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Applying principal component analysis (PCA), we determined climate zones in a topographic gradient in the central-northeastern part of México. We employed nearly 30 years of monthly temperature and precipitation data at 173 meteorological stations. The climate classification was carried out applying the Köppen system modified for the conditions of...
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... with rains the whole year (Cfga). The precipitation in the humid season should not be larger than ten times the highest monthly precipitation and less than three times the driest monthly precipitation. Regions satisfying these conditions are located in Huasteca, where the warmest monthly mean of temperature reach values larger than 22 °C (Fig. ...
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... further refinement of the climate humid temperate with rains the whole year is represented by (Cfgb 3 ). It has the same rainfall classification as the previous one, but with at least four monthly mean temperatures larger than 10 °C. This kind of climate is also found in the Huasteca region (Fig. ...
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... for the most humid month in summer should be larger than ten times that of the driest month and it must be larger than 40 mm. The coldest monthly mean temperature should be between -3 and 18 °C. In addition, the annual average temperature should be larger than 10 °C and the maximum temperature for the hottest month should be larger than 22 °C (Fig. 6c). The Cw 2 ga climate belongs to the same classification, but it is typified by more humidity (Table I and Fig. 6d). ...
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... than 40 mm. The coldest monthly mean temperature should be between -3 and 18 °C. In addition, the annual average temperature should be larger than 10 °C and the maximum temperature for the hottest month should be larger than 22 °C (Fig. 6c). The Cw 2 ga climate belongs to the same classification, but it is typified by more humidity (Table I and Fig. 6d). ...
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... trends in unsustainable land use change Two sub-types of dry or arid climates (BS 0 , BS 1 ) are found in the zone of Altiplano. They are characterized by low rainfall, less than 500 mm, in the summer season. According to García´s criterion, the subdivision is done using the relation P/T as an indicator of the degree of dryness (Table II and Figs. 6e, 6f). practices (exemplified by conversion of desert vegetation into croplands) and changes in vegetation which could point to effects of climate change. We proceeded as follows: applying the classification of the six climates BS0, BS1, Cfga, Cfgb 3 , Cw1ga and Cw2ga in combination with information on types of vegetation, we defined 85 ...
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... The use of Pr as one of the variables was encountered in 25 cases out of 99 (25.3%). This is due to the use of this variable in the popular KG method [102], which uses monthly AT and Pr for classification, and due to the frequent use of this variable in hot climate countries, such as Brazil [101], the Dominican Republic [120], Chile [73], Colombia [151], Mexico [133], and the Philippines [152]. Wind component as a climate variable is not common in CZB and was found in 12 cases (12.1%) in combination with other variables. ...
... It can simultaneously involve many climatic and geographic variables or even be combined with some building properties, avoiding oversimplification and obtaining more meaningful results [84]. Although the most common type of ML classification in the development of climate maps is CA, other various ML approaches for CZB were revealed during this review (neural networks, principal component analysis, sensitivity analysis, etc.) [39,45,52,53,72,119,133]. Accordingly, we extended this category and used the new MLM term. ...
Understanding the link between the energy-efficiency of buildings and climatic conditions can improve the design of energy-efficient housing. Due to global climate change and growing requirements for building energy-efficiency, the number of publications on climate zoning for buildings has grown over the last 20 years. This review attempted to give the reader an up-to-date assessment of the scientific literature in the field of climate mapping for buildings on a global and national scale, filling in the gaps of previous works and focusing on details that were not presented before. There were 105 scientific sources examined. The most dominant climate zoning variables were thoroughly analyzed. A clear categorization of climate zoning methods with specific criteria was shown. The most used methods were evaluated, emphasizing their similarities and differences, as well as their essential components and advantages. The main literature review was supported with bibliometric and bibliographic analysis. The existence of many climate zoning methods can be an indicator of the lack of agreement on the most effective strategy. A tendency has been established for the popularization among scientists of methods based on machine learning and building energy simulations, which are relatively easy to use and have proven to be the most reliable climate zoning methods. A transformation is emerging by shifting from a climate-based to a building performance-based climate zoning approach.
... Climate regionalization of the territory is essential for characterizing spatial and temporal climatic variability (Pineda-Martínez et al., 2007), producing meteorological forecasts, and analyzing trends at different scales based on the spatial homogeneity of phenomena. Regionalization also helps to ensure differentiated planning in the management of the territory's water resources (Srinivas, 2013) and to measure the impact of climate change in different regions (Almazroui et al., 2015), thus contributing to better decision-making in the ordering and management of the territory (Virmani, 1980). ...
Climate regionalization is essential for characterizing spatial and temporal climatic variability, producing meteorological forecasts, analyzing trends at different scales and, determining the climatic impact on human activities. The aim was to propose a climatic regionalization for Santa Cruz province, based on gridded data of rainfall and temperature (period 1995 to 2014), and subsequent characterization. To achieve this goal, we applied the non-hierarchical k-means clustering method to monthly accumulated rainfall and monthly average temperature databases. The Thornthwaite classification modified by Feddema was used to classify each cluster. Results from this study showed that Santa Cruz province is divided into 11 climatic regions based on rainfall and temperature. The driest and warmest regions are located in the center and northeast of the province and the most humid and coldest ones in the south and southwest. Regionalization is an important component of many applied climate studies and it can be used in other studies related to agriculture, energy production, water resource management, extreme weather events, and climate change, among others. This regionalization in particular can be used to examine the impacts of climate change in regional studies of climatic scale reduction in Santa Cruz province. As well as this tool can be essential in the study of drought and its impacts and contributes to a better understanding of the climatic phenomena that condition drought.
... In this research, rainfall is the 5th essential variable with a normalized weight of 0.08 (Table 3). In the SLPB, the rainy season encompasses from June to September [125], and according to [46], the complex orography determines the rainfall patterns over the ...
... In this research, rainfall is the 5th essential variable with a normalized weight of 0.08 (Table 3). In the SLPB, the rainy season encompasses from June to September [125], and according to [46], the complex orography determines the rainfall patterns over the area. As shown in Figure 8, the rainfall distribution clearly indicates that towards the Sierras, high levels of rain (from 421 to 501 mm) occur, classifying them as high-very high groundwater potential (Table 5). ...
Groundwater occurrence in semi-arid regions is variable in space and time due to climate patterns, terrain features, and aquifer properties. Thus, accurate delineation of Groundwater Potential Zones (GWPZs) is essential for sustainable water resources management in these environments. The present research aims to delineate and assess GWPZs in a semi-arid basin of San Luis Potosi (SLP), Mexico, through the integration of Remote Sensing (RS), Geographic Information System (GIS), and Analytic Hierarchy Process (AHP). Seven thematic layers (geology, lineament density, land use and land cover, topographic wetness index (TWI), rainfall, drainage density, and slope) were generated in raster format. After the AHP procedure and rank assignment, the thematic layers were integrated using the raster calculator to obtain the GWPZs map. The results indicated that 68.21% of the area is classified as low groundwater potential, whereas 26.30% is classified as moderate. Validation was done by assessing the water residence time data from 15 wells distributed in the study area. Furthermore, the Receiver Operating Characteristics (ROC) curve was obtained, indicating a satisfactory accuracy prediction (AUC = 0.677). This study provides valuable information for decision-makers regarding the conservation and sustainable management of groundwater resources
... In this research, rainfall is the 5th essential variable with a normalized weight of 0.08 (Table 3). In the SLPB, the rainy season encompasses from June to September [125], and according to [46], the complex orography determines the rainfall patterns over the ...
... In this research, rainfall is the 5th essential variable with a normalized weight of 0.08 (Table 3). In the SLPB, the rainy season encompasses from June to September [125], and according to [46], the complex orography determines the rainfall patterns over the area. As shown in Figure 8, the rainfall distribution clearly indicates that towards the Sierras, high levels of rain (from 421 to 501 mm) occur, classifying them as high-very high groundwater potential (Table 5). ...
Groundwater occurrence in semi-arid regions is variable in space and time due to climate
patterns, terrain features, and aquifer properties. Thus, accurate delineation of Groundwater Potential Zones (GWPZs) is essential for sustainable water resources management in these environments. The present research aims to delineate and assess GWPZs in a semi-arid basin of San Luis Potosi (SLP), Mexico, through the integration of Remote Sensing (RS), Geographic Information System (GIS), and Analytic Hierarchy Process (AHP). Seven thematic layers (geology, lineament density, land use and land cover, topographic wetness index (TWI), rainfall, drainage density, and slope) were generated in raster format. After the AHP procedure and rank assignment, the thematic layers were integrated using the raster calculator to obtain the GWPZs map. The results indicated that 68.21% of the area is classified as low groundwater potential, whereas 26.30% is classified as moderate. Validation was done by assessing the water residence time data from 15 wells distributed in the study area. Furthermore, the Receiver Operating Characteristics (ROC) curve was obtained, indicating a satisfactory accuracy prediction (AUC = 0.677). This study provides valuable information for decision-makers regarding the conservation and sustainable management of groundwater resources.
... Globally, Pineda-Martinez et al. (2007) zoned the bioclimatic regions of central and northeastern Mexico using factor analysis. The calculation of PC1 and PC2 indicates that climate variability is closely related to mean annual precipitation and extraordinary climate events. ...
... In order to zoning Sistan and Baluchestan Province, Iran, Saligeh et al. (2008) utilized factor analysis and climate clustering methods fitted with 20 variables and found that climate of the province is formed by five factors including precipitation, temperature, sunlight, wind, and thunder. Pineda-Martinez et al. (2007) used factor analysis for zoning of bioclimatic regions in central, northern, and northeastern Mexico. With the same spirit, Yunus (2011) applied factor analysis and cluster methods for bioclimatic classification in Malaysia. ...
Identification and assessment of the relationship between medicinal plant species and climatic factors are important lines of research, which provides insights into reasonable exploitation and sustainable development of these species. In addition, multivariate statistical methods have been proved successful in revealing the relationship between climatic factors and the distribution of plant species. Hence, this study aims to investigate bioclimatic characteristics of Daphne mucronata by determining climatic factors in its distribution range in Fars Province, Iran, in the year 2020, where this species has a wide spatial distribution (27% of the total area). Towards this aim, 50 climatic variables related to January, April and July were used. To reduce the number of variables and determine the most important factors, Factor Analysis and Principal Component Analysis (PCA) were used. The results showed that four principal components namely the temperature, precipitation, cloudiness, and wind components account for 38.56, 32.85, 11.03, and 9.09 % (91.54% in total) of data variance, respectively. The results also indicated that temperature and precipitation had the greatest impact on the species distribution. Moreover, the mean temperature within the species distribution range was about 15°C with minimum precipitation of 379 mm. This research identifies the characteristics of vegetative climate to determine the possibility of expansion and development of this plant in natural areas and provides better management.
... In contrast, natural vegetation would have the opposite effect, creating microclimatic conditions that resist the Revista Bio Ciencias 9, e1173. opuesto, creando condiciones microclimáticas capaces de hacer frente a la influencia de la variabilidad climática global (Pineda- Martínez et al., 2007;Carvajal & Pabón, 2016). ...
... influence of global climate variability (Pineda-Martínez et al., 2007;Carvajal & Pabón, 2016). ...
Studying climate variations over time is one way to observe signs of climate change in different areas. Variations are generally analyzed and presented descriptively, which is a problem when integrating climate variability as an element of climate change vulnerability studies. Although validated indices of climate variability exist internationally, these use daily data values, so it is a challenge to apply them in Mexico, where most of the available climate data are monthly compiled. Therefore, this research aimed to propose a list of functional indicators from monthly data to represent climate variability in an index. As a case study for validation, we choose a fragment of the Pacific Coastal Plain, a warm climate zone in the northwest of the country. The method consisted of conceptual analysis (choice of dimensions from references research) and operational analysis (definition of indicators and statistical validation process). We obtained 24 indicators, grouped into six variables and four dimensions 1) climate extreme, 2) climate anomalies, 3) natural climate variability teleconnection, and 4) long-term climate change. According to the case study results, 1980-1989 and 2010-2018 were the most variant periods concerning the average. The resulting indicators are expected to be useful in future vulnerability assessment studies.
Annexes: https://doi.org/10.6084/m9.figshare.21201797.v1
... These six factors accounted for about 82% of the data variance, and in order of importance were heating temperature (37.32%), spring and summer precipitation (22.54%), wind (7.18%), autumn-winter precipitation (6.61%), dusty days (4.22%), and cloudiness days (4.15%), respectively. In many studies conducted by Lashani et al. (2011), Khodagholi et al. (2007, Yunus (2011), Pineda-Martinez et al. (2007, and Hossel et al. (2003), temperature, precipitation, and wind were the most important climatic factors. Hessel and et al. (2003) showed that precipitation, temperature, wind speed, evaporation potential, and sunshine hours accounted for about 97% of the variance of the initial variables and separated the different regions of Ireland and Britain. ...
The relationship between plant species and climatic factors has always been a fundamental issue in plant ecology, and the use of multivariate statistical methods can be effective in revealing the relationship between climatic factors and plant species distribution. Therefore, in this study, climatic factors affecting the distribution of Artemisia sieberi and Artemisia aucheri, widely distributed in Iran, were investigated. For this purpose, 117 climatic factors were used, and to reduce the number of factors and determine the most important effective ones, a factor analysis was used by principal component analysis. The results showed that six factors including heating temperature, spring and summer precipitation, wind, autumn–winter precipitation, and dusty and cloudiness days explained 37.32%, 22.54%, 7.18%, 6.6%, 4.22%, and 4.15% of data variation, respectively. Together these seven factors account for 82% of data variation. The autumn–winter precipitation and heating temperature had the greatest impact on the presence of Artemisia sieberi and Artemisia aucheri, respectively, so that the autumn–winter precipitation was negative in areas where Ar.sieberi is observed. The heating temperature factor is negative in areas where Ar.aucheri is present, while it is positive in areas lacking Ar.aucheri. The study of the effect of environmental factors on Artemisia species distribution is very important in the planning and management of natural resources, and Artemisia is one of the most important plants in the country’s rangelands; therefore, the results of this research can be used for practical planning, management, and reclamation of these rangelands.
... Selection of homogeneous regions can be conducted through a variety of cluster analysis (CA) techniques, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), factorial analysis (FA), and self-organizing maps (SOM) (see e.g. Baeriswyl and Rebetez 1997;Beaudoin and Rousselle 1982;Comrie and Glenn 1998;Hassan and Ping 2012;Karl et al. 1982;Malekinezhad et al. 2011;Mallants and Feyen 1990;Markonis and Strnad 2020;Modarres and Sarhadi 2011;Munoz-Díaz and Rodrigo 2004;Ngongondo et al. 2011;Pineda-Martínez et al. 2007;Santos et al. 2015;Van Regenmortel 1995), complemented by some type of statistical homogeneity testing; see e.g. Hosking and Wallis (1997) and Sect. ...
We investigate and discuss limitations of the approach based on homogeneous regions (hereafter referred to as regional approach) in describing the frequency distribution of annual rainfall maxima in space, and compare its performance with that of a boundaryless approach. The latter is based on geostatistical interpolation of the at-site estimates of all distribution parameters, using kriging for uncertain data. Both approaches are implemented using a generalized extreme value theoretical distribution model to describe the frequency of annual rainfall maxima at a daily resolution, obtained from a network of 256 raingauges in Sardinia (Italy) with more than 30 years of complete recordings, and approximate density of 1 gauge per 100 km². We show that the regional approach exhibits limitations in describing local precipitation features, especially in areas characterized by complex terrain, where sharp changes to the shape and scale parameters of the fitted distribution models may occur. We also emphasize limitations and possible ambiguities arising when inferring the distribution of annual rainfall maxima at locations close to the interface of contiguous homogeneous regions. Through implementation of a leave-one-out cross-validation procedure, we evaluate and compare the performances of the regional and boundaryless approaches miming ungauged conditions, clearly showing the superiority of the boundaryless approach in describing local precipitation features, while avoiding abrupt changes of distribution parameters and associated precipitation estimates, induced by splitting the study area into contiguous homogeneous regions.
... The results showed that 4 factors of humidity, weather disturbance, drought and temperature capability justifies 88.6% of the variance of the initial data. Martinez et al. [14] by multivariate statistical methods, determined of bioclimatic classification of Mexico City, for this study climatic data were extracted from 37 meteorological stations over a period of 30 years. The results showed that there are two large climatic regions in the region, which themselves are divided into four small climatic regions that had similar climatic characteristics. ...
Traditional classification approaches tend to classify climates in terms of one or a few climatic variables given the specific circumstances. However, climate incorporates wide varieties of variables and serves as a cumulative process. In the present research, the number of 197 climatic parameters were selected and evaluated drawing upon data from the Iranian Meteorological Organization at 164 selected stations during the period of 1981 to 2018. After accuracy verification (by statistics of reliable synoptic stations in the area), such parameters were applied to develop a database. Then, upon reviewing of the library documents while considering the effective climatic elements in the formation and distribution of vegetation in different regions of Iran, finally 137 climatic variables were identified, in turn used as input data to perform factor analysis in SPSS software. Such variables greatly affect the climate of each region, and in fact, are determinant factors of the climate in each area. Subsequently, cluster analysis was performed on climatic factors obtained from the factor analysis method. According to the results, throughout the Iran, nine factors of temperature, relative humidity, cold season rainfall, warm-season rainfall, wind speed, semi-cloudy days, thunderstorm and snowy day with eigenvalues greater than one were 29.71, 22.32, 9.58, 7.52, 6.22, 6.80, 4.25, 3.69, 2.22%, respectively and totally accounted for 92.35% of the variance the data. Cluster analysis was an important step in determining the number of homogeneous clusters in this study. For this, the wards method was applied. First, by GIS software, the whole of Iran was divided into two climatic zones, and the climatic factors separating Iran into two zones were identified. Then two areas were divided into three sub-areas and this was continued until the final stage and at each stage, the factors separating the climatic zones were identified. By the final stage, it means stage at which the number of suitable and final zones is obtained. Subsequently, we reached from the climatic zone of 25 to zone of 26, so that all suitable climate zones of Iran were obtained by cluster analysis. While we proceeded from the zoning phase of 26 to 27 and up, it had no effect on climate factors, and no factor was identified as a separating factor of climatic zones. Eventually, based on the multivariate statistical methods, 26 bio-climatic zones for Iran was obtained. Then, the overall vegetation map of Iran was networked by the kriging method and matched to the Iranian map of climatic zones. And exactly the dominant vegetation types were studied and introduced within each climatic zones. Finally, the results of the multivariate statistical method were compared with those of the traditional Pabout method and the weaknesses and strengths of the Pabout method and also multivariate statistical methods used in the bio-climatic zonation of Iran were investigated.
... México cuenta con una gran diversidad de ecosistemas terrestres que van, desde la vegetación de dunas y manglares en las costas, hasta las selvas altas en el sur de la República. Esta gran diversidad de ecosistemas se debe, en gran parte, a aspectos orográficos y atmosféricos que generan una compleja climatología en el territorio mexicano (Pineda-Martínez et al., 2007). Los climas cálidos y secos de las planicies costeras del norte contrastan con los cálidos y húmedos del suroeste y, los templados y secos de la altiplanicie central, con los fríos y húmedos de las altas montañas del Eje Neovolcánico (Rzedowski, 2006). ...
