1. INTRODUCTION
One of the fundamental principles in wood science is that wood or wooden materials must be suited to their exposed climate to prevent premature failures. Such problems have been particularly reported in various regions of North America (Chouinard and Lawton, 2001; Cornic et al., 2002; Cornick and Dalgliesh, 2003). Thus, determining the moisture loading level in a given location is important for the prevention of deterioration and the maintenance of exposed surfaces of wooden constructions.
Currently, several climate indexes based on temperature–rainfall combinations have been developed to help designers and builders (Lstiburek, 2001; Russo, 1971). They were determined based on either wind-driven rain or the decay risk of wood used for exterior aboveground applications (Boyd, 1963; Kim et al., 2011; Kim and Ra, 2013, 2014; Ra, 2017; Setliff, 1986). Although these climate classifications were useful, they were not developed to evaluate potential moisture loading on the envelopes of materials, such as wood. The requirement for a more quantitative index than the available indexes resulted in the development of a moisture index (MI) (Cornick et al., 2002; Cornick and Dalgliesh, 2003).
MI is a function of wetting index (WI) and drying index (DI) that express the moisture entry and exit, respectively (Bailey, 1958; Cornick et al., 2002; Cornick and Dalgliesh, 2003). Since WI is defined as the wettability of wood, it can be effectively measured by determining the availability of water using rainfall and driving-rain indexes (Cornick et al., 2002). DI is defined as the potential evaporation for a given location. The potential evaporation is defined as the possible room in a parcel of air that is available for moisture take up. Cornick et al., (2002) combined WI and DI to determine the MI of cities in Canada and the United States.
The potential for moisture loading in various climates can be determined by calculating the MI using the available climate data. Because the method of calculating the MI is general, it can also be used to compare the amount of moisture loading in different regions. This study aims to determine the MIs in various regions of Korea to obtain basic climate information for effective moisture control.
2. MATERIALS and METHODS
Domestic hourly weather data recorded from 2009 to 2017 were obtained from the Korea Meteorological Administration (KMA) for 82 stations, including Seoul. Most weather data were measured every third hour prior to 2009. The hourly data of interest were temperature, precipitation, relative humidity, vapor pressure, and spot atmospheric pressure. Spot atmospheric pressure is the atmospheric pressure at the measured site, not the pressure modified to the sea level.
Herein, rainfall was used as WI. Rainfall was the preferred parameter over driving-rain indexes because it is strongly correlated and commonly available for most locations (Cornick et al., 2002). And DI was calculated using equations (1), (2), and (3). It is more convenient to use long-term climate data instead of hourly data because of low amount of data. However, the use of long-term data has been reported to underestimate the DI by 21% (Cornick et al., 2002).
where w is the humidity ratio (kg water/kg air), vp is the vapour pressure (kPa), p is the total mixture pressure (kPa), DI is the Drying Index (kg water/kg air-year), n is the number of years under consideration, and k is the number of hours in a particular year.
DI and WI were calculated for each year for a particular location and were combined into a MI. A higher MI suggests a greater potential for moisture loading (Cornick and Dalgliesh, 2003).
Both WI and DI were normalized to have values from 0 to 1 and combined into a MI using equation (4).
For comparison, WIs and DIs of 20 cities in Canada and the United States determined by Cornick et al., (2002) were included in the dataset. The obtained WIs were normalized to that in Seogwipo, Korea, and DIs were normalized to that in Phoenix, Arizona, which had indexes of 2091.94 and 129.47, respectively.
Wetting and drying were assumed as equally important phenomena and were therefore given equal weight in the determination of the MI using equation (5).
The determined MIs were compared and analyzed to evaluate the level of moisture loading.
3. RESULTS and DISCUSSION
Table 1 lists the average domestic weather data and the determined values of the DI, WI, and MI. Herein, WI was defined based on the total rainfall in a region and the DI was determined using the hourly weather data obtained from 82 locations from 2009 to 2017.
The average annual temperature varied from 7.39°C to 16.96°C depending on the location. Seogwipo showed the highest temperature, followed by Jeju, Gosan, and Seongsan. The location with the lowest temperature was Daegwallyeong. The average annual precipitation showed values between 739.17 and 2091.94 mm. Seogwipo and Seongsan had an annual precipitation level of >2,000 mm, and Baengnyeongdo had the lowest annual precipitation of 739.17 mm. The annual relative humidity (RH) was ~65%. The highest annual average RH was 80% in Mokpo, and the lowest was 57.94% in Gangneung. According to Russo’s classification scheme (Russo, 1971), most domestic regions can be classified as mild and wet regions.
DI was calculated using the hourly climate data and ranged between 10.7 and 18.03. Seogwipo had the highest DI, followed by Gosan, Seongsan, and Mokpo. Daegwallyeong had the lowest DI. A high DI suggests that the potential drying capacity would be higher. Among the North American cities, Vancouver and Victoria, BC, Canada, have DIs similar to those in Korea. Their values are 16.10 and 17.03, respectively (Cornick et al., 2002; Cornick and Dalgliesh, 2004).
Fig. 1 plots DI versus WI, where each point represents the nine-year average indexes calculated from the data for 82 locations. Cornick et al., (2002) divided the climate zones into four categories using DI and WI. A DI of ~20 and a WI of ~800 were used as references to distinguish between hot and cold regions as well as wet and dry regions, respectively. According to this scheme, all regions of Korea, except Baengnyeongdo, can be classified as cold and wet regions. Baengnyeongdo is the only cold and dry region. Cities occupying the lower right-hand corner of the plot have low DIs and high WIs and can be assumed to have a high potential for moisture loading. The plot also suggests that the potential moisture loading would increase with an increasing WI.
The variation in DI between the regions in Korea is not severe when compared with those in Canada and the United States. If the range of the DI is varied significantly, the ranking will be changed, making it impossible to compare the MI between regions. Therefore, the determined WIs and DIs of several cities in Canada and the United States were normalized. In this study, the WI was normalized to that in Seogwipo, Korea, whereas the DI was normalized to that in Phoenix, Arizona, because Seogwipo and Phoenix had the maximum WI of 2091.94 and the maximum DI of 129.47, respectively, in the given dataset.
In Korea, Baengnyeongdo had the lowest MI of 0.99. Other locations had MIs greater than those in Victoria, BC, Canada. Cornick and Dalgliesh (2003) used five divisions to categorize moisture loading. Their classifications ranged from zone 1 with low moisture loading to zone 5 with severe moisture loading. Victoria that has the MIs distinguishing between severe and high moisture loading zones according to Cornick et al., (2002), showed the MI of 1.02. Table 2 lists the calculated MIs of the cities in Canada and the United States. According to this criterion, Korea appears to be in zone 5, a division of severe moisture loading.
The high MI of Korea is due to its higher precipitation and RH. Although Korea lies in a severe moisture loading zone, it is not in a severe decay hazard zone. According to the Scheffer index, Korea is generally classified as a middle decay hazard region (Kim and Ra, 2013, 2014). The reason for this is unclear but may be explained by the application of equal weighting for the DI and WI when combining them into a MI. Neglecting wet or dry spells may also be a reason.
A mathematical evaluation of the relation between climate and wood moisture content, particularly on wood surfaces, may facilitate better understanding of premature failures that frequently occur in wooden houses or structures from a maintenance point of view. The MI approach offers a way to group and compare countries into zones related to potential moisture problems. In addition, it has the potential to quantitatively assess the mechanisms of the effect of climate on wood moisture content. Further research to investigate the relation between the MI and the wood moisture content is required to provide a more integrated interpretation for practical purposes.
4. CONCLUSION
The following conclusions can be drawn from this study.
MI was determined using hourly weather data for 82 locations recorded from 2009 to 2017.
Seogwipo had the highest MI of 1.34, followed by Seongsan, Namhae, and Geoje. Baengnyeongdo had the lowest MI of 0.99.
Almost all the locations in Korea can be classified as regions of severe moisture loading.
Further research is required to investigate the relation between MI and moisture content on wood surfaces and to gain insights into practical applications.
