An expert system called EXPLORE has been developed to manage the
Seville City water supply system. The main difficulties are: the lack of
information on management procedures, the complicated electrical tariff,
the dynamical character of the water network and the dependency of the
water demand that is a major uncontrollable factor. EXPLORE reduces the
cost of operation derived from the pumps to the different water storage
tanks; for this task EXPLORE employs the water demand forecast to obtain
an optimal pump daily schedule. EXPLORE has several additional benefits
to collect the operator experience. EXPLORE also permits the refinement
of operation processes through the simulation of new strategies and
provides the training for novice operators. The system has been applied
to the management of the Seville City water supply system. EXPLORE has
been tested reaching a great improvement in electrical cost (a saving of
25% concerning the current operation satisfying water demand)
Models that are used for future based scenarios should be calibrated with historical water supply and use data. Historical water records in Australia are discontinuous, incomplete and often incongruently disaggregated. We present a systematic method to produce a coherent reconstruction of the historical provision and consumption of water in Victorian catchments. This is demonstrated using WAS: an accounting and simulation tool that tracks the stocks and flows of physical quantities relating to the water system. The WAS is also part of, and informed by, an integrated framework of stocks and flows calculators for simulating long-term interactions between other sectors of the physical economy. Both the WAS and related frameworks consider a wide scope of inputs regarding population, land use, energy and water. The physical history of the water sector is reconstructed by integrating water data with these information sources using a data modelling process that resolves conflicts and deduces missing information. The WAS allows strategic exploration of water and energy implications of scenarios of water sourcing, treatment, delivery and end use cognisant of historical records.
An economic-engineering optimization model of California's major water supply system is presented. The model's development, calibration, limitations, and results are reviewed. The major methodological conclusions are that large-scale water resources optimization models driven by economic objective functions are both possible and practical; deterministic models are useful despite their limitations; and data management, reconciliation, and documentation are important benefits of large-scale system modeling. Specific results for California indicate a great potential for water markets and conjunctive use to improve economic performance and significant economic value for expanding some conveyance facilities. Overall, economic-engineering optimization #even if deterministic# can suggest a variety of promising approaches for managing large systems. These approaches can then be refined and tested using more detailed simulation models. The process of developing large-scale models also motivates the systematic and integrated treatment of surface water, groundwater, facility, and water demand data, and identification of particularly important data problems, something of long-term value for all types of water resources analysis.
Introduction When one reads the water resources literature, it appears that engineers either belong to a stochastic or deterministic school of thought. It is rare to find an engineer who has a balanced view of the world, which simultaneously embraces both stochastic and deterministic elements of a modeling problem. This difference in world view stems from our educational system which tends to propagate such distinctions and which tends to emphasize deterministic elements. High school graduates have had twelve years of mathematics, often without a single course in either probability or statistics. A single course in probability and/or statistics is the most that one can expect in most undergraduate engineering college curricula. How can one expect engineers to understand statistics, with only a single course? Entire departments are built around the subject of statistics; how can a single course suffice? Would we feel comfortable with our understanding of calculus if we had only
The importance of water distribution network rehabilitation, replacement and expansion is discussed. The problem of choosing the best possible set of network improvements to make with a limited budget is presented as a large optimisation problem to which conventional optimisation techniques are poorly suited. A multi-objective approach is described, using capital cost and benefit as dual objectives, enabling a range of non-inferior solutions of varying cost to be derived. A Structured Messy Genetic Algorithm is developed, incorporating some of the principles of the Messy Genetic Algorithm, such as strings which increase in length during the evolution of designs. The algorithm is shown to be an effective tool for the current optimisation problem, being particularly suited both to the multi-objective approach and to problems which involve the selection of small sets of variables from large numbers of possibilities. Two examples are included which demonstrate the features of the method an...
We present a model for optimizing the placement of sensors in municipal water networks to detect maliciously-injected contaminants. An optimal sensor configuration minimizes the expected fraction of the population at risk. We formulate this problem as an integer program, which can be solved with generally available IP solvers. We find optimal sensor placements for three real networks with synthetic risk and population data. Our experiments illustrate that this formulation can be solved relatively quickly, and that the predicted sensor configuration is relatively insensitive to uncertainties in the data used for prediction.
Climate warming is altering the flow and temperature regimes in
California's Sierra Nevada mountain range by reducing snowpack, causing
earlier runoff and raising stream temperatures. Managing reservoir
releases for downstream temperatures is a promising adaptation option.
In this study, we developed a linear programming model to optimally
release water from multiple thermal layers in a seasonally stratified
reservoir to minimize deviations from desired downstream temperatures.
An explicit objective of the work was to develop a method that can be
readily integrated into a watershed-scale, multi-reservoir optimization
model using a node-link representation of system features. The objective
function is to minimize managed temperature deviations from target
temperatures based on the natural temperature regime. Thermal dynamics
of reservoirs and streams are included in the constraint set. For a case
study, the model is applied to Lake Spaulding, a multi-purpose reservoir
in the western Sierra Nevada that thermally stratifies seasonally and
that could be used to manage temperatures for a downstream cold water
fishery. We demonstrate how the model can effectively manage releases
from thermal pools when compared to only a single, low-level outlet (no
selective withdrawal). The model hedges the release of cold water to
decrease summer stream temperatures, but at a cost of warmer stream
temperatures in the winter. This method can be extended to include other
nearby reservoirs to optimally manage releases from multiple reservoirs
for multiple downstream temperature targets to help buffer aquatic
ecosystems against anticipated stream temperature increases.
In this paper, using a stylized mathematical simulation model, the participation of farmers in spot and options markets is analyzed in a two-period setting. Farmers assess the benefits from entering one of the two markets based upon anticipated water supplies in a subsequent period and relative expected profits in water and agricultural markets. Supply of water for urban use is a function of uncertain weather conditions and water demands for agricultural uses. The analysis offers insight on water market participation by deriving conditions that favor spot or options market participation. Findings highlight the importance of market size, agricultural profitability and other parameters in predicting the potential success of spot and options market formation.
A decision support system (DSS) is presented for conjunctive management of surface water and ground water under prior appropriation. The DSS is constructed around the generalized river basin network flow model MODSIM, providing an open architecture allowing access to input and output database and modification and verification at all levels of the modeling process. The graphical user interface for the MODSIM DSS provides spatially referenced database capabilities whereby the user can create and link river-basin network objects on the display and populate and import data for that object interactively. Geographic information system tools are used to prepare grid-based spatial data for input into MODRSP, a modified version of the U.S. Geological Survey (USGS) three-dimensional finite-difference ground water model MODFLOW. Response functions generated by MODRSP are provided to MODSIM for simulating spatially varied and time-lagged return/depletion flows from stream-aquifer interactions. Capabilities of the MODSIM DSS are demonstrated on a case study for a portion of the Lower South Platte River Basin, Colorado. Results of the case study indicate significant differences between using ground water response coefficients developed from preassigned stream depletion factor (SDF) values as currently used in the basin, and those generated using a finite-difference ground water model.
Many water authorities, both nationally and internationally, have been forced to rethink their strategies for achieving water balance as a result of growing water demands, droughts, reduced no-failure yields and environmental sustainability considerations. In particular, regulatory bodies in Australia are demanding that water managers exhaust network management efficiencies before considering new water source options (e.g. dams, desalination, pipelines etc.). Demand management incentive schemes in conjunction with water recycling and pressure and leakage management (PLM) initiatives are a few examples of least-cost planning strategies being adopted by water authorities to achieve water balance without expanding the water infrastructure asset requirements. Potential benefits of PLM strategies have been predicted by these authorities worldwide, in areas such as: deferred capital works; reduced corrective maintenance; reduced treatment costs; energy savings; reduced reclaimed water discharges; and improvements to customer service. However, justification for PLM options remains difficult due to the limited amount of quantified evidence for most of the above-mentioned benefits over an urban water systems life cycle. As the first stage in the development of a holistic PLM decision support system this paper quantifies the benefits derived from a PLM strategy in a trial area located on the Gold Coast, in Queensland, Australia. The results of the trial provide evidence to support claims that PLM can reduce water consumption and the frequency of infrastructure failures if implemented throughout the entire Gold Coast City. Further to this, the research concludes that PLM impacts on the total water cycle and has broad implications for ensuring the future sustainability of potable water services. Yes Yes
This study demonstrates the use of high-order Pareto optimization (i.e., optimizing a system for more than two objectives) on a long-term monitoring (LTM) application. The LTM application combines quantile kriging and the nondominated sorted genetic algorithm-II (NSGA-II) to successfully balance four objectives: (1) minimizing sampling costs, (2) maximizing the accuracy of interpolated plume maps, (3) maximizing the relative accuracy of contaminant mass estimates, and (4) minimizing estimation uncertainty. Optimizing the LTM application with respect to these objectives reduced the decision space of the problem from a total of 500 million designs to a set of 1,156 designs identified on the Pareto surface. Visualization of a total of eight designs aided in understanding and balancing the objectives of the application en route to a single compromise solution. This study shows that high-order Pareto optimization holds significant potential as a tool that can be used in the balanced design of water resources systems.
A modified version of the Bureau of Reclamation (Reclamation) long-term
planning model, Colorado River Simulation System (CRSS), is used to
evaluate whether hydrologic model choice has an impact on critical
decision variables within the San Juan River Basin when evaluating
potential impacts of climate change through 2099. The distributed
Variable Infiltration Capacity (VIC) model and the lumped National
Weather Service (NWS) River Forecast System (RFS) were each used to
project future streamflow; these projections of streamflow were then
used to force Reclamation's CRSS model over the San Juan River Basin.
Both hydrologic models were compared to evaluate whether or not
uncertainty in climatic input generated from General Circulation Models
outweighed differences between the hydrologic models. Differences in
methodologies employed by each hydrologic model had a significant impact
on projected streamflow within the basin. Both models project decreased
water availability under changing climate conditions within the San Juan
River Basin, but disagree on the magnitude of the decrease. On average,
total naturalized inflow within the San Juan River Basin into the Navajo
Reservoir is approximately 15% higher using inflows derived using the
VIC model than those inflows developed using the RFS model; average
projected tributary inflow from the San Juan River Basin to the Colorado
River is approximately 25% higher using inflows derived using the VIC
model than those inflows developed using the RFS. Overall, there is a
higher risk and magnitude of shortage within the San Juan River Basin
using streamflow developed using the RFS model as compared to inflow
scenarios developed using the VIC model. Model choice was found to have
a significant impact on the evaluation of climate change impacts over
the San Juan River Basin.
A decision support system (DSS) for the integration of hydrologic process modeling and risk evaluation of the surface water management alternatives in a river basin is developed. The DSS, named CTIWM, is aimed at supporting the testing and evaluation of water management policies and at facilitating integration of user-selected scenarios into planning strategies of the water resource system in the Chikugo River basin, a multipurpose multireservoir system. CTIWM uses a module library that contains compatible modules for simulating a variety of hydrologic processes. Different numerical models are invoked through a user interface menu, which facilitates communications between users and models in a friendly way. The source code was developed by using object-oriented programming techniques. The result shows that the use of DSSs may effectively improve the speed and quality of water management and give users more flexibility in analyzing different scenarios.
This paper describes an approach for establishing overhaul repair schedules for clear water pump stations that balance the goal of minimizing the total life-cycle cost of the pumps with the goal of minimizing the CO2 emissions associated with the generation of the electricity used to operate the pumps. The specific focus of this study is on pump stations with a parallel pump configuration in which clear water is pumped from a clear water tank at a groundwater treatment plant (GWTP) to a service reservoir. The approach uses models for the operation and deterioration of such pumps, and forms the basis for a model-based decision support tool (DSS) that has been developed to assist in budgeting and planning decisions associated with pump overhaul repairs.
The professed goal of World Health Organization—to supply potable water to the poorest of the poor in the developing countries by the year 1990—appears to be quite an optimistic one in view of the existing situation in the developing countries, where potable water is still out of the reach of 80% of the populace. Supplying potable water to the rural population in these countries is quite a gigantic task owing to lack of physical and institutional resources. Any major effort at providing safe potable water to rural populations should first aim at improvement of the small community water supply systems. Small community water supply systems in these countries generally include: (1) Groundwater supplies; (2) rain water storage basins; and (3) surface water storage basins. Existing technologies of small community water supply systems in Indonesia and India are discussed and recommendations are made for improving their technical performance to meet the professed United Nations potable water supply goals.
This paper presents and compares four types of stochastic dynamic programming (SDP) for on-line reservoir operation, relying on observed or forecasted inflows. The models are different because of the assumptions regarding the inflow in the next time period. If this inflow is known (or a forecast is possible with 100% reliability) models with expected value of the future returns are possible (present returns are deterministic). Otherwise, a simple forecast based on conditional probabilities is necessary, and present and future returns are random. The objective is to maximize expected annual hydropower generation. In a case study of the Feitsui Reservoir in Taiwan, SDP models appear to provide efficient longterm operating policies. The simulation of on-line operation of the reservoir reveals that the SDP model that relies on the observed inflows of the preceeding time step provides the best performance. Nevertheless, under different hydrological regimes this finding might be not universal, but dependent upon the characteristics of the particular water resources system.
The problem of optimal multiunit hydropower system configuration is analyzed considering long-term operational aspects. The spatial distribution of individual system elements such as reservoirs and the vertical configurations representing heights of various dams, full supply levels, and minimum operating levels of the reservoirs are selected for optimum energy generation potential. The objective of maximizing the monthly firm energy has been successfully achieved first by applying the Incremental Dynamic Programming (IDP) technique, and then by the Stochastic Dynamic Programming (SDP) method which incorporates the streamflow stochasticity into the system. SDP maximizes the expected total annual energy generation subject to a prespecified monthly firm power output. Transitional probabilities are derived from the available historical streamflow records. The results obtained could be used in planning a reservoir system and in deriving a long-term operational policy. In this study, they are used in deciding the optimum system configuration in a river basin development problem in Nepal.
Using long-term (1948-1996) pan evaporation measurements, a 6% increase in warm-season (May-October) actual evapotranspiration (ET) is computed over the conterminous United States between 1949 and 1996 via the complementary hypothesis. This predicted increase in ET is in agreement with the measured precipitation increase for the same period if long-term wet-surface ET is assumed to be constant. Long-term relative humidity and air temperature measurements express an increase in mean air temperature and water vapor concentration but not a statistically significant change in vapor pressure deficit. The latter implies a smaller than 6% increase in actual warm-season ET. Water-balance estimates for six watersheds, covering about 50% of the land area of the contiguous states of the United States, indicate a 3% increase in annual ET over the same period.
A personal computer (PC) based decision-support system (DSS) can provide real-time information for improved flood prevention and control in the Trinity River basin in Texas. It uses specialized versions of the well-known programs HEC-1 and HEC-5 for forecasting runoff and simulating reservoir operation, respectively. The DSS integrates these components with a specialized program manager. Although not yet used as designed, potential improved flood prevention and control with the DSS are demonstrated with historical storm data. David Ford Consulting Engineers, Inc. and Halff Associates, Inc. Research in Water Resources, Center for
This work presents a novel approach for solving groundwater management problems with reduced computational effort. We replace a groundwater flow model governed by a partial differential equation with a simple model governed by an ordinary differential equation. Model reduction is achieved with empirical orthogonal functions, i.e., principal components. Replacement of the full-scale model by a reduced model allows implementation of the embedding approach for optimal groundwater management. Comparing the results obtained with the full-scale simulation model, preliminary analyses show that the reduced model is able to reproduce head variations in the flow domain with good accuracy and, to a certain degree, the sensitivities of head with respect to pumping. A key advantage of the reduced model is that it is simple and easy to solve, and in many instances captures the dominating characteristics of the original model. In view of the many sources of uncertainty influencing groundwater simulation, the accuracy provided by a reduced model may be sufficient for planning purposes. As with other examples of model reduction presented in recent research efforts, the methodology shows promise in presenting general trends, but does not eliminate the need for the original model when more detailed analyses are needed.
One of the first steps in developing an optimal water resources design model is creating appropriate objective functions that represent the primary goals of the design. In many cases, one major objective is minimizing cost. A more realistic cost function, with detailed cost terms, may yield more accurate results but will require more development effort. This research examines the benefits of developing a realistic cost function using two multiobjective groundwater remediation case studies. The results show that realistic cost functions find better solutions than the simplified cost functions, as well as identifying more optimal solutions on the Pareto frontier than the other functions. The realistic cost functions achieved up to 14% improvement in total cost, although the degree of loss in accuracy varies substantially for the two case studies considered in this work and for different parameter settings within each case study. Given the difficulties of predicting which case studies or parameter settings would have significant loss of performance from using simplified cost functions, investments in developing accurate site-specific cost functions appear to be worthwhile.
This paper presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm optimization (EMPSO) technique, to derive reservoir operation policies for multipurpose reservoir systems. Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between many simple individuals called particles. In this study the standard particle swarm optimization (PSO) algorithm is further improved by incorporating a new strategic mechanism called elitist-mutation to improve its performance. The proposed approach is first tested on a hypothetical multireservoir system, used by earlier researchers. EMPSO showed promising results, when compared with other techniques. To show practical utility, EMPSO is then applied to a realistic case study, the Bhadra reservoir system in India, which serves multiple purposes, namely irrigation and hydropower generation. To handle multiple objectives of the problem, a weighted approach is adopted. The results obtained demonstrate that EMPSO is consistently performing better than the standard PSO and genetic algorithm techniques. It is seen that EMPSO is yielding better quality solutions with less number of function evaluations.
Water distribution networks are not inert transport systems. The high-quality water produced at water treatment works is subject to a variety of complex and interacting physical, chemical, and biological interactions within these highly variable, high-surface reactors. In particular, the aging and deteriorating asset condition in water distribution systems can result in a degradation of water quality delivered to the customer, often experienced as discoloration caused by increasing amounts of fine particulate matter. Here, it is proposed that by assessing measured turbidity over time, in particular its correlation with local hydraulics, an assessment of change in risk of fouling can be obtained and asset deterioration inferred. This paper presents a methodology for pairwise monitoring of a hydraulic parameter (flow or pressure) and turbidity using wavelet-based semblance analysis—a novel methodology from another domain, which is applied for the first time to water quality data in distribution systems. It is suggested and subsequently explored through case studies that an increasing (anti) correlation of the turbidity with the (pressure) flow diurnal cycle will be indicative of increasing fouling risk. This can be further supported through evaluation of the rate and magnitude of drift and through assessment of the change in magnitude of the daily turbidity profile. The composite of these approaches is applied to an extensive data set from a United Kingdom distribution system revealing the effectiveness of the analysis preflushing and postflushing (reducing discoloration events by 64–89%). With increasing proliferation of monitoring devices and real-time data acquisition the potential for online systems and well-informed proactive management is apparent.