Journal of Water Resources Planning and Management

This paper presents a water resources sustainability index that makes it possible to evaluate and compare different water management policies with respect to their sustainability. The sustainability index identifies policies that preserve or improve the desired water management characteristics of the basin in the future. This index is based on a previous sustainability index with improvements in its structure, scale, and content to make it more flexible and adjustable to the requirements of each water user, type of use, and basin. The Rio Grande transboundary basin is used as a case study demonstrating the use of the index. Tailor-made sustainability indexes are defined for water users in Mexico, the United States, the environment, and for meeting system requirements (international treaty obligations). Group sustainability indexes are calculated to summarize the results for groups of water users of each country, the environment, and the basin as a whole. Sustainability indexes by subbasins are calculated to identify areas of potential improvement and regions at risk.

This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration’s (NOAA’s) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1–7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers’ practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA’s imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4–8.5% gain in profit and 11.0–26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role of forecasts for irrigation scheduling in a more effective way.

In many places in the world, increasing water demands have led to the development of infrastructure for freshwater storage and irrigation. Especially in water-scarce regions, this development has led to a growing number of concerns about basin closure. These concerns ask for a structured approach for analyzing the occurrence of basin closure to facilitate sustainable responses. In this paper, the downstreamness concept is proposed for analyzing the availability and commitments of freshwater in river basins. The downstreamness of a location is the ratio of its upstream catchment area to the entire river basin area. The downstreamness of a function on the basin, such as water availability or water demand, is defined as the downstreamness-weighted integral of that function divided by its regular integral. An approach to determine the downstreamness of surface water storage capacity, stored surface water volumes, and water demands is described. Applying the proposed approach is helpful for the spatiotemporally explicit assessment of basin closure and its drivers. Water management policies rely on the valuations of trade-offs between commitments of upstream and downstream uses and technical implementations of upstream and downstream measures. The concept of downstreamness can assist in analyzing subbasin points of view as an integral part of a basin perspective. In this way, the effects of anthropogenic processes driving basin closure can be better understood. To illustrate the use of the concept, the authors describe its application to the Jaguaribe Basin in the semiarid northeast of Brazil.

A modeling approach based on mass balance coupled with the Hydrologic Simulation Program in FORTRAN (HSPF) is developed in this study to evaluate bacteria reduction strategies and their impact on water quality for an urban watershed in Texas. This approach allowed for spatial and temporal representation of bacteria loading from the adjacent watershed that was balanced with in-stream loading. This decreased the uncertainty in source loading estimates and reduced the number of variables requiring calibration. The resulting model is used to investigate whether reduction goals calculated by using the mass balance data alone would actually achieve water quality standards. Results indicate that 100% elimination of runoff loads would be required to achieve the applicable standards, which is a somewhat unrealistic and costly endeavor. The utility of the developed model is further demonstrated by investigating spatial reduction strategies targeting the various bacteria sources in the watershed. Results indicate that a 99% elimination of the upstream dry weather loading led to water quality compliance in the downstream subbasins. The modeling approach also indicates that the water body had a higher probability of achieving the single-sample standard than the geometric mean standard.

Numerous studies have developed new methods for skillful long-lead seasonal streamflow forecasts, especially in the western United States, and most assume that the streamflow forecast skills translate into improved water resources decision making, but there has been little comprehensive demonstration of this. This translation is not straightforward because the decision system is nonlinear. This paper develops a prototype decision support system (DSS) to systematically evaluate the translation of streamflow forecast skills to water resource operational variables and decision variables in the Gunnison River basin (GRB). The DSS consists of two main modules: (1) a multimodel ensemble streamflow forecast module that uses large-scale climate information and issues forecast ensembles, and (2) a decision support module (developed in the generalized decision tool, RiverWare) that captures the seasonal operations and management of the GRB water resources system. Ensembles of streamflow forecast scenarios drive the decision support module, resulting in an ensemble of various variables pertaining to operations of the reservoirs. The DSS is run with streamflow forecast ensembles for the spring season issued on January 1 and April 1 and also the climatological and observed streamflows. The skills of the various variables, e.g., power generated and end of season reservoir storages, are computed. The results demonstrate that the skills from the streamflow forecasts are well transferred to the operational variables, enabling more effective decision making as early as January, when the snow information is partial at best. This integrated and comprehensive prototype DSS is believed to be one of the early attempts to demonstrate the utility of streamflow forecasts in a decision-making context. This DSS framework can be easily transferred to other basins.

In Tanzania, like in many other countries in the Sub-Saharan region, the government is implementing an ambitious rural water supply and sanitation plan (RWSSP) to increase access to water from 53% in 2005 to 90% by 2025. This paper exploits water point mapping (WPM) data to evaluate the principles underlying the design of this program and reveals some challenges for the rapid increase of access to water. The data set of water points analyzed accounts for 15% of the country’s total rural population. The results show that the inaccuracy of the baseline used for the program design, difficulties faced by underserved districts in keeping the water points functional, and the differences between the expected and real long-term functionality of the water points, especially for hand pumps, are the primary threats to the achievement of the targets. It is suggested that more effort be put into capacity building, particularly the supervision of construction, and the support of community-owned management during the first years of operation.

Nearly all water supply systems for high-rise buildings in Egypt include storage cisterns to feed the upper floors with cold water for different uses. Although these cisterns are the most important component in the distribution system, they are generally the least understood in terms of their effect on water quality. In this study, chlorine residual, algae genus and concentration, and pH value were measured before and after storage cisterns that exist above a 12-floor building in Assiut City, Egypt, to check their effect on water potability, through 48 h of observation. EPANET quality model is applied to simulate the chlorine residual and water age in the distribution system and storage cisterns through extended period simulations. The model is used to study the influence of the storage cisterns size and the turnover depth on residual chlorine decay and water age in the tanks. Results indicated that oversized storage cisterns can have negative impacts on water quality, including increased water age, reduced disinfectant residuals, and increased growth of disinfectant by-products (DBPs). However, there are other key roles that storage plays in the distribution system, e.g., security of supply during supply interruption and reserve storage for firefighting. Some of the microbiological and chemical effects of the cisterns on water quality have been investigated.

Accurate estimation and prediction of the demand patterns of the customers of a water works would enable more accurate network hydraulic management. In this study, a probabilistic model to generate residential demand patterns for single-family and semidetached houses is formed. To form these pattern models, an automated meter reading technique has been utilized to gather the necessary information from a sample of residences, which are used to model the demand behavior in a way that is applicable to a much wider range of residences. A linear regression model was constructed to predict the measured average weekly consumption from the calculated average weekly consumption. The residences were clustered by their weekly water demand into four distinct classes using the $k$-means algorithm. For the final result, probability models developed on the basis of mixtures of Gaussians for each class, in conjunction with the prediction model of the weekly water consumption, were utilized so that estimates for the demand pattern are obtained by sampling the probability distributions for individual single-family and semidetached houses.