The Impacts of Spatially Variable Demand Patterns on Water Distribution System Design and Operation

Open Access articleResilient water distribution systems (WDSs) need to minimize the level of service failure in terms of magnitude and duration over its design life when subject to exceptional conditions. This requires WDS design to consider scenarios as close as possible to real conditions of the WDS to avoid any unexpected level of service failure in future operation (e.g., insufficient pressure, much higher operational cost, water quality issues, etc.). Thus, this research aims at exploring the impacts of design flow scenarios (i.e., spatial-variant demand patterns) on water distribution system design and operation. WDSs are traditionally designed by using a uniform demand pattern for the whole system. Nevertheless, in reality, the patterns are highly related to the number of consumers, service areas, and the duration of peak flows. Thus, water distribution systems are comprised of distribution blocks (communities) organized in a hierarchical structure. As each community may be significantly different from the others in scale and water use, the WDSs have spatially variable demand patterns. Hence, there might be considerable variability of real flow patterns for different parts of the system. Consequently, the system operation might not reach the expected performance determined during the design stage, since all corresponding facilities are commonly tailor-made to serve the design flow scenario instead of the real situation. To quantify the impacts, WDSs’ performances under both uniform and spatial distributed patterns are compared based on case studies. The corresponding impacts on system performances are then quantified based on three major metrics; i.e., capital cost, energy cost, and water quality. This study exemplifies that designing a WDS using spatial distributed demand patterns might result in decreased life-cycle cost (i.e., lower capital cost and nearly the same pump operating cost) and longer water ages. The outcomes of this study provide valuable information regarding design and operation of water supply infrastructures; e.g., assisting the optimal design

Changing Dimensions of India's Growth Process: A State Level Analysis

Growth of the Indian economy has been quite impressive during 2004-07. This paper chronicles the performance of the states in India during this high growth phase. The growth performance during 2000-03 is taken as the benchmark to compare and contrast the changing growth patterns across sectors in different states. Apart from sectoral growth, sectoral contributions to state output, variability of sectoral output and contribution of different sectors to overall growth in the spatial dimension have been studied. The results broadly indicate a decline in the importance of the service sector during the high growth phase and increased variability in output in the states. While maintaining the high growth rates of the primary and secondary sectors remains a challenge, increased variability of output raises serious concerns on the continuity of the high growth momentum in the future.Sectoral Growth, Viability of Growth, Contribution to Growth

Visualizing the underlying trends of component latencies affecting service operation performance

This paper presents a technology agnostic method for extracting the underlying distinct patterns of variations in the overall performance of a service operation for changes to different application components supporting the service operation in a computer based service provider to consumer contract. This short paper advocates that visualizing these patterns would help in early projection of the operation's performance due to modification of the application components/processing catering to the operation, without the need of repetitive performance and load testing of the whole service. Lookup datasets against different component configurations are created to associate the variability of component processing impedances to the service operation's performance and best fit regression types are applied to enable trend extrapolation and interpolation

Pattern-based multi-cloud architecture migration

Many organizations migrate on-premise software applications to the cloud. However, current coarse-grained cloud migration solutions have made such migrations a non transparent task, an endeavor based on trial-anderror. This paper presents Variability-based, Pattern-driven Architecture Migration .V-PAM), a migration method based on (i) a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud, (ii) a situational migration process framework to guide pattern selection and composition, and (iii) a variability model to structure system migration into a coherent framework. The proposed migration patterns are based on empirical evidence from several migration projects, best practice for cloud architectures and a systematic literature review of existing research. Variability-based, Pattern-driven Architecture Migration allows an organization to (i) select appropriate migration patterns, (ii) compose them to define a migration plan, and (iii) extend them based on the identification of new patterns in new contexts. The patterns are at the core of our solution, embedded into a process model, with their selection governed by a variability model

Continental scale variability in ecosystem processes: Models, data, and the role of disturbance

Management of ecosystems at large regional or continental scales and determination of the vulnerability of ecosystems to large-scale changes in climate or atmospheric chemistry require understanding how ecosystem processes are governed at large spatial scales. A collaborative project, the Vegetation and Ecosystem Modeling and Analysis Project (VEMAP), addressed modeling of multiple resource limitation at the scale of the conterminous United States, and the responses of ecosystems to environmental change. In this paper, we evaluate the model-generated patterns of spatial variability within and between ecosystems using Century, TEM, and Biome-BGC, and the relationships between modeled water balance, nutrients, and carbon dynamics. We present evaluations of models against mapped and site-specific data. In this analysis, we compare model-generated patterns of variability in net primary productivity (NPP) and soil organic carbon (SOC) to, respectively, a satellite proxy and mapped SOC from the VEMAP soils database (derived from USDA-NRCS [Natural Resources Conservation Service] information) and also compare modeled results to site-specific data from forests and grasslands. The VEMAP models simulated spatial variability in ecosystem processes in substantially different ways, reflecting the models’ differing implementations of multiple resource limitation of NPP. The models had substantially higher correlations across vegetation types compared to within vegetation types. All three models showed correlation among water use, nitrogen availability, and primary production, indicating that water and nutrient limitations of NPP were equilibrated with each other at steady state. This model result may explain a number of seemingly contradictory observations and provides a series of testable predictions. The VEMAP ecosystem models were implicitly or explicitly sensitive to disturbance in their simulation of NPP and carbon storage. Knowledge of the effects of disturbance (human and natural) and spatial data describing disturbance regimes are needed for spatial modeling of ecosystems. Improved consideration of disturbance is a key ‘‘next step’’ for spatial ecosystem models

Migration signatures across the decades: Net migration by age in U.S. counties, 1950−2010

Background: Migration is the primary population redistribution process in the United States. Selective migration by age, race/ethnic group, and spatial location governs population integration, affects community and economic development, contributes to land use change, and structures service needs. Objective: Delineate historical net migration patterns by age, race/ethnic, and rural-urban dimensions for United States counties. Methods: Net migration rates by age for all US counties are aggregated from 1950−2010, summarized by rural-urban location and compared to explore differential race/ethnic patterns of age-specific net migration over time. Results: We identify distinct age-specific net migration ‘signatures’ that are consistent over time within county types, but different by rural-urban location and race/ethnic group. There is evidence of moderate population deconcentration and diminished racial segregation between 1990 and 2010. This includes a net outflow of Blacks from large urban core counties to suburban and smaller metropolitan counties, continued Hispanic deconcentration, and a slowdown in White counterurbanization. Conclusions: This paper contributes to a fuller understanding of the complex patterns of migration that have redistributed the U.S. population over the past six decades. It documents the variability in county age-specific net migration patterns both temporally and spatially, as well as the longitudinal consistency in migration signatures among county types and race/ethnic groups

Effect of environmental constraints on multi-segment coordination patterns during the tennis service in expert performers

The aims of this study were to examine the effect of different environmental constraints on kinematic multi-segment coordination patterns during the service and its coordination with service time variability. Ten expert tennis players (Age: 34.1±5.3) volunteered to take part in this study. Participants served 30 times in 3 different conditions: control, target and opposition. The order of conditions was counterbalanced between participants. A wireless 3D motion capture system (STT Co, Spain) was used to measure 7 joint motions, with a 17 degrees of freedom biomechanical model created to capture the entire service action. Results of the principal component analysis showed that 4 synergies were created; however, their roles were changed relative to the perception of the environment. The results of repeated-measures analysis of variance did not show any significant difference on total variance and individual principal components between conditions; however, one synergy pattern significantly predicted the service time variability in both control and opposition conditions. In conclusion, the findings demonstrated that expert performers reduce the joint dimensionality by creating functional synergies in different phases of service and adapt the service action according to the perception of the environment

Exploring the bullwhip effect by means of spreadsheet simulation.

An important supply chain research problem is the bullwhip effect: demand fluctuations increase as one moves up the supply chain from retailer to manufacturer. It has been recognized that demand forecasting and ordering policies are two of the key causes of the bullwhip effect. In this paper we present a spreadsheet application, which explores a series of replenishment policies and forecasting techniques under different demand patterns. It illustrates how tuning the parameters of the replenishment policy induces or reduces the bullwhip effect. Moreover, we demonstrate how bullwhip reduction (order variability dampening) may have an adverse impact on inventory holdings. Indeed, order smoothing may increase inventory fluctuations resulting in poorer customer service. As such, the spreadsheets can be used as an educational tool to gain a clear insight into the use or abuse of inventory control policies and improper forecasting in relation to the bullwhip effect and customer service. Keywords: Bullwhip effect, forecasting techniques, replenishment rules, inventory fluctuations, spreadsheet simulationBullwhip; Bullwhip effect; Forecasting techniques; Inventory fluctuations; Replenishment rule; Simulation; Spreadsheet simulation;

Seasonal predictions of energy-relevant climate variables through Euro-Atlantic Teleconnections

The goal of this analysis is the better understanding of how the large-scale atmospheric patterns affect the renewable resources over Europe and to investigate to what extent the dynamical predictions of the large-scale variability might be used to formulate empirical prediction of local climate conditions (relevant for the energy sector). The increasing integration of renewable energy into the power mix is making the electricity supply more vulnerable to climate variability, therefore increasing the need for skillful weather and climate predictions. Forecasting seasonal variations of energy relevant climate variables can help the transition to renewable energy and the entire energy industry to make better informed decision-making. At seasonal timescale climate variability can be described by recurring and persistent, large-scale patterns of atmospheric pressure and circulation anomalies that interest vast geographical areas. The main patterns of the North Atlantic region (Euro Atlantic Teleconnections, EATCs) drive variations in the surface climate over Europe. We analyze reanalysis dataset ERA5 and the multi-system seasonal forecast service provided by Copernicus Climate Change Service (C3S). We found that the observed EATC indices are strongly correlated with surface variables. However, the observed relationship between EATC patterns and surface impacts is not accurately reproduced by seasonal prediction systems. This opens the door to employ hybrid dynamical-statistical methods. The idea consists in combining the dynamical seasonal predictions of EATC indices with the observed relationship between EATCs and surface variables. We reconstructed the surface anomalies for multiple seasonal prediction systems and benchmarked these hybrid forecasts with the direct variable forecasts from the systems and also with the climatology. The analysis suggests that hybrid methodology can bring several improvements to the predictions of energy relevant Essential Climate Variables.This work was supported by the European Union’s Horizon 2020 research and innovation programme [Grant Numbers. No 776787, H2020 S2S4E] and by the National Italian project PAR 2019–2021 1.8 ‘Energia dal Mare’.Peer ReviewedPostprint (published version