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Laboratory experiments were performed to study the impact of the relative particle protrusion P/D (P is the protrusion height and D is the diameter of the target particle) on the mechanism of entra...

Urbanisation changes the nature of urban catchments by altering landcover, soil conditions and runoff storage capacity, resulting in increased runoff volume and peak flow due to disruption of the hydrological cycle. Contemporary stormwater management approaches such as Best Management Practice (BMP), Water Sensitive Design (WSD) and Sponge cities have introduced a new philosophy in stormwater management to reduce the impact of urbanization on surface runoff and improving the hydrological cycle in urban areas. These approaches mainly focus on hydrological mitigation by minimizing the impact of increasing runoff volume and peak flow. However, there are no robust approaches to evaluate the effect of changes in the hydrological characteristics of urban developed catchments on the resilience of stormwater management system to flooding. This paper introduces a novel framework to characterize the resilience of stormwater management systems in terms of the hydrological characteristics of an urban catchment. The framework is based on the differences in the peak flow and runoff volume of a catchment between the urbanized and greenfield conditions. A set of indices are defined to represent relationships between catchment performance capacities in stormwater management and design rainfall, culminating in the quantification of the hydrological resilience degree (\({R}_{hg}\)). The framework is tested in an urban catchment in Auckland, New Zealand to demonstrate the reduction in \({R}_{hg}\) as a function of rainfall depth and the influence of changes in hydrological characteristics. Different scenarios are analysed to demonstrate the effect of stormwater runoff control systems on resilience, with volume control devices providing the greatest increase in resilience. Mapping of the indices across sub-catchments provides a spatial representation of these changes and can be used as a tool in catchment management plans to optimize the hydrological mitigation and resilience of stormwater management systems.

This paper aims to improve summer power generation of the Yeywa Hydropower Reservoir in Myanmar using the modified multi-step ahead time-varying hedging (TVH) rule as a case study. The results of the TVH rules were compared with the standard operation policy (SOP) rule, the binary standard operation policy (BSOP) rule, the discrete hedging (DH) rule, the standard hedging (SH) rule, the one-point hedging (OPH) rule, and the two-point hedging (TPH) rule. The Multi-Objective Genetic Algorithm (MOGA) was utilized to drive the optimal Pareto fronts for the hedging rules. The results demonstrated that the TVH rules had higher performance than the other rules and showed improvements in power generation not only during the summer period but also over the entire period.

Impacts of the global climate change in hydrology and water resources are accessed by downscaling of local daily rainfall from large-scale climate variables. This study developed a statistical downscaling model based on the Volterra series, principal components and ridge regression. This model is known, hereafter as SDCRR. The proposed model is applied at four different stations of the Manawatu River basin, in the North Island of New Zealand to downscale daily rainfall. The large-scale climate variables from the National Centers for Environmental Predictions (NCEP) reanalysis data are used in the present study to obtain with the wide range (WR) and the restricted range (RR) of predictors. The developed SDCRR model incorporated the climate change signals sufficiently by working with WR predictors. Further, principal component analysis (PC) was applied to the set of WR predictors, which were also used as the orthogonal filter in the ridge regression model to deal with the multi-collinearity. The ridge regression coefficients determined were less sensitive to random errors, and were capable of reducing the mean square error between the observed and the simulated daily precipitation data. Thus, the combined application of principal component analysis (PCA) and ridge regression improved the performance of the model. This combination is steady enough to capture appropriate information from predictors of the region. The performance of the SDCRR model is compared with that of the widely used statistical downscaling model (SDSM). The results of the study show the SDCRR model has better performance than the SDSM.

Local scour is an important design factor for submerged weirs. This study conducted a clear-water scour experiment at a submerged weir, using the particle tracking velocimetry technique to measure ...

Critical infrastructure systems are typically managed as isolated systems, however, past events have demonstrated that disruptions to electricity supply can initiate major social and economic impac...

To reduce losses from the various disasters, regional water security evaluation and risk control model is studied. The model is built upon different kinds of indices in water security system, proceeding from the whole structure and its parts of evaluation, forewarning and decision making analysis. Based on China's national conditions, this study firstly advances an evaluation index system of regional water security, which includes three subsystems of water resource security, water environment security, and water disaster control security. Secondly, fuzzy analytic hierarchy process based on accelerating genetic algorithm (AGA-FAHP) combines with entropy weight method (EW) to determine the objective weights of evaluation indexes. The subjective and objective weights can be integrated by the principle of minimum relative information entropy. The subsystem weights are obtained by using AGA-FAHP. Then regional water security evaluation model is established. Thirdly, the comparison judging method is adopted to divide warning degree of water security with the comprehensive evaluation index and forewarning standards, and then the local conditions for proposing planning schemes. Finally, decision making analysis is employed to find the effective indices based on projection pursuit technique with the ideal point method in multi-index decision. This study takes Jiangsu province, China as an example. The evaluation results from 2000 to 2015 show that the development trend of water security is increasing on the whole except in several individual years. Risk forewarning doesn't take place in recent years. But risk is always there. So, project and non-project measures are proposed for the corresponding forewarning levels. From light warnings for three times and moderate warning for once in 2000, 2001, 2002, and 2004, index 1, 3, 4, 11, 13, 17, and 18 are selected as the effective indices to decision making analysis in common. Then, the solution schemes are given as the processing method accordingly. This conclusion is reasonable and its method is practical that match the reality. It suggests that the presented model is feasible with theory and application, which can offer advice in regional water security management to some extent.

Statistical and dynamic downscaling approaches are commonly used to downscale large-scale climatic variables from global circulation (GCM) and regional circulation (RCM) model outputs to local precipitation. The performance of these two approaches may differ from each other for daily precipitation projections when applied in the same region. This is examined in this study based on the estimation of extreme precipitation. Daily precipitation series are generated from GCM HadCM3, CGCM3/T47 and RCM HadCM3 models for both historical hindcasts and future projections in accordance with the period from 1971 to 2070. The Waikato catchment of New Zealand is selected as a case study. Deterministic and probabilistic performances of the GCM and RCM simulations are evaluated using root-mean-square-error (RMSE) coefficient, percent bias (PBIAS) coefficient and equitable threat score (ETS). The value of RMSE, PBIAS and ETS is 2.89, − 2.16, 0.171 and 8.72, − 4.01, 0.442 for mean areal and at-site daily precipitation estimations, respectively. The study results reveal that the use of frequency analysis of partial duration series (FA/PDS) is very effective in evaluating the accuracy of downscaled daily precipitation series. Both the statistical and the dynamic downscaling perform well for simulating daily precipitation at station level for a return period equal to or less than 100 years. However, the latter outperforms the former for daily precipitation simulation at catchment level.

Watershed hydrology changes are mostly influenced by changes in climate and land use and understanding of the interaction between these changes is required for the sustainable water management of water resources. In this study, an integrated approach of land-use modelling, downscaling climate data, and river flow simulation is carried out to detect hydrologic responses of the Myitnge River basin in Myanmar under land use and climate changes. A set of Landsat satellite images (2010, 2011, and 2017) are classified, and Land Change Modeller (LCM) module in the TerrSet model is used to simulate land-use maps for 2030 and 2050. The CanESM2 large-scale general circulation model (GCM) is used to project the near future (2020–2059) climate data. Then, future river flow changes are simulated using the SWAT hydrologic model under changing future climate and land-use conditions. The results of the study show that the annual mean river flow will slightly increase under future climate change, and the changes will be exaggerated when the land-use change occurred simultaneously. This study offers a perspective on the possible hydrologic alteration in the Myitnge River basin under future land use and climate conditions, which can be beneficial for formulating adaption strategies.

This study is motivated by the potential improvement in water supply reliability and better forecasts of extreme rainfall and floods linked to Atmospheric rivers (ARs) in New Zealand. Results indic...

Tsunamis are among the most destructive natural disasters. Recent major tsunamis in Indonesia (2004), Chile (2010), and Japan (2011) resulted in significant loss of human lives and damage t...

Atmospheric Rivers (ARs) are filamentary channels of strong poleward water vapour transport in the midlatitudes. Recent studies have found that ARs are northwesterly and northeasterly orientated an...

The Ngongotaha Stream was used as a case study to assess the applicability of fiber optic distributed temperature sensing (FODTS) to identify the location of springs and quantify their discharge. Thirteen springs were identified, mostly located within a 115 m reach, five discharged from the right bank and eight from the left bank. To quantify groundwater discharge, a new approach was developed in which the one-dimensional transient heat transport model was fitted to the FODTS measurements, where the main calibration parameters of interest were the unknown spring discharges. The spatial disposition of the groundwater discharge estimation problem was constrained by two sources of information; first, the stream gains ∼500 L/s as determined by streamflow gauging. Second, the temperature profiles of the left and right banks provide the spatial disposition of springs and their relative discharges. FODTS was used to measure stream temperature near the left and right banks, which created two temperature datasets. A weighted average of the two datasets was then calculated, where the weights reflected the degree of mixing between the right and left banks downstream of a spring. The new approach in this study marks a departure from previous studies, in which the general approach was to use the steady-state thermal mixing model (Selker et al. 2006a; Westhoff et al. 2007; Briggs et al. 2012) to infer groundwater discharge, which is then used as an input into a transient model of the general form of equation to simulate stream temperature (Westhoff et al. 2007).

Climate change, increasing urbanisation and a growing concern over existing stormwater management systems (SWMSs) has resulted in the development of various approaches to improve urban resilience to flooding and the performance of SWMSs. However, previous studies have focused on urban resilience and the hydraulic reliability of urban drainage systems, without considering all dimensions of a SWMS as the main urban flood control infrastructure. This paper presents an approach to quantify the resilience of the hydraulic dimension of primary SWMSs to flooding. Resilience was quantified based on the Hydraulic Performance Capacity (HPC), a new metric developed to represent the functionality of a SWMS over time using the temporal hydraulic characteristics across a catchment. The effect of network properties, catchment characteristics, and design storm events can be assessed through this approach based on the outputs of standard One Dimensional (1D) hydraulic modelling. The approach was applied to a case study urban catchment and was able to demonstrate the effect of different storm events and pipe material properties on resilience, robustness, and recovery. This framework can be used by decision makers to benchmark SWMS network resilience, optimise network capacity for design, and assess methods for reducing flood hazard in urban catchments.

Climate change is projected to affect the flow regime in urban waterways adversely. This study investigates the climate change impacts on the flow regime in the Lucas Creek catchment under ...

Downscaling of future projections of climatic variables from global climate models (GCMs) to urban catchment scales is required for stormwater studies as existing GCMs are unable to predict the rainfall at high temporal and spatial resolutions. In the current study, the capability of dynamical and statistical downscaling methods is evaluated and compared using the rainfall data of current climate and future extreme events at a small urban catchment scale. Regional climate model (RCM) and statistical downscaling model (SDSM) are utilized to downscale rainfall data from twelve GCMs under three representative concentration pathways (RCPs) (i.e., RCP2.6, RCP 4.5 and RCP 8.5). The daily rainfall data (1985–2015) of Lucas Creek catchment located in Auckland, New Zealand is used as a baseline/current climate. The future downscaled rainfall data is analyzed in the 2090s (2071–2100). The results showed that both the methods performed well in downscaling the current climate. For future projections, SDSM underestimated mean daily rainfall at the start of the annual cycle and overestimated towards the middle of the year compared to RCM. Similarly, monthly variance and skewness were overestimated for some months by SDSM. The GCMs of both the methods also showed variations in the future rainfall projections amongst themselves. However, significant alterations in the future rainfall were observed compared to the current climate. Rainfall frequency analysis was performed by applying the Gumbel distribution to the baseline and downscaled data for 1, 2, 3, 4, 5, 10, 20, 30, 50 and 100 years return periods. The investigation revealed that RCM and SDSM show similar results for low return periods and different results for high return periods for the current and future climate. Both the methods forecasted an increase in magnitudes of future events however, RCM projections were lower compared to SDSM. The results illustrate the downscaling abilities of both the methods at a small urban catchment scale however, the contrasting implication in downscaling the rainfall data is related to their different downscaling mechanisms.

This study quantifies the impact of atmospheric rivers (ARs) on rainfall in New Zealand. Using an automated AR detection algorithm, daily rainfall records from 654 rain gauges, and various atmospheric reanalysis datasets, we investigate the climatology of ARs, the characteristics of landfalling ARs, the contribution of ARs to annual and seasonal rainfall totals, and extreme rainfall events between 1979 and 2018 across the country. Results indicate that these filamentary synoptic features play an essential role in regional water resources and are responsible for many extreme rainfall events on the western side of mountainous areas and northern New Zealand. In these regions, depending on the season, 40–86% of the rainfall totals and 50–98% of extreme rainfall events are shown to be associated with ARs, with the largest contributions predominantly occurring during the austral summer. Furthermore, the median daily rainfall associated with ARs is 2–3 times than that associated with other storms. The results of this study extend the knowledge on the critical roles of ARs on hydrology and highlight the need for further investigation on the landfalling AR physical processes in relation to global circulation features and AR sources, and hydrological hazards caused by ARs in New Zealand.

The headwater catchment of the Siem Reap River has supplied the Angkor Temple Complex and surrounding communities since the twelfth century. The Angkor Temple Complex area consists of historical moats and barays (reservoirs) which are currently used to store the water from the Siem Reap River to maintain temple foundation, irrigate cultivation areas and provide floodwater storage. The Angkor Wat Temple, which is located in the complex, was constructed on a sandy alluvial substrate and needs a stable supply of water to avert land subsidence and destabilization of the temple foundation. In light of changing climate, land use and land cover (LULC) trends, it is crucial to examine the wide-ranging implications of reduced water supply for the Angkor Temple Complex. Using the Soil and Water Assessment Tool, this study seeks to assess the conditions necessary to provide sustainable streamflow to the Angkor Temple Complex. We modelled 30 scenarios of co-varied LULC and precipitation regime under a changing climate. The results show that under most LULC scenarios, sufficient water resources can be harvested to supply the complex—however—any further loss of forest cover is likely to impact groundwater conditions, flood management and dry season shortages. Conversely, the water supply to the complex is shown to be sensitive under the range of climate scenarios explored; a reduction of more than 10–20% in mean annual precipitation was enough to put the water supply under stress for the current and future conditions of the complex.

Scour at bridge piers is time-dependent. In this paper, temporal evolution of clear-water scour at complex bridge piers is studied experimentally. The pier model has a typical form comprisi...

Critical infrastructure networks are geographically distributed systems spanning multiple scales. These networks are increasingly interdependent for normal operations, which causes localized asset failures from natural hazards or man-made interference to propagate across multiple networks, affecting those far removed from an initiating failure event. This paper provides methodology to identify such failure propagation effects by quantifying the spatial variability in magnitude, frequency, and disruptive reach of failures across national infrastructure networks. To achieve this, we present methodology to combine functionally interdependent infrastructure networks with geographic interdependencies by simulating complete asset failures across a national scale grid of spatially localized hazards. A range of metrics are introduced to compare the systemic vulnerabilities of infrastructure systems and the resulting spatial variability in both the potential for initiating widespread failures and the risk of being impacted by distant hazards. We demonstrate the approach through an application in New Zealand of infrastructures across the energy (electricity, petroleum supply), water and waste (water supply, wastewater, solid waste), telecommunications (mobile networks), and transportation sectors (passenger rail, ferry, air, and state highways). In addition to identifying nationally significant systemic vulnerabilities, we observe that nearly half (46%) of the total disruptions across the simulation set can be attributed to network propagation initiated asset failures. This highlights the importance in considering interdependencies when assessing infrastructure risks and prioritizing investment decisions for enhancing resilience of national networks.

This study used a new approach in data integration to fully evaluate water consumption across different housing types, income groups and seasons in Auckland, New Zealand. Using urban GIS-based databases, we developed a sample of 60,000 dwellings through integration of water consumption, land use and census microdata. We find that household size is the major determinant of water use across all housing types in Auckland. But household water consumption, and its seasonal and spatial variations, is significantly higher in detached houses than in apartments due to greater outdoor water use.

This study investigates tsunami bore impact on a cylindrical structure. A physical study was carried out in a large wave flume with a horizontal floor. Experiments covered a range of bore heights (140–210 mm) and bore velocities (1.98–2.45 m/s). The vertical and angular distributions of the applied pressure were measured using a vertical array of pressure sensors. The cylindrical structure was rotated so that the sensors were orientated at several angles between 0° and 135°, giving in effect a three-dimensional pressure distribution around the structure. The total stream-wise force was computed as a summation of the hydrostatic and hydrodynamic forces. A hydrodynamic coefficient of 0.65 was found to be appropriate for this study. The height of the centre of effort of the force on the cylinder surface was estimated by dividing the measured moment by the measured force, and was found to be proportional to the actual water height at the front face of the cylinder.

Siem Reap River plays a crucial role in maintaining the stability of the Angkor Temple Complex and the livelihoods of the people in the basin. Land use and land cover (LULC) have changed considerably over the last few decades, as well as climatic regime, and these, in turn, have an influence on streamflow. The objective of this study was to assess the potential impacts of future LULC and climate change on streamflow upstream from the Angkor Temple Complex. LULC maps, classification of LULC units, and projections of change were developed using Maximum Likelihood Supervised Classification and the Land Change Modeler (LCM). Artificial neural network (ANN) downscaling was applied to downscale precipitation and temperatures. The Soil and Water Assessment Tool (SWAT) was applied to assess the effects of future LULC and climate change within the upper Siem Reap catchment. The Mann-Kendall test was employed to detect any potential trends within the current and future (modelled) streamflow. The results indicate that there would be reduction of streamflow at seasonal and annual timescales, under every scenario. The annual average streamflow was reduced −69%, −63% and −75% under the Representative Concentration Pathway (RCP) scenarios RCP 2.6, RCP 4.5 and RCP 8.5 respectively. It seems that there is a sufficient water supply to sustain the Angkor Temple Complex under the current LULC and climate regime, but there would be an insufficient water supply under every future modelled scenario. Moreover, trends in groundwater levels must be taken into account, because growing population will inevitably influence groundwater balance and surface water which could potentially threaten the stability of Angkor Wat Temple, a UNESCO World Heritage Site.

Local scour at pile-supported bridge foundations in coastal and offshore areas are an essential factor to engineering design and evaluation. This study experimentally investigates live-bed local scour at complex bridge piers under the influence of bed-form migration. The results show that the variation of the mean scour depth as a function of flow intensity ratio U / U c ( U c = threshold velocity for incipient motion) depends on the pile-cap elevation and is significantly different to single piers. Instantaneous bed level, cross-correlation analysis, and power spectra show that bed-forms approaching an exposed pile group may be damped, leading to mean bed level fluctuation that are slightly smaller than the bed-form heights. In contrast, when the pile-cap is completely buried by the original bed but can be exposed during scour, approaching bed-forms tend to keep their individual features and cause larger scour fluctuation at pile-cap's leading edge. The enlargement is due to the concurrence between the enhanced (or weakened) scouring capacity and the decreased (or increased) upstream sediment supply. The scour predictor proposed by previous studies is extended to live-bed range and performs better than existing predictors.

Considering network topologies and structures of the artificial neural network (ANN) used in the field of hydrology, one can categorize them into two different generic types: feedforward and feedback (recurrent) networks. Different types of feedforward and recurrent ANNs are available, but multilayer perceptron type of feedforward ANN is most commonly used in hydrology for the development of wavelet coupled neural network (WNN) models. This study is conducted to compare performance of the various wavelet based feedforward artificial neural network (ANN) models. The feedforward ANN types used in the study include the multilayer perceptron neural network (MLPNN), generalized feedforward neural network (GFFNN), radial basis function neural network (RBFNN), modular neural network (MNN) and neuro-fuzzy neural network (NFNN) models. The rainfall-runoff data of four catchments located in different hydro-climatic regions of the world is used in the study. The discrete wavelet transformation (DWT) is used in the present study to decompose input rainfall data using db8 wavelet function. A total of 220 models are developed in this study to evaluate the performance of various feedforward neural network models. Performance of the developed WNN models is compared with their counterpart simple models developed without applying wavelet transformation (WT). The results of the study are further compared with - multiple linear regression (MLR) model which suggest that the WNN models outperformed their counterpart simple models. The hybrid wavelet models developed using MLPNN, the GFFNN and the MNN models performed best among the six selected data driven models explored in the study. Moreover, performance of the three best models is found to be similar and thus the hybrid wavelet GFFNN and the MNN models can be considered as an alternative to the most commonly used hybrid WNN models developed using MLPNN. The study further reveals that the wavelet coupled models outperformed their counterpart simple models only with the parsimonious input vector.

Studies evaluating the determinants of water demand typically use household-scale data or aggregated data. The household-scale data basically is preferred since it can reveal the heterogeneity in responses to the demand drivers across different consumer groups. However, the scarcity of household-scale data and its high data collection cost generally have limited the studies to rely on small samples of household data. Thus, they failed to show the spatial variation of water demand. In contrast, the aggregated studies have assessed the spatial variation of water use however they overlooked the variations across households. Using a rich source of GIS-based urban databases in Auckland, New Zealand, this study overcame this challenge by developing a large sample of 31000 single-unit housing through integration of household-level water consumption and property data with micro-scale household demographics information. This large dataset enabled this study to evaluate the water consumption both at the household scale and the census area unit scale. Panel data models were used for the water demand analysis in both scales. The proposed multi-scale analysis approach provided detailed knowledge about water consumption and its major determinants across different consumer groups and urban areas. This information may help water planners to more reliably plan water supply systems and manage consumption in the complex urban environments.

Diffusers are energy conversion devices in submersible pump structures, and they play a key role in improving the pump performance and operational stability. However, investigations pertaining to the factors influencing the performance of diffusers, in particular the effect of the coupling of factors on the performance of submersible pumps, are still rare. This study focuses on the effect of coupled parameters, such as the inlet angle, inlet width, axial length and number of blades of diffusers, for improving the performance of submersible pumps. First, the orthogonal test method is used to design the diffuser scheme, and sixteen models involving the coupling of different diffuser parameters are established and simulated. Next, the path analysis method is used to extensively understand the effect mode and influence degree of diffuser parameter coupling on the performance of submersible pumps. The results indicate that the mode of action of the diffuser parameters on the performance of the submersible pump can divided into two aspects: the direct action of the parameter itself and the indirect effect of the parameter coupling induced by other parameters. The orders of significance of the parameters affecting the head and efficiency are α3n > L > b3 > z and L > α3n > b3 > z, respectively. The contribution rates of the inlet angle, inlet width, axial length and number of blades to the pump efficiency are 31.56%, 16.53%, 37.05% and 14.86%, respectively. The numerical results show that the combination of the diffuser parameters—blade inlet angle of 30°, inlet width of 45 mm, axial length of 100 mm and number of blades equal to 8—can help realize the maximum efficiency (83.5%) of a submersible pump with a 18.9 m head. In the production design, the parameters can be quantitatively evaluated in advance by using path analysis. On this basis, the key parameters can be selected according to the order of magnitude of the influence degree. Furthermore, taking into account the production process and manufacturing costs, it is necessary to consider the active role of the non-key parameters in improving the submersible pump performance.

Siem Reap River has played a crucial role in maintaining the Angkor temple complex and livelihood of the people in the basin since the 12th century. Land use in this watershed has changed considerably over the last few decades, which is thought to have had an influence on river. This study was carried out as part of assessing the land use and climate change on hydrology of the upper Siem Reap River. The objective was to reconstruct patterns of annual deforestation from 1988 to 2018 and to explore scenarios of land use 40 and 80 years into the future. A supervised maximum likelihood classification was applied to investigate forest cover change in the last three decades. Multi-layer perceptron neural network-Markov chain (MLPNN-MC) was used to forecast land use and land cover (LULC) change for the years 2058 and 2098. The results show that there has been a significantly decreasing trend in forest cover at the rate 1.22% over the last three decades, and there would be a continuous upward trend of deforestation and downward trend of forest cover in the future. This study emphasizes the impacts of land use change on water supply for the Angkor temple complex (World Heritage Site) and the surrounding population.

An investigation of the characteristics of live-bed scour at circular and skewed rectangular (slab) piers in comparatively shallow flows is presented. The 110 experiments were undertaken in...