ORCID Profile
0000-0002-5568-4732
Current Organisations
University of Melbourne
,
Monash University
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Publisher: Copernicus GmbH
Date: 23-12-2013
DOI: 10.5194/NHESS-13-3405-2013
Abstract: Abstract. In the lower River Murray in Australia, a combination of a reduction in the frequency, duration and magnitude of natural floods, rising saline water tables in floodplains, and excessive evapotranspiration have led to an irrigation-induced groundwater mound forcing the naturally saline groundwater onto the floodplain. It is during the attenuation phase of floods that these large salt accumulations are likely to be mobilised and discharged into the river. This has been highlighted as the most significant risk in the Murray–Darling Basin and the South Australian Government and catchment management authorities have subsequently developed salt interception schemes (SIS). The aim of these schemes is to reduce the hydraulic gradient that drives the regional saline groundwater towards the River Murray. This paper investigates the interactions between a river (River Murray in South Australia) and a saline semi-arid floodplain (Clark's floodplain) that is significantly influenced by groundwater lowering due to a particular SIS. The results confirm that groundwater extraction maintains a lower water table and a higher amount of fresh river water flux to the saline floodplain aquifer. In terms of salinity, this may lead to less solute stored in the floodplain aquifer. This occurs through three mechanisms, namely extraction of the solute mass from the system, reducing the saline groundwater flux from the highland to the floodplain and changing the floodplain groundwater regime from a losing to a gaining one. It is shown that groundwater extraction is able to remove some of the solute stored in the unsaturated zone and this can mitigate the floodplain salinity risk. A conceptual model of the impact of groundwater extraction on floodplain salinization has been developed.
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2016
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 10-2014
Publisher: MDPI AG
Date: 12-04-2019
DOI: 10.3390/EN12081416
Abstract: The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 07-2021
Publisher: Springer Science and Business Media LLC
Date: 03-2014
Publisher: Elsevier BV
Date: 2020
Publisher: MDPI AG
Date: 10-06-2016
DOI: 10.3390/RS8060492
Publisher: Springer Science and Business Media LLC
Date: 15-04-2016
Publisher: Elsevier BV
Date: 07-2019
Publisher: Inderscience Publishers
Date: 2012
Publisher: Elsevier BV
Date: 03-2020
Publisher: Wiley
Date: 06-2020
DOI: 10.1002/HYP.13790
Publisher: MDPI AG
Date: 09-04-2020
DOI: 10.3390/W12041067
Abstract: Soil salinization is a major environmental issue in arid and semi-arid regions, and has been accelerated in some areas by removal of native vegetation cover. Partial afforestation can be a practical mitigation strategy if efficiently integrated with farms and pastures. Using an integrated surface-subsurface hydrological model, this study evaluates the water and salt dynamics and soil salinization conditions of a rural intermittent catchment in the semi-arid climate of southeast Australia subjected to four different partial afforestation configurations under different climate change scenarios, as predicted by several general circulation models. The results show that the locations of afforested areas can induce a retarding effect in the outflow of groundwater salt, with tree planting at lower elevations showing the steadier salt depletion rates. Moreover, except for the configuration with trees planted near the outlet of the catchment, the streamflow is maintained under all other configurations. It appears that under both Representative Concentration Pathways considered (RCP 4.5 and RCP 8.5), the Hadley Centre Global Environmental Model represents the fastest salt export scheme, whereas the Canadian Earth System Model and the Model for Interdisciplinary Research on Climate represent the slowest salt export scheme. Overall, it is found that the location of partial afforestation generally plays a more significant role than the climate change scenarios.
Publisher: Elsevier BV
Date: 05-2019
Publisher: MDPI AG
Date: 24-05-2023
DOI: 10.3390/HYDROLOGY10060117
Abstract: The intensity–duration–frequency (IDF) curve is a commonly utilized tool for estimating extreme rainfall events that are used for many purposes including flood analysis. Extreme rainfall events are expected to become more intense under the changing climate, and there is a need to account for non-stationarity IDF curves to mitigate an underestimation of the risks associated with extreme rainfall events. Sydney, Australia, has recently started experiencing flooding under climate change and more intense rainfall events. This study evaluated the impact of climate change on altering the precipitation frequency estimates (PFs) used in generating IDF curves at Sydney Airport. Seven general circulation models (GCMs) were obtained, and the best models in terms of providing the extreme series were selected. The ensemble of the best models was used for comparing the projected 24 h PFs in 2031–2060 with historical values provided by Australian Rainfall and Runoff (ARR). The historical PFs consistently underestimate the projected 24 h PFs for all return periods. The projected 24 h 100 yr rainfall events are increased by 9% to 41% for the least and worst-case scenario compared to ARR historical PFs. These findings highlight the need for incorporating the impact of climate change on PFs and IDF curves in Sydney toward building a more prepared and resilient community. The findings of this study can also aid other communities in adapting the same framework for developing more robust and adaptive approaches to reducing extreme rainfall events’ repercussions under changing climates.
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 02-2017
Publisher: MDPI AG
Date: 06-04-2019
DOI: 10.3390/W11040709
Abstract: Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 2016
Publisher: MDPI AG
Date: 09-08-2018
DOI: 10.3390/SU10082826
Abstract: More well-maintained green spaces leading toward sustainable, smart green cities mean that alternative water resources (e.g., wastewater) are needed to fulfill the water demand of urban greenery. These alternative resources may introduce some environmental hazards, such as salt leaching through wastewater irrigation. Despite the necessity of salinity monitoring and management in urban green spaces, most attention has been on agricultural fields. This study was defined to investigate the capability and feasibility of monitoring and predicting soil salinity using proximal sensing and remote sensing approaches. The innovation of the study lies in the fact that it is one of the first research studies to investigate soil salinity in heterogeneous urban vegetation with two approaches: proximal sensing salinity mapping using Electromagnetic-induction Meter (EM38) surveys and remote sensing using the high-resolution multispectral image of WorldView3. The possible spectral band combinations that form spectral indices were calculated using remote sensing techniques. The results from the EM38 survey were validated by testing soil s les in the laboratory. These findings were compared to remote sensing-based soil salinity indicators to examine their competence on mapping and predicting spatial variation of soil salinity in urban greenery. Several regression models were fitted the mixed effect modeling was selected as the most appropriate to analyze data, as it takes into account the systematic observation-specific unobserved heterogeneity. Our results showed that Soil Adjusted Vegetation Index (SAVI) was the only salinity index that could be considered for predicting soil salinity in urban greenery using high-resolution images, yet further investigation is recommended.
Publisher: Pleiades Publishing Ltd
Date: 05-2013
Publisher: Wiley
Date: 12-2019
DOI: 10.1002/HYP.13610
Publisher: MDPI AG
Date: 02-04-2019
DOI: 10.3390/SU11071947
Abstract: Biofiltration systems, as one of the best management practices, have good potentials to improve stormwater quality and hydrology of urban catchments. While biofiltration systems are well-studied in developed countries, the majority of those studies are conducted for temperate climate and there is a lack of lab-scale and field-scale studies on such systems under tropical conditions. This paper focuses on the performance of a lab-scale prototype biofiltration systems in stormwater retention efficiency as well as pollutants removal (including heavy metals and nutrients) from synthetic stormwater reproducing tropical rainfall events. A three-layer sand-based filter media with two different native plants including Pedilanthus tithymaloides and Cyperus alternifolius was selected for this study. Results showed that the system with Cyperus has a better stormwater retention capacity compared to the one with Pedilanthus. In addition, the observed infiltration rate in Cyperus and Pedilanthus were 338 mm/h and 267 mm/h, respectively. The better hydraulic performance in the system with Cyperus was attributed to the deeper and more extensive root penetration of this plant (as deep as 800 mm) compared to Pedilanthus (as deep as 250 mm). While both systems failed to perform well in removing total nitrogen, they performed significantly better in removing total phosphorus (Cyperus and Pedilanthus removed 67.3% and 62.5% of total phosphorus, respectively). The statistical analysis of results showed that the top 100 mm layer of filter media is the main contributor to total phosphorus removal. However, no major differences were observed between the two systems in phosphorus removal. Moreover, both systems were also capable of removing the available heavy metals (i.e., Fe, Cu, Mn, Ni, Pb, and Zn) as the removal efficiencies exceeded 90%, except for Fe (76%). Similar to phosphorus, it was concluded that the top layer is the major contributor to the heavy metals removal. Overall, the biofiltration system using Cyperus was found to be a successful system for operating under tropical conditions.
Publisher: Elsevier BV
Date: 10-2020
Publisher: MDPI AG
Date: 29-12-2018
DOI: 10.3390/W11010052
Abstract: 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.
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 08-2018
Publisher: Elsevier BV
Date: 04-2017
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 2019
Publisher: Wiley
Date: 15-12-2020
DOI: 10.1002/IRD.2395
Publisher: Desalination Publications
Date: 2018
Location: Taiwan, Province of China
No related grants have been discovered for Sina Alaghmand.