Long-term Salinity Prediction with Uncertainty Analysis Jim Prairie CADSWES (Masters’ Thesis Status) September 11, 2000

Law of the River - Water Quality Mexico Treaty Minute No. 242 assured water received by Mexico have an average salinity of no more than 115 ppm +/- 30 ppm above the average annual salinity at Imperial Dam Colorado River Basin Salinity Control Act of 1974 meet United States obligation to Mexico under Minute No. 242 authorized construction of desalting plant and additional salinity control projects

Salinity Control Forum Created by Basin States in response to Federal Water Pollution Control Act Amendments of 1972 Developed numerical salinity standards 723 mg/L below Hoover Dam 747 mg/L below Parker Dam 879 mg/L at Imperial Dam review standards on 3 year intervals Develop basin wide plan for salinity control

Salinity Control Forum Salinity Control efforts in place removed 621,400 tons from the system in 1998. This accounted for 9% of the salt mass at Imperial Dam total expenditure through 1998 $426,020,000 Projected 1,476,600 tons of salinity controls needed by 2015 projects additional expediture through 2015 $169,698,000 (data taken from Quality of Water, Progress Report 19)

Colorado River Simulation System - Fortran First implemented in the early 1980’s Basin wide model for water and salinity modeling CRSS model was an essential tool for decision support model used to determine required salt removal to maintain standards

Colorado River Simulation System - RiverWare CRSS was replaced by a poilcy model in RiverWare in 1996 (RW-CRSS) Recent attempts to verify RW-CRSS against historic data from 1970 to 1990 failed

USBR and Research Objectives Investigating the salinity methodologies currently used and improving them as necessary for future predictions Verify the data and calibrating the current model for both water quantity and water quality (total dissolved solids, or TDS) Development of a long-term, basin-scale salinity model for the Colorado River Basin, with the following features: model flow weighted salinity concentrations with uncertainties at locations with salinity standards develop methodology for operational and long-term policy analysis Funding provided by the USBR salinity group for USBR and Research Objectives

Current Salinity Modeling Methodology Separation of gauged flow and natural flow necessary for predictions gauged flow influenced by changing human factors natural and human-induced salt Salt can enter the system from two sources natural flows salt loading (predominately agriculture) Salt modeled as a conservative substance Reservoirs modeled fully mixed Monthly timestep

Review of Current Model Problems Inconsistent data and methodologies status reports outlined new methods to develop the natural flow database and store model inputs and outputs Calibration problems quantified the over-prediction throughout the basin Low-flow problems Limited uncertainty analysis

Calibration Problems

Low-Flow Problems

Limited Uncertainty analysis Natural variability of flows Index sequential modeling generates synthetic streamflow that exactly match the historical record, shifted in time

Research Objectives Development of a long-term, basin-scale salinity model for the Colorado River Basin, with the following features: model flow weighted salinity concentrations with uncertainties at locations with salinity standards develop methodology for operational and long-term policy analysis

Literature Review Water Resource, GEOBASE and GeoRef databases were searched Multiple keyword searches found a total of approximately 700 records Articles discussing groundwater, estuary, or salt intrusion models were eliminated leaving approximately 100 articles Review of these abstracts found 2 papers directly related to our research

Directly Related Articles Basin Scale Water Quality Models for the Colorado River Basin Stochastic Analysis of Water Quality. Malone, R., Bowles, D., Grenney, W., Windham, M., Utah Water Research Laboratory. Considered uncertainty with salt loading A Stochastic Model of River Water Quality: Application to Salinity in the Colorado River. Lee, D., Howitt, R., Marino, M., Water Resources Research. Considered uncertainty due to natural variability of flow

Additional Papers Reviewed Water Quality Models with Uncertainty Modeling Salt Processes Modeling Methods for Uncertainty

Uncertainty in Water Quality greatest uncertainty least uncertainty

Uncertainty in Water Quantity greatest uncertainty least uncertainty

Separation of Natural Flow from Gauged Flow Need separation for predictions ? Can historical gauged flow be used for predictions ? Introduces additional uncertainty Need to separate natural salt load from gauged salt load neither natural nor human-induced salt loading can be directly measured

Representation of Stochastic Hydrology Natural variability Sustained low-flow periods Time series analysis Stochastic Analysis, Modeling and Simulation (SAMS) generates synthetic streamflow sequences that have not occurred in the past, but are statistically possible critical low-flow analysis Could another type of analysis be used?

Detailed Reach Study Upper Colorado and Gunnison River most complete water quantity dataset calculation of salinity pickup in the Grand Valley Research a process to describe salt loading into the system and associated uncertainty

Calibration and Use of Model for Projections Current calibration is only a patch removes over-predicted salt with calibration nodes Development of a final calibration will not be complete until a dataset for the basin is developed Projections SAMS to represent natural variability land use as a parameter in predictive model

Calibrated Model

Research Issues to Discuss SAMS for representation of natural variability Separating natural salt load from gauged salt load neither natural nor human-induced salt loading can be directly measured difficult to produce good prediction or quantified uncertainty Developing a process to describe salt loading into the system and associated uncertainty Modeling changes in salt loading due to future land use changes