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Controlling Salt Problems on the San Joaquin River

A B S T R A C T

Stan E. Malinky1 and Nigel W.T. Quinn2

The San Joaquin River (SJR) is the longest river in Central California. The 366-mile long river starts in the high Sierra Nevada at an elevation of 9,839 feet and flows through the rich agricultural region known as the San Joaquin Valley before reaching the San Francisco Bay-Delta and ultimately the Pacific Ocean. For this reason the SJR is among the most heavily dammed and diverted rivers in the state. The Grassland Water District (GWD) salt monitoring and management pilot project is part of a San Joaquin basin-wide effort to implement real-time salinity management as a more cost-effective alternative to TMDL-based salinity. The GWD pilot project focuses on the management of seasonal wetland drainage of elevated salinity to the San Joaquin River. The project aims to develop a functional sensor network with real-time data management providing an exemplar for other wetland and agricultural drainage entities in the basin. The initial phase of the project has involved design of a Water Information Management System (WIMS) that will provide both a software solution and hardware for collecting, managing, evaluating and automatically correcting water quality data. Once the WIMS is fully operational it will provide a higher quality of data to be utilized by water quality forecasting models of the SJR and associated watersheds. The solution will help improve scheduling of saline drainage from wetland and agricultural sources to the river. Future goals include sharing real-time data with basin stakeholders, so as to minimize requests on east-side reservoirs to provide dilution when water quality objectives at the Vernalis compliance station are exceeded.

Figure 1. San Joaquin River basin

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INTRODUCTION With a basin size of 32,000 square miles and a river length of 330 miles, the SJR channel cuts through the rich farm land of the San Joaquin Valley. Once the richest river ecosystem in California, the river is now threatened by pollution in many areas where it runs through the valley. A Real-Time Management Program (RTMP) was initiated in 2004 as a long-term, innovative plan to meet the salinity objectives at Vernalis compliance monitoring station through the cooperation of basin stake-holders and coordination of efforts to manage salt loading. The RTMP helps to maximize the salt exports in the Lower SJR while maintaining compliance with salinity water quality objectives defined at Vernalis. The RTMP and real-time load allocations replace the monthly salt load allocations specified in the 2004 TMDL and existing Water Quality Basin Plan for the basin. The California Department of Water Resources and the United States Geological Survey operate flow and water quality monitoring stations along the SJR and its tributaries. These and other stations provide real-time data input to models that forecast flow and water quality at specified locations along the river. This will allow SJR basin management to coordinate drainage flows and eastside reservoir operations to meet the salinity compliance objectives at Vernalis. The sensor network within the basin will be fully enhanced and data sharing protocols established. Under the RTMP, salts are only discharged from eastside and westside watersheds when assimilative capacity is in the River, rather than constrained by mandated monthly load allocations. Managing the use of assimilative capacity is anticipated to reduce reliance on fresh water releases from New Melones Reservoir that typically are made to assist in meeting salinity objectives at Vernalis during dry years.

RTMP AREA OF FOCUS

Real-time salinity management has been written into the Basin Water Quality Plan for the SJR. It is the most cost-effective long-term solution to salinity problems within the basin. The plan balances the dual objectives of maximizing out-of-basin salt export while maintaining compliance with strict 30-day rolling average salinity objectives, set independently for summer and winter periods. The summer objective of 700 uS/cm is protective of downstream irrigated agriculture where water supplies are diverted directly from the SJR. The winter objective of 1,000 uS/cm is higher since agricultural constraints are not a limiting factor during this season. The proposed solution or mechanism to meet and maintain water quality in the San Joaquin River as stated in the RTMP is

1. Develop a fully functional and comprehensive sensor network of flow and EC monitoring stations.

2. Develop IT capability to allow data collection, management and sharing information basin-wide in real-time.

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3. Calibrate and improve the accuracy of existing San Joaquin River and watershed computer-based models to produce two-week forecasts of SJR assimilative capacity as measured at the Vernalis compliance monitoring station.

4. Work with basin stakeholders to improve salt management and control capabilities to schedule westside salt loading during periods of sufficient SJR assimilative capacity.

Additional strategies may limit the loading of salt via the Delta Mendota Canal, which is the main source of agricultural and wetland supply water from the Delta. Controlling salt load imported to the basin is considered a separate concern. The remaining portion of this paper will be focused on the monitoring and management of the salt load that is discharged to the SJR from westside sources to coincide with forecast flows from the eastside reservoirs to meet water quality objectives.

Figure 2. San Joaquin River Basin Study Area

GRASSLAND WATER DISTRICT PILOT PROJECT

Several State and Federal grants were awarded to Grassland Water District and Berkeley National Laboratory, which allowed for the development of a sensor network with 50 flow and salinity monitoring stations. The District is a water purveyor on the westside of the San Joaquin Basin. It serves 160 individual duck clubs and land and cattle operations within an area of approximately 51,700 acres. Hardware vendors were selected and equipment was deployed (YSI-ECONET) for automated collection and transmission of continuous data to a remote server. Data were managed and presented on a hosted website provided by the hardware vendor. The overarching goal of these pilot projects was to deter-mine the potential benefits and environmental impacts of local district-level management of saline wetland drainage to the San Joaquin River.

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A particular oversight of the system was the inability to perform data quality assurance in real-time or on a daily basis. The ability to do so would allow development of real-time drainage salt load forecasts to coincide with daily SJR assimilative capacity forecasts. The oversight also limited the type of data sharing between stakeholders that is considered essential for basin-scale real-time salinity management. The project team further determined that the software solution should be a commercially available, off-the-shelf solution with the ability to apply business rules specific to the District’s needs for automatic validation, correction and presentation of data in graphical form and visualization of information with web services. Selecting a software solution that has been adopted by other water districts has two advantages: (1) districts that want to upgrade operations tend to more readily adopt technologies being used by neighboring districts, and (2) information exchange between district stakeholders is more easily facilitated when districts share the same software. Data sharing is imperative to real-time salinity management. It allows westside drainage of salt loads to be matched with eastside reservoir releases that generate salt-assimilative capacity. In addition real-time data quality assurance is an essential criterion for a successful long-term solution. The project team selected KISTERS software solutions for water data management and the WISKI platform. Project managers worked closely with the IT firm to develop a data management system and associated tools for monitoring site data to be simultaneously reported to the YSI-ECONET server and a database housed at the District. The long-term goal was to develop the capability for a stable, robust data management system and enable migration from YSI-ECONET. The result would be a subsequent cost savings of more than $30,000 annually in cellular service fees and software maintenance. The product manager requested the IT firm participate in the pilot to specifically support the District by installing and configuring the software. When the web solution is implemented and new hardware installed within the District to migrate a mixed cellular/RF radio system to a mostly RF system with a base station located within the District headquarters, the project will be complete. The incremental deployment strategy adopted by the project team has allowed local wetland managers to adopt the technology at a manageable pace. The result is development of a more quantitative approach to wetland water and salinity management.

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Figure 3. Grassland Water District Project Area

WHY WAS A WIMS SELECTED INSTEAD OF A SCADA SOLUTION?

The constantly changing conditions and dynamic state of the data necessary for managing flows and computing salt concentrations in drains and the SJR required a real-time data management solution. Real-time data management is often reserved for solutions such as Supervisory Control and Data Acquisition (SCADA). However SCADA is most often associated with the collection and presentation of current conditions so appropriate changes can be made to ensure the smooth operation of a facility.

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The use of the term “real-time data” can be misleading. Real-Time Data (RTD) refers to data collected at a frequency typically in less than one minute increments and delivered immediately after collection and primarily reserved for SCADA applications. This type of solution is necessary for operators to manage facilities such as hydroelectric generating stations, wastewater plants, etc. Near Real-Time Data (NRTD) refers to data collected in intervals of 5-, 15-, 30-minute or greater intervals of time and then delivered at the collection rate or faster depending upon telemetry cost, power management, and needs of the data users. Applying data for decision support, presentation, management, and modeling requires the best or highest quality data available. This can be difficult and labor intensive if large quantities of data are imported at a higher rate. Data collected by monitoring station sensors necessitates frequent maintenance and on-going calibration. Raw data are often plagued with fly outs, dropouts or missing data - yet this data is used to make decisions on a weekly, daily, or hourly basis. An automated method for cleaning or correcting the data makes real-time decision support systems feasible.

APPLYING BUSINESS RULES TO DATA FOR USE IN REAL-TIME

Without oversight and evaluation, data use by an organization such as a water district to make informed decisions becomes extremely difficult. Data that do not conform to a reasonable standard of quality are not dependable and should not be used for decision making purposes. KISTERS’ data evaluation tools when combined with specific business rules can automatically evaluate in near real time, and supply the user with the highest quality of data available based on the incoming raw data sets. The design of business rules for data management is specific to the type of data collected and intended use of the data. The following are two typical problems often observed with data:

Data is missing. The business rule for missing information can be as simple as no attempt to fill the gap. However a model that uses time series data for its forecasting needs means a null value is not acceptable; it would require compensatory data. If a one-hour model is run and data for a duration in excess of an hour are missing, then a result cannot be forecasted. Missing data can be managed automatically in a number of ways depending upon the gap. If data are predictable and the period of missing data falls within an acceptable time period -- for example an hour’s worth of data collected in 15-minute intervals -- filling the information deficit with linear interpolation could be acceptable. This level of data validation however must be approved by the users of the data or the authority.

Data spikes and dropouts. Business rules for spikes and dropouts depend on the severity of the sudden increase or decrease of information. Water quality data are notorious for spiking and dropping out. Data with a normal range of 1,000 units that spike to 7,000 units are somewhat easy to manage depending on the duration of the spike. This is true with dropouts as well.

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With water quality data, acceptable minimum and maximum limits are usually well understood for the reach being monitored. Threshold values can be developed for these values to clip incoming data to the reasonable limit. Since a dropout cannot fall below the value of zero, a rule that clips data less than zero can be defined and applied. Spikes and dropouts with less dramatic magnitudes are more difficult to detect and correct – but validation can still be done. Business rules for spikes and dropouts in this category require testing over time with large data sets to ensure corrected points fall within reasonable limits.

Business rules used for the correction of noisy data can make the difference between meeting and not meeting required compliance.

EXAMPLES OF APPLYING BUSINESS RULES TO NOISY DATA ON THE SAN JOAQUIN RIVER

Data from one of the agency-supported stations along the SJR were chosen to illustrate expanded capabilities of the project team at GWD and LBL with the use of the WISKI database solution. The 15- minute data from these stations are posted to the California Data Exchange (CDEC) and data are automatically downloaded and imported into WISKI as soon as it is made available by CDEC. The first example station is the SJR at Maze Boulevard. Figure 4, below, shows data plagued with spikes and dropouts. Data have had a simple business rule applied: clip all data less than zero. This simple rule can be applied to all data values below zero. Notice the large spike that extends from an average of 1,000 units to more than 40,000 units, which is a common problem with continuously recording water quality sensors. The development of business rules for the real-time data correction is defined for a specific parameter such as EC at a specific geographical location. The rules are defined and then arranged into a series. The simplest rule that corrects the largest amount of data is applied first, and then the next rule is applied. The rule defined for one location may or may not apply to other parameters in other geographical locations.

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Figure 4. Values of conductivity from the San Joaquin River at Maze Blvd.

Figure 5, below, is an enlarged area of the spike from Figure 4, above. A closer look at details in the data reveals noise that requires further refinement. Additional business rules are essential to further validate and correct data.

Figure 5. A closer look at a portion of data from Figure 4

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Below, Figure 6 shows the final corrected data after all of the business rules have been applied. More information about this site and discussions with staff members who are responsible for maintaining stations and sensors may lead to further data refinement. Additionally, downstream or upstream EC stations may yield information about other issues with data collected at this location. If there are no influencing factors between the upstream or downstream monitoring stations, a business rule can be defined to compare data from these stations, in order to further qualify any questionable data.

Figure 6. Time series after all business rules have been applied and data corrected

Figure 7 combines both the raw data (red line) and the validated data (blue line) for comparison.

Figure 7. Comparison of the raw data (red line) and the corrected data (blue line)

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The second example is a monitoring station located on the San Joaquin River near Patterson. The data set is plagued with noisy data, spikes that exceed 200,000 units as compared to an average value of 2,000 units, and multiple values less than zero.

Figure 8. Comparison of the raw data (red line) and the corrected data (blue line)

The last example is from the compliance monitoring station on the San Joaquin River near Vernalis. This monitoring station is the target site for meeting the water quality objectives of the SJR.

Table 1, below, is the water quality objective as agreed upon in the Water Quality Control Plan for the Sacramento River and San Joaquin River basins in the Control of Salt and Boron Discharges into the Lower San Joaquin River, July 2004 also known as the 2004 TMDL.

Table 1. Salinity and boron objectives for the Lower San Joaquin River at Vernalis

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Below, Figure 9 displays both the raw and the validated data for comparison as collected from Vernalis monitoring station. Notice there is little error between the raw and corrected data during this period.

Figure 9. from San Joaquin River near Vernalis comparison of the original data (red line) and the corrected data (blue line)

Figure 10 displays corrected data, 30-day average, and seasonal limits as defined in Table 1, above. The 30-day running average allows reservoir operators the flexibility needed to achieve compliance while managing water more efficiently and thus, less water is released for dilution while more is delivered to junior water rights holders in the basin.

Figure 10. San Joaquin River near Vernalis 30-day running average and seasonal EC limits

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As seen in the figures above, applying business rules to data can provide a better quality for modeling and decision support. Figure 10 shows the 30-day running average, and demonstrates the advantages of giving flexibility to reservoir operators in order to meet the seasonal salt compliance. The same figure also shows many days are out of compliance due to insufficient monitoring and data sharing infrastructure throughout the basin. With the acceptance of this pilot project and basin-wide expansion of the sensor network, real-time data management and data sharing, as well as the use of WIMS and the WISKI solution, the Lower San Joaquin River water quality objectives as defined in the RTMP can be accomplished.

LONG TERM PLAN FOR CONTROLLING SALT AT THE WATER DISTRICT LEVEL

The pilot project was designed to look at the cost and efforts associated with a district-level project to monitor and manage saline drainage releases to the San Joaquin River. This project provides the basis for implementing similar programs at other key water districts on the westside that discharge either agricultural or wetland water to the river. It also applies to two municipal discharges entering the San Joaquin River between Lander Avenue and Vernalis. The WIMS software will allow district managers to time releases when the river can support the additional salt load. After districts have implemented a monitoring program, they will use data sharing capabilities of the WIMS in the form of a web portal -- thus allowing all stakeholders to monitor water quality of the San Joaquin Basin in real-time. Additionally, two key districts on the eastside have implemented KISTERS’ software solutions and could play a major role in supplying salinity dilutions flows when needed. Finally a completely automated system with sufficient data can be designed around optimization soft-ware that would operate in real-time and provide critical information to all stakeholders involved in decision-making. With sufficient data, an optimization solution will ingest all flow and EC values from all critical locations in the basin and compute all releases necessary to reach the objectives at Vernalis. A tool can be run on demand and identify optimum operating conditions in minutes, depending on the size of the system. For example, if the tool runs hourly, best possible conditions for the subsequent day can be reported. If the tool runs for a day, it can identify the optimum for the following 10 days. A notification schedule can be implemented to let key districts be aware of an impending problem as well.

CONCLUSIONS

The next steps in the program will require (1) complete migration of the current GWD YSI-ECONET platform to a local data server running WISKI software and (2) development of communication software to allow direct, automated data downloading to the local district-maintained database. This system will meet the low-cost objectives of the program and become an exemplar for adoption in other wetland management entities such as the California Department of Fish and Wildlife, the U.S. Fish and Wildlife Service, wetland managers, and a large number of independent water districts that collectively belong to the San Joaquin Valley (Westside) Drainage Authority (SJVDA) which bears responsibility for data monitoring networks being developed within the basin. Teamed with an eastside San Joaquin Valley drainage authority, the SJVDA will provide the institutional organizing impetus for successful implementation of this new concept in basin-scale salinity management.

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AUTHOR AFFILIATIONS

Stan E. Malinky is a water data solutions consultant for the environmental IT firm KISTERS North America. Nigel W.T. Quinn is the Group Research Leader for HydroEcological Engineering Advanced Decision Support (HEADS) at Lawrence Berkeley National Laboratory.

REFERENCES

Quinn N.W.T, R. Tassey and J. Wang. 2014. Use of online data and computational resources to implement real-time salinity management - an efficient regulatory alternative to TMDL-mandated waste discharge requirements in: Gaurina-Medjimurec, Nediljka (ed), Handbook of Research on Advancements in Environmental Engineering. Advances in Environmental Engineering and Green Technologies (AEEGT) series. IGI Global, Hershey, PA. 89-117. DOI: 10.4018/978-1-4666-7336-6.ch004

Quinn N.W.T., R. Ortega and L. Holm. 2011. Environmental sensor networks and continuous data quality assurance to manage salinity within a highly regulated river basin in: Manos, Basil (ed), Decision Support Systems in Agriculture, Food and the Environment: Trends, Applications and Advances. IGI Global, Hershey, PA. 420-436. DOI: 10.4018/978-1-61520-881-4.ch019 Quinn N.W.T., G. Lee and D. Cozad. 2010. Information technology and decision support tools for stakeholder-driven river basin salinity management. Proceedings of the 43rd Annual Hawaii International Conference on System Sciences (HICSS) 2010 Jan 5-8, Kauai, HI. Piscataway, NJ: IEEE, 2010. 458-468. DOI: 10.1109/HICSS.2010.458 Quinn N.W.T., R. Ortega, P.J.A. Rahilly and C.W. Royer. 2010. Use of environmental sensors and sensor networks to develop water and salinity budgets for seasonal wetland real-time water quality manage-ment in: Environmental Modelling and Software. 25(9), 1045-1058. DOI: 10.1016/j.envsoft.2009.10.011 Quinn N.W.T. 2009. Environmental decision support system development for seasonal wetland salt management in a river basin subjected to water quality regulation in: Agricultural Water Management, 96(2), 247-254. DOI: 10.1016/j.agwat.2008.08.003 Quinn, N.W.T., L.F. Grober, J. Kipps, C.W. Chen and E. Cummings. 1997. Computer model improves real-time management of water quality. California Agriculture, 51(5), 14-20. DOI: 10.3733/ca.v051n05p14