scholarly journals Numerical Representation of Groundwater-Surface Water Exchange and the Effect on Streamflow Contribution Estimates

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1923
Author(s):  
Sachin Karan ◽  
Martin Jacobsen ◽  
Jolanta Kazmierczak ◽  
José A. Reyna-Gutiérrez ◽  
Thomas Breum ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Exchange ◽  
Large Data ◽  
Field Measurements ◽  
Drainage Area ◽  
Numerical Representation ◽  
Catchment Scale ◽  
Stream Network ◽  
Data Set ◽  
Cell Refinement

The effects of streams and drainage representation in 3D numerical catchment scale models on estimated streamflow contribution were investigated. MODFLOW-USG was used to represent complex geology and a stream network with two different conceptualizations—one with equal cell discretization in the entire model domain and another with refined cell discretization along stream reaches. Both models were calibrated against a large data set including hydraulic heads and streamflow measurements. Though the optimized hydraulic parameters and statistical performance of both model conceptualizations were comparable, their estimated streamflow contribution differed substantially. In the conceptualization with equal cell discretization, the drainage contribution to the streamflow was 13% compared to 41% in the conceptualization with refined cell discretization. The increase in drainage contribution to streamflow was attributed to the increase in drainage area in proximity to the stream reaches arising from the refined discretization. e.g., the cell refinement along stream reaches reduced the area occupied by stream cells allowing for increased drain area adjacent to the stream reaches. As such, an increase in drainage area equivalent to 7% yielded a 146% increase in drainage contribution to streamflow. In-stream field measurements of groundwater-surface water exchange fluxes that were qualitatively compared to calculated fluxes from the models indicated that estimates from the refined model discretization were more representative. Hence, the results of this study accentuate the importance of being able to represent stream and drain flow contribution correctly, that is, to achieve representative exchange fluxes that are crucial in simulating groundwater–surface water exchange of both flow and solute transport in catchment scale modeling. To that end, the in-stream measurements of exchange fluxes showed the potential to serve as a proxy to numerically estimate drainage contribution that is not readily available at the catchment scale.

scholarly journals Measurements and Analysis of Primary Ship Waves in the Stockholm Archipelago, Sweden

2020 ◽  
Vol 8 (10) ◽  
pp. 743
Author(s):  
Björn Almström ◽  
Magnus Larson
Keyword(s):  
Large Data ◽  
Field Measurements ◽  
Physical Processes ◽  
Data Set ◽  
Ship Waves ◽  
Stockholm Archipelago ◽  
New Equation ◽  
Water Level Measurements ◽  
Marine Vessels ◽  
Better Than

Primary ship waves generated by conventional marine vessels were investigated in the Furusund fairway located in the Stockholm archipelago, Sweden. Continuous water level measurements at two locations in the fairway were analyzed. In total, 466 such events were extracted during two months of measurements. The collected data were used to evaluate 13 existing predictive equations for drawdown height or squat. However, none of the equations were able to satisfactorily predict the drawdown height. Instead, a new equation for drawdown height and period was derived based on simplified descriptions of the main physical processes together with field measurements, employing multiple regression analysis to derive coefficients in the equation. The proposed equation for drawdown height performed better than the existing equations with an R2 value of 0.65, whereas the equation for the drawdown period was R2 = 0.64. The main conclusion from this study is that an empirical equation can satisfactorily predict primary ship waves for a large data set.

Comparison of Non-Destructive Examination Techniques for Crack Inspection

2020 ◽  
Author(s):  
Axel Aulin ◽  
Khurram Shahzad ◽  
Robert MacKenzie ◽  
Steven Bott
Keyword(s):  
Internal Surface ◽  
Large Data ◽  
Field Measurements ◽  
Management Program ◽  
Destructive Testing ◽  
Data Set ◽  
Examination Techniques ◽  
Welded Pipe ◽  
Non Destructive

Abstract Effective and efficient crack management programs for liquids pipelines require consistent, high quality non-destructive examination (NDE) to allow validation of crack in-line inspection (ILI) results. Enbridge leveraged multiple NDE techniques on a 26-inch flash-welded pipe as part of a crack management program. This line is challenging to inspect given the presence of irregular geometry of the weld. In addition, the majority of the flaws are located on the internal surface, so buffing to obtain accurate measurements in the ditch is not possible. As such, to ensure a robust validation of crack ILI performance on the line, phased array ultrasonic testing (PAUT), time-of-flight diffraction (TOFD), and a full matrix capture (FMC) technology were all used as part of the validation dig program. PAUT and FMC were used on most of the flaws characterized as part of the dig program providing a relatively large data set for further analysis. Encoded scans on the flash welded long seam weld were collected in the ditch and additional analyses were performed off-site to characterize and size the flaws. Buff-sizing where possible and coupon cutouts were selected and completed to assist with providing an additional source of truth. Secondary review of results by an NDE specialist improved the quality of the results and identified locations for rescanning due to data quality concerns. Physical defect examinations completed after destructive testing of sample coupon cutouts were utilized to generate a correlation between the actual defect size from fracture surface observation and the field measurements using various NDE methods. This paper will review the findings from the program, including quality-related learnings implemented into standard NDE procedures as well as comparisons of detection and sizing from each methodology. Finally, a summary of the benefits and limitations of each technique based on the experience from a challenging inspection program will be summarized.

Empirical model for predicting a catchment-scale metric of surface water transit time in streams

2005 ◽  
Vol 62 (3) ◽  
pp. 492-504 ◽  
Author(s):  
Erwin E Van Nieuwenhuyse
Keyword(s):  
Surface Water ◽  
Transit Time ◽  
Longitudinal Velocity ◽  
Hydraulic Geometry ◽  
Catchment Scale ◽  
Data Set ◽  
Velocity Gradients ◽  
Average Water ◽  
Bivariate Regression

Estimates of average water velocity (vw) extracted from tracer dye studies (vdye) or calculated from velocity–discharge relationships at continuous-flow gauges (vgage) were combined with catchment area (A) and other readily available data for 111 streams throughout the conterminous United States. The resulting data set (n = 305) represented broad ranges of A (65 – 62 419 km2), mainstem length (Lmax, 15.6–867 km), slope (S, 0.14–11.5 m·km–1), and daily average discharge (Q, 0.09–634 m3·s–1). A catchment-scale metric of surface water transit time (Tw, Lmaxvdye–1) ranged from 0.3 to 40 days, averaging 7.2 days. A bivariate regression model using log10 A and log10 Q explained 83% of the variation in log10 Tw and predicted Tw with an average precision of ±49%. By contrast, a previously published model based on hydraulic geometry relationships overestimated Tw by 100%. Application of my model to five streams nested in a ninth-order (ω = 9) catchment indicated that under dry (September) and wet (March), long-term (1954–2001) median flow conditions, vw increased with Q (vw ∝ Q0.3) as far downstream as ω = 8 and then remained constant or declined. The slope of this longitudinal vw–Q relationship was three times greater than the expected value. Longitudinal velocity gradients in many streams may thus be much steeper than commonly assumed.

Revealing hidden peat structures along the stream network of a boreal forest catchment

2020 ◽  
Author(s):  
Thomas Grabs ◽  
José L.J Ledesma ◽  
Hjalmar Laudon ◽  
Jan Seibert ◽  
Stephan Köhler ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Boreal Forest ◽  
Field Data ◽  
Spatial Information ◽  
Parent Material ◽  
Terrain Analysis ◽  
Stream Network ◽  
Data Set ◽  
Lateral Extent

<p><span>Peat stored in large wetlands plays an important role in the carbon cycle and strongly influences water quality of terrestrial surface water bodies. At the same time, peat is also stored in the direct vicinity of many boreal forest streams. From this strategic position, peat can receive and chemically reset hillslope water before it reaches the stream network. Yet, in contrast to large wetlands, only little spatial information is available on the lateral extent of near-stream peat and even less about its vertical variation. Here, we present field data on peat depth and lateral extent collected from approximately 200 transects (with 12 soil profiles taken per transect) distributed across the entire stream network of the Krycklan boreal catchment in Northern Sweden. This soil profile data revealed a considerable heterogeneity of peat and organic horizon thicknesses. By combining the field data with morphological and geological maps, we show how parent material, stream order and local topography influence near stream peat structures. Furthermore, we discuss potential consequences on surface water quality by linking the detailed peat data set to estimates of lateral, shallow groundwater inflows derived from hydrometric measurements and digital terrain analysis.</span></p>

scholarly journals Revealing the influence of hyporheic water exchange on the composition and abundance of bottom-dwelling macroinvertebrates in a temperate lowland river

2021 ◽  
pp. 37
Author(s):  
Mateusz Grygoruk ◽  
Ewelina Szałkiewicz ◽  
Maria Grodzka-Łukaszewska ◽  
Dorota Mirosław-Świątek ◽  
Paweł Oglęcki ◽  
...  
Keyword(s):  
Surface Water ◽  
Pressure Gradient ◽  
Benthic Macroinvertebrates ◽  
Water Exchange ◽  
Hyporheic Zone ◽  
Field Measurements ◽  
Lowland River ◽  
Taxonomic Richness ◽  
Macroinvertebrate Fauna ◽  
Upwelling And Downwelling

We studied distributions and abundances of macroinvertebrates in relation to hyporheic water exchange (HWE) patterns of the upper Biebrza − a small, lowland, low dynamic European river located in Northeast Poland. On a 6-km stretch of the river; we determined the variability of water exchange in the hyporheic zone by using direct field measurements of the pressure gradient to determine groundwater–surface water interactions. We identified locations with upwelling and downwelling fluxes of HWE as well as ambiguous hydraulic contact between groundwater and surface water along the river. In these locations, we sampled bottom-dwelling macroinvertebrates. In total, 627 individuals of benthic macroinvertebrates of 34 taxa were identified. We revealed that bottom-dwelling macroinvertebrate fauna is more abundant and diverse in river stretches where water from the river infiltrates the hyporheic zone. Results also show higher taxonomic richness and abundances of benthic macroinvertebrates in stretches with diagnosed infiltrating conditions (downwelling flux in a hyporheic zone) compared to in stretches where the river drained groundwater (upwelling flux in a hyporheic zone), but the recorded differences were not statistically significant.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Groundwater protection and diffuse nitrogen emissions to surface waters – which catchment areas have to be considered?

2007 ◽  
Vol 7 (3) ◽  
pp. 103-110
Author(s):  
C. Schilling ◽  
M. Zessner ◽  
A.P. Blaschke ◽  
D. Gutknecht ◽  
H. Kroiss
Keyword(s):  
Surface Water ◽  
Total Nitrogen ◽  
Surface Waters ◽  
Flow Path ◽  
Emission Model ◽  
Catchment Scale ◽  
Danube Basin ◽  
Nitrogen Emissions ◽  
Nitrogen Levels ◽  
Catchment Areas

Two Austrian case study regions within the Danube basin have been selected for detailed investigations of groundwater and surface water quality at the catchment scale. Water balance calculations have been performed using the conceptual continuous time SWAT 2000 model to characterise catchment hydrology and to identify individual runoff components contributing to river discharge. Nitrogen emission calculations have been performed using the empirical emission model MONERIS to relate individual runoff components to specific nitrogen emissions and for the quantification of total nitrogen emissions to surface waters. Calculated total nitrogen emissions to surface waters using the MONERIS model were significantly influenced by hydrological conditions. For both catchments the groundwater could be identified as major emission pathway of nitrogen emissions to the surface waters. Since most of the nitrogen is emitted by groundwater to the surface water, denitrification in groundwater is of considerable importance reducing nitrogen levels in groundwater along the flow path towards the surface water. An approach was adopted for the grid-oriented estimation of diffuse nitrogen emissions based on calculated groundwater residence time distributions. Denitrification in groundwater was considered using a half life time approach. It could be shown that more than 90% of the total diffuse nitrogen emissions were contributed by areas with low groundwater residence times and short distances to the surface water. Thus, managing diffuse nitrogen emissions the location of catchment areas has to be considered as well as hydrological and hydrogeological conditions, which significantly influence denitrification in the groundwater and reduce nitrogen levels in groundwater on the flow path towards the surface water.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals The effect of streambed heterogeneity on groundwater-surface water exchange fluxes inferred from temperature time series

2015 ◽  
Vol 51 (1) ◽  
pp. 198-212 ◽  
Author(s):  
Dylan J. Irvine ◽  
Roger H. Cranswick ◽  
Craig T. Simmons ◽  
Margaret A. Shanafield ◽  
Laura K. Lautz
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Water Exchange ◽  
Temperature Time Series ◽  
Temperature Time ◽  
Streambed Heterogeneity

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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