systems etc. The numerous infiltration models based on the Green-Ampt approach have been widely investigated for their applicability in various scenarios of homogeneous soils. However, recent advances in physically based distributed rainfall-runoff modeling demands for the use of improved infiltration models for layered soils with non-uniform initial moisture conditions under varying rainfall patterns to capture the actual infiltration process that exists in nature. The difficulty that modelers are facing now-a-days includes the estimation of time of ponding and the application of the infiltration model to unsteady rainfall events occurring in heterogeneous soil conditions. The investigation in this direction exhibits that only few infiltration models can handle these situations. Hence, it is of vital importance to analyze the usefulness of different variants of the Green-Ampt infiltration models in terms of their degree of accuracy, complexity and applicability limits. This paper provides a brief review of these infiltration models to bring out their usefulness in the rainfall-runoff and irrigation modeling studies as well as the drawbacks associated with these models.