scholarly journals Effects of climate change on the moisture performance and durability of brick veneer walls of wood frame construction in Canada

2021 ◽  
Vol 2069 (1) ◽  
pp. 012063
Author(s):  
M Defo ◽  
M A Lacasse ◽  
L Wang
Keyword(s):  
Climate Change ◽  
Growth Index ◽  
Annual Rainfall ◽  
Mold Growth ◽  
Climate Data ◽  
Minimum Requirements ◽  
Moisture Performance ◽  
Brick Veneer ◽  
Wood Frame ◽  
Frame Construction

Abstract The objective of this study was to assess the potential effects of climate change on the moisture performance and durability of red matt clay brick veneer walls of wood frame construction on the basis of results derived from hygrothermal simulations. One-dimensional simulations were run using DELPHIN 5.9 for selected moisture reference years of the 15 realizations of modelled historical (H: 1986-2016) and future (F: 2062-2092) climate data of 12 Canadian cities. The mold growth index at the outer layer of the OSB sheathing panel was used to compare the moisture performance under H and F periods. Results for the base design that meet the minimum requirements of the National Building Code of Canada showed that cities within the interior of the country, characterized by a low annual rainfall, are less likely to develop significant mold growth under H and F periods, whereas cities in coastal areas, characterized by high annual rainfall, present a heightened risk to mold growth under both H and F periods. For cities located on the west coast, a possible solution could be to use a 38-mm ventilated drainage cavity as this measure would help dissipate moisture from within the cavity. On the east coast, apart from using a 38-mm ventilated drainage cavity, other measures aiming at reducing the wind-driven rain deposition (i.e., increasing overhang ratio or the height of the roof) could be introduced. However, the feasibility of such measures needs to be considered in respect to whether these are to be implemented as part of a new building or retrofit of an existing one.

scholarly journals Effects of Climate Change on the Moisture Performance of Tallwood Building Envelope

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 35
Author(s):  
Maurice Defo ◽  
Michael A. Lacasse
Keyword(s):  
Climate Change ◽  
Growth Index ◽  
Building Envelope ◽  
Historical Period ◽  
Mold Growth ◽  
Water Penetration ◽  
Moisture Performance ◽  
Wall Assembly ◽  
Mass Timber ◽  
Relative Change

The objective of this study was to assess the potential effects of climate change on the moisture performance and durability of massive timber walls on the basis of results derived from hygrothermal simulations. One-dimensional simulations were run using DELPHIN 5.9.4 for 31 consecutive years of the 15 realizations of the modeled historical (1986–2016) and future (2062–2092) climates of five cities located across Canada. For all cities, water penetration in the wall assembly was assumed to be 1% wind-driven rain, and the air changes per hour in the drainage cavity was assumed to be 10. The mold growth index on the outer layer of the cross-laminated timber panel was used to compare the moisture performance for the historical and future periods. The simulation results showed that the risk of mold growth would increase in all the cities considered. However, the relative change varied from city to city. In the cities of Ottawa, Calgary and Winnipeg, the relative change in the mold growth index was higher than in the cities of Vancouver and St. John’s. For Vancouver and St. John’s, and under the assumptions used for these simulations, the risk was already higher under the historical period. This means that the mass timber walls in these two cities could not withstand a water penetration rate of 1% wind-driven rain, as used in the simulations, with a drainage cavity of 19 mm and an air changes per hour value of 10. Additional wall designs will be explored in respect to the moisture performance, and the results of these studies will be reported in a future publication.

A comparison of hygrothermal simulation results derived from four simulation tools

2021 ◽  
pp. 174425912098876
Author(s):  
Maurice Defo ◽  
Michael Lacasse ◽  
Abdelaziz Laouadi
Keyword(s):  
Relative Humidity ◽  
Outer Layer ◽  
Growth Index ◽  
Mould Growth ◽  
Simulation Tools ◽  
Assembly Design ◽  
Hygrothermal Simulation ◽  
Moisture Performance ◽  
Wood Frame ◽  
Wall Assembly

The objective of this work was to compare the hygrothermal responses and the moisture performance of four wood-frame walls as predicted by four hygrothermal (HAM) simulation tools, namely: DELPHIN, WUFI, hygIRC and COMSOL. The four wall systems differ only in their cladding type; these were fibreboard, vinyl, stucco and brick. Three Canadian cities having different climates were selected for simulations: Ottawa, Ontario; Vancouver, British Columbia and Calgary, Alberta. In each city, simulations were run for 2 years. Temperature and relative humidity of the outer layer of OSB sheathing were compared amongst the four simulation tools. The mould growth index on the outer layer of the OSB sheathing was used to compare the moisture performance predicted by the respective hygrothermal simulation tools. Temperature profiles of the outer layer of the OSB sheathing were all in good agreement for the four HAM tools in the three locations. For relative humidity, the highest discrepancies amongst the four tools were found with stucco cladding where differences as high as 20% could be found from time to time. Mould growth indices predicted by the four HAM tools were similar in some cases but different in other cases. The discrepancies amongst the different HAM tools were likely related to: the material property processing, how the quantity of wind-driven rain absorbed at the cladding surface is computed and some implementation details. Despite these discrepancies, The tools generally yielded consistent results and could be used for comparing the impacts of different designs on the risk of premature deterioration, as well as for evaluating the relative effects of climate change on a given wall assembly design.

Beneficial impacts of climate change on pastoral and broadacre agriculture in cool-temperate Tasmania

2014 ◽  
Vol 65 (2) ◽  
pp. 194 ◽  
Author(s):  
D. C. Phelan ◽  
D. Parsons ◽  
S. N. Lisson ◽  
G. K. Holz ◽  
N. D. MacLeod
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Annual Rainfall ◽  
Wheat Crop ◽  
Daily Maximum ◽  
Climate Data ◽  
Diverse Range ◽  
Wheat Production ◽  
Elevated Atmospheric Co2

Although geographically small, Tasmania has a diverse range of regional climates that are affected by different synoptic influences. Consequently, changes in climate variables and climate-change impacts will likely vary in different regions of the state. This study aims to quantify the regional effects of projected climate change on the productivity of rainfed pastoral and wheat crop systems at five sites across Tasmania. Projected climate data for each site were obtained from the Climate Futures for Tasmania project (CFT). Six General Circulation Models were dynamically downscaled to ~10-km grid cells using the CSIRO Conformal Cubic Atmospheric Model under the A2 emissions scenario for the period 1961–2100. Mean daily maximum and minimum temperatures at each site are projected to increase from a baseline period (1981–2010) to 2085 (2071–2100) by 2.3–2.7°C. Mean annual rainfall is projected to increase slightly at all sites. Impacts on pasture and wheat production were simulated for each site using the projected CFT climate data. Mean annual pasture yields are projected to increase from the baseline to 2085 largely due to an increase in spring pasture growth. However, summer growth of temperate pasture species may become limited by 2085 due to greater soil moisture deficits. Wheat yields are also projected to increase, particularly at sites presently temperature-limited. This study suggests that increased temperatures and elevated atmospheric CO2 concentrations are likely to increase regional rainfed pasture and wheat production in the absence of any significant changes in rainfall patterns.

scholarly journals Development of moisture reference years for assessing long-term mould growth risk of wood-frame building envelopes

2021 ◽  
Vol 2069 (1) ◽  
pp. 012015
Author(s):  
Lin Wang ◽  
Maurice Defo ◽  
Abhishek Gaur ◽  
Michael A Lacasse
Keyword(s):  
Selection Process ◽  
Growth Index ◽  
Latin Hypercube Sampling ◽  
Mould Growth ◽  
Climate Data ◽  
Moisture Index ◽  
Data Set ◽  
Building Envelopes ◽  
Wood Frame

Abstract A moisture reference year (MRY) is generally used to assess the durability, or long-term performance of building envelopes within a long climatological time period, e.g. a 31 year timeframe. The intent of this paper is to develop a set of moisture reference years that can be used to assess risk to the formation of mould growth in wood-frame buildings over the long-term. The set of moisture reference years have been developed based on 15 realizations of 31-year climate data. Replicated Latin Hypercube Sampling is applied to select 15 sub-realizations with 7 representative years having different levels of moisture index (MI) from each realization. Thereafter, hygrothermal simulations are performed for a brick veneer clad wood-frame wall assembly using the 15 sub-realizations; that sub-realization which produces the highest value of maximum mould growth index over 7-year period is selected as the MRY. The selection process is then implemented for all 15 realizations of the 31-years of data sets, from which 15 sets of 7-year long MRYs are selected to represent the original 15 realizations. It is shown that the 15 sets of 7-year long MRYs can produce the same value of maximum mould growth index as well as the uncertainty as compared to the original 15 realizations having a 31-year climate data set.

scholarly journals Stochastic generation of annual, monthly and daily climate data: A review

2001 ◽  
Vol 5 (4) ◽  
pp. 653-670 ◽  
Author(s):  
R. Srikanthan ◽  
T. A. McMahon
Keyword(s):  
Climate Change ◽  
Historical Data ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Model Parameters ◽  
Annual Data ◽  
Climate Data ◽  
Time Step ◽  
Method Of Fragments

Abstract. The generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed.

scholarly journals Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

2021 ◽  
Author(s):  
Mary Funke Olabanji ◽  
Nerhene Davis ◽  
Thando Ndarana ◽  
Anesu Gelfand Kuhudzai ◽  
Dawn Mahlobo
Keyword(s):  
Climate Change ◽  
South Africa ◽  
Smallholder Farmers ◽  
Meteorological Data ◽  
Group Discussion ◽  
Kendall Test ◽  
Adaptation Strategies ◽  
Annual Rainfall ◽  
Climate Data ◽  
Climate Trend

Abstract Climate change is expected to affect the livelihood of rural farmers in South Africa particularly the smallholder farmers, due to their overwhelming dependence on rain-fed agriculture. This study examines smallholder farmers' perception of climate change, the adaptation strategies adopted and factors that influences their adaptive decisions. The unit of data collection was household interview and focus group discussion. Climate data for the Olifants catchment (1986–2015) were also collected to validate farmers' perception of climate change with actual climate trend. Data collected were analysed using descriptive statistics, Mann–Kendall trend, Sen's slope estimator and multinomial logit regression model. Results revealed that smallholder farmers are aware of climate change (98%), their perception of these changes aligns with actual meteorological data, as the Mann–Kendall test confirms a decreasing inter-annual rainfall trend (−0.172) and an increasing temperature trend (0.004). These changes in temperature and precipitation have prompted the adoption of various adaptation responses, among which the use of improved seeds, application of chemical fertilizer and changing planting dates were the most commonly practised. The main barriers to the adoption of adaptation strategies were lack of access to credit facility, market, irrigation, information about climate change and lack of extension service. The implication of this study is to provide information to policy-makers on the current adaptation responses adopted by farmers and ways in which their adaptive capacity can be improved in order to ensure food security.

scholarly journals Application of Support Vector Regression to the Prediction of the Long-Term Impacts of Climate Change on the Moisture Performance of Wood Frame and Massive Timber Walls

Buildings ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 188
Author(s):  
Naman Bansal ◽  
Maurice Defo ◽  
Michael A. Lacasse
Keyword(s):  
Relative Humidity ◽  
Full Range ◽  
Support Vector ◽  
Data Series ◽  
Climate Variables ◽  
Climate Data ◽  
Moisture Performance ◽  
Wood Frame ◽  
Massive Timber

The objective of this study was to explore the potential of a machine learning algorithm, the Support Vector Machine Regression (SVR), to forecast long-term hygrothermal responses and the moisture performance of light wood frame and massive timber walls. Hygrothermal simulations were performed using a 31-year long series of climate data in three cities across Canada. Then, the first 5 years of the series were used in each case to train the model, which was then used to forecast the hygrothermal responses (temperature and relative humidity) and moisture performance indicator (mold growth index) for the remaining years of the series. The location of interest was the exterior layer of the OSB and cross-laminated timber in the case of the wood frame wall and massive timber wall, respectively. A sliding window approach was used to incorporate the dependence of the hygrothermal response on the past climatic conditions, which allowed SVR to capture time, implicitly. The variable selection was performed using the Least Absolute Shrinkage and Selection Operator, which revealed wind-driven rain, relative humidity, temperature, and direct radiation as the most contributing climate variables. The results show that SVR can be effectively used to forecast hygrothermal responses and moisture performance on a long climate data series for most of the cases studied. In some cases, discrepancies were observed due to the lack of capturing the full range of variability of climate variables during the first 5 years.

scholarly journals Rainfall, temperature, and Classic Maya conflict: A comparison of hypotheses using Bayesian time-series analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253043
Author(s):  
Mark Collard ◽  
W. Christopher Carleton ◽  
David A. Campbell
Keyword(s):  
Climate Change ◽  
Classic Maya ◽  
Annual Rainfall ◽  
Climate Data ◽  
Impact Of Climate Change ◽  
Rainfall Variation ◽  
Times Series ◽  
Summer Temperatures ◽  
Bayesian Time Series ◽  
The Impact

Studies published over the last decade have reached contrasting conclusions regarding the impact of climate change on conflict among the Classic Maya (ca. 250-900 CE). Some researchers have argued that rainfall declines exacerbated conflict in this civilisation. However, other researchers have found that the relevant climate variable was increasing summer temperatures and not decreasing rainfall. The goal of the study reported here was to test between these two hypotheses. To do so, we collated annually-resolved conflict and climate data, and then subjected them to a recently developed Bayesian method for analysing count-based times-series. The results indicated that increasing summer temperature exacerbated conflict while annual rainfall variation had no effect. This finding not only has important implications for our understanding of conflict in the Maya region during the Classic Period. It also contributes to the ongoing discussion about the likely impact of contemporary climate change on conflict levels. Specifically, when our finding is placed alongside the results of other studies that have examined temperature and conflict over the long term, it is clear that the impact of climate change on conflict is context dependent.

scholarly journals Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management

2020 ◽  
Vol 12 (18) ◽  
pp. 2984
Author(s):  
Ali Hamoud AL-Falahi ◽  
Naeem Saddique ◽  
Uwe Spank ◽  
Solomon H. Gebrechorkos ◽  
Christian Bernhofer
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Rainfall Variability ◽  
Annual Rainfall ◽  
Climate Data ◽  
Average Deviation ◽  
Monthly Basis ◽  
Highland Region ◽  
Single Station ◽  
Trmm 3B42

Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

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