River Modelling For Flood Risk Map Prediction: Case Study Of Sungai Kayu Ara

Penyelidikan ini memberikan tumpuan terhadap kepentingan kebanjiran sungai di kawasan bandar yang menyebabkan kehilangan nyawa dan kerosakan harta benda. The research illustrates an importance of river flood in urban areas which cause lost of lives and properties damages

River Modelling For Flood Risk Map Prediction: Case Study Of Sungai Kayu Ara

Penyelidikan ini memberikan tumpuan terhadap kepentingan kebanjiran sungai di kawasan bandar yang menyebabkan kehilangan nyawa dan kerosakan harta benda. The research illustrates an importance of river flood in urban areas which cause lost of lives and properties damages

Long-term Impacts of Partial Afforestation on Water and Salt Dynamics of an Intermittent Catchment under Climate Change

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

Rainfall-runoff Modeling Using Dynamic Evolving Neural Fuzzy Inference System with Online Learning

This is an open access article under the CC BY-NC-ND license.Neuro-Fuzzy Systems (NFS) are computational intelligence tools that have recently been employed in hydrological modeling. In many of the common NFS the learning algorithms used are based on batch learning where all the parameters of the fuzzy system are optimized off-line. Although these models have frequently been used, there is a criticism on such learning process as the number of rules are needed to be predefined by the user. This will reduce the flexibility of the NFS architecture while dealing with different data with different level of complexity. On the other hand, online or local learning evolves through local adjustments in the model as new data is introduced in sequence. In this study, dynamic evolving neural fuzzy inference system (DENFIS) is used in which an evolving, online clustering algorithm called the Evolving Clustering Method (ECM) is implemented. ECM is an online, maximum distance-based clustering method which is able to estimate the number of clusters in a data set and find their current centers in the input space through its fast, one-pass algorithm. The 10-minutes rainfall-runoff time series from a small (23.22 km2) tropical catchment named Sungai Kayu Ara in Selangor, Malaysia, was used in this study. Out of the 40 major events, 12 were used for training and 28 for testing. Results obtained by DENFIS were then compared with the ones obtained by physically-based rainfall-runoff model HEC-HMS and a regression model ARX. It was concluded that DENFIS results were comparable to HEC-HMS and superior to ARX model. This indicates a strong potential for DENFIS to be used in rainfall-runoff modeling

Non-Stationary Precipitation Frequency Estimates for Resilient Infrastructure Design in a Changing Climate: A Case Study in Sydney

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

A review of the numerical modelling of salt mobilization from groundwater-surface water interactions

Salinization of land and water is a significant challenge in most continents and particularly in arid and semi arid regions. The need to accurately forecast surface and groundwater interactions has promoted the use of physically based numerical modelling approaches in many studies. In this regard, two issues can be considered as the main research challenges. First, in contrast with surface water, there is generally less observed level and salinity data available for groundwater systems. These data are critical in the validation and verification of numerical models. The second challenge is to develop an integrated surface groundwater numerical model that is capable of salt mobilization modelling but which can be validated and verified against accurate observed data. This paper reviews the current state of understanding of groundwater and surface water interactions with particular respect to the numerical modelling of salt mobilization. 3D physically based fully coupled surface subsurface numerical model with the capability of modelling density dependent, saturated unsaturated solute transport is an ideal tool for groundwater surface water interaction studies. It is concluded that there is a clear need to develop modelling capabilities for the movement of salt to, from, and within wetlands to provide temporal predictions of wetland salinity which can be used to assess ecosystem outcomes.

Water and salt balance modelling of intermittent catchments using a physically-based integrated model

none5simixedDaneshmand, Hossein*; Alaghmand, Sina; Camporese, Matteo; Talei, Amin; Daly, EdoardoDaneshmand, Hossein; Alaghmand, Sina; Camporese, Matteo; Talei, Amin; Daly, Edoard

River basin-scale flood hazard assessment using a modified multi-criteria decision analysis approach: A case study

Flood is a major natural hazard with extremely large impact on social-ecological systems. Therefore, developing reliable and efficient tools to identify areas vulnerable to potential flooding is vital for water managers, engineers and decision makers. Moreover, being able to accurately classify the level of hazard is a step forward towards more efficient flood hazard mapping. This study presents a multi-criteria index approach to classify potential flood hazards at the river basin scale. The presented methodology was implemented in the Mashhad Plain basin in North-east Iran, where flood has been a major issue in the last few decades. In the present study, seven factors, selected based on their greater influence towards flooding, were identified and extracted from the basic thematic layers to be used to generate a five-class Flood Hazard Index (FHI) map. The Soil and Water Assessment Tool (SWAT) was used to develop a runoff coefficient map, which was found to be the most influential factor. A sensitivity analysis was performed and the results incorporated to generate a modified Flood Hazard Index (mFHI) map. The accuracy of the proposed method was evaluated against the well-documented flood records in the last 42 years at the study area. The results showed that, for both FHI and mFHI maps, more than 97% of historical flood events have occurred in moderate to very high flood hazard areas. This demonstrates that incorporating hydrological model (such as SWAT) and multi-criteria analysis introduces a robust methodology to generate comprehensive potential flood hazard maps. Moreover, the proposed modified methodology can be used to identify high potential flood hazard zones and work towards more efficient flood management and mitigation strategies

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

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