Construction-dependent methods require hygrothermal simulation models and modeling information on specific construction types. Moisture conditions within a construction serve as criteria to determine the one in ten severe years of the weather data. Since the moisture response of the construction is the outcome that designers are looking for, this approach appears to be sensible. The caveat lies in the response of different construction types. Not all assemblies will be sensitive to the same combination of climate parameters. Therefore, these methods may result in different climate files for different construction types. The principles used by these methods can be summarized in four steps [
34]. Firstly, a set of typical wall constructions and typical boundary conditions should be defined. Secondly, the critical construction orientation should be prescribed. Third, hygrothermal simulations for all the years of hourly data should be performed. Lastly, HRYs should be determined by proper criteria to determine ranks of the simulated years: the mean/maximum moisture content of a material layer or a whole construction and certain damage functions of the expected moisture-related problems.
3.2.1. Moisture Content Method
The Rode method [
45] is a construction-dependent method that requires the use of a reliable hygrothermal simulation tool in determining the hourly HRY. An HRY represents the year characterized by the occurrence of the worst moisture condition. It can be the highest moisture content for the construction or the critical layer within a construction. The procedure involves conducting hygrothermal simulations for as many years as there are available hourly data, for several different constructions and with different orientations. The moisture content is calculated for each year, which is then ranked in accordance with the severity of moisture conditions. The higher the moisture content integral, the worst moisture conditions in the construction. Juráš and Žilinský [
46] followed the same method to determine a hygrothermal reference year for Bratislava, Slovenia. A total of two walls were considered: aerated clay brick wall with/without an external thermal insulation composite system made of expanded polystyrene insulation.
The Geving method [
15] was developed within the framework of the IEA Annex 24. For the purpose of moisture accumulation effect in wintertime, it was examined for the applicability of this method for different climates, six construction types, orientations, indoor climate conditions, initial moisture content of the construction, and duration, as shown in
Table 2.
The author used a 1-D coupled transient heat and mass transfer computer software to perform calculations. For each preselected construction type, the maximum moisture content of the hygroscopic layer just outboard of the insulation and the mean moisture content of the entire construction were gained from the simulation results. From the normal distribution function, it is determined for the 10% level (90th-percentile) moisture content for both maximum and average moisture content criteria of each construction, and calculated mean and standard deviation. Each construction will have two corresponding values and a total of 12 HRYs values are generated for each location. The next steps are as follows:
Rank every year and for each construction in accordance with the average moisture content value determined with a hygrothermal simulation. The year with the highest average moisture content is ranked 1st, with the subsequent and monotonously decreasing years being ranked 2, 3, 4, 5, and so on. For example, with 30 years of weather data, the 10% level will be represented by the 3rd year.
Select all the years that occurred in the 5 worst year groups for half or more of the constructions and calculate the sum of the rankings for every construction. For example,
Table 3 [
15] shows that 5 years 1989, 1990, 1991, 1992, and 1994 were each found to be in the 5 worst years in at least 5 of the 6 constructions.
Select two or three years with the lowest mean rankings.
Assuming a normal distribution function, calculate the average probability level for each selected year using the average moisture content.
Repeat the first three steps using the maximum moisture content criteria. It is important that the same years as those selected for the average moisture content (in the second step) must be selected. In this case, the years 1989, 1990, 1991, 1992 and 1994 must be selected.
Calculate the average probability level for the average and the maximum moisture content for each year selected.
The year with the lowest probability level is selected to be the HRY for the specific location.
3.2.2. Damage Function Method
The damage function method is suitable for the location where a typical damage pattern caused by the external environment is well known. In addition, the main ideas of this method can be applied for many purposes if thresholds that moisture damages start have been clearly proved. The general procedure of the damage function method follows consecutive steps. The wall systems and weather data for locations should be defined. Computer simulations of their hygrothermal performance should be performed, and the damage function using the simulation outputs of interest should be calculated. Then, an inverse analysis of the damage-function values with the data of climatic variables should be conducted. Lastly, selection of the critical weather year.
The current ASHRAE Standard 160 [
19] uses the simple approximate method that was developed by Salonvaara [
47] using a damage function method. The author analyzed several existing HRY selection methods and compared their performance by simulating two wall systems: one lightweight wall and one massive concrete masonry wall. The analysis included 30 years of hourly weather data for 12 locations in the US and Canada. However, the analysis showed that none of the existing methods produced consistent simulation results. Therefore, the author proposed a new construction-independent method by analyzing the weather data and the simulation results of the constructions. The basic idea was to use yearly average weather data parameters and use regression analysis to fit the parameters of an equation that can be used to calculate a predicted damage function value for each year. A simple approximate model was developed as Equation (7):
with the severity index as a predicted damage function,
Isev [−], the vapor pressure,
pv [Pa], the relative humidity,
φ [−] where 0 ≤
φ ≤ 1, the dry-bulb temperature,
To [°C], the driving rain,
rwd [kg/(m
2∙h)], the cloud index,
Icl [−] where 0 ≤
Icl ≤ 8, and the solar radiation on the wall with orientation receiving the least solar radiation,
Ev [W/m
2].
The year with the third highest severity index value is proposed as the year to be selected for hygrothermal designs. For the regression analysis, the severity index was derived from the damage function RHT-index in the OSB layer of the lightweight wood frame wall. The RHT-index [
48] is calculated as Equation (8):
with the temperature,
T [°C], the relative humidity,
RH [% rh], in the OSB layer of the lightweight wood frame wall from the results of hygrothermal simulations and the limiting values,
TL = 0 °C,
RHL = 70 % rh.
The significance of individual coefficients to the severity index was identified by t-values. Only the regression equations having an absolute value of at least 2.0 were applied to the equation to minimize the error between the damage function values resulting from simulations and the predictions from the equation. The authors showed the matrix of correlation between the damage function and the mean annual weather data.
J. Kocí et al. [
49] utilized the damage function method to select a critical weather year for the Czech Republic. The severity of every year was ranked by the newly expressed damage function, Winter Index. Afterwards, the mathematical relations between the weather data and its severity to an investigated structure are determined. Lastly, the derived mathematical formulas are used to select the critical weather year using the monthly means of partial weather data for the locations. The freeze/thaw cycles in the external surface layers of the building envelope were determined as typical moisture-related damage by the external environment. The Winter Index (WI) was calculated using the RHT-index [
48] as Equation (9):
with the temperature,
T [°C], the relative humidity,
RH [% rh], of the external plaster otherwise the position 10 mm under the exterior surface and the limiting values,
TL = 0 °C,
RHL = 95% rh.
The selection process continued by ranking each chosen by the Winter Index damage function with the defined damage function and the necessary weather data. Following this, the relation between the damage function values and the selected principal parameters of the weather years was derived for each envelope of the 16 types. In this way, the mathematical relations between the weather data and the Winter Index were found as Equation (10):
with the predicted value of the Winter Index,
Ypred [−]; the optimized coefficients for each construction type,
c0–
c4; the monthly average of temperature, relative humidity, and precipitation in the winter period from November to March,
Tw [°C],
RHw [% rh],
RRw [mm], respectively; and those in the summer period from April to October,
Ts [°C],
RHs [% rh],
RRs [mm], respectively.
The optimized coefficients for each construction type, c0–c4, were determined by the inverse relation for 16 constructions. As a final step, the derived mathematical formulas were used to select the critical weather year using the monthly mean weather data for the given location.
The damage function method can be successful in selecting the worst years in all analyzed locations and is consistent in its predictions in similar climate regions. However, the method should include all the input data necessary for the hygrothermal simulation and address the specific problem under investigation. Furthermore, the approximate equations and coefficients are optimized by weather data, simulation results, and damage functions, so future users must beware of using the proposed equations without much consideration for moisture control design by hygrothermal building envelope simulation.
3.2.3. Hybrid Climate Analysis and Hygrothermal Performance Method
Another proceeding, described in [
50], couples both statistical methods to preselect single real measured months to combine them to a representative year and hygrothermal simulations of rather moisture-sensitive constructions to further adapt the single climate elements in a way that repeated use of the created year leads to similar conditions like a simulation with real measured long-term data. Therefore, it is called a hybrid model. Temperature and driving rain were chosen as the main influencing factors for selecting the single measured months. On a monthly basis, the statistical key numbers such as mean, minimum, maximum, and median value, as well as 75th- and 25th-percentile, were matched with the same numbers for a mean year out of an eight-year period. This was performed for each month of the year, and the HRY was generated as a combination of the months which best match the mean of the indices of both climate elements. In a second step, hygrothermal simulations of constructions were performed, which are particularly sensitive concerning moisture accumulation depending on the influence of the different outdoor climate elements. In case of relevant differences in the moisture performance of the assemblies simulated with the new HRY or with long-term measured data, the relevant climate elements were identified and adjusted in an appropriate manner. The method developed within the framework of [
51] was used to create, in total, 11 HRY data sets that represented the different climate regions in Germany and were adopted by [
20] for hygrothermal design purposes. In addition to the slightly critical but representative years, which shall be normally used; also, cold years were proposed, which can be used to assess on a single-year basis non-cumulative damage mechanisms such as frost or mold growth on thermal bridges, which occur in particularly cold winters.