Salinity Management in the Upper Colorado River Basin: Modeling, Monitoring, and Cost-Equity Challenges

Salinity issues in the Upper Colorado River Basin have been a serious concern to the western United States and northern Mexico. The Colorado River salinity is mainly come from geologic materials located in the Upper Colorado River Basin. Natural weathering and human activities, such as irrigation, accelerate the dissolution of saline materials. Economic damages due to salinity in the Colorado River Basin are estimated at $295 million in 2010, for example, reduced crop yield, plugging of water pipes and fixtures, and ecological health of rivers. In order to manage salinity in the Upper Colorado River Basin, SPARROW model has been applied to simulate salinity sources and transport. However, the model application discontinued during recent past due to lack of data. Given the motivation and importance of salinity issues in the Colorado River Basin, the overall goal of this research is to develop a decision-making framework for an effective salinity management in the Upper Colorado River Basin. First, this research introduced a methodology for reliable analysis of salinity sources and transport in the Upper Colorado River Basin. However, recent decreasing trend of number of monitoring stations may cause increase of model uncertainty. Therefore, a decision-making methodology for an effective water quality monitoring network was developed. From the results of monitoring network analysis, the redundancy or scarcity of monitoring stations in each watershed can be identified under the given operational costs. Finally, salinity management scenarios considering cost and equity were developed. Management options considering cost only can neglect the fairness in the allocation of salinity control responsibilities among stakeholders. To overcome this limitation in management, the methodology developed in this research considers cost of salinity control, equitable distributions among stakeholders, and cost efficiency. The methodologies developed in this research provide a comprehensive decision-making framework for an effective salinity management in the Upper Colorado River Basin. Moreover, this framework is not limited to the management of salinity in the Upper Colorado River only, but also can be applied to other water quality management problems

Cost Effective Salinity Removal Strategies for the Upper Colorado River Basin

The Colorado River Basin is currently affected from high salinity generated from both anthropogenic causes and natural geology. The annual salt loading of the Colorado River Basin (CRB) is around 9 million tons at the Hoover Dam, and the corresponding economic damage is estimated at 383 million dollars based on 2009 salinity concentrations. Generally, the Upper CRB is a major contributor of salinity, and the Lower CRB is a major user of impaired water. Therefore, the total salinity removal target of the Colorado River is aimed at the UCRB. Fifty nine 8-digit hydrologic unit code (HUC) watersheds in the Upper CRB are considered responsible for salinity. In this research, cost effective allocation strategy of salinity is proposed by cost minimizing optimization. The objective function is formulated by using a cost function of salinity control that was derived using regression analysis of salinity control amounts and the corresponding control costs from the existing salinity control units. Salinity removal by irrigated lands is only considered in this research assuming that maximum salinity removal in the Upper CRB can be obtained by entire retirement of irrigated lands. Salinity generation after retirement can be considered as salinity from natural sources. In addition, the maximum possible salinity removal from each watershed cannot exceed the differences between the current salinity loading and the projected salinity loading when irrigated lands are retired. The SPARROW surface water quality model of U.S. Geological Survey (USGS) is used to estimate salinity generation. SPARROW is a basin wide, statistical, and a process-based model and it is able to calculate instream salinity contribution of each salinity source and delivery parameter. Fifty four watersheds that have irrigated lands are used in cost optimization. A simple salinity load reduction method based on relative contribution from each watershed was used for comparison with the optimized allocation. Cost effective allocation strategies provide economically competitive solutions compared to the simple weighted allocation and shows different priorities in salinity removal of watersheds. The outcome and procedure of this research can be used to determine better load reduction strategies using both cost and equity as priorities

Predicting Annual Variation of Salinity Production from the Upper Colorado River Basin Using SPARROW

The Colorado River Basin has suffered high salinity from both natural conditions and anthropogenic activities which precipitate dissolving salts. The annual salt load at the Hoover Dam located in the lower part Colorado River recorded 9 million tons, and the economic damage is estimated at 383 million dollars based on 2009 salinity concentrations. In order to manage salinity in the Colorado River Basin, water quality standards given as TDS concentration are established at three monitoring stations. Therefore, efforts have been underway to study salinity generation and transport, and develop salinity mitigation strategies. As a part of these efforts, the SPARROW (SPAtially Referenced Regressions On Watershed attributes) surface water quality model developed by U.S. Geological Survey was applied in an earlier study to the Upper Colorado River Basin to predict salinity production and to estimate how the salinity-related parameters affect in the water year 1991. Since this application about 20 years ago, there is a need to extend this earlier work to evaluate the applicability of SPARROW in modeling salinity in the past two decades. Also hydrologic and climatic conditions together with land cover have changed since 1991. In this work, SPARROW modeling was extended up to 2011 with updated land cover, and hydrologic and climatic data. In addition, the observed salinity loads were updated at each monitoring station for each year as well. The results from this recent modeling effort revealed that the salinity loads generally follow the trend of stream discharge. From the SPARROW model, it is shown that geologic sources and point sources do not have any particular trend in the recent decade; however, irrigated lands that occupy a small percent of the total land area have increasing salinity production trends indicating that agricultural is producing significant amounts of salinity to the lower basin

Introducing the Ensemble-Based Dual Entropy and Multiobjective Optimization for Hydrometric Network Design Problems: EnDEMO

Entropy applications in hydrometric network design problems have been extensively studied in the most recent decade. Although many studies have successfully found the optimal networks, there have been assumptions which could not be logically integrated into their methodology. One of the major assumptions is the uncertainty that can arise from data processing, such as time series simulation for the potential stations, and the necessary data quantization in entropy calculations. This paper introduces a methodology called ensemble-based dual entropy and multiobjective optimization (EnDEMO), which considers uncertainty from the ensemble generation of the input data. The suggested methodology was applied to design hydrometric networks in the Nelson-Churchill River Basin in central Canada. First, the current network was evaluated by transinformation analysis. Then, the optimal networks were explored using the traditional deterministic network design method and the newly proposed ensemble-based method. Result comparison showed that the most frequently selected stations by EnDEMO were fewer and appeared more reliable for practical use. The maps of station selection frequency from both DEMO and EnDEMO allowed us to identify preferential locations for additional stations; however, EnDEMO provided a more robust outcome than the traditional approach

Entropy Applications to Water Monitoring Network Design: A Review

Having reliable water monitoring networks is an essential component of water resources and environmental management. A standardized process for the design of water monitoring networks does not exist with the exception of the World Meteorological Organization (WMO) general guidelines about the minimum network density. While one of the major challenges in the design of optimal hydrometric networks has been establishing design objectives, information theory has been successfully adopted to network design problems by providing measures of the information content that can be deliverable from a station or a network. This review firstly summarizes the common entropy terms that have been used in water monitoring network designs. Then, this paper deals with the recent applications of the entropy concept for water monitoring network designs, which are categorized into (1) precipitation; (2) streamflow and water level; (3) water quality; and (4) soil moisture and groundwater networks. The integrated design method for multivariate monitoring networks is also covered. Despite several issues, entropy theory has been well suited to water monitoring network design. However, further work is still required to provide design standards and guidelines for operational use