Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers

Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.     

Estimation of suspended sediment loads in a seasonal stream in the wet‐dry tropics,Northern Territory,Australia

One year of instantaneous suspended sediment concentration, C, and instantaneous discharge, Q, data collected at Ngarradj downstream of the Jabiluka mine site indicate that the use of a simple C–Q rating curve is not a reliable method for estimating suspended sediment loads from the Ngarradj catchment. The C–Q data are not only complicated by hysteresis effects within the rising and falling stages of individual events, but also by variable depletion of available suspended sediment through multipeaked runoff events. Parameter values were fitted to an event‐based suspended sediment load–Q relationship as an alternative to the C–Q relationship. Total suspended sediment load and Q data for 10 observed events in the Ngarradj stream catchment were used to fit parameter values to a suspended sediment load–Q relationship, using (a) log–log regression and (b) iterative parameter fitting techniques. A more reliable and statistically significant prediction of suspended sediment load from the Ngarradj catchment is obtained using an event‐based suspended sediment load–Q relationship. Fitting parameters to the event‐based suspended sediment load–Q relationship using iterative techniques better predicts long‐term suspended sediment loads compared with log–log regression techniques. Copyright © 2004 John Wiley & Sons, Ltd.     

Long-range forecasting of suspended sediment

This study examines the employment of two methods, multiple linear regression (MLR) and an artificial neural network (ANN), for multistep ahead forecasting of suspended sediment. The autoregressive integrated moving average (ARIMA) model is considered for one-step ahead forecasting of sediment series in order to provide a comparison with the MLR and ANN methods. For one- and two-step ahead forecasting, the ANN model performance is superior to that of the MLR model. For longer ranges, MLR models provide better accuracy, but there is an important assumption violation. The Durbin-Watson statistics of the MLR models show a noticeable decrease from 1.3 to 0.5, indicating that the residuals are not dependent over time. The scatterplots of the three methods (MLR, ARIMA and ANN) for one-step ahead forecasting for the validation period illustrate close fits with the regression line, with the ANN configuration having a slightly higher R² value.     

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

ABSTRACT

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.     

The effect of El Niño Southern Oscillation cycles on the decadal scale suspended sediment behavior of a coastal dry‐summer subtropical catchment

Rivers display temporal dependence in suspended sediment–water discharge relationships. Although most work has focused on multi‐decadal trends, river sediment behavior often displays sub‐decadal scale fluctuations that have received little attention. The objectives of this study were to identify inter‐annual to decadal scale fluctuations in the suspended sediment–discharge relationship of a dry‐summer subtropical river, infer the mechanisms behind these fluctuations, and examine the role of El Niño Southern Oscillation climate cycles. The Salinas River (California) is a moderate sized (11 000 km²), coastal dry‐summer subtropical catchment with a mean discharge (Q_mean) of 11.6 m³ s^?1. This watershed is located at the northern most extent of the Pacific coastal North America region that experiences increased storm frequency during El Niño years. Event to inter‐annual scale suspended sediment behavior in this system was known to be influenced by antecedent hydrologic conditions, whereby previous hydrologic activity regulates the suspended sediment concentration–water discharge relationship. Fine and sand suspended sediment in the lower Salinas River exhibited persistent, decadal scale periods of positive and negative discharge corrected concentrations. The decadal scale variability in suspended sediment behavior was influenced by inter‐annual to decadal scale fluctuations in hydrologic characteristics, including: elapsed time since small (~0.1 × Q_mean), and moderate (~10 × Q_mean) threshold discharge values, the number of preceding days that low/no flow occurred, and annual water yield. El Niño climatic activity was found to have little effect on decadal‐scale fluctuations in the fine suspended sediment–discharge relationship due to low or no effect on the frequency of moderate to low discharge magnitudes, annual precipitation, and water yield. However, sand concentrations generally increased in El Niño years due to the increased frequency of moderate to high magnitude discharge events, which generally increase sand supply. Copyright © 2014 John Wiley & Sons, Ltd.     

Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones

Abstract

The abilities of neuro-fuzzy (NF) and neural network (NN) approaches to model the streamflow–suspended sediment relationship are investigated. The NF and NN models are established for estimating current suspended sediment values using the streamflow and antecedent sediment data. The sediment rating curve and multi-linear regression are also applied to the same data. Statistic measures were used to evaluate the performance of the models. The daily streamflow and suspended sediment data for two stations—Quebrada Blanca station and Rio Valenciano station—operated by the US Geological Survey were used as case studies. Based on comparison of the results, it is found that the NF model gives better estimates than the other techniques.     

Analyses of suspended sediment loads in Slovenian rivers

ABSTRACT

Suspended solids are present in every river, but high quantities can worsen the ecological conditions of streams; therefore, effective monitoring and analysis of this hydrological variable are necessary. Frequency, seasonality, inter-correlation, extreme events, trends and lag analyses were carried out for peaks of suspended sediment concentration (SSC) and discharge (Q) data from Slovenian streams using officially monitored data from 1955 to 2006 that were made available by the Slovenian Environment Agency. In total more than 500 station-years of daily Q and SSC data were used. No uniform (positive or negative) trend was found in the SSC series; however, all the statistically significant trends were decreasing. No generalization is possible for the best fit distribution function. A seasonality analysis showed that most of the SSC peaks occurred in the summer (short-term intense convective precipitation produced by thunderstorms) and in the autumn (prolonged frontal precipitation). Correlations between Q and SSC values were generally relatively small (Pearson correlation coefficient values from 0.05 to 0.59), which means that the often applied Q–SSC curves should be used with caution when estimating annual suspended sediment loads. On average, flood peak Q occurred after the corresponding SSC peak (clockwise-positive hysteresis loops), but the average lag time was rather small (less than 1 day).

Editor M.C. Acreman; Associate editor Y. Gyasi-Agyei     

Suspended sediment dynamics in a steep,glacier‐fed mountain stream,Place Creek,Canada

This study examined suspended sediment concentration (SSC) during the ablation seasons of 2000 and 2001 in Place Creek, Canada, a steep, glacier‐fed mountain stream. Comparison of stream flow in Place Creek with that in an adjacent, almost unglacierized catchment provided a rational basis for separating the ablation seasons into nival, nival–glacial, glacial and autumn recession subseasons. Distinct groupings of points in plots of electrical conductivity against discharge supported the validity of the subseasonal divisions in terms of varying hydrological conditions. Relationships between SSC and discharge (Q) varied between the two study seasons, and between subseasons. Hysteresis in the SSC–Q relationship was evident at both event and weekly time‐scales. Some suspended sediment released from pro‐glacial Place Lake (the source of Place Creek) appeared to be lost to channel storage at low flows, especially early in the ablation season, with re‐entrainment at higher flows. Multiple regression models were derived for the subseasons using predictor variables including Q, Q², the change in Q over the previous 3 h, cumulative discharge over the ablation season, total precipitation over the previous 24 h and SSC measured at 1500 hours as an index value for each day. The models produced adjusted R² values ranging from 0·71 to 0·91, and provided tentative insights into the differences in SSC dynamics amongst subseasons. Introduction of the index value of SSC significantly improved the model fit during the nival–glacial and glacial subseasons for both years, as it adjusts the model to the current condition of sediment supply. Copyright © 2003 John Wiley & Sons, Ltd.     

Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques / Modélisation de la charge journalière en matières en suspension dans des rivières turques à l'aide de plusieurs techniques empiriques

Abstract

The transport of sediment load in rivers is important with respect to pollution, channel navigability, reservoir filling, longevity of hydroelectric equipment, fish habitat, river aesthetics and scientific interest. However, conventional sediment rating curves cannot estimate sediment load accurately. An adaptive neuro-fuzzy technique is investigated for its ability to improve the accuracy of the streamflow—suspended sediment rating curve for daily suspended sediment estimation. The daily streamflow and suspended sediment data for four stations in the Black Sea region of Turkey are used as case studies. A comparison is made between the estimates provided by the neuro-fuzzy model and those of the following models: radial basis neural network (RBNN), feed-forward neural network (FFNN), generalized regression neural network (GRNN), multi-linear regression (MLR) and sediment rating curve (SRC). Comparison of results reveals that the neuro-fuzzy model, in general, gives better estimates than the other techniques. Among the neural network techniques, the RBNN is found to perform better than the FFNN and GRNN.     

Predicting suspended sediment concentrations in the Meuse river using a supply‐based rating curve

A rating curve provides a reasonable estimate of the suspended sediment concentration at a given discharge. However, analysis of a detailed 9‐year time‐series of suspended sediment concentration (SSC) and discharge Q of the Meuse River in The Netherlands indicates that SSC is (besides discharge) controlled by exhaustion and replenishment of different sediment sources. Clockwise hysteresis and other effects of sediment exhaustion can be observed during and after flood events, and the effects of stockpiling of sediment in the river bed during low‐discharge periods are obvious in the SSC of the next flood. In a single regression equation we have implemented a parameter that represents the presence or absence of stock for sediment uptake. In comparison with a rating curve of SSC and Q, adding this parameter is shown to be a more reliable and comprehensive method to predict SSCs at all discharge regimes with all preceding discharge conditions, for single‐peaked and multi‐peaked runoff events as well as for low flow conditions. The method is probably applicable to other small‐ to medium‐scaled river basins. Copyright © 2007 John Wiley & Sons, Ltd.     

Enhanced hysteresis of suspended sediment transport in response to upstream damming: An example of the middle Yangtze River downstream of the Three Gorges Dam

The peak in sediment transport in alluvial rivers generally lags behind the peak in discharge. It is thus not clear how the hysteresis in the sediment/discharge relationship may be impacted by damming, which can fundamentally alter the water and sediment regimes in the downstream reaches of the river. In this study, a total of 500 flood events in the Yichang–Chenglingji Reach (YCR) of the Middle Yangtze River immediately downstream of the Three Gorges Dam (TGD) are analysed to study the impacts of dam operations on the hysteresis of suspended sediment transport. Sediment rating curves, hysteresis patterns, as well as lag times, are investigated to determine the relationship between suspended sediment concentration (SSC) and flow discharge (Q) at different temporal scales, from inter-annual to individual flood events, for the pre- and post-TGD period from 1992 to 2002 and from 2003 to 2017, respectively. The results showed that the TGD operation decreased the frequency and magnitude of floods. The decrease in peak flow and increase in base flow weakened the flood contribution to the annual discharge by nearly 20%. However, the relative suspended sediment load contribution during flood events was much higher than the discharge contribution, and was little impacted by the dam. At seasonal and monthly scales, more than 80% of the suspended sediment was transported by ~65% of the water discharge in the summer and early autumn. The monthly SSC–Q relationship changed from a figure-eight to an anti-clockwise pattern after the construction of the TGD. For single flood events, the TGD operations significantly modified the downstream SSC–Q hysteresis patterns, increasing the frequency of anti-clockwise loops and the lag time between peak Q and peak SSC. These adjustments were mainly caused by differences in the propagation velocities of flood and sediment waves and the sediment ‘storage–mobilization–depletion’ process, whereas the influence of lateral diversions was small. © 2020 John Wiley & Sons, Ltd.     

Investigating the extrapolation performance of neural network models in suspended sediment data

Suspended sediment modelling is a quite significant issue in hydrology. The prediction of suspended sediment has taken the attention of several scientists in water resources. With extrapolation, the forecasting ability of the employed forecasting model beyond the calibration range is investigated. In the present study, different smoothing parameters are used to differentiate the kurtosis of the local critical points (local minima and maxima). The two models used are an artificial neural network (ANN) model and a multiple linear regression (MLR) model for prediction in order to examine the model extrapolation ability. The ANN model provides closer estimations to the observed peaks, being higher than the corresponding MLR ones. For the local minima, the ANN predictions are higher than the MLR predictions. As there are limited local points, all the remaining ANN predictions are lower than the MLR ones except for one point.     

Estimating suspended sediment concentrations in areas with limited hydrological data using a mixed‐effects model

Sediment rating curves are commonly used to estimate the suspended sediment load in rivers and streams under the assumption of a constant relation between discharge (Q) and suspended sediment concentrations (SSC) over time. However, temporal variation in the sediment supply of a watershed results in shifts in this relation by increasing variability and by introducing nonlinearities in the form of hysteresis or a path‐dependent relation. In this study, we used a mixed‐effects linear model to estimate an average SSC–Q relation for different periods of time within the hydrologic cycle while accounting for seasonality and hysteresis. We tested the performance of the mixed‐effects model against the standard rating curve, represented by a generalized least squares regression, by comparing observed and predicted sediment loads for a test case on the Chilliwack River, British Columbia, Canada. In our analyses, the mixed‐effects model reflected more accurate patterns of interpolated SSC from Q data than the rating curve, especially for the low‐flow summer months when the SSC–Q relation is less clear. Akaike information criterion scores were lower for the mixed‐effects model than for the standard model, and the mixed‐effects model explained nearly twice as much variance as the standard model (52% vs 27%). The improved performance was achieved by accounting for variability in the SSC–Q relation within each month and across years for the same month using fixed and random effects, respectively, a characteristic disregarded in the sediment rating curve. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier

The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.     

Dynamics of suspended sediment transport at field‐scale drain channels of irrigation‐dominated watersheds in the Sonoran Desert,southeastern California

Suspended sediment is a major source of pollution in irrigation‐dominated watersheds. However, little is known about the process and mechanisms of suspended sediment transport in drain channels directly connected to agricultural fields. This paper explains sediment dynamics using averaged 5 min flow discharge Q (m³ s^?1) and suspended sediment concentration C (mg l^?1) collected during one crop season in a small catchment containing a first‐order drain channel and its connected six agricultural fields within the Salton Sea watershed. The statistical properties and average trends of Q and C were investigated for both early (i.e. November) and late (i.e. January) stages of a crop season. Further in‐depth analysis on sediment dynamics was performed by selecting two typical single‐field irrigation events and two multiple‐field irrigation events. For each set of irrigation events, the process of suspended sediment transport was revealed by examining hydrograph and sediment graph responses. The mechanisms underlying suspended sediment transport were investigated by analysing the types of corresponding hysteresis loop. Finally, sediment rating curves for both hourly and daily data at early and late stages and for the entire crop season were established to seek possible sediment‐transport predictive model(s). The study suggests that the complicated processes of suspended sediment transport in irrigation‐dominated watersheds require stochastic rather than deterministic forecasting. Copyright © 2007 John Wiley & Sons, Ltd.     

Some remarks on Q-compensated sparse deconvolution without knowing the quality factor Q

The subsurface media are not perfectly elastic, thus anelastic absorption, attenuation and dispersion (aka Q filtering) effects occur during wave propagation, diminishing seismic resolution. Compensating for anelastic effects is imperative for resolution enhancement. Q values are required for most of conventional Q-compensation methods, and the source wavelet is additionally required for some of them. Based on the previous work of non-stationary sparse reflectivity inversion, we evaluate a series of methods for Q-compensation with/without knowing Q and with/without knowing wavelet. We demonstrate that if Q-compensation takes the wavelet into account, it generates better results for the severely attenuated components, benefiting from the sparsity promotion. We then evaluate a two-phase Q-compensation method in the frequency domain to eliminate Q requirement. In phase 1, the observed seismogram is disintegrated into the least number of Q-filtered wavelets chosen from a dictionary by optimizing a basis pursuit denoising problem, where the dictionary is composed of the known wavelet with different propagation times, each filtered with a range of possible values. The elements of the dictionary are weighted by the infinity norm of the corresponding column and further preconditioned to provide wavelets of different values and different propagation times equal probability to entry into the solution space. In phase 2, we derive analytic solutions for estimates of reflectivity and Q and solve an over-determined equation to obtain the final reflectivity series and Q values, where both the amplitude and phase information are utilized to estimate the Q values. The evaluated inversion-based Q estimation method handles the wave-interference effects better than conventional spectral-ratio-based methods. For Q-compensation, we investigate why sparsity promoting does matter. Numerical and field data experiments indicate the feasibility of the evaluated method of Q-compensation without knowing Q but with wavelet given.     

A fuzzy dynamic flood routing model for natural channels

A fuzzy dynamic flood routing model (FDFRM) for natural channels is presented, wherein the flood wave can be approximated to a monoclinal wave. This study is based on modification of an earlier published work by the same authors, where the nature of the wave was of gravity type. Momentum equation of the dynamic wave model is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. Hence, the FDFRM gets rid of the assumptions associated with the momentum equation. Also, it overcomes the necessity of calculating friction slope (S_f) in flood routing and hence the associated uncertainties are eliminated. The fuzzy rule based model is developed on an equation for wave velocity, which is obtained in terms of discontinuities in the gradient of flow parameters. The channel reach is divided into a number of approximately uniform sub‐reaches. Training set required for development of the fuzzy rule based model for each sub‐reach is obtained from discharge‐area relationship at its mean section. For highly heterogeneous sub‐reaches, optimized fuzzy rule based models are obtained by means of a neuro‐fuzzy algorithm. For demonstration, the FDFRM is applied to flood routing problems in a fictitious channel with single uniform reach, in a fictitious channel with two uniform sub‐reaches and also in a natural channel with a number of approximately uniform sub‐reaches. It is observed that in cases of the fictitious channels, the FDFRM outputs match well with those of an implicit numerical model (INM), which solves the dynamic wave equations using an implicit numerical scheme. For the natural channel, the FDFRM outputs are comparable to those of the HEC‐RAS model. Copyright © 2009 John Wiley & Sons, Ltd.     

Hydrometeorological assessments and suspended sediment delivery from a central Himalayan glacier in the upper Ganga basin

Integrated hydrometeorological investigations are not frequently available at a regional scale over a longer time period, especially near the terminus of Indian Himalayan glaciers. An integrated approach to the collection of hydrological data has major advantages for understanding the runoff generation mechanisms at basin scale, particularly when coupled with meteorological observations. The current study involves time series analysis of hydrometeorological records collected near the terminus of the Chorabari Glacier, for four consecutive ablation seasons(June-Sept.) 2009-2012. The analysis shows that variation in rainfall was higher(c_v= 0.9) at the same elevation over proximal sites, while the intensity of extreme rainfall events was 121-160 mm/d. The diurnal temperature range(DTR) has a tendency to reduce over the ablation season because of the onset of the Indian Summer Monsoon(ISM) and then further increases during the ISM withdrawal indicating humid-temperate conditions. The peak discharge(Qpeak) was found to be higher during July and August. Snow and glacier melt contributed 76% of the total suspended sediment transport during peak ISM months(July and August) reflecting seasonal evolution of the hydrologic conduits. The results indicate that Karakoram and western Himalayan glaciers produce comparatively low sediment yield compared to central Himalayan glaciers. The hydrological variations are depicted through flow duration curves(FDC) for meltwater discharge and sediment load. The flow corresponding to Q_(50), Q_(75), and Q_(90)(where Qx is the discharge that is exceeded x percent of the time referred to as % dependability) are 4.2, 3.7, and 2.8 m~3/s; and the corresponding dependability for suspended sediment loads(SSLs) are 409.0, 266.0, and 157.2 t/d, respectively. The daily SSL and discharge(Q) from 2009 to 2012 were used to develop a sediment rating curve(SSL = 39.55 × Q~(1.588). R~2 = 0.8).Multiple regressions are used to determine the impacts of meteorological parameters on glacier melt.The meteorological conditions, hydrological characteristics, and suspended sediment delivery for the Chorabari Glacier provide insight on meltwater generation processes and sediment transport patterns during the ISM season.     

Building and Testing an <Emphasis Type="Italic">a priori</Emphasis> Geophysical Model for Western Eurasia and North Africa

We construct and evaluate a new three-dimensional model of crust and upper mantle structure in Western Eurasia and North Africa (WENA) extending to 700 km depth and having 1° parameterization. The model is compiled in an a priori fashion entirely from existing geophysical literature, specifically, combining two regionalized crustal models with a high-resolution global sediment model and a global upper mantle model. The resulting WENA1.0 model consists of 24 layers: water, three sediment layers, upper, middle, and lower crust, uppermost mantle, and 16 additional upper mantle layers. Each of the layers is specified by its depth, compressional and shear velocity, density, and attenuation (quality factors, Q _P and Q _S ). The model is tested by comparing the model predictions with geophysical observations including: crustal thickness, surface wave group and phase velocities, upper mantle _n velocities, receiver functions, P-wave travel times, waveform characteristics, regional 1-D velocities, and Bouguer gravity. We find generally good agreement between WENA1.0 model predictions and empirical observations for a wide variety of independent data sets. We believe this model is representative of our current knowledge of crust and upper mantle structure in the WENA region and can successfully be used to model the propagation characteristics of regional seismic waveform data. The WENA1.0 model will continue to evolve as new data are incorporated into future validations and any new deficiencies in the model are identified. Eventually this a priori model will serve as the initial starting model for a multiple data set tomographic inversion for structure of the Eurasian continent.