Journal of Water Resources Planning and Management

A multistage stochastic optimization model using linear programming was developed to provide planning tools for Lower Colorado River Authority in the operation of the Highland Lakes system, as well as a framework for examining the operation of four irrigation districts. Three primary objectives were maximized in the model: (1) Revenues from rice production, (2) recreation benefits associated with lake use, and (3) revenues from hydropower generation. The model includes stochastic inflows, weather-dependent irrigation demands, an interruptible contract decision function, a reservoir space rule to balance storage volumes between reservoirs, hydropower production, municipal and irrigation return flows, and bay and estuary inflow requirements. Model weights and coefficients were calibrated to reflect actual market prices and economic constraints, or to represent water management priorities. Stochastic optimization is used to account for meteorological uncertainty using representative inflow scenarios. Considering the uncertainty in the inflows, the model predicts optimum acreage levels, reservoir storage levels, reservoir releases, and other decisions, to maximize total expected benefits in the system.

Many watersheds in Canada and in the northern United States see most of their precipitation in the form of snow. Many of these watersheds are the sites of important hydropower development projects. During snowmelt, watershed managers require information on snowpack depletion in order to optimize power production while minimizing flooding risk. In many cases, management techniques are based on simple correlations extracted from data collected in previous years or on inappropriate tools for data interpretation. Both of these factors can affect the reliability of forecasts and result in production losses or increased risk in downstream areas. This paper presents an approach to improve snowmelt forecasts. A simple distributed snowmelt model based on the degree-days approach is used to predict snowmelt based on weather forecasts. To improve forecasts, a feedback algorithm is presented that allows for real-time model adjustment using the integration of NOAA-AVHRR remote sensing data. A case study is presented based on a central Quebec watershed for the 1999 snowmelt season. This watershed is mostly under the management of Alcan Inc., which uses hydropower for the production of aluminum in its Jonquière, Que., Canada plant. A real-time simulation was carried out that resulted in a significant improvement of the timing of the flood peak forecast. With only one satellite image, the forecasting error of the flood peak was decreased by $5\text{days}$ (from 7 to 2) and reduced to less than $1\text{day}$ with the use of a second image, acquired $2\text{days}$ later. For this watershed, each one-day improvement in the timing of the peak flood forecast is worth tens of thousands of dollars in hydropower.

Two of the main performance criteria of a water distribution system are that it is inexpensive to construct, operate, and maintain and that it operates in a reliable manner. These two criteria are often conflicting, thus implying that the decision maker needs to reach a compromise in scheduling mains for replacement. One way to facilitate this decision is to generate a trade-off surface that represents the compromise between the two objectives. This paper demonstrates the use of genetic algorithms to generate trade-off curves between cost and reliability for pipe replacement decisions. Curves for two planning scenarios are generated. The first identifies the trade-offs necessary for the current conditions. The second allows the water authority to determine the required levels of future expenditure, given funding constraints, to meet a specified level of service over the entire planning horizon. Alternatively, it provides an estimate of the impact on performance of delaying expenditure.

Excess stormwater runoff is a serious problem in a large number of urban areas, causing flooding, water pollution, groundwater recharge deficits, and ecological damage to urban streams. Solutions currently proposed to deal with this problem often involve large centralized infrastructure and high expense. Phase II of the Environmental Protection Agency’s stormwater regulation is now requiring smaller communities nationwide to make important decisions about the potentially expensive management of excess stormwater runoff. This paper builds on research investigating the use of economic incentives to promote dispersed placement of smaller-scale best management practices (BMPs) for water detention to control excess runoff. We estimate a hedonic price function for houses in the area of a pilot project, and include the estimated part worth of yard area as our lower bound for opportunity cost in the cost function of the residential BMPs. We then show the effects of the inclusion of opportunity cost on two potentially useful incentive-based policy instruments available to communities.

We describe an approach to seasonal streamflow forecasting for the Snake River Basin in the Pacific Northwest that uses the variable infiltration capacity (VIC) macroscale hydrology model in conjunction with adjustments of the model’s initial snow state using moderate resolution imaging spectroradiometer (MODIS) imagery for Winters 2000–2004. We evaluated seasonal streamflow forecasts made on March 1, April 1, and May 1 through the end of July, as well as short lead forecasts for two-week durations beginning on April 15 and May 15 retrospectively for 2000–2004 (2004 forecasts are archived forecasts made in real time). In general, reductions in mean absolute error were more prevalent and larger for the two-week forecasts than for the longer seasonal forecasts, and the forecast skill improvements resulting from updating were greatest for forecasts made earliest in the season. Inclusion of MODIS data resulted in forecast error reduction (or no change in forecasts) in 59% of the seasonal forecasts (54% of the two-week forecasts), however when only the largest adjustments were considered, the fraction of improvement was much higher. We also evaluated the effect of MODIS adjustment on reservoir storage volume forecasts using a monthly time step reservoir simulation model. For two reservoirs (Dworshak and American Falls) where the reservoir model performed well in retrospective simulations, storage forecast errors were reduced for most forecasts at Dworshak, but were mostly unchanged or degraded at American Falls. The differences are attributed to reservoir operating characteristics, which are more constrained at American Falls than at Dworshak.

An optimization procedure called SDPR that includes dynamic programming, stochastic dynamic programming (SDP), simulation, and trial and error adjustment of risk coefficient is developed and applied to determine the optimal operation policy of the proposed Kok-Ing-Nan transbasin diversion system in Thailand. Subject to hydrologic uncertainty, transition probabilities of inflows and its related uncertainty were considered. Due to dimensionality problems, the system is decomposed into three serially linked subsystems: two for the proposed upstream Kok and Ing diversion storages and one for the existing Sirikit reservoir. Optimization of each subsystem is done sequentially from upstream to downstream with specified sets of hydrologic state variables and diversion/release targets. The targets of the three subsystems are interrelated and link the subsystems together. From the derived optimal operation policies, simulation results show that the transbasin diversion increases the Sirikit reservoir release, irrigation reliability and net benefit of the system each by about 50–60%. Compared to SDP, the SDPR optimal operation policy increases both the maximum irrigation reliability and maximum system net benefit by about 10%.

Cost of effective design or rehabilitation of the pipe networks depends largely on the available budget and satisfying pressures expected at the demand nodes. With the available market pipe sizes, one or more solutions of the same cost, the least cost, and simultaneously meeting the pressure constraints can be obtained from EPANET, a water distribution network simulation model, coupled with an optimization algorithm. Often, however, the least cost may be prohibitive from the overall budget consideration especially in developing countries and a less optimal solution is therefore the only option. The study shows a scheme in which solution selection is made, among solutions, which (1) is within allowable budget; and (2) yields acceptable total pressure deficit distributed more equally at several nodes instead of loaded on one or a few nodes only. A multiobjective optimization algorithm (NSGA-II) is coupled with water distribution network simulation software (EPANET) to provide the much needed Pareto front of the cost and nodal pressure deficit. A two-looped simple network is used to demonstrate the application of the scheme.