In any research project that bases its methodology on hypothesized quantifications of natural phenomena, there can be many sources of uncertainty. In this project, errors were not additive throughout the process because of the way in which the methodology was conducted (for example, the selective elimination of sites from the analysis at certain stages). Thus, the potential sources of error must be examined at each step independently of one another. Though a formal error analysis could not be done because of low confidence in the historical dataset as a whole, the following discussion attempts to quantify potential sources of uncertainty. Specific recommendations for future research to address some of these issues can be found in Chapter 7. Historical temperature data at the HJA have been gathered using partially shielded mercury bulb thermometers and thermisters. Instrumentation error for mercury thermometers (used for about two-thirds of the total period of record) was approximately ± 2.0°C, with another ± 2.0°C error introduced when digitizing the paper charts. Thermisters, installed by the early 1990s at all sites, are accurate to approximately ± 0.4°C (J. Moreau, pers. comm.). The inconsistency of sensor heights above the ground may also have been a source of error, though probably a small one. Mean monthly temperatures were less likely to have been affected by these observational errors than the original daily datasets. In Chapter 4, mean monthly temperatures at sites with short records were adjusted to the full 30-year period using the highest correlated long-term site. For maximum temperature adjustments, mean absolute errors for periods of record ranged from 1.1°C for a one-year period of record to 0.2°C for a 24-year period of record (0.6°C to 0.2°C for minimum temperatures, from Figures 4.2 and 4.3). The shorter the period of record for a short-term site, the greater the error, but potential temperature errors never exceeded 0.7°C because any site with less than three years of original data was not considered (mean absolute errors for maximum and minimum temperatures were 0.7°C to 0.6°C for three-year periods of record, respectively). Thus, errors introduced into the procedure by temporal adjustments were likely minimal compared to observational errors. The most significant source of error in the project probably stems from radiation adjustments to the datasets (adjusting temperatures to simulate flat, open siting conditions for input into PRISM). Monthly cloud factors at UPLMET were taken to be representative of the HJA as a whole. Though the HJA is a small geographic area, it is probable that cloud factors varied somewhat across the watershed. Hemispherical fisheye photographs, which played a major role in our analysis, are temporally unreliable records of radiation and sky view factor attenuation. Canopy characteristics may have changed significantly over the 30-year period of record, and our images documented vegetation conditions at one instant in time only. Given the general trend of increasing canopy closure over time, the probable effect was a bias toward too much canopy correction for the early years of record. Attempts were made to use only climate stations in our analysis for which fisheye images were deemed ‘reliable’ and most likely to represent long-term canopy characteristics, but this was a significant source of error. We did not account for the role that obstacle distance might play in determining longwave radiation attenuation. For example, clouds, mountain ridges, and nearby trees probably do not mitigate thermal radiation loss equally. It was difficult to quantify fisheye sources of error, but the author’s best estimate is 5% uncertainty for very open or closed canopy sites (continuous canopies), and 25% uncertainty for sites with partially open canopies. The slopes of the regression functions developed in Chapter 4 can be used to estimate the potential effects of radiation and sky view factor errors on temperature adjustments. The regression functions incorporated many of the potential sources of error in our methodology, so these error estimates give a good idea of the overall effect of several factors on actual temperature estimates. Consider a 2.52 MJ/m²·day radiation difference between site pairs in December, the month with the steepest maximum temperature/radiation regression line slope (Table 4.21). This is the greatest radiation difference between any site pair used to calculate the maximum temperature/radiation regression function for that month (Table 4.20). The ‘best and worst case’ scenarios assuming 5% and 25% error in the radiation estimates, correspond to margins of error of ± 0.13 and ± 0.63 MJ/m²·day, respectively. The resulting uncertainty in maximum temperature adjustment values range from ± 0.18°C to ± 0.89°C. The greatest radiation difference between any site pair in July (the month with the shallowest regression line slope but largest radiation differences) was 19.91 MJ/m²·day. The ‘best and worst case’ scenarios gave radiation difference ranges of ± 1.00 and ± 4.98 MJ/m²·day, resulting in ranges in maximum temperature adjustment values from ± 0.2°C to ± 1.0°C, respectively. Thus, even when radiation estimates were made from fisheye photographs having a ± 25% margin of error, maximum temperature adjustment errors never exceeded 1.0°C, an amount well within the limits of observational error. A similar analysis performed on minimum temperature adjustments reveals an even lower potential margin of error. Months with the steepest and shallowest minimum temperature/sky view factor regression line slopes were August and January, respectively, and the greatest difference in sky view factor proportions between any site pair was 0.64 (Table 4.22). ‘Best and worst case’ scenarios assuming 5% and 25% error in the sky view factor estimates correspond to errors of ± 0.03 and ± 0.16, respectively. These values give error ranges in minimum temperature estimates from ± 0.1°C to ± 0.6°C in August to ± 0.0°C to ± 0.2°C in January. Thus, errors in minimum temperature adjustments from the minimum temperature/sky view factor regression functions were small. Error estimates of the temperature interpolation process were made using a jackknife cross-validation procedure within PRISM. At each station location, PRISM was run without that station to estimate the temperature at its location, and the predicted values were compared to the observed station value. Mean absolute errors, which are the average of the absolute value of error, ranged from 0.5°C to 0.9°C for maximum temperatures, and from 0.1°C to 0.3°C for minimum temperatures throughout the year. Biases, which assess how high or low estimates are across the entire grid, ranged from +0.1°C to +0.3°C for maximum temperatures, and from 0.0°C to +0.1°C for minimum temperatures. All of these values are well within observational error, and show that spatial interpolation of temperatures introduced low levels of uncertainty to the process. There were other possible sources of error in the original temperature datasets. Forest edges (boundary areas between clearings and forests) and streams probably affected long-term monthly temperature values. Many climate stations in the HJA have been and are located within distances that may be affected by edges and streams. These physical features could not be accounted for in this study because necessary datasets did not exist to quantify them. This study also did not quantify scale-dependent temperature advection processes that may affect temperatures in the HJA. For example, temperature regimes on an even, broad north-facing slope are likely different than those on a small north-facing slope having several slopes of varying orientation nearby.
