Modeling stormwater management at the city district level in response to changes in land use and low impact development

Mitigating the impact of increasing impervious surfaces on stormwater runoff by low impact development (LID) is currently being widely promoted at site and local scales. In turn, the series of distributed LID implementations may produce cumulative effects and benefit stormwater management at larger, regional scales. However, the potential of multiple LID implementations to mitigate the broad-scale impacts of urban stormwater is not yet fully understood, particularly among different design strategies to reduce directly connected impervious areas (DCIA). In this study, the hydrological responses of stormwater runoff characteristics to four different land use conversion scenarios at the city scale were explored using GIS-based Stormwater Management Model (SWMM). Model simulation results confirmed the effectiveness of LID controls; however, they also indicated that even with the most beneficial scenarios hydrological performance of developed areas was still not yet up to the pre-development level, especially where there were pronounced changes from pervious to impervious land.     

Applicability of Hydrologic Response Units in low topographic relief catchments and evaluation using high resolution aerial photograph analysis

Topography and geomorphology of a catchment are major drivers of runoff generation. In an earlier publication by the authors, a novel approach of delineating Hydrologic Response Units (HRUs) was investigated with reference to high topographic relief catchments. In the present study, this approach is evaluated for low topographic relief catchment and found to be applicable. To verify the proposed HRUs delineation logic, the thresholds derived from the DEM analysis are evaluated here at catchment scale using high resolution aerial photograph analysis and associated site visits. The thresholds derived from the DEM analysis are found in the same range as obtained from the aerial photographs analysis. The adequacy of the HRUs delineation approach is further verified by soil moisture movement modelling across four cross-sections and sensitivity analysis test for one cross-section in the Little River catchment which further supports the application of HRUs delineation approach in low topographic relief catchment.     

A syllable-scale framework for language identification

Whilst several examples of segment based approaches to language identification (LID) have been published, they have been typically conducted using only a small number of languages, or varying feature sets, thus making it difficult to determine how the segment length influences the accuracy of LID systems. In this study, phone-triplets are used as crude approximates for a syllable-length sub-word segmental unit. The proposed pseudo-syllabic length framework is subsequently used for both qualitative and quantitative examination of the contributions made by acoustic, phonotactic and prosodic information sources, and trialled in accordance with the NIST 1996 LID protocol. Firstly, a series of experimental comparisons are conducted which examine the utility of using segmental units for modelling short term acoustic features. These include comparisons between language specific Gaussian mixture models (GMMs), language specific GMMs for each segmental unit, and finally language specific hidden Markov models (HMM) for each segment, undertaken in an attempt to better model the temporal evolution of acoustic features. In a second tier of experiments, the contribution of both broad and fine class phonotactic information, when considered over an extended time frame, is contrasted with an implementation of the currently popular parallel phone recognition language modelling (PPRLM) technique. Results indicate that this information can be used to complement existing PPRLM systems to obtain improved performance. The pseudo-syllabic framework is also used to model prosodic dynamics and compared to an implemented version of a recently published system, achieving comparable levels of performance.     

Performance assessment and improvement of recursive digital baseflow filters for catchments with different physical characteristics and hydrological inputs

Recursive digital filters (RDFs) are one of the most commonly used methods of baseflow separation. However, how accurately they estimate baseflow and how to select appropriate values of filter parameters is generally unknown. In this paper, the output of fully integrated surface water/groundwater (SW/GW) models is used to obtain optimal parameters for, and assess the accuracy of, three commonly used RDFs under a range of physical catchment characteristics and hydrological inputs. The results indicate that the Lyne and Hollick (LH) filter performs better than the Boughton and Eckhardt filters, over a larger range of conditions. In addition, the optimal values of the filter parameters vary considerably for all three filters, depending on catchment characteristics and hydrological inputs. The dataset of the 66 catchment characteristics and hydrological inputs, as well as the corresponding simulated total streamflow and baseflow hydrographs obtained using the SW/GW model, can be downloaded as Supplementary material.     

Identifying low impact development strategies for flood mitigation using a fuzzy-probabilistic approach

Low impact development (LID) includes strategies and practices that are designed to control surface runoff at its sources in a sustainable way. The performance of these strategies has been frequently addressed through curve number approach. This approach however subjects to a great deal of uncertainties owing to uncertain nature of curve numbers and temporal/spatial variability of flood events. This paper represents a novel methodology to deal with both inherent flood uncertainties and epistemic uncertainties identifying optimal LID strategies for flood mitigation. The proposed methodology integrates a great variety of mathematical tools including copula functions, MCS method, hydrological and hydraulic models, NSGA-II algorithm as well as ANN and fuzzy set theory. The obtained results from a case study clearly demonstrate that the proposed methodology not only presents cost-effective measures, but also can simultaneously handle both inherent and epistemic uncertainties in flood risk management.     

Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted.     

Urban runoff modelling uncertainty: Comparison among Bayesian and pseudo-Bayesian methods

Urban stormwater quality modelling plays a central role in evaluation of the quality of the receiving water body. However, the complexity of the physical processes that must be simulated and the limited amount of data available for calibration may lead to high uncertainty in the model results. This study was conducted to assess modelling uncertainty associated with catchment surface pollution evaluation. Eight models were compared based on the results of a case study in which there was limited data available for calibration. Uncertainty analysis was then conducted using three different methods: the Bayesian Monte Carlo method, the GLUE pseudo-Bayesian method and the GLUE method revised by means of a formal distribution of residuals between the model and measured data (GLUE_f). The uncertainty assessment of the models enabled evaluation of the advantages and limitations of the three methodologies adopted. The models were then tested using the quantity–quality data gathered for the Fossolo catchment in Bologna, Italy. The results revealed that all of the models evaluated here provided good calibration results, even if the model reliability (in terms of related uncertainty) varied, which suggests the adoption of a specific modelling approach with respect to the others. Additionally, a comparison of uncertainty analysis approaches showed that, regarding the models evaluated here, the classical Bayesian method is more effective at discriminating models according to their uncertainty, but the GLUE approach performs similarly when it is based on the same founding assumptions as the Bayesian method.     

Modelling chain for the effect of road traffic on air and water quality: Techniques,current status and future prospects

Modelling approaches for simulating air and stormwater pollution due to on-road vehicles are reviewed and discussed. Models for traffic, emissions, atmospheric dispersion, and stormwater contamination are studied with particular emphasis on their couplings to create a modelling chain. The models must be carefully selected according to the requirements and level of detail of the integrated modelling chain. Although a fair amount of research has been conducted to link air pollution and road traffic, many questions related to spatio-temporal scales, domains of validity, consistency among models, uncertainties of model simulation results, and interfaces between models remain open. The aim of this work is to review the current status of the relationships between traffic, emissions, air quality, and water quality models, to recommend modelling approaches and to propose some directions for improving the state of the science. The difficulties and challenges associated with model coupling are illustrated with specific examples.     

Modeling the atmospheric deposition and stormwater washoff of nitrogen compounds

We investigated the suitability of integrating deterministic models to estimate the relative contributions of atmospheric dry and wet deposition onto an urban surface and the subsequent amounts removed by stormwater runoff. The CIT airshed model and the United States Environmental Protection Agency Storm Water Management Model (SWMM) were linked in order to simulate the fate and transport of nitrogen species through the atmosphere and storm drainage system in Los Angeles, California, USA. Coupling CIT and SWMM involved defining and resolving five critical issues: (1) reconciling the different modeling domain sizes, (2) accounting for dry deposition due to plant uptake, (3) estimating the fraction of deposited contaminant available for washoff, (4) defining wet deposition inputs to SWMM, and (5) parameterizing the SWMM washoff algorithm. The CIT–SWMM interface was demonstrated by simulating dry deposition, wet deposition, and stormwater runoff events to represent the time period from November 18, 1987 to December 4, 1987 for a heavily urbanized Los Angeles watershed discharging to Santa Monica Bay. From November 18th to December 3rd the simulated average dry deposition flux of nitrogen was 0.195 kg N/ha-day to the watershed and 0.016 kg N/ha-day to Santa Monica Bay. The simulated rainfall concentrations during the December 4th rainfall event ranged from 3.76 to 8.23 mg/l for nitrate and from 0.067 to 0.220 mg/l for ammonium. The simulated stormwater runoff event mean concentrations from the watershed were 4.86 mg/l and 0.12 mg/l for nitrate and ammonium, respectively. Considering the meteorology during the simulation period, the CIT and SWMM predictions compare well with observations in the Los Angeles area and in other urban areas in the United States.     

An assessment of modelling capacity to identify the impacts of climate variability on catchment hydrology

The aim is to investigate the consistency or variability of catchment response over time and space and evaluate the predictive error caused by the impacts of climate variability on streamflow. For this purpose, both data- and top-down model-based analyses of the dynamic relation between rainfall and runoff for selected sub-catchments have been undertaken. Data analysis techniques (e.g. trend analysis, deconvolution and baseflow filtering) were used to assess the temporal and spatial variation in the hydrologic response characteristics for each site. The lumped conceptual rainfall-runoff model IHACRES CMD (Catchment Moisture Deficit) version is applied to the sub-catchments to assess the adequacy of the model response in representing the impact of weather patterns on streamflow. Several performance criteria have been used to evaluate the performance of the model in each calibration period using a multi-criteria approach. The IHACRES-3S (3 Storage) model is applied to assess low flow behaviour and capture the timing in the switch between baseflow and no flow periods. Rainfall-runoff model performance characteristics of each sub-catchment are quite related to their incident rainfall regime. Sub-catchments which are located in a lower rainfall regime show poor to average model performance. The reduction in performance in R² is due to the poor fitting to the peaks for both large and small streamflow events, with the model underestimating the highest flow peaks, and overestimating smaller peaks. Further work will be needed to assess observed data reliability and improve model performance in order to separate the impacts of climate variations and land use change on hydrological response. An appropriate model structure having a variable partitioning between quick and slow flow components is under consideration and techniques are being used to identify problematic periods and events with high error in the observational data.     

基于卷积神经网络的语种识别系统

从给定语音中提取有效语音段表示是语种识别的关键点。近年来深度学习在语种识别应用中有重要的进展,通过深度神经网络可以提取音素相关特征,并有效提升系统性能。基于深度学习的端对端语种识别系统也表现出其优异的识别性能。本文针对语种识别任务提出了基于卷积神经网络的端对端语种识别系统,利用神经网络强大的特征提取能力及区分性建模能力,提取具有语种区分性的基本单元,再通过池化层得到有效语音段表示,最后输入全连接层得到识别结果。实验表明,在NIST LRE 2009数据集上,相比于现阶段国际主流语种识别系统,提出的系统在30 s,10 s和3 s等语音段上错误率分别相对下降了1.35%,12.79%和29.84%,且平均错误代价在3种时长上均相对下降30%以上。     

Consistent Modelling of Users, Devices and Sensors in a Ubiquitous Computing Environment

This paper describes the use of an accretion-resolution user modelling representation to model people, places and objects. We explain the motivation for the key properties of the representation, especially those of particular importance for ubiquitous computing: firstly, for flexibility in interpreting the typically noisy and potentially conflicting evidence about users’ locations; secondly, to support users in scrutinising their user model, the processes that determine its contents and the way that it is used in the ubiquitous computing environment. A novel and important aspect of this work is our extension of the representation beyond modelling just users, using it also to represent the other elements such as devices, sensors, rooms and buildings. We illustrate our approach in terms of models we have been building for a system which enables users to gain personalised information about the sensors and services in a ubiquitous computing environment. We report experiments on the scalability and the management of inconsistency in modelling of location, based on accretion-resolution     

Learning pixel-wise signal energy for understanding semantics

Visual interpretation of events requires both an appropriate representation of change occurring in the scene and the application of semantics for differentiating between different types of change. Conventional approaches for tracking objects and modelling object dynamics make use of either temporal region-correlation or pre-learnt shape or appearance models. We propose a new pixel-level approach for learning the temporal characteristics of change at individual pixels. Gaussian mixture models are used to model slow long-term changes in pixel distributions while pixel energy histories are used to extract fast-change signatures from short-term events and modelled by CONDENSATION matching.     

Reliability analysis for evaluation of factors affecting pollutant load reduction in urban stormwater BMP systems

Detention basins are used in best management practices (BMPs) of urban stormwater systems, and total suspended solids (TSS) are used as an indicator of urban nonpoint-source pollutant. To characterize the uncertainty of BMP effluent loads resulting from uncertainty in BMP performance models, this study incorporated uncertainty analysis into a model that predicts the performance of stormwater BMPs based on watershed imperviousness, BMP volume, and BMP surface area. The objective of this study was to examine an approach to urban stormwater BMPs for use in performance analyses. The k-C* model, which incorporates uncertainty analysis, was applied to the uncertainty of stormwater effluent concentrations in urban stormwater systems. To characterize the uncertainty of BMP effluent loads, frequency analysis of the runoff volume and BMP overflow was done, and the load frequency curve (LFC) was simulated with and without BMP conditions, and then verified using the observed TSS load. Finally, the effects of watershed imperviousness, BMP volume, and BMP surface area were investigated using reliability analysis. The results of this study could be used to estimate the appropriate BMP size to achieve a specific watershed-runoff-pollutant load. They could also be used to produce appropriate BMPs to meet the defined nonpoint-source pollutant regulations, including the total maximum daily load.     

Predicting daily streamflow using rainfall forecasts,a simple loss module and unit hydrographs: Two Brazilian catchments

The performance of a simple, spatially-lumped, rainfall–streamflow model is compared with that of a more complex, spatially-distributed model. In terms of two model-fit statistics it is shown that for two catchments in Brazil (about 30,000 km² and 34,000 km²) with different flow regimes, the simpler catchment models, which are unit hydrograph-based and require only rainfall, streamflow and air temperature data for calibration, perform about as well as more complex catchment models that require additional information from satellite images and digitized maps of elevation, land-use and soils. Simple catchment models are applied in forecasting mode, using daily rainfall forecasts from a regional weather forecasting model. The value of the rainfall forecasts, relative to the case where rainfall is known, is assessed for both catchments. The results are discussed in the context of on-going work to compare different modelling approaches for many other Brazilian catchments, and to apply improved forecasting algorithms based on the simple modelling approach to the same, and other, catchments.     

Representing effects of micro-topography on runoff generation and sub-surface flow patterns by using superficial rill/depression storage height variations

An adequate representation of micro-topography in spatially explicit, physically based models can be crucial in modeling runoff generation, surface/subsurface flow interactions or subsurface flow patterns in hydrological systems with pronounced micro-topography. However, representation of micro-topography in numerical models usually requires high grid resolutions to capture relevant small scale variations in topography at the range of centimeters to meters. High grid resolutions usually result in longer simulation times, especially if fully integrated model approaches are used where the governing partial differential equations for surface and subsurface flow are solved simultaneously. This often restricts the implementation of micro-topography to plot scale models where the overall model domain is small to minimize computational cost resulting from a high grid resolution. In this study an approach is presented where a highly resolved digital elevation model (DEM) for a hummocky topography in a plot scale wetland model (10 m × 21 m × 2 m), is represented by spatially distributed rill/depression storage zones in a numerical model with a planar surface. By replacing the explicit micro-topography with spatially distributed rill/depression storage zones, important effects of micro-topography on surface flow generation and subsurface transport characteristics (e.g. residence time distributions) are being preserved, while at the same time the number of computational nodes is reduced significantly. We demonstrate that the rill/depression storage concept, which has been used for some time to represent time delays in the generation of surface runoff, can also be used to mimic subsurface flow patterns caused by micro-topography. Results further indicate that the rill/depression storage concept is an efficient tool to represent micro-topography in plot scale models because model computation times drop significantly. As important aspects of surface and subsurface flows induced by micro-topography can be mimicked adequately by applying the rill/depression storage concept on a coarser grid, it may also be a useful tool to represent micro-topography in numerical flow models beyond the plot scale.     

Language identification using phase information

The present work investigates the importance of phase in language identification (LID). We have proposed three phase based features for the language recognition task. In this work, auto-regressive model with scale factor error augmentation have been used for better representation of phase based features. We have developed three group delay based systems, namely, normal group delay based system, auto-regressive model group delay based system and auto-regressive group delay with scale factor augmentation based system. As mel-frequency cepstral coefficients (MFCCs) are extracted from the magnitude of the Fourier transform, we have combined this MFCC-based system with our phase-based systems to exploit the complete information contained in a speech signal. In this work, we have used IITKGP-MLILSC speech database and OGI Multi-language Telephone Speech (OGI-MLTS) corpus for our experiments. We have used Gaussian mixture models for building the language models. From the experimental results it is observed that the LID accuracy obtained from our proposed phase based features is comparable with MFCC features. We have also observed some performance improvement in the LID accuracy on combining the proposed phase-based systems with the state of the art MFCC-based system.     

Performance of ten rainfall–runoff models applied to an arid catchment in Oman

When selecting a rainfall–runoff model for application to an arid region, the literature dictates the need to consider the spatial features of rainfall and the variability and non-linearity of losses, and to match model complexity to the availability and quality of data. In light of this, the metric-conceptual IHACRES model is applied to hourly data from a 734 km² catchment in Oman, using a semi-distributed representation of rainfall input. Sensitivity analysis is used to guide reduction of the model from a 9-parameter version to simpler versions. The performances of the alternative versions, for predicting flow volumes and peaks at the catchment outlet, are inter-compared. Performances are also compared with those achieved using lumped versions of IHACRES, a physics-based model and a 2-parameter regression model. For peak flows, a 2-parameter non-linear loss model with 2-parameter linear routing, applied in semi-distributed mode, achieves the best overall performance. For flow volumes, the same model was preferred although the routing component was not required. The principal reasons for the success of these models are thought to be their parsimony, representation of spatial rainfall, and ability to compensate for systematic rainfall and flow observation errors. Extra performance is achieved by using a score-based calibration criterion, which is more robust to extreme errors than the fit-based criteria. Although the best performances are poor, with an average absolute relative error across events of 53% for flow peaks and 36% for flow volumes, this is not disappointing compared to other applications of this type. Prediction uncertainty is high due to variability of effective parameter values over events, and uncertainty analysis must explicitly represent this variability in order to explain the observations.     

Nutrient monitoring,simulation and management within a major lowland UK river system: the Kennet

This paper describes an assessment of the nitrogen and phosphorus dynamics of the River Kennet in the south east of England. The Kennet catchment (1200 km²) is a predominantly groundwater fed river impacted by agricultural and sewage sources of nutrient (nitrogen and phosphorus) pollution. The results from a suite of simulation models are integrated to assess the key spatial and temporal variations in the nitrogen (N) and phosphorus (P) chemistry, and the influence of changes in phosphorous inputs from a Sewage Treatment Works on the macrophyte and epiphyte growth patterns. The models used are the Export Co-efficient model, the Integrated Nitrogen in Catchments model, and a new model of in-stream phosphorus and macrophyte dynamics: the ‘Kennet’ model. The paper concludes with a discussion on the present state of knowledge regarding the water quality functioning, future research needs regarding environmental modelling and the use of models as management tools for large, nutrient impacted riverine systems.