How the Climate Variables Were Estimated
As part of the Evapotranspiration of Hawai‘i project, it was necessary to make maps of numerous climate and land characteristic variables in order to estimate evapotranspiration. In all, more than 40 variables were mapped. The spatial patterns of each climate variable were estimated through a combination of measurements and modeling techniques. In each case, data not used to make the estimates was used to check the results, a process called validation. A complete description of the methods used to estimate each variable is given in the ET Project final report. Below are brief descriptions of the methods used for some of the key variables.
Air Temperature
Monthly maximum, minimum, and mean temperatures were estimated as functions of elevation, within an assumed two-layer atmosphere separated by the trade-wind inversion (assumed to be at a height of 2150 m), and rainfall. At right, January maximum, minimum, and mean temperatures are shown as a function of elevation for mean annual rainfall of 500 mm (solid lines) and 1000 mm (dotted lines).
Humidity
Mean relative humidity was mapped for each hour of the diurnal cycle of each month as a function of elevation. First, functions were fit to station data to represent the vertical changes of minimum and maximum relative humidity with elevation. As an example, the January profiles of minimum and maximum relative humidity are shown on the left.
Based on the minimum and maximum, the 24 hourly relative humidity values were estimated using a sinusoidal function. The January diurnal cycles for elevations of 100 and 1000 m are shown in the graph to the right.
Wind Speed
The mean annual wind map developed for Hawai‘i by AWS Truewind were used in this study. Using their map of wind speed at 50 m above the ground, we adjusted it to a reference level of two meters above the vegetation. Using stations data from HaleNet (photo on left shows the HaleNet Treeline station at 2260 m elevation on windward Haleakalā), we found the month-to-month differences in wind speed did not follow a consistent pattern. However, the wind speed does differ strongly according to time of day, but with differences that depend on elevation. We used that information to develop 24 hourly wind speed maps, applicable in all months of the year.
| Raingage in Haleakalā, Maui. Photo credit: John DeLay |
Rainfall
In the Rainfall Atlas of Hawai‘i, raingage data and expert knowledge were supplemented with rainfall estimates derived from the PRISM rainfall analysis (Daly et al., 2006), NEXRAD radar rainfall observations, MM5 mesoscale meteorological model simulations, and patterns of vegetation. We used a variety of innovative techniques to evaluate and merge these different estimates of rainfall to produce the best estimates of mean rainfall and its uncertainty at any given location. For complete background information, data, interactive maps, historical information, and methodology, please refer to the Rainfall Atlas of Hawai‘i website.
Net Radiation
The key variable in the energy term of the Penman-Monteith equation is Net Radiation. Much of the work done in this project went into estimating this variable. Net radiation has four components, each of which had to be mapped for each time interval (24 hours x 12 months). The most important component of Net Radiation is Solar Radiation, which has distinct diurnal, annual, and spatial patterns related to sun angle, atmospheric clarity, and cloud cover.
Solar Radiation
We mapped solar radiation by first estimating clear-sky solar radiation, the amount of sunlight received with no clouds at a given location, time of year, and time of day. Then the effects of clouds and shading by surrounding terrain were incorporated to produce maps of solar radiation. We analyzed patterns of cloud frequency based on imagery from the MODIS and GOES satellite platforms. For more information on the methods used to map solar radiation, please refer to the Solar Radiation of Hawai‘i website.
