Journal of Water Resources Planning and Management

Systems analysis can assist in decision making to achieve water quality goals in urban watersheds that are typically associated with streams having poor water quality and degraded aquatic habitats caused by storm-water runoff. The need for an effective systems analysis methodology is particularly urgent in watersheds facing regulatory action through the imposition of a total maximum daily load (TMDL). Systems analysis methodologies are available that approach the problem at different levels of complexity corresponding to the methods that are used to model the physical, technological, and economic aspects of watershed storm-water management. Important roles exist for both simulation and analytical optimization, and they can be used together in ways that improve the applicability of both. These tools, along with geographic analysis and field monitoring, are used in a four-stage process that can lead to the development of a cost-effective strategy for moving an impaired watershed toward the restoration of water quality and removal of the impaired status. Details of the methodology are described, and results are presented from its successful application in the development of an action plan for an impaired watershed that drains multiple municipalities in suburban Philadelphia, PA.

Selecting approaches to managing nonpoint source pollution is challenging due to the complex generation and transport processes that influence the quantity of pollutant that eventually reaches a receiving water. Pollutant transport is influenced by land surface characteristics along the transport path, and these effects should be considered when optimizing management approaches for nonpoint source pollution. A fully distributed sediment-generation and transport watershed model is presented within an optimization framework to enable the development of spatially precise solutions to sediment-trapping best management practice (BMP) placement at the watershed scale. To focus on the BMP-siting problem, a stylized representation of a BMP is assumed to be capable of reducing sediment mass by a fixed fraction, and optimal arrangements of this assumed BMP type are developed with a genetic algorithm. The results suggest that the optimal location for sediment mass reduction is not necessarily at locations of only high mass generation or at locations of only high transport capacity. The results also suggest that there are efficient locations for management that produce a relatively large reduction in storm sediment load.

Linear and dynamic programming formulations are introduced for optimizing the placement of distributed best management practices (BMPs) at the watershed scale. The results of linear programming optimization of infiltration-based stormwater management BMPs are compared with the results of genetic algorithm (GA)optimization using a nonlinear distributed model. Additionally, linear and dynamic programming optimization of sediment-trapping BMPs are compared with GA optimization using a nonlinear distributed model. The results indicate that the solution to stormwater peak-flow reduction is influenced primarily by distributed-flow arrival time, and a linear programming analog to a nonlinear optimization model can efficiently reproduce much of the same solution structure. Linear and dynamic programming solutions to the storm sediment-management problem indicate natural sediment trapping is an important consideration, and a solution to the sediment-management-optimization problem can be efficiently found using a dynamic programming formulation.

Water storing facilities in a watershed are very often operated independently one to another to meet specific operating objectives, with no information sharing among the operators. This uncoordinated approach might result in upstream-downstream disputes and conflicts among different water users or inefficiencies in the watershed management when looked at from the viewpoint of an ideal central decision-maker. In this paper, the authors propose an approach in two steps to design coordination mechanisms at the watershed scale with the ultimate goal of enlarging the space for negotiated agreements between competing uses and improving the overall system efficiency. First, the multiobjective centralized solution is computed to assess the maximum potential benefits of a shift from a sector-by-sector to an ideal fully coordinated perspective. Then, the Pareto-optimal operating policies are analyzed to gain insight into suitable strategies to foster cooperation or impose coordination among the involved agents. The approach is demonstrated on an Alpine watershed in Italy where a long lasting conflict exists between upstream hydropower production and downstream irrigation water users. Results show that a coordination mechanism can be designed that drive the current uncoordinated structure towards the performance of the ideal centralized operation.

This paper presents an agroeconomic approach to assess the economic impact of improving nitrogen and irrigation management practices in California’s Tulare Lake Basin and the Salinas Valley. The approach employs a self-calibrated mathematical programming model with a constant elasticity of substitution production function and two nests: one for irrigation and one for nitrogen. Agricultural crop yields are maintained as a worst-case for improving nitrogen use efficiency. Small reductions ($<25\%$) in nitrate load to groundwater can be achieved at relatively low costs. Load reductions of 50% may require more costly nitrogen management practices and a broader education strategy with higher reductions in farm net revenues and irrigated area. Other policy instruments such as a tax and levees on applied nitrogen may help reduce groundwater load and raise revenues for alternate drinking water supplies in affected areas. The model also provides further evidence that it is possible to integrate agronomic and economic models that account for substitutability of applied nitrogen and water in agricultural production for policy analysis.

In this study, a combination of two modified versions of the SWAT 2005 model are used to estimate the impact of several best management practices on phosphorus loading to the Cannonsville reservoir in upstate New York as a function of variable weather and climate. The management scenarios in this study include a base case scenario in which management practices are not changed and three other scenarios in which new practices are implemented to reduce the amount of phosphorus applied to the watershed. The long-term impacts of these scenarios are investigated using historical data and stochastically generated weather data that incorporate projected climate change. The projected changed climate data are used to investigate the impact of a changed climate on the nutrient loading and the effectiveness of the three management practices on reducing nutrient loading to the reservoir. The methodology used here can be helpful to other watersheds as well to assess long term response to best management practices or their absence in the face of uncertainty about future weather and climate.

Research has revealed the impacts of climate change on the hydrologic cycle. These impacts are intensified in the urban water system due to special characteristics of urban watersheds, such as a high percentage of impervious areas and the presence of a microclimate. A slight change in rainfall intensity and duration can cause severe floods, with a significant incremental increase in life and property losses. Therefore, it is a key issue to modify the urban watershed characteristics in a systematic way to deal with these probable changes. In this paper, a multicriteria optimization model is developed to select best management practices (BMPs) for flood management in urban watershed systems. The addressed criteria include maximizing reliability of the drainage system, as well as minimizing flood damages and BMP costs. A new reliability index for dealing with floods is suggested in this study that considers both severity and duration of flooding. The optimization model is solved using a two-stage multicriteria decision making method which employs the nondominated sorting genetic algorithm II (NSGA II) and data envelopment analysis (DEA) for the selection of best alternatives. The performance of the suggested model is tested in an urban watershed located in the northeastern part of Tehran, the capital of Iran. The drainage system of this watershed is simulated using the storm water management model (SWMM) developed by United States Environmental Protection Agency (USEPA). The climate characteristics of this region are simulated under a climate change scenario based on Hadley Center Coupled Model, version 3 General Circulation Model (HadCM3 GCM) outputs. The rainfall events, with flooding potential, are determined and the optimization model is used to select the best BMPs for flooding conditions. The results show the important value of using the proposed algorithm for reducing flooding volume and increasing the drainage system’s reliability. The results show further investigations are needed for real case applications to incorporate the uncertainties in climate change impacts.

Rural nonpoint sources of water pollution are particularly difficult to control, with relatively little progress having been made compared to point sources. Management choices are difficult because of large uncertainties in both the monitoring of nonpoint pollution and the effectiveness of various actions to reduce that pollution. This study includes a proposed framework for selecting the optimal combination of research, monitoring, and management actions. The approach combines Bayesian inference and multiobjective linear programming to explicitly represent uncertainty in the effectiveness and cost of controls and to quantify the value of reducing uncertainty through research and monitoring. The authors illustrate the framework using the problem of reducing turbidity from rural sediment sources in the Minnesota River basin. The results show that a combination of research methods in different subbasins usually yields the most valuable information and is predicted to result in benefits via reduced cost and increased effectiveness of sediment reduction.

Systems thinking and system dynamics simulation can provide insights for developing effective plans to protect the environmental integrity of natural systems impacted by human activities. In this study, a system archetype known as growth and underinvestment is hypothesized to explain the eutrophication problem of Lake Allegan in Michigan and identify policy leverage points for mitigation. An integrated system dynamics model is developed to simulate the interaction between key socioeconomic subsystems and natural processes driving eutrophication. The model is applied to holistically characterize the lake’s recovery from its hypereutrophic state and assess a number of proposed total maximum daily load (TMDL) reduction policies, including phosphorus load reductions from point sources and nonpoint sources. It is shown that for a TMDL plan to be effective, it should be considered a component of a continuous sustainability process, addressing the functionality of dynamic feedback relationships between socioeconomic growth, land use change, and environmental conditions.

Urban water resources should be managed to meet conflicting demands for environmental health, economic prosperity, and social equity for present and future generations. While the sustainability of water resources can depend on dynamic interactions among natural, social, and infrastructure systems, typical water resource planning and management approaches are based on methodologies that ignore feedbacks and adaptations among these systems. This research develops and demonstrates a new complex adaptive systems approach to model the dynamic interactions among population growth, land-use change, the hydrologic cycle, residential water use, and interbasin transfers. Agent-based and cellular automaton models, representing consumers and policymakers who make land- and water-use decisions, are coupled with hydrologic models. The framework is applied for an illustrative case study to simulate urbanization and the water supply system over a long-term planning horizon. Results indicate that interactions among the decentralized decisions of individual residents can significantly influence system-wide sustainability. Adaptive management policies are included to restrict the water use and land use of consumers as the availability of water decreases. These strategies are simulated and assessed based on their abilities to increase the sustainability of the water supply system under the stresses of population growth, land-use change, and drought.

Over the past decade, water-centric research has increasingly taken into consideration the interactions between the water cycle and the social, economic, and biogeophysical processes that drive watershed dynamics. In parallel, water management has made great strides in data sharing and collaborative modeling that support decision making through integrated planning and stakeholder involvement. Both research and management communities require data and simulation models that cover large spatial scales and workflows that enable investigations and decision making in real time with participation of multiple watershed actors. To efficiently accomplish their goals, these two communities are tapping into the capabilities of advanced cyberinfrastructure (CI) platforms that facilitate an understanding of watershed processes, knowledge management, visualization, interaction, and collaboration in multiple watershed science and engineering disciplines. This paper illustrates an implementation of an end-to-end CI system for understanding of the ecological threats, shifts in soil conservation practices, and public perception of environmental health with preservation of the economic benefits of agricultural production at the watershed scale. The systems were implemented in a $270{\mathrm{km}}^2$ Clear Creek catchment in eastern Iowa.

Economic theory suggests that water pricing can contribute to efficient management of water scarcity. The European Union (EU) Water Framework Directive (WFD) is a major legislative effort to introduce the use of economic instruments to encourage efficient water use and achieve environmental management objectives. However, the design and implementation of economic instruments for water management, including water pricing, has emerged as a challenging aspect of WFD implementation. This study demonstrates the use of a systems analysis approach to designing and comparing two economic approaches to efficient management of groundwater and surface water given EU WFD ecological flow requirements. Under the first approach, all wholesale water users in a river basin face the same volumetric price for water. This water price does not vary in space or in time, and surface water and groundwater are priced at the same rate. Under the second approach, surface water is priced using a volumetric price, while groundwater use is controlled through adjustments to the price of energy, which is assumed to control the cost of groundwater pumping. For both pricing policies, optimization is used to identify optimal prices, with the objective of maximizing welfare while reducing human water use in order to meet constraints associated with EU WFD ecological and groundwater sustainability objectives. The systems analysis approach demonstrates the successful integration of economic, hydrologic, and environmental components into an integrated framework for the design and testing of water pricing policies. In comparison to the first pricing policy, the second pricing policy, in which the energy price is used as a surrogate for a groundwater price, shifts a portion of costs imposed by higher water prices from low-value crops to high-value crops and from small urban/domestic locations to larger locations. Because growers of low-value crops will suffer the most from water price increases, the use of energy costs to control groundwater use offers the advantage of reducing this burden.

By ignoring the opportunity cost of water use, water is undervalued, which can lead to significant errors in investments and water allocation decisions. The marginal resource opportunity cost (MROC) varies in time and space, as resource availability, demands, and users’ WTP vary. This spatial and temporal variability can only be captured by basinwide hydro-economic models integrating water demands and environmental requirements, resources, infrastructure, and operational and institutional restrictions. This paper presents a method for the simulation of water pricing policies linked to water availability, and the design of efficient pricing policies that incorporate the basinwide marginal value of water. Two approaches were applied: priority-based simulation and economic optimization. The improvement in economic efficiency was assessed by comparing the results from simulation of the current system operation and the pricing schedule. The difference between the benefits for the simulated current management and the upper bound benefits from optimization indicates the maximum gap that could be bridged with pricing. In the application to a synthetic case, a storage-dependent step pricing schedule derived from average MROC values led to benefits that capture 80% of the gap of net benefits between management without pricing and the economically optimal management. Different pricing policies were tested, depending not only on reservoir storage but also on previous inflows. The results show that the method is useful for designing pricing policies that enhance the economic benefits, leading to more efficient resource allocations over time and across the competing uses.