Analysis of storage facilities for urban stormwater quantity control

Storage facilities for urban drainage systems are frequently planned and implemented to mitigate the negative impacts of stormwater discharges on receiving waters. For screening level analysis of various runoff control alternatives, cost-effective planning and design of the storage facilities could significantly benefit from analytical tools with explicit solutions to the determination of the relative magnitudes of the storage capacity and the controlled outflow capacity in conjunction with the desired level of system performance. This paper presents methodologies for the development of closed-form mathematical expressions of system performance measures, with which existing drainage system performance and a wider range of alternative designs can be evaluated. As an alternative to continuous simulation for urban stormwater runoff control analysis at the planning stage, these analytical models for stormwater control analysis are developed with the derived probability distribution approach whereby the probability density functions (PDFs) of rainfall characteristics of the catchment are mathematically transformed by rainfall–runoff transformation to create the PDFs of system outputs, such as spill volumes from the storage facility, runoff capture efficiency, etc. This study demonstrates that analytical models, with consideration of the entire spectrum of meteorological conditions, are capable of providing comparable results to continuous simulation models and can be employed as effective tools in urban stormwater management planning.     

Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index

Vegetation processes are seldom considered in lumped conceptual rainfall–runoff (RR) models although they have significant impacts on runoff via the control of evapotranspiration. This paper incorporates the remotely-sensed the moderate resolution imaging spectrometer mounted on the polar-orbiting terra satellite-leaf area index (MODIS-LAI) data into Xinanjiang rainfall–runoff model and assesses the model performance on 210 catchments in south-east Australia. The results show that the inclusion of LAI data improves both the model calibration results as well as the daily runoff prediction in ungauged catchments. It is likely that more significant improvements to the model structure to integrate the remotely-sensed vegetation and other data can further reduce the uncertainty in runoff prediction in ungauged catchments.     

A modelling approach to assessing variations of total suspended solids (tss) mass fluxes during storm events

Connections between the catchment hydrology and accumulation, washoff and transport of pollutants in wet weather greatly affect the management of urban drainage and its wet‐weather effluents. In recent years, the concept of the first flush has gained on prominence and was further developed for analyzing the interaction between the hydrology and transport of runoff pollutants. One of the most important definitions of the first flush can be derived from the analysis of the m(v) curves (i.e. the curves in which the normalized cumulative pollutant mass is plotted vs the normalized cumulative runoff volume). Indeed the m(v) curves, indicating the distribution of pollutant mass versus volume in wet‐weather flow (WWF) discharges, are commonly used for comparing pollutant discharges for different rainfall events and catchments. In this study, the m(v) curves were used to define the concepts of flow‐limited and mass‐limited WWF events. These two different behaviours have been analysed for rainfall/runoff events observed in the urbanized part of the Liguori catchment in Cosenza (Italy). In order to advance the understanding of the intra‐event variability of m(v) curves, the mathematical rainfall/runoff model Storm Water Management Model of the US Environmental Protection Agency (SWMM) was calibrated for eight observed rainfall/runoff events and the differences between observed and simulated m(v) curves were analysed. The results showed a good correlation between the observed and simulated m(v) curves, and this finding offers further benefits in SWMM model calibration. Copyright © 2013 John Wiley & Sons, Ltd.     

A deterministic linearized recurrent neural network for recognizing the transition of rainfall–runoff processes

Characterizing the dynamic relationship between rainfall and runoff is a highly interesting modeling problem in hydrology. This study develops a deterministic linearized recurrent neural network (denoted as DLRNN) that deals with the system’s nonlinearity by recalibration at each time interval, and relates the weights of DLRNN to unit hydrographs in order to describe the transition of the rainfall–runoff processes. Case studies of 38 events, from 1966 to 1997, are implemented in the Wu-Tu watershed of Taiwan, where the runoff path-lines are short and steep. A comparison between the DLRNN and a feed-forward neural network demonstrates the advantage of DLRNN as a dynamic system model. It is concluded that DLRNN shows superiority in the performance of rainfall–runoff simulations and the ability to recognize transitions in hydrological processes.     

Effects of land use intensity on stormwater runoff and its temporal occurrence in cold climates

The impacts of land use intensity, here defined as the degree of imperviousness, on stormwater volumes, runoff rates and their temporal occurrence were studied at three urban catchments in a cold region in southern Finland. The catchments with ‘High’ and ‘Intermediate’ land use intensity, located around the city centre, were characterized by 89% and 62% impervious surfaces, respectively. The ‘Low’ catchment was situated in a residential area of 19% imperviousness. During a 2‐year study period with divergent weather conditions, the generation of stormwater correlated positively with catchment imperviousness: The largest annual stormwater volumes and the highest runoff coefficients and number of stormwater runoff events occurred in the High catchment. Land use intensity also altered the seasonality of stormwater runoff: Most stormwater in the High catchment was generated during the warm period of the year, whereas the largest contribution to annual stormwater generation in the Low catchment took place during the cold period. In the two most urbanized catchments, spring snow melt occurred a few weeks earlier than in the Low catchment. The rate of stormwater runoff in the High and Intermediate catchments was higher in summer than during spring snow melt, and summer runoff rates in these more urbanized catchments were several times higher than in the Low catchment. Our study suggests that the effects of land use intensity on stormwater runoff are season dependent in cold climates and that cold seasons diminish the differences between land use intensities. Copyright © 2013 John Wiley & Sons, Ltd.     

Stormwater pollutant runoff: A stochastic approach

Since stormwater wash-off of pollutants in urban areas is largely affected by environmental variability, it is very difficult to predict the amount of pollutants transported by stormwater runoff during and after individual rainfall events. We investigated the addition of a random component into an exponential wash-off equation of total suspended solids (TSS) and total nitrogen (TN) to model the variability of runoff pollutant concentrations. The model can be analytically solved to describe the probability distributions of TSS and TN concentrations as a function of increasing runoff depths. TSS data from six Australian catchments and TN data from three of these catchments were used to calibrate the model and evaluate its applicability. Using the results of the model, its potential use to determine the appropriate size of stormwater treatment systems is discussed, stressing how probabilistic considerations should be included in the design of such systems. Specifically, stormwater depths retained by a treatment system should result from a compromise between the recurrence of specific runoff depths and the probability to discharge a target pollutant concentration when such a runoff depth is exceeded.     

Characterization and prediction of stormwater runoff quality in sub-tropical rural catchments

Due to scarcity of local data on stormwater pollution levels and rainfall-runoff generation process, very few attempts have been made towards the management of stormwater in sub-tropical rural catchments. An attempt has been made in the present study to characterize and predict the stormwater runoff characteristics using regression modeling from five rural catchments in north-west India. Stormwater samples and flow data were collected from 75 storm events. Samples were analyzed for pH, total suspended solids (TSS), 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), total kjeldhal nitrogen (TKN), total phosphorous (TP), nitrate-nitrogen (NO ₃ ^- –N), total coliform count (TC), fecal coliform count (FC), Zn, Cu and Fe. It was found that size of the catchment and the land use practices influenced the stormwater quality even in predominantly rural areas, otherwise thought to be homogeneous. The results obtained were related with the antecedent dry days (ADD) and average rainfall. ADD was found to be positively correlated with pollutant loads whereas average rainfall showed negative correlation. The study highlights the importance of ADD in causing greater mean pollutant concentrations except for TKN, TP and NO ₃ ^- –N. Regression models were developed for the studied catchments to estimate mean pollutant concentrations as a function of rainfall variables. Results revealed that measured pollutant concentrations demonstrated high variability with ADD and average rainfall in small rural catchments, whereas in large catchments, factors like land use, extent of imperviousness etc. resulted in low predictability of measured parameters.     

Artificial neural networks for daily rainfall—runoff modelling

Abstract

The application of artificial neural network (ANN) methodology for modelling daily flows during monsoon flood events for a large size catchment of the Narmada River in Madhya Pradesh (India) is presented. The spatial variation of rainfall is accounted for by subdividing the catchment and treating the average rainfall of each subcatchment as a parallel and separate lumped input to the model. A linear multiple-input single-output (MISO) model coupled with the ANN is shown to provide a better representation of the rainfall-runoff relationship in such large size catchments compared with linear and nonlinear MISO models. The present model provides a systematic approach for runoff estimation and represents improvement in prediction accuracy over the other models studied herein.     

Modelling the impact of stormwater source control infiltration techniques on catchment baseflow

Stormwater management increasingly recognises the need to emulate, to the maximum extent possible, the flow regime of receiving waters in their pre‐development state. Hydrological models play a central role in assessing the catchment‐scale impacts of alternative stormwater management strategies. However, because of the complexity of physical processes involved in urban hydrology, particularly subsurface flows, the predictive performance of such models is often low. We investigated how the structure of hydrological models influenced the prediction of urbanisation and stormwater management impacts on baseflow. We calibrated three conceptual models of the same reference catchment and compared the modelled flow regime from different stormwater management scenarios, using each of the three model structures. Scenarios were assessed using six metrics, characterising the whole streamflow regime and in particular baseflow. Although the three models of the reference catchment represented the observed hydrograph well, the most complex structure developed using a thorough diagnostic of the catchment behaviour better captured the change in hydrological regime during dry years. Predictions of baseflow changes due to urbanisation varied significantly according to the model structure. Similarly, the models showed distinct responses to the stormwater management scenarios applied, especially for scenarios involving infiltration of stormwater at source. Our results confirm the importance of predicting the consequences of land use changes with conceptual models that are consistent with the hydrological behaviour of the study catchment. Future work should help to quantify the uncertainties due to model structure and thus provide practical guidance to the use of catchment models for assessing stormwater management strategies. Copyright © 2013 John Wiley & Sons, Ltd.     

Effectiveness of landscape‐based green infrastructure for stormwater management in suburban catchments

Land cover changes associated with urbanization have negative effects on downstream ecosystems. Contemporary urban development attempts to mitigate these effects by designing stormwater infrastructure to mimic predevelopment hydrology, but their performance is highly variable. This study used in situ monitoring of recently built neighbourhoods to evaluate the catchment‐scale effectiveness of landscape decentralized stormwater control measures (SCMs) in the form of street connected vegetated swales for reducing runoff volumes and flow rates relative to curb‐and‐gutter infrastructure. Effectiveness of the SCMs was quantified by monitoring runoff for 8 months at the outlets of 4 suburban catchments (0.76–5.25 ha) in Maryland, USA. Three “grey” catchments installed curb‐and‐gutter stormwater conveyances, whereas the fourth “green” catchment built parcel‐level vegetated swales. The catchment with decentralized SCMs reduced runoff, runoff ratio, and peak runoff compared with the grey infrastructure catchments. In addition, the green catchment delayed runoff, resulting in longer precipitation–runoff lag times. Runoff ratios across the monitoring period were 0.13 at the green catchment and 0.37, 0.35, and 0.18 at the 3 grey catchments. Runoff only commenced after 6 mm of precipitation at the decentralized SCM catchment, whereas runoff occurred even during the smallest events at the grey catchments. However, as precipitation magnitudes reached 20 mm, the green catchment runoff characteristics were similar to those at the grey catchments, which made up 37% of the total precipitation in only 10 of 72 events. Therefore, volume‐based reduction goals for stormwater using decentralized SCMs such as vegetated swales require additional redundant SCMs in a treatment train as source control and/or end‐of‐pipe detention to capture a larger fraction of runoff and more effectively mimic predevelopment hydrology for the relatively rare but larger precipitation events.     

Urban stormwater thermal gene expression models for protection of sensitive receiving streams

Thermal impact of typical high‐density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold, and cool‐water streams. This is the first study of its kind that provides simple easy‐to‐use equations, developed using gene expression programming (GEP) that can guide the assessment and the design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed 3 GEP models using data collected during 3 years (2009–2011) from 4 urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90–0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis, we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff.     

Upscaling of saturated conductivity for Hortonian runoff modelling

Stochastic and deterministic upscaling techniques are developed that upscale saturated conductivity at the support of 0.04 m² to representative actual infiltration (I_b) for support units (blocks) of 10¹–10⁴ m², as a function of steady state rainfall and runon to the block, under Hortonian runoff (infiltration excess overland flow). Parameters in the upscaling techniques represent the surface runoff flow pattern and the spatial probability distribution of saturated conductivity within the 10¹–10⁴ m² block. The stochastic upscaling technique represents the spatial process of infiltration and runoff using a simple process-imitating model, estimating I_b using Monte Carlo simulation. The deterministic upscaling technique aggregates these processes by a deterministic function relating rainfall and runon to I_b. The stochastic upscaling technique is shown to be capable to upscale saturated conductivity derived from ring infiltrometers to I_b values of plots (1 m²) corresponding to measured I_b values using rainfall simulators. It is shown that both upscaling techniques can be used to estimate I_b for each time step and each block in transient rainfall–runoff models, giving better estimates of cumulative runoff from a hillslope and a small catchment than model runs that do not use upscaling techniques.     

Evaluating sources of uncertainty in Australian runoff projections

Generating estimates of the future impacts of climate change on human and natural systems is confounded by cascading uncertainties which propagate through the impact assessment. Here, a simple stochastic rainfall–runoff model representing 238 river basins on the Australian continent was used to assess the sensitivity of the risk of runoff changes to various sources of uncertainty. Uncertainties included global mean temperature change, greenhouse gas stabilisation targets, catchment sensitivities to climatic change, and the seasonality of runoff, rainfall, and evaporation. Model simulations provided estimates of the first-order risk of climate change to Australian catchments, with several regions having high likelihoods of experiencing significant reductions in future runoff. Climate uncertainty (at global and regional scales) was identified as the dominant driving force in hydrological risk assessments. Uncertainties in catchment sensitivities to climatic changes also influenced risk, provided they were sufficiently large, whereas structural assumptions of the model were generally negligible. Collectively, these results indicate that rigorous assessment of climate risk to water resources over relatively long time-scales is largely a function of adequately exploring the uncertainty space of future climate changes.     

Evaluation of rainfall–runoff model performance under non-stationary hydroclimatic conditions

ABSTRACT

Understanding of rainfall–runoff model performance under non-stationary hydroclimatic conditions is limited. This study compared lumped (IHACRES), semi-distributed (HEC-HMS) and fully-distributed (SWATgrid) hydrological models to determine which most realistically simulates runoff in catchments where non-stationarity in rainfall–runoff relationships exists. The models were calibrated and validated under different hydroclimatic conditions (Average, Wet and Dry) for two heterogeneous catchments in southeast Australia (SEA). SWATgrid realistically simulates runoff in the smaller catchment under most hydroclimatic conditions but fails when the model is calibrated in Dry conditions and validated in Wet. All three models perform poorly in the larger catchment irrespective of hydroclimatic conditions. This highlights the need for more research aimed at improving the ability of hydrological models to realistically incorporate the physical processes causing non-stationarity in rainfall–runoff relationships. Although the study is focussed on SEA, the insights gained are useful for all regions which experience large hydroclimatic variability and multi-year/decadal droughts.     

Stormwater quality during residential construction activities: influential variables

Research on the water quality of urban runoff has so far focussed on the post‐development phase of urban catchments, whereas water quality in developing areas under construction has remained less understood. The construction phase, however, may constitute a considerable source of diffuse pollution in urban areas. This study investigated the mechanisms affecting water quality in residential areas during the construction and post‐construction phases. Water quality (suspended solids, total phosphorus, total nitrogen and chemical oxygen demand) and runoff were monitored over a period of 5 years in three catchments located in the city of Espoo in Southern Finland. The catchments included an urbanizing area under construction and low‐density and high‐density residential areas. The water quality was quantified in terms of event mean concentrations and loads. The key influential variables explaining the water quality in a multiple linear regression analysis included hydrological variables (event volume and intensity), antecedent conditions and a variable describing the ongoing construction projects. Construction activities in the developing catchment had a profound impact on water quality. Inclusion of the variables describing activities, such as earth moving works, paving, house construction and temporary wastewater discharges, was necessary to explain water quality variations in the developing catchment. The importance of antecedent conditions as an explanatory variable depended on the site and the pollutant in question. Copyright © 2015 John Wiley & Sons, Ltd.     

Modified NLPM-ANN model and its application

The non-linear perturbation model based on artificial neural network (NLPM-ANN) takes advantage of the consideration of seasonal information by the linear perturbation model (LPM) and the notable non-linear simulation capability of artificial neural network (ANN). However, this model does not take account of antecedent catchment wetness that may effect the simulation and forecasting accuracy. A modified NLPM-ANN model is proposed and developed to take the consideration of antecedent catchment wetness. The output perturbing terms of the response function in the simple linear model (SLM) in an auxiliary component are taken as inputs of ANN to represent catchment wetness. The simulated total runoff is obtained by integrating the outputs of ANN with that of the seasonal model. The rainfall–runoff data of eight catchments were selected and used to compare the modified NLPM-ANN with the NLPM-ANN models. Results show that the modified NLPM-ANN is significantly superior to the NLPM-ANN, and the model component efficiency index values are 16.82% and 16.74% over the NLPM-ANN during calibration and verification periods, respectively.     

Storm runoff response to rainfall pattern,magnitude and urbanization in a developing urban catchment

This study explored the hydrological impacts of urbanization, rainfall pattern and magnitude in a developing catchment. The Stormwater Management Model was parameterized, calibrated and validated in three development phases, which had the same catchment area (12.3 ha) but different land use intensities. The model calibration and validation by using sub‐hourly hydro‐meteorological data demonstrated a good performance of the model in predicting stormwater runoff in the different development phases. Based on the results, a threshold between minor and major rainfall events was identified and conservatively determined to be about 17.5 mm in depth. Direct runoff for minor storm events has a linear relationship with rainfall; however, events with a rainfall depth greater than the threshold yield a rainfall–runoff regression line with a clearly steeper slope. The difference in urban runoff generation between minor and major rainfall events diminishes with the increase of imperviousness. Urbanization leads to an increase in the production of stormwater runoff, but during infrequent major storms, the runoff contribution from pervious surfaces reduces the runoff changes owing to urbanization. Rainfall pattern exerts an important effect on urban runoff, which is reflected in pervious runoff. With the same magnitude, prolonged rainfall events with unvarying low intensity yield the smallest peak flow and the smallest total runoff, yet rainfall events with high peak intensity produce the largest runoff volume. These results demonstrate the different roles of impervious and pervious surfaces in runoff generation, and how runoff responds to rainstorms in urban catchments depends on hyetograph and event magnitude. Furthermore, the study provides a scientific basis of the design guideline sustainable urban drainage systems, which are still arbitrary in many countries. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of pollutant build-up and wash-off from selected land uses at the Port of Brisbane, Australia

The quality of stormwater runoff from seaports can be an important source of pollution to the marine environment. Currently, little knowledge exists with regards to the pollutant generation capacity specific to seaports as they do not necessarily compare well with conventional urban land use. The research project focussed on the assessment of pollutant build-up and wash-off. The study was undertaken using rainfall simulation and small impervious plots for different port land uses with the results obtained compared to typical urban land uses.The study outcomes confirmed that the Port land uses exhibit comparatively lower pollutant concentrations. However, the pollutant characteristics varied across different land uses. Hence, the provision of stereotypical water quality improvement measures could be of limited value. Particle size <150 μm was predominant in suspended solids. Therefore, if suspended solids are targeted as the surrogate parameter for water quality improvement, this particle size range needs to be removed.     

Assessment of Hurricane Ivan impact on chlorophyll-a in Pensacola Bay by MODIS 250 m remote sensing

The impact of Hurricane Ivan on water quality in Pensacola Bay was investigated by MODIS 250 m remote sensing of chlorophyll-a concentrations at different time slots before and after the hurricane event. Before the hurricane, the mean chlorophyll-a in the Bay was 5.3 μg/L. Heavy rainfall occurred during the hurricane landfall. The 48 h rainfall reached 40 cm and the peak storm surge reached 3 m on 9/16. After the rainstorm and during the storm surge on 9/17/2004, the mean chlorophyll-a concentration substantially increased to 14.7 μg/L. 26.3% water area was in the poor-water-quality condition (chl-a > 20 μg/L). This indicates that heavy nutrient loads from urban stormwater runoff and storm-surge inundation simulated chlorophyll bloom. After the end of the storm surge on 9/18/2004, the mean chlorophyll dropped to 2.0 μg/L, suggesting the effective flushing of polluted water from the bay to the Gulf of Mexico by the storm-surge. The good water quality condition lasted for almost several weeks after the storm surge. The peak river flow, arriving on the 4th day after the peak storm surge, did not alter the good water quality situation in the bay. This indicates that urban stormwater runoff rather than the river inflow is the major pollutant source for water quality in Pensacola Bay during the hurricane.     

PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments

The Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and – optionally, if backwater effects have a significant impact on the flow regime – a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) – portraying the rainfall–runoff process – and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF – essentially consisting of the coupled “hydrologic” PoNN and “hydrodynamic” MLFN – to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km²). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.