Surface Water Supply Index Water resources managers across the west benefit by knowing how much water to expect from snowmelt runoff. Forecasts are generally made by correlating snowpack, precipitation, antecedent moisture conditions, climatic variables such as SOI, (Southern Oscillation Index) etc with streamflow. Other models are also employed to forecast streamflow ranging from a seasonal to a daily basis. In a management sense, it is often very useful to know if the current year is similar to preceeding years for which you have a record - not only of the hydroclimatic conditions (snowpack, precipitation, runoff characteristics, temperatures, etc) but of the past management actions for that year as well. This gives one the information on what was done for a given set of circumstances, why it was done and the ultimate outcome of those actions both positive and negative. This is typcally called an ‘analog year’ type analysis. It is most valuable when the current year closely mimics the selected analog in all characteristics or when certain areas/characteristics are not close but the differences are objectively quantifiable and the differences in the outcome are predictable and quantifiable. This also different outcomes, critique: gives a manager the opportunity to assess management strategies and their potential by having one or more analogs to did the outcomes meet the objective and if not, what other options exist that could be employed given the current circumstances. Surface water supply indexes (SWSI’s) are calculated to provide an indicator of how much surface water will be available for use in any give season. As the name implies, it is only surface water categorized here. Ground water may be an important component in the total water supply picture, but in general, groundwater information is more difficult to obtain, especially timely information for processing, it is generally more constant water source with pumping costs being the variable of interest. Given the data, total water supply indexes could be generated (including ground water sources) however, for convenience, jurisdictional considerations and timely product generation - and consistency from area to area, surface water is generally what is categorized. Cost also enters in this equation - ground water is expensive relative to surface water and given an option - all other factors being equal, managers use the cheapest source first and most. SWSI’s came about in the mid 80’s as a way to quantify in objective terms the total resource available to managers. The question generally was “how are we doing water wise this year?” and the answer was generally some variation of ‘runoff will be very poor - however reservoir storage is really high’ so all is ok, or runoff will be poor and reservoir storage is equally bad - bad drought, runoff is great and reservoir storage is overflowing - floods, or runoff will be great but reservoir storage is poor -still ok. All these combinations needed some kind of objective analysis so that folks weren’t just shooting from the hip. Thus SWSI’s were conjured up - a way to objectively quantify where we are today with respect to historical occurrences. Early SWSI’s were first developed in Colorado and Oregon. They used snowpack, precipitation as indeces of future runoff, current streamflow and reservoir storage as indeces of current conditions. These tended to be problematic in terms of data collection - streamflow was a weak link in the system. Newer version dropped the streamflow component in favor of forecast runoff, typically an April-july forecast volume or in some cases, April- September. Other problems cropped up as various states began to develop SWSI’s and the problem of consistency or comparability once again popped up. Many states did not have year round reservoir storage data, especially for smaller reservoirs managed by small water districts. During the ‘non management season’ many reservoir operators do not keep storage data, inflow or outflow data for their operations - basically a set the gate and walk away till the runoff and irrigation season starts again. Also, many areas did not see a need for a continuous, 12 months per year SWSI, preferring a relatively simple application that would be a January through June system. There was basically no need for product generation during the fall and winter. There are still inconsistencies from area to area but they are becoming more consistent over time. Forecast values of runoff have replaced observed values of snowpack and precipitation as one of the main components of the SWSI. The rational behind this is: forecast runoff is based on snowpack, precipitation, and other variables. Thus by using forecast runoff, those variables are automatically incorporated into the system, reducing the amount of processing required. Observed or projected reservoir storage is the other main components currently used. The process is available to all who wish to use it for whatever purpose - such as an engineering firm developing a SWSI for a client. The Process: 1. Historical data collection and quality control a. observed runoff for the point and time frame in question (typically April-July runoff, or monthly if running a continuous system) b. Reservoir storage - (monthly for April July, January-July or all months depending on what type of SWSI you intend to set up.) * Side note: remember that reservoir data are typically ‘EOM’ or end of the month values so that an April EOM is taken on April 30, not on April 1 I know this seems stupid and yes it is (technically EOM is at 00:00 which could be taken as the first or the 30th of any month but that’s how it is in reservoir data base archival) therefore, an April EOM would correspond with May 1 forecast, snowpack and precipitation conditions. Also on reservoir data, make sure you know what the data represent: total storage, live storage, some combination of live storage (conservation pool, dead storage, fish pool, etc) or useable capacity and that whatever data you use will be replicatable in the future. For example - the total storage in Bear Lake is near 5 million acre feet and some but the published lake content is 1,421,000 or 1.4 million acre feet which is the ‘useable capacity’ or that amount of water that can be withdrawn for use, that which is tied to a water right (somebody owns it and can call for it). 2. If using more than one reservoir (basin wide SWSI), collect all the appropriate reservoir and streamflow data - a SWSI can be as large or small as the application requires. 3. Construct a matrix in a spreadsheet of the following form: 1. Number 2. Year 3. Resv 4. stream 5. Comb 6. 7. Prob SWSI Where: number is the number of years, 1 through whatever the highest year is, Year is the year of occurrence Resv is the total reservoir storage, in acre feet (single reservoir or the combined storage of all) Stream is the observed runoff data in acre feet for the SWSI point Comb is the sum of the reservoir storage data and the observed streamflow data in acre feet. Prob is the probability of occurrence. average condition. 50% is the SWSI is the actual SWSI value. The final 3 columns are calculated from the first 4 columns. The combined reservoir and streamflow column is self explanatory - simply the summ of Yi (streamflow and reservoir) for any year. This is the total surface water supply available for that season. Probability is actually the plotting position as no probability density function is assumed - you may if you wish, assume a function and simply apply the appropriate formulas to assign a probability to the point. Most PP’s use the Weibull equation of M/(n+1) * Sidenote: the probability is the best overall categorization for reference. It is intuitive, easily explained and can be used to compare area to area in quantitative terms. The SWSI value on the other hand, is a scale of -4 to +4, a conversion from the probability scale that makes the SWSI “comparable to the Palmer soil moisture Index” also of the same scale. it is not intuitive, not easily understood nor comparable from area to area due to the linear conversion from a probability function to an artificial scale. (a SWSI of -2.1 is not the same from area to area, depending on the sample population and its fit with respect to the PDF, whereas a probability of 30% is) I prefer to use probability and candidly think that both the SWSI and palmer scales stink. SWSI calculation: this simply converts a scale of 0 to 100 to a scale of -4 to +4. (probability - 50)/12 You now have a matrix ready for an ascending sort function. The probability column, number column and the SWSI column are static - they are never included in the sort function. The sorting must be done on the combined reservoir and streamflow column as the principal column -it is this column that dictates the total water supply for any season and is of most interest. The other 3 columns, the year, streamflow and reservoir data are sorted along with the combined data as a BLOCK - the combined column determines where each year ends up, but we want the year and the other data to remain with their associated combined total. What you have now is a matrix sorted in ascending order - lowest to highest of water supply conditions. You can see the probability of any occurrence either high or low. You can see what component caused it to be in its specific position and you can insert the current years data to determine where you are. Given this position, you can see the years that are analogs to this year for management analysis. This system is simple, uses a minimum of data, easily maintained and operated and conveys a maximum of information for operations. Anything above 90%: roll up your pants and take the brick out of the toilet. Anything below 10%: shut the reservoir gates and conserve all you can. Historically, you can then go back and determine where various events were classified such as agricultural water shortages and set relative SWSI values: anything below a SWSI of X.X and water shortages are likely, or water rationing or flooding, etc.