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.

scholarly journals Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 840 ◽  
Author(s):  
Salam A. Abbas ◽  
Yunqing Xuan
Keyword(s):  
High Resolution ◽  
Model Calibration ◽  
Hydrological Model ◽  
Estimation Method ◽  
Model Performance ◽  
Point Estimation ◽  
Processing Methods ◽  
Essential Components ◽  
The Impact ◽  
Point Estimation Method

Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing is often nontrivial as precipitation data are very limited in the first place. In this paper, we demonstrate a study using a semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT) to examine the impact of different precipitation pre-processing methods on model calibration and the overall model performance with regards to the operational use. A river catchment in the UK is modelled to test against the three pre-processing methods: the Centroid Point Estimation Method (CPEM), the Grid Area Method (GAM) and the Grid Point Method (GPM). Cross-calibration and validation are then carried out by using the high-resolution Centre for Ecology & Hydrology–Gridded Estimate Areal Rainfall (CEH-GEAR) dataset. The results show that the proposed methods GAM and GPM can improve the model calibration significantly against the one calibrated with the existing CPEM method used by the model; the performance differences in the validation among the calibrated models, however, remain small and become irrelevant. The findings indicate that it is preferable to always make use of high-quality rainfall data, when available, with a better pre-processing method, even with models that are previously calibrated with low-quality rainfall inputs. It is also shown that such improvements are affected by the size of catchment and become less significant for smaller catchments.

scholarly journals The impact of US wildland fires on ozone and particulate matter: a comparison of measurements and CMAQ model predictions from 2008 to 2012

2018 ◽  
Vol 27 (10) ◽  
pp. 684 ◽  
Author(s):  
Joseph L. Wilkins ◽  
George Pouliot ◽  
Kristen Foley ◽  
Wyat Appel ◽  
Thomas Pierce
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Wildland Fire ◽  
Fine Particulate Matter ◽  
Model Performance ◽  
Grid Cell ◽  
Emissions Inventory ◽  
Fire Emissions ◽  
The Mean ◽  
The Impact

Wildland fire emissions are routinely estimated in the US Environmental Protection Agency’s National Emissions Inventory, specifically for fine particulate matter (PM2.5) and precursors to ozone (O3); however, there is a large amount of uncertainty in this sector. We employ a brute-force zero-out sensitivity method to estimate the impact of wildland fire emissions on air quality across the contiguous US using the Community Multiscale Air Quality (CMAQ) modelling system. These simulations are designed to assess the importance of wildland fire emissions on CMAQ model performance and are not intended for regulatory assessments. CMAQ ver. 5.0.1 estimated that fires contributed 11% to the mean PM2.5 and less than 1% to the mean O3 concentrations during 2008–2012. Adding fires to CMAQ increases the number of ‘grid-cell days’ with PM2.5 above 35 µg m−3 by a factor of 4 and the number of grid-cell days with maximum daily 8-h average O3 above 70 ppb by 14%. Although CMAQ simulations of specific fires have improved with the latest model version (e.g. for the 2008 California wildfire episode, the correlation r = 0.82 with CMAQ ver. 5.0.1 v. r = 0.68 for CMAQ ver. 4.7.1), the model still exhibits a low bias at higher observed concentrations and a high bias at lower observed concentrations. Given the large impact of wildland fire emissions on simulated concentrations of elevated PM2.5 and O3, improvements are recommended on how these emissions are characterised and distributed vertically in the model.

scholarly journals Assessing the impact of grid cell properties in spatial discretization of groundwater model

2017 ◽  
Vol 109 ◽  
pp. 012037 ◽  
Author(s):  
DKSY Klaas ◽  
M A Imteaz ◽  
A Arulrajah ◽  
I Sudiayem ◽  
EME Klaas ◽  
...  
Keyword(s):  
Grid Cell ◽  
Groundwater Model ◽  
Spatial Discretization ◽  
The Impact

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 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

Institutional Quality and Trade Flow: Empirical Evidence from Malaysia and Other OIC Member Countries in Africa

2021 ◽  
pp. 097508782098717
Author(s):  
Hammed Agboola Yusuf ◽  
Luqman Olanrewaju Afolabi ◽  
Waliu Olawale Shittu ◽  
Kafilah Lola Gold ◽  
Murtala Muhammad
Keyword(s):  
Economic Growth ◽  
Bilateral Trade ◽  
Trade Flow ◽  
Estimation Method ◽  
Institutional Quality ◽  
Likelihood Estimation ◽  
Political Stability ◽  
Bilateral Trade Flow ◽  
World Governance Indicators ◽  
The Impact

This article examines the impact of institutional quality on bilateral trade flow between Malaysia and selected 25 African Organisation of Islamic Cooperation (OIC) member countries. Four institutional qualities were selected from World Governance Indicators with other trade predictors from the period from 1985 to 2016. Using gravity model of trade and Poisson pseudo-maximum likelihood estimation method (PPML) technique, the results confirm that government effectiveness, regulatory quality and political stability have an adverse effect on bilateral trade flow among the OIC countries in Africa. On the other hand, these institutional quality variables were considered as a strength for Malaysian economic growth. Therefore, better institutional quality reforms are needed among OIC member countries in Africa in order to accelerate trade, economic growth and development in their region.

scholarly journals Reliability Assessment of a Multi-State HVDC System by Combining Markov and Matrix-Based Methods

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3097
Author(s):  
Roberto Benato ◽  
Antonio Chiarelli ◽  
Sebastian Dambone Sessa
Keyword(s):  
Current System ◽  
Estimation Method ◽  
Reliability Estimation ◽  
Voltage Source ◽  
Voltage Source Converter ◽  
Hvdc System ◽  
Critical Elements ◽  
The Matrix ◽  
The Impact

The purpose of this paper is to highlight that, in order to assess the availability of different HVDC cable transmission systems, a more detailed characterization of the cable management significantly affects the availability estimation since the cable represents one of the most critical elements of such systems. The analyzed case study consists of a multi-terminal direct current system based on both line commutated converter and voltage source converter technologies in different configurations, whose availability is computed for different transmitted power capacities. For these analyses, the matrix-based reliability estimation method is exploited together with the Monte Carlo approach and the Markov state space one. This paper shows how reliability analysis requires a deep knowledge of the real installation conditions. The impact of these conditions on the reliability evaluation and the involved benefits are also presented.

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.

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