Статті в журналах з теми "Urban runoff Australia Mathematical models"

Abstract The Runoff assessment is a crucial parameter in understanding the urban flooding scenario. This estimation becomes the deciding factor because of the uneven distribution of rainfall. Physics-based models for simulation of Runoff from catchments are composite models based on learning algorithms. The application of models to water resource problems is complex due to the incredible spatial variability of the characteristics of watershed and precipitation forms — the pattern-learning algorithms. Fuzzy-based algorithms, Artificial Neural Networks (ANNs), etc., have gained wide recognition in simulating the Rainfall-Runoff (RR), producing a comparable accuracy. In the present study, RR modeling is carried out targeting the application and estimation of Runoff using mathematical modeling. The investigations were carried out for the Malkajgiri catchment adopting 16 years of daily data from 2005 to 2021. The statistical learning theory-based pattern-learning algorithm is further utilized to evaluate the value of Runoff for the year 2021. The results were found to have fair accordance with the analytical outcomes.

In this paper we present a benchmarking methodology, which aims at comparing urban runoff quality models, based on the Bayesian theory. After choosing the different configurations of models to be tested, this methodology uses the Metropolis algorithm, a general MCMC sampling method, to estimate the posterior distributions of the models' parameters. The analysis of these posterior distributions allows a quantitative assessment of the parameters' uncertainties and their interaction structure, and provides information about the sensitivity of the probability distribution of the model output to parameters. The effectiveness and efficiency of this methodology are illustrated in the context of 4 configurations of pollutants' accumulation/erosion models, tested on 4 street subcatchments. Calibration results demonstrate that the Metropolis algorithm produces reliable inferences of parameters thus, helping on the improvement of the mathematical concept of model equations.

Abstract The purpose of mathematical modelling of sewer networks is mainly to assess the hydraulic capacity and monitor its behaviour under different conditions to predict the future state. Sewerage network models are also part of the design process. Their advantage is the possibility of simulating the future state of the network and the precipitation and runoff process in the context of climate change. With the help of simulations, it is possible to anticipate future conditions in urban catchments and thus effectively design new networks. The aim of this paper is to summarize mathematical simulation models that are used to model sewer networks.

This article discusses the use of green roofs as rainfall water storage in its soil matrix. The methodology is analytical based on mathematical models, where runoff produced in an urban area is compared with current conditions of ordinary roofs with ceramic or bituminous materials as the original scenario, against another where green roofs are used. The study area is located in the Palavecino municipality of Lara state in Venezuela, in the flood zone of Quebrada Tabure. In this research, a quantitative comparison of the direct runoff hydrographs of the proposed scenarios was used, obtaining as a main result the reduction of runoff between 60% and 80% according to the period of return. An interesting point of this research was the incorporation of the routing of hydrographs on the roofs, reducing even more the peak flow over 90%, and delaying the peak time of the generated hydrographs between 10 and 12 minutes while the total duration of the hydrographs increase more than three times.

There is increasing awareness about uncertainties in the modelling of urban drainage systems and, as such, many new methods for uncertainty analyses have been developed. Despite this, all available methods have limitations which restrict their widespread application among practitioners. Here, a modified Monte-Carlo based method is presented that reduces the subjectivity inherent in typical uncertainty approaches (e.g. cut-off thresholds), while using tangible concepts and providing practical outcomes for practitioners. The method compares the model's uncertainty bands to the uncertainty inherent in each measured/observed datapoint; an issue that is commonly overlooked in the uncertainty analysis of urban drainage models. This comparison allows the user to intuitively estimate the optimum number of simulations required to conduct uncertainty analyses. The output of the method includes parameter probability distributions (often used for sensitivity analyses) and prediction intervals. To demonstrate the new method, it is applied to a conceptual rainfall-runoff model (MOPUS) using a dataset collected from Melbourne, Australia.

The article considers one of the important directions of innovative technologies in the urban economy, application of digital terrain models in the design, development and operation of utility networks. The author considers the five tasks of using the digital model sequentially: ) development of a digital terrain model, 2) allocation of watersheds and facies, 3) plotting contours (ridges) for all facies, 4) two-level modeling of surface runoff and storm sewer, 5) solving practical problems of determining silting zones and optimizing snow removal. The original principle proposed in the article is a multi-funnel model of surface runoff, in which each facies (local catchment) is replaced by an equivalent inclined funnel. This greatly simplifies the calculations, and also allows you to combine mathematical modeling with physical modeling.

Many runoff models are currently in use to predict both the quantity and quality of storm water runoff. In most models, the quality algorithms need further development to gain the confidence of model users. The writers have attempted to disaggregate the accumulation process and to develop improved algorithms for pollutant buildup. The factors and processes that affect buildup include atmospheric dustfall due to plumes of dust-laden air, wind effects, vehicles, intentional removals (e.g., street cleaning), special activities (such as construction and demolition), biological decomposition, and population-related activities (e.g., vegetation density, insecticides, herbicides, fertilizers, and lawn cutting). Mathematical expressions for each of these mechanisms are presented and utilized to develop algorithms in the RUNOFF module of the SWMM3 package.A separate multiregression model is used to generate atmospheric dustfall from meteorological information; this is input to the new program (NEWBLD) to calculate pollutant accumulation on individual subcatchments. NEWBLD is interfaced with the RUNOFF block of SWMM3. A sensitivity analysis is carried out using data for the Chedoke Creek catchment in Hamilton, Ontario. The modified version of the SWMM3 RUNOFF block developed herein by incorporating the new water quality algorithms is called CHGQUAL. It is applied to an urban catchment in Hamilton, Ontario. Key words: storm water models, dust and dirt buildup, storm water pollution, urban hydrology, air pollution.

Quantifying hydrological losses in a catchment is crucial for developing an effective flood forecasting system and estimating design floods. This can be a complicated and challenging task when the catchment is urbanised as the interaction of pervious and impervious (both directly connected and indirectly connected) areas makes responses to rainfall hard to predict. This paper presents the challenges faced in estimating initial losses (IL) and proportional losses (PL) of the partly urbanised Brownhill Creek catchment in South Australia. The loss components were calculated for 57 runoff generating rainfall events using the non-parametric IL-PL method and parametric method based on two runoff routing models, Runoff Routing Burroughs (RORB) and Rainfall-Runoff Routing (RRR). The analysis showed that the RORB model provided the most representative median IL and PL for the rural portion of the study area as 9 mm and 0.81, respectively. However, none of the methods can provide a reliable loss value for the urban portion because there is no runoff contribution from unconnected areas for each event. However, the estimated non-parametric IL of 1.37 mm can be considered as IL of EIA of the urban portion. Several challenges were identified in the loss estimation process, mainly when selecting appropriate storm events, collecting data with the available temporal resolution, extracting baseflow, and determining the main-stream transmission losses, which reduced the urban flow by 5.7%. The effect of hydrograph shape in non-parametric loss estimation and how combined runoff from the effective impervious area and unconnected (combined indirectly connected impervious and pervious) areas affects the loss estimation process using the RORB and RRR models are further discussed. We also demonstrate the importance of identifying the catchment specific conditions appropriately when quantifying baseflow and runoff of selected events for loss estimation.

Runoff is one of the main sources of diffuse pollution of surface water. Suspended solids are the most typical contaminants of runoff. Suspended solids have a great influence on water quality and ecological status of water bodies. The content of suspended solids in water bodies is determined not only by their receipt from external sources, but also by the ability to transport sediments by flow. There is a permanent exchange of suspended solids between water bodies and bottom deposits. This fact stimulates specific requirements for modeling the transfer of suspended solids. For the most cases, models which describe the transfer of suspensions in a turbulent flow are based on the three-dimensional equation of turbulent diffusion or its two-dimensional simplification, which allows to take into account the spatial distribution of substances or it’s distribution to the width of stream. The use of such models requires a significant amount of initial data to determine the parameters of the models and is associated with a significant amount of calculations. At the same time, one-dimensional interpretation of processes is common and practically sufficient for shallow watercourses. It is more important to take into account the dynamics of the exchange of suspended solids between water mass and bottom deposits. The article is devoted to the development of a mathematical model for estimating the influence of non - point sources of pollution on the content of suspended solids in narrow watercourses. The model is based on the principle of mass balance of substances entering the flow section and takes into account the processes of sedimentation and resedimentation of suspended solids. A mathematical model in the form of a differential equation for the case of normal and overloaded flow is developed. Analytical solutions of equations for both cases are obtained. The influence of hydraulic size of the suspension on the content of suspended solids in watercourses and its distribution along the flow length is analyzed. The developed model was used for estimation of the impact of runoff from the urban area of Kharkiv city (Ukraine) on the river Lopan. The model demonstrated satisfactory compliance with field data.

Urbanization increases imperviousness and reduces infiltration, retention, and evapotranspiration, frequently aggravating urban flooding due to greater runoff and higher and faster discharge peaks. Effective strategies to mitigate flood risks require a better understanding of the watershed dynamics and space to reverse the negative impacts. However, often cities do not have proper data sets to feed mathematical models that would be helpful in mapping water dynamics. Attempts to reduce flood risks have been made for decades by means of structural interventions but were frequently designed within the logic of a local scale, using limited available spaces and often merely shifting flooding downstream. Therefore, assessing urban floods requires a modeling approach capable of reflecting the watershed scale, considering interactions between hydraulic structures and urban landscape, where best practices and non-structural measures aim to improve community flood resilience through the reduction of social and financial costs in the long run. This paper proposes an integrated approach to analyze low impact development (LID) practices complemented by non-structural measures in a case study in southern Italy, supported by mathematical modeling in a strategy to overcome a context of almost no available data and limited urban open spaces.

The use of urban drainage models requires careful calibration, where model parameters are selected in order to minimize the difference between measured and simulated results. It has been recognized that often more than one set of calibration parameters can achieve similar model accuracy. A probability distribution of model parameters should therefore be constructed to examine the model's sensitivity to its parameters. With increasing complexity of models, it also becomes important to analyze the model parameter sensitivity while taking into account uncertainties in input and calibration data. In this study a Bayesian approach was used to develop a framework for quantification of impacts of uncertainties in the model inputs on the parameters of a simple integrated stormwater model for calculating runoff, total suspended solids and total nitrogen loads. The framework was applied to two catchments in Australia. It was found that only systematic rainfall errors have a significant impact on flow model parameters. The most sensitive flow parameter was the effective impervious area, which can be calibrated to completely compensate for the input data uncertainties. The pollution model parameters were influenced by both systematic and random rainfall errors. Additionally an impact of circumstances (e.g. catchment type, data availability) has been recognized.

In the search for benefits to justify transport projects, economic appraisals have increasingly incorporated the valuation of impacts to the wider economy. Computable general equilibrium (CGE) models provide a framework to estimate these impacts by simulating the interactions of urban economies and transport networks. In CGE models, households and firms are represented by microeconomic behavioral functions, and markets adjust according to prices. As markets both inside and outside the transport network are taken into account, a wide variety of measures that can assist in economic appraisals can be extracted. However, urban CGE models are computationally burdensome and require detailed, spatially disaggregate data. This paper discusses the methodology used to develop a database, including an input–output table, for the calibration of an urban CGE model for Sydney, Australia. Official and publicly available data sources were manipulated by using a number of mathematical and statistical techniques to compile a table for 249 regions and 20 sectors across Sydney. Issues, such as determining the appropriate level of aggregation, generating incomplete data, and managing conflicting data, that other input–output table developers may encounter when constructing multiregional tables were addressed in the study. The table entries themselves were mapped and explored, as they provide a useful study of the spatial economy of Sydney. Future work will focus on streamlining the construction of input–output tables and incorporating new data sources.

Numerical simulation of mixed flows combining free surface and pressurized flows is a practical tool to prevent possible flood situations in urban environments. When dealing with intense storm events, the limited capacity of the drainage network conduits can cause undesirable flooding situations. Computational simulation of the involved processes can lead to better management of the drainage network of urban areas. In particular, it is interesting to simultaneuously calculate the possible pressurization of the pipe network and the surface water dynamics in case of overflow. In this work, the coupling of two models is presented. The surface flow model is based on two-dimensional shallow water equations with which it is possible to solve the overland water dynamics as well as the transformation of rainfall into runoff through different submodels of infiltration. The underground drainage system assumes mostly free surface flow that can be pressurized in specific situations. The pipe network is modeled by means of one-dimensional sections coupled with the surface model in specific regions of the domain, such as drains or sewers. The numerical techniques considered for the resolution of both mathematical models are based on finite volume schemes with a first-order upwind discretization. The coupling of the models is verified using laboratory experimental data. Furthermore, the potential usefulness of the approach is demonstrated using real flooding data in a urban environment.

Demand for water in Australia is increasing along with growing pressure to maximise the efficiency of irrigation water use and seek additional and alternative irrigation supplies. The scarcity of water supplies coupled with the need for urban communities to dispose of large quantities of treated recycled water from sewage treatment plants has led to increasing interest from urban and rural communities in the reticulation of this water for irrigating adjacent crop-production areas. Proposals to use recycled water inevitably lead to a complex range of issues that need to be addressed, including: costs and benefits of supplying an additional source of water to current or new cropping systems; optimum irrigation design and management, particularly where there are multiple sources of irrigation water; management of overflow from on-farm water storages; and environmental implications with regard to salinity, runoff, drainage, nitrate leaching, and environmental flows. Simulation models can capture many of the key factors and processes influencing irrigated crop production systems, and can play a useful role in exploring these issues. In this paper, we have described an approach that couples agricultural production system and economic models in a way that enables analysis of the likely benefits and risks of investing in recycled water, although the analysis is equally relevant to any assessment of the value of an additional source of irrigation water, particularly saline water. The approach has been illustrated with a case study of a mixed-crop farm in the Darling Downs region of Queensland, Australia, in which the farm-scale crop production, economic, and environmental implications of investing in recycled water were considered.

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.

An innovative concept of dynamic stormwater storage in sponge-like porous bodies (SPBs) is presented and modelled using first principles, for down-flow and up-flow variants of SPBs. The rate of inflow driven by absorption and/or capillary action into various porous material structures was computed as a function of time and found to be critically dependent on the type of structure and the porous material used. In a case study, the rates of inflow and storage filling were modelled for various conditions and found to match, or exceed, the rates of rainwater inflow and volume accumulation associated with two types of Swedish rainfalls, of 60-min duration and a return period of 10 years. Hence, the mathematical models indicated that the SPB devices studied could capture relevant amounts of water. The theoretical study also showed that the SPB concepts could be further optimized. Such findings confirmed the potential of dynamic SPB storage to control stormwater runoff and serve as one of numerous elements contributing to restoration of pre-urban hydrology in urban catchments. Finally, the issues to be considered in bringing this theoretical concept to a higher Technological Readiness Level were discussed briefly, including operational challenges. However, it should be noted that a proper analysis of such issues requires a separate study building on the current presentation of theoretical concepts.

Households are an important location for the transmission of communicable diseases. Social contact between household members is typically more frequent, of greater intensity, and is more likely to involve people of different age groups than contact occurring in the general community. Understanding household structure in different populations is therefore fundamental to explaining patterns of disease transmission in these populations. Indigenous populations in Australia tend to live in larger households than non-Indigenous populations, but limited data are available on the structure of these households, and how they differ between remote and urban communities. We have developed a novel approach to the collection of household structure data, suitable for use in a variety of contexts, which provides a detailed view of age, gender, and room occupancy patterns in remote and urban Australian Indigenous households. Here we report analysis of data collected using this tool, which quantifies the extent of crowding in Indigenous households, particularly in remote areas. We use these data to generate matrices of age-specific contact rates, as used by mathematical models of infectious disease transmission. To demonstrate the impact of household structure, we use a mathematical model to simulate an influenza-like illness in different populations. Our simulations suggest that outbreaks in remote populations are likely to spread more rapidly and to a greater extent than outbreaks in non-Indigenous populations.

MANEJO DE BACIAS HIDROGRÁFICAS E SUA INFLUÊNCIASOBRE OS RECURSOS HÍDRICOS CAMILA FERNANDES FERREIRA APARECIDO1; LUIZ SERGIO VANZELA2; GISELE HERBST VAZQUEZ3 E RONALDO CINTRA LIMA4 1Mestranda em Ciências Ambientais, UNICASTELO, Campus de Fernandópolis - SP, CEP 15600-000. camilaff_gyn@hotmail.com2Prof. Titular do Departamento de Agricultura, Campus de Fernandópolis - SP, CEP 15600-000. lsvanzela@yahoo.com.br3Prof. Titular do Departamento de Agricultura, Campus de Fernandópolis - SP, CEP 15600-000. gisele-agro@uol.com.br4Prof. Professor Assistente Doutor da Coordenadoria de Curso de Engenharia Agronômica, FCAT-UNESP, CEP 17900-000. Dracena – SP. rclima@dracena.unesp.br 1 RESUMO Os estudos sobre o uso e ocupação do solo explorado pelos mais diversos seguimentos da sociedade, sejam eles, urbanos, rurais e industriais, associados às características das bacias hidrográficas são de extrema importância para o gerenciamento e a sustentabilidade dos recursos hídricos. Assim, este trabalho objetivou avaliar a influência de diferentes manejos de uso e ocupação do solo associados a declividade e ao escoamento superficial, sobre os recursos hídricos de bacias hidrográficas localizadas no Triângulo Mineiro - MG. O trabalho foi desenvolvido por meio do monitoramento das vazões e sedimentos em 4 bacias, no período de setembro de 2013 a outubro de 2014, além da determinação dos usos e ocupações do solo, declividade e dos escoamentos superficiais no período. Os modelos matemáticos obtidos demonstraram que a proporção entre os diferentes usos e ocupações do solo interfere na produção específica e na concentração de sedimentos, bem como na vazão específica das bacias hidrográficas, enquanto a quantidade de escoamento superficial interfere na produção específica de sedimentos. O resultado das simulações do manejo do uso e ocupação das bacias hidrográficas demonstrou que a conservação de matas nativas, as técnicas conservacionistas de uso do solo e as técnicas de minimização do transporte difuso originado das áreas urbanas, são essenciais na manutenção da disponibilidade e qualidade de água. Palavras-chave: escoamento superficial, uso e ocupação, declividade. APARECIDO, C. F. F.; VANZELA, L. S.; VAZQUEZ, G. H.; LIMA, R. C.WATERSHED MANAGEMENT AND ITS INFLUENCE ON THE HYDRIC RESOURCES 2 ABSTRACT Studies on the use and occupation of soil explored by various segments of urban, rural and industrial societies, associated to the watershed characteristics are extremely important for water resources management and sustainability. Therefore, this work aimed to evaluate the influence of different managements of soil use and occupation associated to declivity and superficial flow water resources of watersheds located in Triângulo Mineiro – MG. The study was conducted by monitoring the flow and sediment in 4 basins from September 2013 to October 2014, besides the determination of soil use and occupation, declivity and superficial flow during this time. The obtained mathematical models showed that the ratio of the different soil uses and occupations interferes with the specific production and sediment concentration and the specific flow of watersheds as well, while the amount of superficial flow interferes with the specific sediment production. The simulations results of the management of watersheds’ use and occupation showed that the conservation of native forests, the conservationist techniques of soil use and the minimization techniques of diffuse transport from urban areas are essential for keeping water availability and quality. Keywords: runoff, land use, slope

Modern Mathematical Models have been developed for studying the complex hydrological processes of a watershed and their direct relation to weather, topography, geology and land use. In this study the hydrology of Indrayani watershed located in Indrayani River basin at the North-East of Pune is modelled, using the Soil and water Assessment Tool (SWAT). It aims to simulate the surface runoff using a Temporal resolution of 10 years LandUse-LandCover (Lu-Lc) maps of 2003-04 and 2013-14. The ArcSWAT interface implemented in the ArcGIS software was used to delineate the study area and its sub-components, combine the data layers and edit the model database. The ArcSWAT model predicted Indrayani watershed hydrologic component of Surface Runoff with weather components of precipitation and temperature of 25 years along with Food and Agriculture Organisation soil layer. The SWAT model run in 2 Phases with Lu-Lc of 2003-04 as Phase-I and also model run using 2013-14 Lu-Lc as phase-II. The runoff predicted within a 10 year temporal changes of Lu-Lc, 919 mm of Surface Runoff obtained with Lu-Lc of 2003-04 and 767 mm of Surface Runoff predicted from 2013-14 Lu-Lc map layer respectively. The higher runoff predicted with Lu-Lc 2003-04, as such it has higher area of Agricultural land with lower urban area covered and less water bodies than 2013-14 Landuse-Lancover classes.

Using a case study approach from past projects in Singapore, Australia, Cambodia, Thailand and Vietnam, we examine the benefits, but also some of the challenges, to implementing green space in urban design. Green space can have multiple physical and psychological wellbeing benefits, as well as environmental benefits, including urban runoff quantity and quality management, urban heat island abatement, air quality improvement, and noise reduction. Water sensitive urban design (WSUD) can be an important element of green space design and here we explore how modeling of ecosystem services and dynamic modeling of WSUD can help to facilitate sound planning and management decision making in support of green space implementation. As we illustrate with examples for Australia, Singapore and Cambodia, we believe that application of an urban ecosystem services modeling approach can elucidate environmental benefits of urban green space that otherwise may not be considered. Engineers may include dynamic modeling of WSUD in support of an urban master plan, or urban redevelopment, but generally urban planners are less conversant in applying models. We discuss some of the challenges to integrating multidisciplinary visioning and modeling of green space design and performance evaluation through our experience with a stormwater and wastewater design study for Cha Am, Thailand, that included landscape architecture and engineering classes at Thammasat University, Mahidol University, and AIT. Through a case study of Phnom Penh, we illustrate how modeling and 3D visualization can be used to effectively explore the benefits of green space. We conclude that a user-friendly decision support system is needed to integrate modeling and visualization tools and thereby bridge the gap between form and function in urban green space design.

In order to find the optimal solution for the drainage of rainwater from roads in urban areas, as well as for the evaluation and ranking of conceptual solutions, appropriate mathematical models and software packages were used in this research. For relevant rain episodes, i.e. rainfall of appropriate duration and intensity, runoff coefficients and flows were taken into account and analyzed according to the rational method, all for the purpose of obtaining data on the amount of rainwater entering the sewage system. Through this research, very good correlations and regressions were established between the cross slope of the road and the parameters of rainwater drainage from the road, as well as the correlation and regression relationships of the cross slope of the road and the efficiency of the drain. Likewise, the dependences of the drainage parameters, the efficiency of the drains and the cross slope of the road were determined, expressed through mathematical functions.

Abstract Groundwater is the main source of fresh water in arid regions. The Saq aquifer is a transboundary sandstone groundwater layer that extended into Saudi Arabia and Jordan. The groundwater level of the aquifer is depleted due to extensive pumping with negligible natural recharge. The objective of this study is to predict the artificial recharge supplied from runoff into the Saq aquifer for a selected area in the Qassim region, KSA using mathematical models. The maximum weekly and daily rainfall was quantified at different return periods for urban areas using graphical and probability distribution methods. The predicted surface water from rainfall is suggested to be stored in ponds, consequently the required volume of ponds was estimated according to the results of weekly maximum rainfall and various return periods. The stored surface water is proposed to be recharged into the groundwater aquifer via designed wells. The estimated quantity of the surface runoff was 4·106 m3, 6.2·106 m3, and 10.3·106 m3 for return periods 25, 50 and 75 years respectively. The study is applicable for similar aquifers that suffer from non-renewable resources.

Lake Erie is the shallowest and most biologically productive system of the Great Lakes, surrounded by large urban, industrial and agricultural areas. This combination prompted extensive efforts to promote Best Management Practices (BMPs) to mitigate non-point source pollution in Lake Erie’s watershed. Recent technical and conceptual advancements caution that significant variability exists in the BMP efficiency to reduce the severity of runoff and nutrient concentrations due to differences in implementation, the dependence of operational performance on local soil and climatic conditions, storm events and seasonality, and declining performance over time owing to imperfect maintenance. Given the uncertainty surrounding the design and efficiency of BMPs in abating non-point source pollution, our primary objective is to review the critical strengths and potential weaknesses of nine agricultural BMPs promoted for use in the Lake Erie watershed. We examine the capacity of the current generation of watershed process-based models to recreate possible BMP-mediated changes in the water and nutrient cycles. After reviewing modelling strategies (dynamic, external forcing, and empirical) to recreate non-linear watershed responses and feedback loops to BMP efficiency, our study recommends adopting dynamic representations of the interplay among key mechanisms, like soil moisture, water table, nutrient availability, plant uptake and subsequent growth. Notwithstanding the increased sophistication of complex mathematical models, their learning capacity is usually compromised by the coarse resolution of environmental data and limited empirical knowledge to accurately parameterize environmental properties and partially understood biogeochemical processes. In this context, we highlight the expression of the value of ecosystem services in monetary terms as a critical information piece when considering trade-offs among costly and diverse policy decisions. Consistent with the Integrated Watershed Management framework, we advocate the adoption of a rigorous mass-balance approach to assess the impact of BMPs on nutrient cycles, as well as the integration of the projected environmental improvements with terrestrial ecosystem services, beneficial use impairments, and aquatic ecosystem services. The proposed strategy has the potential to improve the decision-making process by identifying cost-effective management actions and balancing different goods and services provided by the agroecosystems at different time scales.

Introduction This article explores the changing relational quality of “the shadow of hierarchy”, in the context of the merging of platforms with infrastructure as the source of the shadow of hierarchy. In governance and regulatory studies, the shadow of hierarchy (or variations thereof), describes the space of influence that hierarchal organisations and infrastructures have (Héritier and Lehmkuhl; Lance et al.). A shift in who/what casts the shadow of hierarchy will necessarily result in changes to the attendant relational values, logics, and (techno)socialities that constitute the shadow, and a new arrangement of shadow that presents new challenges and opportunities. This article reflects on relevant literature to consider two different ways the shadow of hierarchy has qualitatively changed as platforms, rather than infrastructures, come to cast the shadow of hierarchy – an increase in scalability; and new socio-technical arrangements of (non)participation – and the opportunities and challenges therein. The article concludes that more concerted efforts are needed to design the shadow, given a seemingly directionless desire to enact data-driven solutions. The Shadow of Hierarchy, Infrastructures, and Platforms The shadow of hierarchy refers to how institutional, infrastructural, and organisational hierarchies create a relational zone of influence over a particular space. This commonly refers to executive decisions and legislation created by nation states, which are cast over private and non-governmental actors (Héritier and Lehmkuhl, 2). Lance et al. (252–53) argue that the shadow of hierarchy is a productive and desirable thing. Exploring the shadow of hierarchy in the context of how geospatial data agencies govern their data, Lance et al. find that the shadow of hierarchy enables the networked governance approaches that agencies adopt. This is because operating in the shadow of institutions provides authority, confers bureaucratic legitimacy and top-down power, and offers financial support. The darkness of the shadow is thus less a moral or ethicopolitical statement (such as that suggested by Fisher and Bolter, who use the idea of darkness to unpack the morality of tourism involving death and human suffering), and instead a relationality; an expression of differing values, logics, and (techno)socialities internal and external to those infrastructures and institutions that cast it (Gehl and McKelvey). The shadow of hierarchy might therefore be thought of as a field of relational influences and power that a social body casts over society, by virtue of a privileged position vis-a-vis society. It modulates society’s “light”; the resources (Bourdieu) and power relationships (Foucault) that run through social life, as parsed through a certain institutional and infrastructural worldview (the thing that blocks the light to create the shadow). In this way the shadow of hierarchy is not a field of absolute blackness that obscures, but instead a gradient of light and dark that creates certain effects. The shadow of hierarchy is now, however, also being cast by decentralised, privately held, and non-hierarchal platforms that are replacing or merging with public infrastructure, creating new social effects. Platforms are digital, socio-technical systems that create relationships between different entities. They are most commonly built around a relatively fixed core function (such as a social media service like Facebook), that then interacts with a peripheral set of complementors (advertising companies and app developers in the case of social media; Baldwin and Woodard), to create new relationships, forms of value, and other interactions (van Dijck, The Culture of Connectivity). In creating these relationships, platforms become inherently political (Gillespie), shaping relationships and content on the platform (Suzor) and in embodied life (Ajunwa; Eubanks). While platforms are often associated with optional consumer platforms (such as streaming services like Spotify), they have increasingly come to occupy the place of public infrastructure, and act as a powerful enabler to different socio-technical, economic, and political relationships (van Dijck, Governing Digital Societies). For instance, Plantin et al. argue that platforms have merged with infrastructures, and that once publicly held and funded institutions and essential services now share many characteristics with for-profit, privately held platforms. For example, Australia has had a long history of outsourcing employment services (Webster and Harding), and nearly privatised its entire visa processing data infrastructure (Jenkins). Platforms therefore have a greater role in casting the shadow of hierarchy than before. In doing so, they cast a shadow that is qualitatively different, modulated through a different set of relational values and (techno)socialities. Scalability A key difference and selling point of platforms is their scalability; since they can rapidly and easily up- and down-scale their functionalities in a way that traditional infrastructure cannot (Plantin et al.). The ability to respond “on-demand” to infrastructural requirements has made platforms the go-to service delivery option in the neo-liberalised public infrastructure environment (van Dijck, Governing Digital Societies). For instance, services providers like Amazon Web Services or Microsoft Azure provide on demand computing capacity for many nations’ most valuable services, including their intelligence and security capabilities (Amoore, Cloud Ethics; Konkel). The value of such platforms to government lies in the reduced cost and risk that comes with using rented capabilities, and the enhanced flexibility to increase or decrease their usage as required, without any of the economic sunk costs attached to owning the infrastructure. Scalability is, however, not just about on-demand technical capability, but about how platforms can change the scale of socio-technical relationships and services that are mediated through the platform. This changes the relational quality of the shadow of hierarchy, as activities and services occurring within the shadow are now connected into a larger and rapidly modulating scale. Scalability allows the shadow of hierarchy to extend from those in proximity to institutions to the broader population in general. For example, individual citizens can more easily “reach up” into governmental services and agencies as a part of completing their everyday business through platform such as MyGov in Australia (Services Australia). Using a smartphone application, citizens are afforded a more personalised and adaptive experience of the welfare state, as engaging with welfare services is no-longer tied to specific “brick-and-mortar” locations, but constantly available through a smartphone app and web portal. Multiple government services including healthcare and taxation are also connected to this platform, allowing users to reach across multiple government service domains to complete their personal business, seeking information and services that would have once required separate communications with different branches of government. The individual’s capacities to engage with the state have therefore upscaled with this change in the shadow, retaining a productivity and capacity enhancing quality that is reminiscent of older infrastructures and institutions, as the individual and their lived context is brought closer to the institutions themselves. Scale, however, comes with complications. The fundamental driver for scalability and its adaptive qualities is datafication. This means individuals and organisations are inflecting their operational and relational logics with the logic of datafication: a need to capture all data, at all times (van Dijck, Datafication; Fourcade and Healy). Platforms, especially privately held platforms, benefit significantly from this, as they rely on data to drive and refine their algorithmic tools, and ultimately create actionable intelligence that benefits their operations. Thus, scalability allows platforms to better “reach down” into individual lives and different social domains to fuel their operations. For example, as public transport services become increasingly datafied into mobility-as-a-service (MAAS) systems, ride sharing and on-demand transportation platforms like Uber and Lyft become incorporated into the public transport ecosystem (Lyons et al.). These platforms capture geospatial, behavioural, and reputational data from users and drivers during their interactions with the platform (Rosenblat and Stark; Attoh et al.). This generates additional value, and profits, for the platform itself with limited value returned to the user or the broader public it supports, outside of the transport service. It also places the platform in a position to gain wider access to the population and their data, by virtue of operating as a part of a public service. In this way the shadow of hierarchy may exacerbate inequity. The (dis)benefits of the shadow of hierarchy become unevenly spread amongst actors within its field, a function of an increased scalability that connects individuals into much broader assemblages of datafication. For Eubank, this can entrench existing economic and social inequalities by forcing those in need to engage with digitally mediated welfare systems that rely on distant and opaque computational judgements. Local services are subject to increased digital surveillance, a removal of agency from frontline advocates, and algorithmic judgement at scale. More fortunate citizens are also still at risk, with Nardi and Ekbia arguing that many digitally scaled relationships are examples of “heteromation”, whereby platforms convince actors in the platform to labour for free, such as through providing ratings which establish a platform’s reputational economy. Such labour fuels the operation of the platform through exploiting users, who become both a product/resource (as a source of data for third party advertisers) and a performer of unrewarded digital labour, such as through providing user reviews that help guide a platform’s algorithm(s). Both these examples represent a particularly disconcerting outcome for the shadow of hierarchy, which has its roots in public sector institutions who operate for a common good through shared and publicly held infrastructure. In shifting towards platforms, especially privately held platforms, value is transmitted to private corporations and not the public or the commons, as was the case with traditional infrastructure. The public also comes to own the risks attached to platforms if they become tied to public services, placing a further burden on the public if the platform fails, while reaping none of the profit and value generated through datafication. This is a poor bargain at best. (Non)Participation Scalability forms the basis for a further predicament: a changing socio-technical dynamic of (non)participation between individuals and services. According to Star (118), infrastructures are defined through their relationships to a given context. These relationships, which often exist as boundary objects between different communities, are “loosely structured in common use, and become tightly bound in particular locations” (Star, 118). While platforms are certainly boundary objects and relationally defined, the affordances of cloud computing have enabled a decoupling from physical location, and the operation of platforms across time and space through distributed digital nodes (smartphones, computers, and other localised hardware) and powerful algorithms that sort and process requests for service. This does not mean location is not important for the cloud (see Amoore, Cloud Geographies), but platforms are less likely to have a physically co-located presence in the same way traditional infrastructures had. Without the same institutional and infrastructural footprint, the modality for participating in and with the shadow of hierarchy that platforms cast becomes qualitatively different and predicated on digital intermediaries. Replacing a physical and human footprint with algorithmically supported and decentralised computing power allows scalability and some efficiency improvements, but it also removes taken-for-granted touchpoints for contestation and recourse. For example, ride-sharing platform Uber operates globally, and has expressed interest in operating in complement to (and perhaps in competition with) public transport services in some cities (Hall et al.; Conger). Given that Uber would come to operate as a part of the shadow of hierarchy that transport authorities cast over said cities, it would not be unreasonable to expect Uber to be subject to comparable advocacy, adjudication, transparency, and complaint-handling requirements. Unfortunately, it is unclear if this would be the case, with examples suggesting that Uber would use the scalability of its platform to avoid these mechanisms. This is revealed by ongoing legal action launched by concerned Uber drivers in the United Kingdom, who have sought access to the profiling data that Uber uses to manage and monitor its drivers (Sawers). The challenge has relied on transnational law (the European Union’s General Data Protection Regulation), with UK-based drivers lodging claims in Amsterdam to initiate the challenge. Such costly and complex actions are beyond the means of many, but demonstrate how reasonable participation in socio-technical and governance relationships (like contestations) might become limited, depending on how the shadow of hierarchy changes with the incorporation of platforms. Even if legal challenges for transparency are successful, they may not produce meaningful change. For instance, O’Neil links algorithmic bias to mathematical shortcomings in the variables used to measure the world; in the creation of irritational feedback loops based on incorrect data; and in the use of unsound data analysis techniques. These three factors contribute to inequitable digital metrics like predictive policing algorithms that disproportionately target racial minorities. Large amounts of selective data on minorities create myopic algorithms that direct police to target minorities, creating more selective data that reinforces the spurious model. These biases, however, are persistently inaccessible, and even when visible are often unintelligible to experts (Ananny and Crawford). The visibility of the technical “installed base” that support institutions and public services is therefore not a panacea, especially when the installed base (un)intentionally obfuscates participation in meaningful engagement like complaints handling. A negative outcome is, however, also not an inevitable thing. It is entirely possible to design platforms to allow individual users to scale up and have opportunities for enhanced participation. For instance, eGovernance and mobile governance literature have explored how citizens engage with state services at scale (Thomas and Streib; Foth et al.), and the open government movement has demonstrated the effectiveness of open data in understanding government operations (Barns; Janssen et al.), although these both have their challenges (Chadwick; Dawes). It is not a fantasy to imagine alternative configurations of the shadow of hierarchy that allow more participatory relationships. Open data could facilitate the governance of platforms at scale (Box et al.), where users are enfranchised into a platform by some form of membership right and given access to financial and governance records, in the same way that corporate shareholders are enfranchised, facilitated by the same app that provides a service. This could also be extended to decision making through voting and polling functions. Such a governance form would require radically different legal, business, and institutional structures to create and enforce this arrangement. Delacoix and Lawrence, for instance, suggest that data trusts, where a trustee is assigned legal and fiduciary responsibility to achieve maximum benefit for a specific group’s data, can be used to negotiate legal and governance relationships that meaningfully benefit the users of the trust. Trustees can be instructed to only share data to services whose algorithms are regularly audited for bias and provide datasets that are accurate representations of their users, for instance, avoiding erroneous proxies that disrupt algorithmic models. While these developments are in their infancy, it is not unreasonable to reflect on such endeavours now, as the technologies to achieve these are already in use. Conclusions There is a persistent myth that data will yield better, faster, more complete results in whatever field it is applied (Lee and Cook; Fourcade and Healy; Mayer-Schönberger and Cukier; Kitchin). This myth has led to data-driven assemblages, including artificial intelligence, platforms, surveillance, and other data-technologies, being deployed throughout social life. The public sector is no exception to this, but the deployment of any technological solution within the traditional institutions of the shadow of hierarchy is fraught with challenges, and often results in failure or unintended consequences (Henman). The complexity of these systems combined with time, budgetary, and political pressures can create a contested environment. It is this environment that moulds societies' light and resources to cast the shadow of hierarchy. Relationality within a shadow of hierarchy that reflects the complicated and competing interests of platforms is likely to present a range of unintended social consequences that are inherently emergent because they are entering into a complex system – society – that is extremely hard to model. The relational qualities of the shadow of hierarchy are therefore now more multidimensional and emergent, and experiences relating to socio-technical features like scale, and as a follow-on (non)participation, are evidence of this. Yet by being emergent, they are also directionless, a product of complex systems rather than designed and strategic intent. This is not an inherently bad thing, but given the potential for data-system and platforms to have negative or unintended consequences, it is worth considering whether remaining directionless is the best outcome. There are many examples of data-driven systems in healthcare (Obermeyer et al.), welfare (Eubanks; Henman and Marston), and economics (MacKenzie), having unintended and negative social consequences. Appropriately guiding the design and deployment of theses system also represents a growing body of knowledge and practical endeavour (Jirotka et al.; Stilgoe et al.). Armed with the knowledge of these social implications, constructing an appropriate social architecture (Box and Lemon; Box et al.) around the platforms and data systems that form the shadow of hierarchy should be encouraged. This social architecture should account for the affordances and emergent potentials of a complex social, institutional, economic, political, and technical environment, and should assist in guiding the shadow of hierarchy away from egregious challenges and towards meaningful opportunities. To be directionless is an opportunity to take a new direction. The intersection of platforms with public institutions and infrastructures has moulded society’s light into an evolving and emergent shadow of hierarchy over many domains. With the scale of the shadow changing, and shaping participation, who benefits and who loses out in the shadow of hierarchy is also changing. Equipped with insights into this change, we should not hesitate to shape this change, creating or preserving relationalities that offer the best outcomes. Defining, understanding, and practically implementing what the “best” outcome(s) are would be a valuable next step in this endeavour, and should prompt considerable discussion. 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