Climatic effects on ice phenology and ice-jam flooding of the Athabasca River in western Canada

ABSTRACT

In cold region environments, any alteration in the hydro-climatic regime can have profound impacts on river ice processes. This paper studies the implications of hydro-climatic trends on river ice processes, particularly on the freeze-up and ice-cover breakup along the Athabasca River in Fort McMurray in western Canada, which is an area very prone to ice-jam flooding. Using a stochastic approach in a one-dimensional hydrodynamic river ice model, a relationship between overbank flow and breakup discharge is established. Furthermore, the likelihood of ice-jam flooding in the future (2041–2070 period) is assessed by forcing a hydrological model with meteorological inputs from the Canadian regional climate model driven by two atmospheric–ocean general circulation climate models. Our results show that the probability of ice-jam flooding for the town of Fort McMurray in the future will be lower, but extreme ice-jam flood events are still probable.     

Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey

This paper proposes the application of a conjunction model (neuro‐wavelet) for forecasting monthly lake levels. The neuro‐wavelet (NW) conjunction model is improved combining two methods, discrete wavelet transform and artificial neural networks. The application of the methodology is presented for the Lake Van, which is the biggest lake in Turkey, and Lake Egirdir. The accuracy of the NW model is investigated for 1‐ and 6‐month‐ahead lake level forecasting. The root mean square errors, mean absolute relative errors and determination coefficient statistics are used for evaluating the accuracy of NW models. The results of the proposed models are compared with those of the neural networks. In the 1‐month‐ahead lake level forecasting, the NW conjunction model reduced the root mean square errors and mean absolute relative errors by 87–34% and 86–31% for the Van and Egirdir lakes, respectively. In the 6‐month‐ahead lake level forecasting, the NW conjunction model reduced the root mean square errors and mean absolute relative errors by 34–48% and 30‐46% for the Van and Egirdir lakes, respectively. The comparison results indicate that the suggested model could significantly increase the short‐ and long‐term forecast accuracy. Copyright © 2009 John Wiley & Sons, Ltd.     

Modeling the Development of Ice Phenomena in Rivers as Applied to the Assessment of Probable Changes in Ice Conditions at Various Scenarios of the Future Climate

The results of investigations of the alteration in river ice regime associated with the global climate warming are presented. It is shown that a simple model based on the relationship between the dates of ice phenomena and the average air temperature for the preceding month can be used for the assessment of probable changes in ice phenomena at various scenarios of the future climate. It is found that as a rule, the allowance made for the rate of streamflow in autumn does not improve the assessment of the probable dates of river freeze-up, whereas the model of the process of river breakup allows improving the estimates of the relevant shifts in the dates.     

Stream flow forecasting using neuro‐wavelet technique

This paper proposes the application of a neuro‐wavelet technique for modelling monthly stream flows. The neuro‐wavelet model is improved by combining two methods, discrete wavelet transform and multi‐layer perceptron, for one‐month‐ahead stream flow forecasting and results are compared with those of the single multi‐layer perceptron (MLP), multi‐linear regression (MLR) and auto‐regressive (AR) models. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in the Eastern Black Sea region of Turkey are used in the study. The comparison results revealed that the suggested model could increase the forecast accuracy and perform better than the MLP, MLR and AR models. Copyright © 2008 John Wiley & Sons, Ltd.     

Hydro‐climatic impacts on the ice cover of the lower Peace River

Since the late 1960s, a paucity of ice‐jam flooding in the lower Peace River has resulted in prolonged dry periods and considerable reduction in the area covered by lakes and ponds that provide habitat for aquatic life in the Peace–Athabasca Delta (PAD) region. Though major ice jams occur at breakup, antecedent conditions play a significant role in their frequency and severity. These conditions are partly defined by the mode of freezeup and the maximum thickness that is attained during the winter, shortly before the onset of spring and development of positive net heat fluxes to the ice cover. Data from hydrometric gauge records and from field surveys are utilized herein to study these conditions. It is shown that freezeup flows are considerably larger at the present time than before regulation, and may be responsible for more frequent formation of porous accumulation covers. Despite a concomitant rise in winter temperatures, solid‐ice thickness has increased since the 1960s. Using a simple ice growth model, specifically developed for the study area, it is shown that porous accumulation covers enhance winter ice growth via accelerated freezing into the porous accumulation. Coupled with a reduction in winter snowfall, this effect can not only negate, but reverse, the effect of warmer winters on ice thickness, thus explaining present conditions. The present model is also shown to be a useful prediction tool, especially for extrapolating incomplete data to the end of the winter. Copyright © 2007 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.     

Validation of a full hydrodynamic model for large‐scale hydrologic modelling in the Amazon

A key aspect of large river basins partially neglected in large‐scale hydrological models is river hydrodynamics. Large‐scale hydrologic models normally simulate river hydrodynamics using simplified models that do not represent aspects such as backwater effects and flood inundation, key factors for some of the largest rivers of the world, such as the Amazon. In a previous paper, we have described a large‐scale hydrodynamic approach resultant from an improvement of the MGB‐IPH hydrological model. It uses full Saint Venant equations, a simple storage model for flood inundation and GIS‐based algorithms to extract model parameters from digital elevation models. In the present paper, we evaluate this model in the Solimões River basin. Discharge results were validated using 18 stream gauges showing that the model is accurate. It represents the large delay and attenuation of flood waves in the Solimões basin, while simplified models, represented here by Muskingum Cunge, provide hydrographs are wrongly noisy and in advance. Validation against 35 stream gauges shows that the model is able to simulate observed water levels with accuracy, representing their amplitude of variation and timing. The model performs better in large rivers, and errors concentrate in small rivers possibly due to uncertainty in river geometry. The validation of flood extent results using remote sensing estimates also shows that the model accuracy is comparable to other flood inundation modelling studies. Results show that (i) river‐floodplain water exchange and storage, and (ii) backwater effects play an important role for the Amazon River basin hydrodynamics. Copyright © 2011 John Wiley & Sons, Ltd.     

River ice cover influence on sediment transportation at present and under projected hydroclimatic conditions

A large number of rivers are frozen annually, and the river ice cover has an influence on the geomorphological processes. These processes in cohesive sediment rivers are not fully understood. Therefore, this paper demonstrates the impact of river ice cover on sediment transport, i.e. turbidity, suspended sediment loads and erosion potential, compared with a river with ice‐free flow conditions. The present sediment transportation conditions during the annual cycle are analysed, and the implications of climate change on wintertime geomorphological processes are estimated. A one‐dimensional hydrodynamic model has been applied to the Kokemäenjoki River in Southwest Finland. The shear stress forces directed to the river bed are simulated with present and projected hydroclimatic conditions. The results of shear stress simulations indicate that a thermally formed smooth ice cover diminishes river bed erosion, compared with an ice‐free river with similar discharges. Based on long‐term field data, the river ice cover reduces turbidity statistically significantly. Furthermore, suspended sediment concentrations measured in ice‐free and ice‐covered river water reveal a diminishing effect of ice cover on riverine sediment load. The hydrodynamic simulations suggest that the influence of rippled ice cover on shear stress is varying. Climate change is projected to increase the winter discharges by 27–77% on average by 2070–2099. Thus, the increasing winter discharges and possible diminishing ice cover periods both increase the erosion potential of the river bed. Hence, the wintertime sediment load of the river is expected to become larger in the future. Copyright © 2015 John Wiley & Sons, Ltd.     

A comparision of interpolation methods for producing digital elevation models at the field scale

Digital elevation models have been used in many applications since they came into use in the late 1950s. It is an essential tool for applications that are concerned with the Earth's surface such as hydrology, geology, cartography, geomorphology, engineering applications, landscape architecture and so on. However, there are some differences in assessing the accuracy of digital elevation models for specific applications. Different applications require different levels of accuracy from digital elevation models. In this study, the magnitudes and spatial patterning of elevation errors were therefore examined, using different interpolation methods. Measurements were performed with theodolite and levelling. Previous research has demonstrated the effects of interpolation methods and the nature of errors in digital elevation models obtained with indirect survey methods for small‐scale areas. The purpose of this study was therefore to investigate the size and spatial patterning of errors in digital elevation models obtained with direct survey methods for large‐scale areas, comparing Inverse Distance Weighting, Radial Basis Functions and Kriging interpolation methods to generate digital elevation models. The study is important because it shows how the accuracy of the digital elevation model is related to data density and the interpolation algorithm used. Cross validation, split‐sample and jack‐knifing validation methods were used to evaluate the errors. Global and local spatial auto‐correlation indices were then used to examine the error clustering. Finally, slope and curvature parameters of the area were modelled depending on the error residuals using ordinary least regression analyses. In this case, the best results were obtained using the thin plate spline algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

Prediction of effective porosity from seismic attributes using locally linear model tree algorithm

In this study, a locally linear model tree algorithm was used to optimize a neuro‐fuzzy model for prediction of effective porosity from seismic attributes in one of Iranian oil fields located southwest of Iran. Valid identification of effective porosity distribution in fractured carbonate reservoirs is extremely essential for reservoir characterization. These high‐accuracy predictions facilitate efficient exploration and management of oil and gas resources. The multi‐attribute stepwise linear regression method was used to select five out of 26 seismic attributes one by one. These attributes introduced into the neuro‐fuzzy model to predict effective porosity. The neuro‐fuzzy model with seven locally linear models resulted in the lowest validation error. Moreover, a blind test was carried out at the location of two wells that were used neither in training nor validation. The results obtained from the validation and blind test of the model confirmed the ability of the proposed algorithm in predicting the effective porosity. In the end, the performance of this neuro‐fuzzy model was compared with two regular neural networks of a multi‐layer perceptron and a radial basis function, and the results show that a locally linear neuro‐fuzzy model trained by a locally linear model tree algorithm resulted in more accurate porosity prediction than standard neural networks, particularly in the case where irregularities increase in the data set. The production data have been also used to verify the reliability of the porosity model. The porosity sections through the two wells demonstrate that the porosity model conforms to the production rate of wells. Comparison of the locally linear neuro‐fuzzy model performance on different wells indicates that there is a distinct discrepancy in the performance of this model compared with the other techniques. This discrepancy in the performance is a function of the correlation between the model inputs and output. In the case where the strength of the relationship between seismic attributes and effective porosity decreases, the neuro‐fuzzy model results in more accurate prediction than regular neural networks, whereas the neuro‐fuzzy model has a close performance to neural networks if there is a strong relationship between seismic attributes and effective porosity. The effective porosity map, presented as the output of the method, shows a high‐porosity area in the centre of zone 2 of the Ilam reservoir. Furthermore, there is an extensive high‐porosity area in zone 4 of Sarvak that extends from the centre to the east of the reservoir.     

Improving flow forecasting by error correction modelling in altered catchment conditions

Despite human is an increasingly significant component of the hydrologic cycle in many river basins, most hydrologic models are still developed to accurately reproduce the natural processes and ignore the effect of human activities on the watershed response. This results in non‐stationary model forecast errors and poor predicting performance every time these models are used in non‐pristine watersheds. In the last decade, the representation of human activities in hydrological models has been extensively studied. However, mathematical models integrating the human and the natural dimension are not very common in hydrological applications and nearly unknown in the day‐to‐day practice. In this paper, we propose a new simple data‐driven flow forecast correction method that can be used to simultaneously tackle forecast errors from structural, parameter and input uncertainty, and errors that arise from neglecting human‐induced alterations in conceptual rainfall–runoff models. The correction system is composed of two layers: (i) a classification system that, based on the current flow condition, detects whether the source of error is natural or human induced and (ii) a set of error correction models that are alternatively activated, each tailored to the specific source of errors. As a case study, we consider the highly anthropized Aniene river basin in Italy, where a flow forecasting system is being established to support the operation of a hydropower dam. Results show that, even by using very basic methods, namely if‐then classification rules and linear correction models, the proposed methodology considerably improves the forecasting capability of the original hydrological model. Copyright © 2013 John Wiley & Sons, Ltd.     

Application of the Takagi–Sugeno fuzzy system for combination forecasting of river flow in semiarid mountain regions

Because of high spatial heterogeneity and the degree of uncertainty about hydrological processes in large‐scale catchments of semiarid mountain areas, satisfactory forecasting of daily discharge is seldom available using a single model in many practical cases. In this paper the Takagi–Sugeno fuzzy system (TS) and the simple average method (SAM) are applied to combine forecasts of three individual models, namely, the simple linear model (SLM), the seasonally based linear perturbation model (LPM) and the nearest neighbour linear perturbation model (NNLPM) for modelling daily discharge, and the performance of modelling results is compared in five catchments of semiarid areas. It is found that the TS combination model gives good predictions. The results confirm that better prediction accuracy can be obtained by combining the forecasts of different models with the Takagi–Sugeno fuzzy system in semi‐arid mountain areas. Copyright © 2009 John Wiley & Sons, Ltd.     

Stability of supraglacial debris

Rock debris on the surface of ablating glaciers is not static, and is often transported across the ice surface as relief evolves during melt. This supraglacial debris transport has a strong influence on the spatial distribution of melt, and is implicated in the formation of hummocky glacial topography in deglaciated terrain. Furthermore, as ice‐dammed lakes and ice‐cored slopes become increasingly common in deglaciating watersheds, there is rising concern about hazards to humans and infrastructure posed by mass‐wasting of ice‐cored debris. The existing quantitative framework for describing these debris transport processes is limited, making it difficult to account for transport in mass balance, hazard assessment, and landscape development models. This paper develops a theoretical framework for assessing slope stability and gravitational mass transport in a debris‐covered ice setting. Excess water pressure at the interface between ablating ice and lowering debris is computed by combining Darcy's law with a meltwater balance. A limit‐equilibrium slope stability analysis is then applied to hypothetical debris layers with end‐member moisture conditions derived from a downslope meltwater balance that includes production and seepage. The resulting model system constrains maximum stable slope angles and lengths that vary with debris texture, thickness, and the rate of meltwater production. Model predictions are compared with field observations and with digital elevation model (DEM)‐derived terrain metrics from two modern debris‐covered glaciers on Mount Rainier, USA. Copyright © 2017 John Wiley & Sons, Ltd.     

A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall—runoff models

Abstract

The performances of three artificial neural network (NN) methods for combining simulated river flows, based on three different neural network structures, are compared. These network structures are: the simple neural network (SNN), the radial basis function neural network (RBFNN) and the multi-layer perceptron neural network (MLPNN). Daily data of eight catchments, located in different parts of the world, and having different hydrological and climatic conditions, are used to enable comparisons of the performances of these three methods to be made. In the case of each catchment, each neural network combination method synchronously uses the simulated river flows of four rainfall—runoff models operating in design non-updating mode to produce the combined river flows. Two of these four models are black-box, the other two being conceptual models. The results of the study show that the performances of all three combination methods are, on average, better than that of the best individual rainfall—runoff model utilized in the combination, i.e. that the combination concept works. In terms of the Nash-Sutcliffe model efficiency index, the MLPNN combination method generally performs better than the other two combination methods tested. For most of the catchments, the differences in the efficiency index values of the SNN and the RBFNN combination methods are not significant but, on average, the SNN form performs marginally better than the more complex RBFNN alternative. Based on the results obtained for the three NN combination methods, the use of the multi-layer perceptron neural network (MLPNN) is recommended as the appropriate NN form for use in the context of combining simulated river flows.     

Effect of transect location,transect spacing and interpolation methods on river bathymetry accuracy

Digital elevation models (DEMs) of river channel bathymetries are developed by interpolating elevations between data collected at discrete points or along transects. The accuracy of interpolated bathymetries depends on measurement error, the density and distribution of point data, and the interpolation method. Whereas point measurement errors can be minimized by selecting the most efficient equipment, the effect of data density and interpolation method on river bathymetry is relatively unknown. Thus, this study focuses on transect‐based collection methods and investigates the effects of transect location, the spacing between transects, and interpolation methods on the accuracy of interpolated bathymetry. This is accomplished by comparing four control bathymetries generated from accurate and high resolution, sub‐meter scale data to bathymetries interpolated from transect data extracted from the control bathymetries using two transect locating methods and four interpolation methods. The transect locating methods are a morphologically‐spaced and an equally‐spaced model. The four interpolation methods are Ordinary Kriging, Delaunay Triangulation, and Simple Linear, which are applied in curvilinear coordinates (Delaunay Triangulation is also applied in Cartesian coordinates), and Natural Neighbor only in Cartesian Coordinates. The bathymetric data were obtained from morphologically simple and complex reaches of a large (average bankfull width = 90 m) and a small (average bankfull width = 17 m) river. The accuracy of the developed DEMs is assessed using statistical analysis of the differences between the control and interpolated bathymetries and hydraulic parameters assessed from bankfull water surface elevations. Results indicate that DEM accuracy is not influenced by the choice of transect location method (with same averaged cross‐section spacing) or a specific interpolation method, but rather by the coordinate system for which the interpolation method is applied and the spacing between transects. They also show negligible differences between the mean depths and surface areas calculated from bathymetries with dense or coarse spacing. Copyright © 2016 John Wiley & Sons, Ltd.     

EFFECTS OF RIVER ICE ON STAGE-DISCHARGE RELATIONSHIPS——A CASE STUDY OF THE YELLOW RIVER

Using field observations at four gauging stations along the Inner Mongolia Reach of the Yellow River in China, this paper explores effects of the ice on the hydraulics of this river reach for four different conditions, namely: under open channel flow, during ice-running period, the ice-covered period, and the river break-up period. The rating curves were found to be well recognized under open channel situations, but were sometimes poorly defined and extremely variable under ice conditions. The results also show that the water level is insensitive to flowing ice prior to freeze-up. However, significant, but hardly surprising, variations were observed during ice-covered conditions. The rating curves for both the ice covered condition and river ice breakup period are developed and some related hydraulic issues are examined. Additionally, the impacts of the ice accumulation and associated riverbed deformation during ice period on the rating curves are discussed.     

EFFECTS OF RIVER ICE ON STAGE——DISCHARGE RELATIONSHIPS

Using field observations at four gauging stations along the Inner Mongolia Reach of the Yellow River in China, this paper explores effects of the ice on the hydraulics of this river reach for four different conditions, namely: under open channel flow, during ice-running period, the ice-covered period, and the river break-up period. The rating curves were found to be well recognized under open channel situations, but were sometimes poorly defined and extremely variable under ice conditions. The results also show that the water level is insensitive to flowing ice prior to freeze-up. However, significant, but hardly surprising, variations were observed during ice-covered conditions. The rating curves for both the ice covered condition and river ice breakup period are developed and some related hydraulic issues are examined. Additionally, the impacts of the ice accumulation and associated riverbed deformation during ice period on the rating curves are discussed.     

Adsorption Kinetic Modeling of Safranin onto Rice Husk Biomatrix Using Pseudo‐first‐ and Pseudo‐second‐order Kinetic Models: Comparison of Linear and Non‐linear Methods

Batch kinetic studies were carried out for the removal of safranin from aqueous solution using a biomatrix prepared from rice husk. The adsorption kinetic data were modeled using the pseudo‐first‐order and pseudo‐second‐order kinetic equations. The linear and non‐linear forms of these two widely used kinetic models were compared in this study. In order to determine the best‐fitting equation, the coefficient of determination (r²), the sum of the squares of the errors (SSE), sum of the absolute errors (SAE), average relative error (ARE), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), and the Chi‐squared test (χ²) were used as error analysis methods. Results showed that the non‐linear forms of pseudo‐first‐order and pseudo‐second‐order models were more suitable than the linear forms for fitting the experimental data. Non‐linear method is thus more appropriate for estimating the kinetic parameters and should primarily be used to describe adsorption kinetics.     

Spatial prediction on river networks: comparison of top‐kriging with regional regression

Top‐kriging is a method for estimating stream flow‐related variables on a river network. Top‐kriging treats these variables as emerging from a two‐dimensional spatially continuous process in the landscape. The top‐kriging weights are estimated by regularising the point variogram over the catchment area (kriging support), which accounts for the nested nature of the catchments. We test the top‐kriging method for a comprehensive Austrian data set of low stream flows. We compare it with the regional regression approach where linear regression models between low stream flow and catchment characteristics are fitted independently for sub‐regions of the study area that are deemed to be homogeneous in terms of flow processes. Leave‐one‐out cross‐validation results indicate that top‐kriging outperforms the regional regression on average over the entire study domain. The coefficients of determination (cross‐validation) of specific low stream flows are 0.75 and 0.68 for the top‐kriging and regional regression methods, respectively. For locations without upstream data points, the performances of the two methods are similar. For locations with upstream data points, top‐kriging performs much better than regional regression as it exploits the low flow information of the neighbouring locations. Copyright © 2012 John Wiley & Sons, Ltd.