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Abstract Influence of NRCS snowcourse measurement date on data accuracy and climatic trends Thomas Pagano 503 414 3010 National.

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Presentation on theme: "Abstract Influence of NRCS snowcourse measurement date on data accuracy and climatic trends Thomas Pagano 503 414 3010 National."— Presentation transcript:

1 Abstract Influence of NRCS snowcourse measurement date on data accuracy and climatic trends Thomas Pagano National Water and Climate Center, Natural Resources Conservation Service-USDA, Portland,OR Introduction Observed surveyor behavior Several quality control screens removed a total of 8.9% of the NRCS snowcourse measurements for the western US including Alaska and excluding California (which has its own network). The remaining are plotted above; Warmer colors indicate relatively common measurements taken on a particular day of the year, for a given year. Along the bottom of the plot is the total number of measurements taken per year, revealing the growth of the network until the snow course reduction program of the 1990s. Along the side of the plot is the total number of measurements per given day, showing peaks near the nominal measurement dates. The relative abundance of March and April first of month measurements are also evident. The diagonal herringbone pattern is associated with weekends. The Natural Resources Conservation Service measures high elevation snowpack manually at snowcourses across the western US. The date of the measurement is nominally the first and fifteenth of the month although in recent years it averages approximately two days earlier to support timely operational water supply forecasts whose production begins on the first. This study found that the primary factors influencing measurement dates are, 1) the epoch of the measurement, 2) the day of the week of the nominal measurement date, 3) the presence or absence of snow at the site and 4) if the measurement is for the first or the fifteenth of the month. Specifically, the measurement date is less variable if snow is absent from the site. Mid-month data are collected closer to the nominal measurement date and first of month data have a bias towards being several days early. Since 1957, there has been a stronger aversion to collecting data on Fridays and weekends whereas before 1957 snow surveyors mostly avoided measuring on Sunday. Further, measurements are taken today, on average, 1.34 days earlier than before. These factors were modeled and the effect on climate trends was found to be small, on the order of less than 5% although in individual circumstances the effect can be significant. Since the early 1900’s, the Natural Resources Conservation Service (NRCS) and cooperating agencies have routinely collected high elevation climate data across the western US. Primarily designed to support operational water supply forecasts, the data from this network of snow courses has become a de facto source for climate change studies. A snow survey involves the extraction and weighing of snow cores along a fixed transect of points at a specific location. The weight of the core can be used to calculate the Snow Water Equivalent (SWE), the liquid water content of the snow. Typically two humans participate in the survey and they travel to these remote mountain locations using a variety of over- snow transport devices (e.g. skis, snowmobiles, helicopters). Nominally, the data are collected on the 1 st and 15 th of the month from Jan-Jun, although sites are commonly measured several days earlier to provide more timely forecasts. This introduces systematic biases into the data that are especially important if measurement dates have changed over the years. In modern times, snowcourses are being replaced by automated SNOTEL sites (left), which measure the data exactly on the nominal measurement date. This may be a discontinuity in the record if co-located SNOTEL and snowcourse data are spliced together. The data were subsetted by several factors (right) and the relative frequency of measurements shown. An example (circle) shows the relative frequency of 1 st of month measurements (top half) before 1957 (left column) where snow was observed (lower), where the nominal measurement date was a Sunday (plot column) and the actual measurement date (plot row) was 2 days earlier (Friday). These are more common than on-time (Sunday, 2 cells below) measurements. The measurements are defined by the following four factors 1.If the measurement was before or after The day of the week of the nominal measurement date (especially weekends versus weekdays) 3.If snow or no snow was observed by the surveyors when they arrived at the site (i.e. had it melted out yet?) 4.If the data had a nominal measurement date of the 1 st of the month or the mid-month. Pre-1957Post-1957 First of month Mid-Month No snow With snow No snow With snow Day of week of nominal measurement date Actual measurement date relative to nominal date Early Late Changes over time The most significant factor relevant to climate change studies is a shift in surveyor behavior in the 1950s. On the left is a plot, by year of the measurement date relative to the nominal measurement date for all months and all regions. Before 1930, there was an equal likelihood that a measurement would be late or early, and many samples were taken exactly on the nominal measurement date. From , just over 50% of the measurements were taken one or more days early and only 20% were sampled one or more days late. After 1957, almost 80% of the measurements occurred before the nominal measurement date. The average measurement lead times from versus were and days (early) respectively. 1. Early season measurements: because the normal is still small, each day contributes a relatively large amount of information to the accumulated total. A progressively earlier measurement date would introduce a long-term artificial downward trend. 2. Dates near the seasonal peak: a changing measurement date introduces more noise into the record, but not a systematic bias. Each day contributes relatively little to the seasonal total. 3. During melt: the snowpack is in a rapid state of flux and the normal is quickly dropping, therefore the data are highly sensitive to the actual date of the measurement. The variance during this period is naturally high meaning the bias would be large in terms of inches or percent of normal, but small in terms of statistical significance; changing to progressively earlier measurements would introduce a long-term artificial upwards trend. Assessing the effects on climatic trends Spatial Patterns of Meas. Dates Bias (Average) > 3 days early days early Late on avg Colorado and Utah are commonly measured many days early, whereas Arizona and Northern states are measured closer to the first of the month. Standard Deviation days days Colorado and Arizona are consistent in their measurement dates, whereas other states are highly variable from year to year. Percent of data collected on a weekend never 0 to 1 out of 14 1/7 to 1/14 2/7 to 1/7 > 2 out of 7 days Some stations (red) have never been measured on a weekend (Saturday or Sunday). Some areas are more likely to be measured on weekends (blue) The observed snowcourse data was corrected by using the daily SWE normal from co-located SNOTEL sites: e.g. Corrected Apr 1 SWE = Obs Mar 27 SWE + Apr 1 Norm SWE – Mar 27 Norm SWE The original trend is plotted (X) versus the change in the trend after correction (Y). Consistent with the above theory, early season measurements currently have an artificial downward trend and late season measurements have an artificial upwards trend, whereas near the peak of the season, the signal is mixed. The artificial trends are generally small compared (~0.02) to the obs trends (~0.40). Discussion and Conclusions: The Bottom Line (at the Top!) Since 1957, data is being collected progressively earlier in the season, in part because there is a stronger avoidance of weekends. There is some evidence that this is being done in the name of surveyor safety (Pers. Comm. NRCS personnel). “Mid-month” data is collected closer to the 15 th of the month than “first of month” data is from the 1 st of the month (perhaps because no operational water supply outlook is produced from the mid-month data and there is not a need for a timely product). When there is no snow at the course, there is also less of a leadtime associated with data collection. On the whole, measurements are taken today, on average, 1.34 days earlier than before The average measurement lead times from versus were and days (early) respectively The shifts in measurement date (from later to earlier) have had a small effect on ones ability to detect long term trends. Early in the season, the (uncorrected) data currently have an artificial downward relative trend. Late in the season, the observed trend towards less snow is geographically widespread although the measured trend is not as strong as it could be if the data were normalized to the nominal measurement date. Again, this effect is small compared to the natural variability (a change in correlation versus time of +/- 0.02, depending on the season). One can therefore conclude that the observed downward trends in snowpack since 1950 are not significantly related to changes in snow course measurement dates. Note too, that snowpack model simulations that use daily data, are not vulnerable to the effect. For the sake of water supply forecast accuracy this effect in the observed data should be removed if possible, however. Losing Gaining GC41A-0101 Days


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