Climate Classification Research Articles

CLIMATIC NICHE DETERMINES THE GEOGRAPHIC DISTRIBUTION OF MYRTACEAE SPECIES IN BRAZILIAN SUBTROPICAL ATLANTIC FOREST

ABSTRACT The understanding of factors determining species geographic distribution is a fundamental aim of ecology. We investigated the environmental niche for three Myrtaceae species in the Brazilian Subtropical Atlantic Forest (BSAF), part of a global conservation hotspot. Based on a literature review, we selected one representative Myrtaceae species in three important forest types in this region: Evergreen Rain Forest (coastal plains and associated mountains ranges); Araucaria Forest, and Seasonal Deciduous Forest (continental upland areas). Geographical coordinates of their distribution were obtained from the BIEN database. As explanatory variables, we considered altitude, climate, cloud cover, and soil classes. We summarized the environmental space occupied by each pair of species using Principal Components Analysis, determined niche overlaps, and applied statistical tests to verify niche equivalences and similarities. The selected species in Evergreen Rain Forest, Araucaria Forest, and Seasonal Deciduous Forest were Myrcia splendens (Sw.) DC., Myrcia guianensis (Aubl.) DC., and Campomanesia xanthocarpa O.Berg., respectively. C. xanthocarpa showed a more restricted geographic distribution than the two Myrcia species that occur from central America to southern Brazil. Species’ geographic distribution were fundamentally determined by temperature and rainfall regimes. Only C. xanthocarpa and M. guianensis, from uplands forest formations, showed environmental niche equivalence. In conclusion, we found that both species of Myrcia showed high climatic niche amplitudes occurring throughout the climatic gradient, while C. xanthocarpa was more subtropical, distributed mostly in the south and southeast of Brazil.

Climatic condition effects on the components of plant diversity in the western Iran grasslands using multiplicative partitioning methods

This study aimed to investigate the effects of different climatic conditions (arid vs. semi-arid) on patterns of species richness and plant diversity as well as plant composition in two over-grazed regions. The regions were located in the west of Iran, closed to each other and different in climatic conditions. Based on the aridity index of De Martonne climate classification, Rika (I = 9.94) and Dalab (I = 18.54) were categorized as arid and semi-arid regions, respectively. In each region, we collected plant vegetation data from a nested sampling design consisted of local (80 plots) and regional (8 sites) scales. Patterns of plant diversity and species richness in each region, and similarity of plant composition between the two regions were analyzed using multiplicative partitioning methods, species richness estimator and cluster analysis, respectively. The results showed that diversity components in all scales had significant difference with their expected values (p <0.000) resulted in non-random distribution of plant species among and within scales. In addition, the results showed that species richness in Rika region (arid, 89 species) was greater than Dalab region (semi-arid, 76 species). The results of cluster analysis suggested that Rika and Dalab regions had different plant species compositions. Based on our results, the role of seed dispersal might be more prominent in smaller scales (plot and sites) compared to region scale, since the highest possible amount of β-diversity was occurred between regions. Our results suggested that plant diversity in the largest scale (region) had the highest amount of diversity components where the rangeland manager should attend on this scale for conservation of plant diversity.

Soybean cultivation prospect based on crop water requirements and the agroclimatic zone in Jambi Province

Soybean (Glicine max) has the potential to be developed because it cannot only be processed into foodstuffs but also become livestock feed. In addition to using technology, enhancement of crop production can be done by looking at water availability and pay attention to the weather factors, especially to increase crop intensity. This research aims to determine the crop water requirements based on the agroclimatic zone. The quantitative descriptive analysis and data processing were conducted using the Cropwat model. This model was created by FAO specialists to provides an opportunity for automation of all the necessary calculations for evapotranspiration determination. Cropwat is an easy-to-operate software that can minimize human error. Climate classification was identified based on precipitation data in Jambi province respectively in Muaro Jambi Regency at Sultan Thaha Station, Jambi Palmerah, and Depati Parbu. The agroclimatic zone for those areas, respectively, are D1, D2, and E2. Based on the rainfall, all three districts are feasible for soybean cultivation with average rainfall between 127.11 mm - 192.51 mm per month. The amount of crop water requirements is also an essential factor for cultivating a plant so that the water can be administered according to the needs of plants and can provide optimal production. Based on water balance, the surplus in Muaro Jambi Regency Jambi Province occurs in January, February, March, April, September, October and December. East Tanjung Jabung Regency surplus is in January, February, March and April. West Tanjung Jabung Regency surplus occurs in January, February, March, October, November and December. The results of this calculation indicate that Jambi Province has the potential to cultivate soybean plants because they have sufficient water availability.

Peyzaj Planlama Aşamasında Önemli Etkenlerden Olan Sıcaklık, Yağış Ve İklim Tiplerinde, Küresel İklim Değişikliğine Bağlı Olarak Meydana Gelebilecek Değişiklikler: Mersin Kent Örneği

Küresel iklim değişikliği bütün dünyada canlıları ve ekosistemleri doğrudan veya dolaylı olarak etkileyecek bir süreç olarak görülmektedir. Bu süreçte iklim parametreleri ve iklim tiplerinde meydana gelecek değişikliklerin önceden belirlenmesi, alınabilecek tedbirler ve sürece hazırlıklı olunması bakımından büyük önem taşımaktadır. Bu çalışmada da ülkemizin önemli kentlerinden birisi olan Mersin il genelinde bazı iklim parametreleri ve iklim tiplerinde küresel iklim değişikliğinin sebep olabileceği değişikliklerin belirlenmesi amaçlanmıştır. Çalışma kapsamında sıcaklık, yağış ve iklim tiplerinin (De Martone ve Emberger iklim sınıflandırmasına göre) mevcut durumları ile RCP 4.5 ve RCP 8.5. senaryoları ışığında 2050 ve 2070 yıllarındaki olası durumlarının kıyaslanması amaçlanmıştır. Çalışma sonuçları Mersin il genelinde sıcaklık, yağış ve bunlara bağlı olarak iklim tiplerinin önemli ölçüde değişeceğini göstermektedir. Günümüzde -0,4°C ile 19°C arasında değişen sıcaklığın, RCP 8.5 senaryosuna göre 2070 yılında il genelinde 4,9°C ile 24°C arasında değişeceği yani sıcaklık değişim aralığında 5°C civarında bir artış olacağı, yağış rejiminin değişeceği, iklim tiplerinin kurak iklim tiplerine doğru kayacağı tahmin edilmektedir. Bu durum peyzaj planlama çalışmalarında, planlama kriterlerinin en önemlilerinden olan iklim faktörlerinin önemli ölçüde değişeceğini göstermektedir. Peyzaj planlama çalışmaları, etkilerini uzun yıllar sürdürdüğünden, sağlıklı bir planlama yapılabilmesi için bu durumun mutlaka göz önüne alınması önerilmektedir. Mersin ili için yapılacak planlama çalışmalarında, bu çalışma sonuçlarından yararlanılmalıdır.

Atmospheric water vapor radiative effects on shortwave radiation under clear skies: A global spatiotemporal analysis

This global study analyzes how the total column water vapor (in terms of precipitable water, PW) is related to the attenuation of shortwave solar radiation (SWR) using data from the MERRA-2 reanalysis during 2000–2014 and SWR simulations obtained with the REST2 clear-sky radiation model. The water vapor radiative effect (WVRE) on SWR attenuation is investigated by comparing the clear-sky SWR predictions under humid and ideally dry atmospheres, whereas the quantitative effect of PW on WVRE is determined using the concept of precipitable water efficiency (PWE).At global scale, WVRE exhibits a distinct seasonal pattern ranging from –80.2 to 0 W m‐2, with lower values in humid regions where PW is typically high (>4 cm), making the water vapor contribution relatively small. This is attributed to the saturation effect in water vapor absorption. Over arid regions, the solar irradiance attenuation is mainly controlled by the high aerosol loads, and less negative WVRE values are found. Over high-altitude regions, the low PW reduces the atmospheric absorption, resulting in low absolute WVRE magnitudes.PWE is found to extend globally between –133.7 and 0 W m‐2 cm‐1 on a monthly scale. This range is smaller on a long-term mean monthly or annual basis. On a long-term basis, the monthly PWE results are mainly concentrated between −20 and 0 W m−2 cm−1, generating leptokurtic and left-skewed distributions peaking between −10 and −8 W m−2 cm−1, depending on month. PWE is inversely related to PW on a long-term annual scale, indicating that PWE tends to zero under very humid conditions. A general nonlinear model is proposed to evaluate the hourly PWE at global scale on a monthly basis, separately for the two hemispheres.A PW trend analysis reveals that the Middle East, equatorial regions, central Europe, and Australia exhibit significant temporal trends, further inducing significant trends in WVRE. For those areas, WVRE trends are calculated between −3.3 and 1.5 W m−2 over the entire 15-year period.In addition, the global WVRE over landmasses is classified in terms of the Köppen-Geiger (KG) climate classification system. The most pronounced radiative effects are found over the equatorial regions, with an average WVRE of −63 W m−2. The Arid KG class provides a complex WVRE pattern ranging from −66 to −24 W m−2. Further segregation of this class shows strongly variable WVRE, induced by local climate differentiation. Over Warm Temperate climates, WVRE is found to vary widely according to the various KG subclasses. For the Cold climate class, PW is mainly concentrated around 1 cm and WVRE is low. The Polar regions include high mountains where WVRE is low, with a median close to −28 W m−2.

Os climas do Rio Grande do Sul: uma proposta de classificação climática

Este trabalho apresenta a classificação climática elaborada para o estado do Rio Grande do Sul, resultado da tese “Os climas do Rio Grande do Sul: variabilidade, tendências e tipologia”. O método de classificação climática contemplou análises qualitativas e quantitativas, baseadas na climatologia genética e dinâmica, em associação com análises estatísticas. Com relação à tipologia climática, o estado do Rio Grande do Sul se situa em área de domínio do Clima subtropical, subdividido em quatro tipos principais: Subtropical I - Pouco Úmido (dividido em Subtropical Ia - Pouco úmido com inverno frio e verão fresco e Subtropical Ib - Pouco úmido com inverno frio e verão quente); Subtropical II - Medianamente úmido com variação longitudinal das temperaturas médias; Subtropical III - Úmido com variação longitudinal das temperaturas médias; e d) Subtropical IV - Muito úmido (dividido em Subtropical IVa - Muito úmido com inverno fresco e verão quente e Subtropical IVb - Muito úmido com inverno frio e verão fresco). O Rio Grande do Sul apresenta regiões climaticamente bem diferenciadas, evidenciando certa heterogeneidade, ao contrário de grande parte das classificações climáticas mais conhecidas do estado.

Climate of the Gilgit-Baltistan Province, Pakistan

The present study focuses on the climate conditions of Gilgit Baltistan. It includes the distribution ofdifferent weather elements like precipitation, temperature, number of rainy days, humidity, wind speed, air pressure,evapotranspiration, sunshine, factors affecting climate, and climate classification. Due to glaciation, the area is badlyeroded and consists of deep valleys and moraines. There is a marked variability between the climates of the eastern andwestern observatories of the area. Mostly, terrace agriculture is practiced in the area with deciduous and alpine forests.Based on temperature, the area has been divided into three distinct climate regions, namely, mild, cool, and cold.Whereas, based on precipitation, it classified into arid, semi-arid and undifferentiated highlands. Chilas is the hottestarea of the province, while Astor and Skardu are the coldest. The study area receives heavy precipitation from localthunderstorms, particularly during April and May. There are four rainy seasons, namely winter, pre-monsoon,monsoon, and post monsoon precipitation. The highest precipitation in the area is observed at Astore, while the lowestat Gupis. The relative humidity is below 55% throughout the year and causes chill with low temperature and crackingin human skins. Based on the variability of weather elements, the province has been divided into three macro, threemeso and six micro climate regions.

Prevalence and risk factors of gastrointestinal nematode infestation of horses, donkeys and mules in tropical, dry and temperate regions in Mexico.

The aims of this study were to determine the prevalence of gastrointestinal nematodes (GIN) in horses, donkeys and mules and its associations with age, sex and climatic factors (derived from satellite data) to identify the potential risk factors of different climate regions in four Mexican states. From May 2017 to April 2018, a total of 560 farm owners or managers answered the questionnaire, and the data were used to establish three Köppen climate classes (tropical, dry, temperate). The overall prevalence of GIN parasites in equines was 77.9% (436 out of 560). The highest percentage of GIN was detected in mules (87.5%). In addition, the highest prevalence among the climate regions was found in the temperate climate (79.9%). The identified nematodes were strongyle type (77.9%), Parascaris equorum (5.7%), Strongyloides (0.7%) and Oxyuris equi (2.9%). The highest mean value of strongylid eggs per g faeces was found in the dry climate (632.6±96.8), followed by that in the tropical climate (518.3±49.7) and temperate climate (383.8±30.2); however, a similar prevalence was observed between different climate regions. Age and sex were identified as risk factors for high shedding of strongyle eggs: the odds ratios for higher shedding intensities were 3.858 for geldings compared to mares, 2.602 for 6-10-year-old equines and 3.597 for ≤16-year-olds compared to young equines (≤5years old).

Physiological characteristics of two accessions of moringa (Moringa oleifera Lam.) at different planting sites in Madura

Moringa plants grow in Sumenep with semi-intensive planting at the moor and several planting sites. Sumenep is one of the districts in Madura Island, which is categorized as a dry area based on the Oldeman climate classification. Based on the color of the leaf stalk, moringa plants growing in Sumenep area are categorized as green and purple moringa. The study aimed to examine the differences in physiological characteristics of two accession of moringa plants at different planting sites in Madura. The study was arranged in Oversite Sub-sampling Design with planting site as first factor consisting of Bluto with E5 climate type and Guluk-guluk with D3 climate type. Meanwhile, the second factor was the type of moringa consisting of moringa with green and purple leaf stalks. The results showed that the different planting sites represented the differences in environmental elements, which influenced the physiological characteristics of moringa plants. Based on the observation in September 2016, moringa plants with green stalks planted in Guluk-guluk had high ANR content. The stomatal opening on moringa leaves with green stalks was larger than that on moringa plants with purple stalks, whereas ANR content in green-stalked moringa was lower than in the purple-stalked ones. Meanwhile, in February 2017, the purple-stalked moringa plants planted at Bluto had the highest transpiration rate and proline content compared to the others.

Investigation of spatiotemporal variation of drought in Iran during the last five decades

The present study was conducted to assess the changes in Iran's drought severity for the duration of 1964 to 2014. For this purpose, the spatial distribution of drought was annually and seasonally evaluated using climate data from 26 synoptic stations over Iranian territory based on standardized precipitation index (SPI). In this regard, the climate classification in the study area was performed applying Dermartone method. Moreover, the annual and seasonal values of SPI were calculated for the whole Iranian territory and each climate region. The SPI index for monotonic trend was calculated in each climate region utilizing Mann-Kendall and Theil Sen estimators. Our results implied that the minimum and maximum values of SPI (-3.86 and 2.89, respectively) appeared during spring in dry and Mediterranean climate regions. In addition, the maximum and minimum values of annual continuous SPI appeared in 1999-2004 and 1974-1982, respectively. The maximum and minimum values of seasonal continuous SPI also appeared for a duration of 9 years during summers respectively in the period of 1977 to 1985 and springs in the period of 2006 to 2014. The application of Mann-Kendall and Theil Sen estimator analyses revealed that 9 out of 26 stations had a significant decreasing SPI trend. Moreover, the annual and seasonal time series in moderately dry regions indicated a meaningfully decreasing trend in winter and annual SPI. Additionally, winter, spring, autumn and annual values of SPI had a meaningful decreasing trend in the Mediterranean climate region. In dry and very wet climate regions, no obvious trend was detected for the annual or seasonal SPI index.

Solar exergy evaluation and empirical model establishment; case study: Iran

Iran with 300 sunny days in more than two thirds of its land is among the countries with high potential of solar energy. Nevertheless, to date no research has been conducted on status of solar exergy in Iran. In this study, in order to expand the perception of solar energy quality and to compensate the lack of research on solar radiation exergy in Iran, long term meteorological and solar data of eight capital provinces of Iran with five different climatic conditions are utilized. These properly distributed stations include Urmia, Bushehr, Isfahan, Ilam, Kerman, Mashhad, Zahedan and Zanjan. The monthly average daily solar radiation exergy on a horizontal surface for each station is obtained first, then it is recognized that the ratio of exergy to energy is almost independent of the month, the climatic condition and the geographical location; thus, can be considered 0.87 for the whole Iran. For predicting the solar exergy at every station, five empirical models with linear, quadratic, cubic, exponential and power functional forms, all dependent only on relative sunshine duration, are calibrated. Then, eight statistical indicators are utilized to evaluate the performance of the established models for every capital province. The best models recognized for Urmia, Bushehr, Isfahan, Ilam, Kerman, Mashhad, Zahedan and Zanjan have cubic, power, exponential, exponential, linear, quadratic, power and cubic functional forms, respectively. These models are simple and easy to apply and can be also utilized for other places with similar climatic classification and conditions.

Assessment of Extremes in Global Precipitation Products: How Reliable Are They?

AbstractGlobal gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates.

Estimating seasonal water budgets in global lakes by using multi-source remote sensing measurements

The seasonal change in lake water storage (LWSsc) reflect periodic fluctuations of the basin-scale water balance. However, the role of LWSsc in regulating the water budget at the global scale has not yet been investigated based on straight-forward observations. Quantifying LWSsc is necessary, especially under the context of global change. Available in-situ measurements of lake water levels and volumes are still scarce. Therefore, the Global Surface Water datasets of Joint Research Centre and multi-source satellite altimetry datasets through mathematical statistics methods are used in this study to address this issue. We estimate the LWSsc of 463 lakes and reservoirs worldwide with areas greater than 10 km2, which represent nearly 64% of the total global lake area and 93% of the total lake volume capacity. Results show that the global seasonal water storage variation of these examined water bodies is 1390.91 ± 78.91 km3, comprising 869.44 ± 67.35 km3 from lakes and 521.46 ± 41.11 km3 from reservoirs. The relatively large estimates of LWSsc are concentrated in North American and African basins. Among the watersheds, the seasonal fluctuations of lakes in the North American Lawrence basin make up the most substantial magnitude of 10.76% of the global LWSsc. The latitudinal direction zonality of LWSsc is relatively significant. The LWSsc is concentrated between 30° N and 60° N in the northern hemisphere and between the equator and 30° S in the southern hemisphere. Considering the geographic similarity and climatological zonality, the global LWSsc estimates are also extrapolated to other lakes without direct satellite altimetry observations on the basis of the average rate of the examined lakes distributed in the same Köppen − Geiger Climate Classification zones. The LWSsc is calculated with a consequence of 488.23 ± 14.72 km3 for these extrapolated lakes, indicating an estimate of 1357.67 ± 68.94 km3 for the LWSsc of the global natural lakes (>10 km2). This initial estimation of LWSsc at a global scale will greatly help the improvement of our understanding of the seasonal behavior of lakes and reservoirs in regulating global and regional water cycles and the contribution of terrestrial water storage to sea level rise.

Analysing the inequitable energy framework for the implementation of nearly zero energy buildings (nZEB) in Spain

Most countries from the European Union work towards a low-carbon horizon in the building sector. As for Spain, the last modification of the Spanish Building Technical Code in 2020 establishes that every building ensuring the fulfilment of the regulation will obtain the category of nearly zero energy building. However, the limit values of the thermal properties of envelopes only distinguish 6 variations according to the winter climate zone. For this reason, this study analyses the potential risk of the existence of energy inequalities due to the fulfilment of the regulation as regards energy efficiency in Spain. A total of 48,786 energy simulations were performed by taking the 8131 municipalities of the country into account, as well as the previous and current regulation. The results considered heating and cooling demands, cluster analyses, and their impact on population, showing that the improvement of thermal properties could guarantee buildings with a better energy performance for a greater number of inhabitants. There was, however, a limitation in the current climate classification, so a more detailed climate classification with new limit values is required. The results also showed the limitations of use of the thermal transmittance to reduce the cooling energy consumption. This study could be the starting point to establish new regulatory parameters to reduce energy consumption equitably, considering the importance of cooling consumption in future climate change scenarios.

The Climate in the European Union and the Enlarged European Region is a Determinant of the COVID-19 Case Fatality Ratio

Climate could influence the COVID-19 pandemic, but while no evidence has been advanced on the influence of colder climates, some studies have provided data to support a possible heat-related protective factor. The objective is to verify whether areas with a Cold Temperate Climate (TC) have a higher Case Fatality Ratio (CFR) for COVID-19 than areas with a Cold Climate (CC) or with a Mediterranean Climate (MC) in the European Union and the Enlarged European Region. Countries or regions were subdivided into 3 groups according to the Köppen climate classification system: TC (Cfa, Cfb and Cfc in the Köppen system); MC (Csa, Csb); CC (D and E in the Köppen system). The total number of cases and the total number of deaths were detected on 13 August 2020 on the COVID-19 Map - Johns Hopkins Coronavirus Resource Center-the CFR was thus calculated by area. Living in TC areas is strongly associated with risk of a high Case Fatality Ratio for COVID-19, OR for MC =0.42, IC 95% 0.41-0.43; OR for CC=0.33, IC 95% 0.33-0.35. The results are confirmed in the EU, OR per MC=0.85, CI 95% 0.84-0.87; OR per CC=0.63, IC 95% 0.61-0.65.The study found that the IC in a humid temperate climate is associated with higher CFR with respect to the coldest and warmest temperate climates in Europe. This does not appear to be the only determinant of the pandemic.

Microhabitat characteristics of brown bear den areas

The aim of this study was to determine the microhabitat, relief and forest structure characteristics of brown bear (Ursus arctos L.) cave dens and other types of dens in the North Dinarides (Velebit Nature Park) in Croatia. In total, 63 dens were identified, consisting of 89% cave dens and 11% other den types (nest dens 6%, stump dens 3%, trunk dens 2%). In the 20-meter radius around each den, the microhabitat, relief and structural characteristics were recorded, including altitude, slope, exposition, rockiness, forest developmental stage, canopy density, tree height, forest site index, tree basal area, and tree species composition. Based on altitude, the climatic category was determined according to the Köppen climate classification. Most cave dens (62%) were in the temperate climate, at altitudes between 900 and 1,100 m, with a southern exposition and slope class between 30 to 60%. The dens situate in partial canopy density of the third forest site index with tree heights between 16 and 20 m and without forest degradation. Slopes and tree heights were lower (p &lt; 0.019) in cave den areas in comparison to other den types.&#x0D; Keywords: cave dens, microhabitat, forest structure, North Dinarides, Velebit Nature Park

Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe

This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.

Bias Correction for Climate Model Output (1) Method Characteristics and Classification

気候変化の影響評価においては気候モデル出力値のバイアスを適切に処理する必要がある．本総説では適切なバイアス補正の実施に資するべく，バイアス補正の手法選択や各手法を用いる上での留意点について整理した．第1部となる本稿ではバイアス補正に関連する既往の研究を概観し，手法選択の基礎となる情報を整理した．まず，多くの場合バイアス補正と共に気候モデル出力値の後処理として行われるダウンスケーリング手法について述べ，本稿で扱うバイアス補正の範囲を明確にした．その上で，バイアス補正を利用することなく目的とする情報を得る事例を説明し，バイアス補正が必要かどうかを検討する重要性について述べた．バイアス補正には対象とする変数を直接補正する方法と，対象とする変数を得る物理的な過程を補正する方法がある．両者の特徴について述べると共に，適用例が多い直接補正する方法を対象として，これまでに国内外で提案された様々な手法を特徴により分類し，それぞれの長所と短所について考察した．また，マルチアンサンブル実験結果のバイアス補正やバイアス補正手法の検証と評価などの観点から，バイアス補正の課題についても考察を行った．

Predicting Cattle Grazing Behavior on Rangeland using Accelerometers

The objective was to determine if 3-axis accelerometers could be used to predict daily activity for cattle grazing rangeland. There were 48 Hereford × Angus 2-yr-old low- or high-residual feed intake (LRFI or HRFI) cows used in this 2-yr trial. Cattle grazed in 4 pasture treatments consisting of continuously grazed, control (CCON); continuously grazed, supplemented (CTRT); rotationally grazed, control (RCON); and rotationally grazed, supplemented pastures (RTRT). Three LRFI- and 3 HRFI-collared cows in each treatment had accelerometers mounted for 29 d in 2016 and 45 d in 2017, beginning mid-October. Grazing time (GT), resting time (RT), and walking time (WLK) were obtained for each cow by direct observation over 3 d each year and compared with accelerometer predicted behavior. In 2016, 1.6% of the days were rejected for halter-mounted accelerometers and 3.6% were rejected in 2017 for collar-mounted accelerometers. The GT and RT were more accurately predicted than was WLK with the percentage error of predicted against observed data being 11.94% for RT, 13.51% for GT, and 30.13% for WLK in 2017. Less observation data were available in 2016, but when considering other sampling periods for the same cows and halters, the error rate was 15.1% for RT, 19.3% for GT, and 52.6% for WLK. The accelerometers successfully identified patterns of grazing behavior and differentiated among climatic, grazing system, supplementation status, and residual feed intake classification influences on GT, RT, and WLK. In a more moderate climate year, HRFI cattle appeared to rest less (P < 0.08) and walk more (P < 0.07) than LRFI cattle. Similar patterns were observed for cattle in the CCON versus CTRT treatments, with supplemented cattle resting more (P < 0.05) and walking less (P < 0.05). Accelerometers appear to be effective in determining mechanistic adaptations in grazing behavior by beef cattle on range.

Thermal comfort in university classrooms: An experimental study in the tropics

Thermal comfort is a paramount objective in university classrooms when the aim is to maximise learning and productivity performance. The challenge to achieve thermal comfort in classrooms in the tropics is even more significant due to the extreme weather conditions and the elevated internal and external heat gains. This study relies on thermal comfort responses from 429 questionnaires collected from December 2017 to January 2018 in three geographic regions in Ecuador. The study compares real thermal sensation and predicted models. The cities and its respective Köppen climate classification are Quito (Cfb), Guayaquil (Aw) and Tena (Af). Several free-running and air-conditioned classrooms were part of the sample in this study. Despite the differences in weather, the building designs and the properties, in terms of materials, are very similar. The Predicted Mean Vote (PMV) and the adaptive methods were used for comparison with actual votes through simple linear regression analysis. Griffiths neutral temperature and the one estimated from regression produce similar results. The neutral temperatures from TSV in Quito, Guayaquil, and Tena were 21.8 °C, 26.3 °C and 26.9 °C respectively. They were aligned to the ones reported in previous studies for locations with similar climate zones. On the other hand, for air-conditioned environments, the PMV model underestimated the occupants' satisfaction with the indoor conditions. Overall, high levels of comfort were observed in free-running classrooms in Quito, regardless of the lower air temperature. Whereas students' preferences in classrooms located in hot-humid climates mechanically conditioned are towards colder environments.