scholarly journals Hydrodynamic modelling and model sensitivities to bed roughness and bathymetry offset in a micro-tidal estuary

2020 ◽  
Vol 22 (6) ◽  
pp. 1536-1553
Author(s):  
Mohammadreza Khanarmuei ◽  
Kabir Suara ◽  
Julius Sumihar ◽  
Richard J. Brown
Keyword(s):  
Water Level ◽  
Model Performance ◽  
Hydrodynamic Modelling ◽  
Tidal Estuary ◽  
Physicochemical Changes ◽  
Improve Model ◽  
Tidal Estuaries ◽  
Bed Roughness ◽  
Highly Correlated ◽  
The Impact

Abstract Tidal estuaries support everyday functions for over 80% of Australia's population living within 50 km of the coastline and thus come under immense pressure of physicochemical changes. Most studies in estuarine applications have used the bed roughness as the single calibration parameter to calibrate hydrodynamic modelling, yet errors in bathymetric data can significantly impose uncertainties into the model outputs. In this study, we evaluated the sensitivity of a hydrodynamic model of a micro-tidal estuary to both the bed roughness and bathymetry offset through comparing observed and modelled water level and velocity. Treating both bathymetry offset and bed roughness as calibration parameters, three calibration scenarios were tested to examine the impact of these parameters. To validate the model, Lagrangian drifter data as a new dataset in shallow estuaries were used. The analysis shows that model outputs are more sensitive to the variation of bathymetry offset than bed roughness. Results show that calibrating the bathymetry offset alone can significantly improve model performance. Simultaneous calibration of both parameters can provide further improvement, particularly for capturing the water level. Drifter and modelled velocities are highly correlated during flood tides, whereas the correlation is low for slack water because of wind-induced current on drifters.

scholarly journals Evaluating the impact of grid cell properties in spatial discretization of groundwater model for a tropical karst catchment in Rote Island, Indonesia

2016 ◽  
Vol 48 (6) ◽  
pp. 1757-1772 ◽  
Author(s):  
Dua K. S. Y. Klaas ◽  
Monzur Alam Imteaz ◽  
Arul Arulrajah
Keyword(s):  
Estimation Method ◽  
Model Performance ◽  
Grid Cell ◽  
Specific Yield ◽  
Spatial Discretization ◽  
Observation Well ◽  
Improve Model ◽  
Karst Catchment ◽  
The Impact ◽  
Tropical Karst

Abstract To assess the effect of three grid cell properties (size, mean slope of the surface and distance between centre of grid and observation well) on groundwater models' performances, a tropical karst catchment characterized by monsoonal season in Rote Island, Indonesia was selected. Here, MODFLOW was used to develop models with five different spatial discretization schemes: 10 × 10 m, 20 × 20 m, 30 × 30 m, 40 × 40 m and 50 × 50 m. Using parameter estimation method, hydraulic conductivity and specific yield values over a selection of pilot points were estimated. The trends of the performances were calculated at each observation well in order to recommend the most appropriate location for observation well placement in terms of topographical characteristic. It is confirmed that the deterioration of model performance is mainly controlled by the increase of distance between well and centre of the cell, and the mean slope of the surface. Results reveal that model performance increases substantially for areas of low slope (<3%) and medium slope (3–10%) for a smaller grid cell size. Therefore, to improve model performance, it is recommended that the observations wells are placed in areas of low and medium slopes.

Long-term Runoff Forecasting Models Based on the Teleconnection coupled with Machine Learning

2020 ◽  
Author(s):  
Teng Zhang ◽  
Zhongjing Wang ◽  
Zixiong Zhang
Keyword(s):  
Support Vector Machine ◽  
Water Resources ◽  
Model Performance ◽  
Support Vector ◽  
Wet Season ◽  
Model Based ◽  
Runoff Forecasting ◽  
Monthly Runoff ◽  
Highly Correlated ◽  
The Impact

<p>Runoff forecast with high precision is important for the efficient utilization of water resources and regional sustainable development, especially in the arid area. The monthly runoff of Changmabao (CMB) station has an upwards trend and an abrupt point in 1998. The impact factor analysis shows that it is highly correlated with the current precipitation and temperature in the wet season while the previous runoff and previous global land temperature in the dry season. Three models including the time-series decomposition model, the model based on teleconnection coupled with the support vector machine, and the model based on teleconnection coupled with the artificial neural network are used to predict the runoff of CMB station. An indicator β is constructed with the correlation coefficient (R) and mean relative deviation (rBias) to evaluate the model performance more conveniently and intuitively. The results suggest that the model based on teleconnection coupled with the support vector machine preforms best. This forecasting method could be applied to the management and dispatch of water resources in arid areas.</p>

scholarly journals Investigation on hydrothermal processes in a large channel-type reservoir using an integrated physics-based model

2019 ◽  
Vol 21 (3) ◽  
pp. 493-509 ◽  
Author(s):  
Jian Sun ◽  
Man Zhang ◽  
Jianjun Zhou ◽  
Binliang Lin
Keyword(s):  
Water Level ◽  
Temperature Difference ◽  
Vertical Mixing ◽  
Hydrothermal Processes ◽  
Large Channel ◽  
Dam Operation ◽  
Highly Correlated ◽  
High Dams ◽  
The Impact ◽  
Channel Type

Abstract Hydrothermal processes are vital for the aquatic ecology and environments of a river. In recent decades, as high dams have been increasingly built in large rivers, many channel-type reservoirs have formed. With a considerable amount of water being impounded, the original riverine hydrothermal regimes are modified or even profoundly changed. Existing studies are mainly focused on the thermal stratification in lake-type reservoirs with weak vertical mixing, while channel-type reservoirs are rarely investigated where the vertical mixing is relatively strong due to the large riverine discharge. In this study, the impact of dam operation on the Three Gorges Reservoir (TGR) was investigated, including the water level, discharge and temperature, by applying an integrated physics-based model developed using field data. The present numerical model was built based on a hydrothermal dynamic model and a box model. The results indicate that the reservoir has caused a significant thermal lag between the inflow and outflow, with the temperature difference being up to 5 °C. A highly correlated dependency has been found between the dam-regulated water level and the inflow/outflow temperature difference. The present method and conclusions are potentially useful for managing the TGR and other channel-type reservoirs.

scholarly journals USING SPECIFICATION OF OVERBURDEN TO IMPROVE NUMERICAL LONGSHORE SEDIMENT TRANSPORT MODELING

2020 ◽  
pp. 10
Author(s):  
Irene Watts ◽  
Gary Zarillo
Keyword(s):  
Sediment Transport ◽  
Model Performance ◽  
Coastal Processes ◽  
Transport Pathways ◽  
Model Domain ◽  
Nearshore Sediment Transport ◽  
Longshore Sediment Transport ◽  
Improve Model ◽  
The Impact ◽  
Improve Model Performance

The Sebastian Inlet Florida Coastal Processes Model computes sediment transport pathways in the nearshore to support sediment management activities. Longshore sediment transport rates are computed by the model and compared with field data. The model is run with two alternative specifications of hard bottom to investigate the impact on computed transport rates. One alternative specifies known hard bottom outcrop locations and the second, a uniform one-meter overburden throughout the model domain. The uniform overburden specification improved longshore sediment transport rate computations throughout the model domain. The goal of this work is to improve upon nearshore sediment transport and morphology by addressing uncertainty in hard bottom locations and ephemeral coverage. This paper documents the modeling effort and the changes necessary to improve model performance in the nearshore.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/u1bNOca5qUo

scholarly journals The Impact of Training Data Sequence on the Performance of Neuro-Fuzzy Rainfall-Runoff Models with Online Learning

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 52 ◽  
Author(s):  
Tak Chang ◽  
Amin Talei ◽  
Lloyd Chua ◽  
Sina Alaghmand
Keyword(s):  
Water Level ◽  
Model Performance ◽  
Training Data ◽  
Rainfall Runoff ◽  
Data Sequence ◽  
Online Clustering ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Study Sites ◽  
The Impact

The learning algorithms in many of conventional Neuro-Fuzzy Systems (NFS) are based on batch or global learning where all parameters of the fuzzy system are optimized off-line. Although these models have frequently been used, they suffer from a reduced flexibility in their architecture as the number of rules need to be predefined by the user. This study uses a Dynamic Evolving Neural Fuzzy Inference System (DENFIS) in which an evolving, online clustering algorithm, the Evolving Clustering Method (ECM), is implemented. This study focused on evaluating the performance of this model in capturing the rainfall-runoff process and rainfall-water level relationship. The two selected study catchments are located in an urban tropical and in a semi-urbanized area, respectively. The first catchment, Sungai Kayu Ara (23.22 km2), is located in Malaysia, with 10-min rainfall-runoff time-series from which 30 major events are used. The second catchment, Dandenong (272 km2), is located in Victoria, Australia, with daily rainfall and river stage (water level) data from which 11 years of data is used. DENFIS results were then compared with two groups of benchmark models: a regression-based data-driven model known as the Autoregressive Model with Exogenous Inputs (ARX) for both study sites, and physical models Hydrologic Engineering Center–Hydrologic Modelling System (HEC–HMS) and Storm Water Management Model (SWMM) for Sungai Kayu Ara and Dandenong catchments, respectively. DENFIS significantly outperformed the ARX model in both study sites. Moreover, DENFIS was found comparable if not superior to HEC–HMS and SWMM in Sungai Kayu Ara and Dandenong catchments, respectively. A sensitivity analysis was then conducted on DENFIS to assess the impact of training data sequence on its performance. Results showed that starting the training with datasets that include high peaks can improve the model performance. Moreover, datasets with more contrasting values that cover wide range of low to high values can also improve the DENFIS model performance.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

scholarly journals The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 322
Author(s):  
Evelina Volpe ◽  
Luca Ciabatta ◽  
Diana Salciarini ◽  
Stefania Camici ◽  
Elisabetta Cattoni ◽  
...  
Keyword(s):  
Probability Density ◽  
Central Italy ◽  
Model Performance ◽  
Probability Density Functions ◽  
Slope Instability ◽  
Complex Nature ◽  
Density Functions ◽  
Umbria Region ◽  
Input Variables ◽  
The Impact

The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes. A consolidated approach in evaluating rainfall-induced landslide hazard is based on the integration of rainfall forecasts and physically based (PB) predictive models through deterministic laws. However, considering the complex nature of the processes and the high variability of the random quantities involved, probabilistic approaches are recommended in order to obtain reliable predictions. A crucial aspect of the stochastic approach is represented by the definition of appropriate probability density functions (pdfs) to model the uncertainty of the input variables as this may have an important effect on the evaluation of the probability of failure (PoF). The role of the pdf definition on reliability analysis is discussed through a comparison of PoF maps generated using Monte Carlo (MC) simulations performed over a study area located in the Umbria region of central Italy. The study revealed that the use of uniform pdfs for the random input variables, often considered when a detailed geotechnical characterization for the soil is not available, could be inappropriate.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

scholarly journals The Influence of Trading Locations on Equity Returns

2016 ◽  
Vol 12 (12) ◽  
pp. 188
Author(s):  
Nguyen N.T. Vo
Keyword(s):  
Risk Premium ◽  
Stock Price ◽  
Listed Companies ◽  
Equity Returns ◽  
Stock Exchanges ◽  
Home Market ◽  
Trade Effect ◽  
Market Index ◽  
Highly Correlated ◽  
The Impact

This paper evaluates the impact of trading locations on equity returns by examining the stock price behaviour of three Anglo-Dutch dual-listed companies which result from mergers where two corporations agree to function as a single operating business, but maintain separate identities. The shares of these stocks are traded not only in their home market but also on several US stock exchanges in the form of American Depository Receipts. Regressing the return differentials on these dual-listed and cross-listed stocks on the relative market index returns and currency changes provides evidence of an apparent violation of the Law of One Price. The regression results show that the return on each part of dual-listed companies is highly correlated with the market on which it is most intensively traded. Similarly, returns on cross-listed stocks have considerably higher co-movement with US market indices and considerably lower co-movement with home-market indices than their home-market counterparts. Market risk premium is not a significant explanatory variable of the location of trade effect.

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