GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning

An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model

In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings.

Thermal performance of ETICS, energy activated with PCM and PV

Installing photo-voltaic (PV) panels on building façades is a growing tendency that helps to achieve both newly built and renovated nearly zero energy buildings. A novel approach to building active facades is to use a phase change material (PCM) behind the flexible PV. The PCM stabilises the PV’s temperature which can lead to an increase in energy production and cuts down the temperature peaks to avoid damage. In this study, the thermal performance of an En-ActivETICS wall was modelled in three different locations across Europe. The model was validated against on-site temperature measurements. The efficiency of the PV was calculated and an optimal PCM thickness and melting temperature were selected. The results show that annual energy production of the PV panel could increase between 2% (in Lodz) to 5% (in Madrid) using a 40mm-thick PCM. The optimal PCM melting temperatures for a certain climate should be chosen as 0 to 10 degrees below maximum air temperature in summer. The maximum peak PV temperatures could be reduced by ca. 20 K (from ∼90 to ∼70°C). Reasonable way to fix the stainless steel casing to the wall would be with four stainless steel anchor bolts – that gives 78% or 93% efficiency in case of EPS or PIR thermal insulation, respectively.

Updated Moving Forecasting Model of Air Maximum Temperature

In the current study, a moving forecasting model is used for the purpose of forecasting maximum air temperature. A number of recordings are used for building the AR model and next, to forecasting some temperature values ahead. Then the AR model coefficients are updating due to shifting the training sample by adding new temperature values in order to involve the change in temperature time series behaviour. The current work shows a high performance all over the temperature time series, which considered in the analysis.

Measured and predicted freeze-thaw days frequencies in climate change conditions in central Poland

The rate of progression of geomorphological phenomena is greatly influenced by freeze-thaw processes. In the face of air temperature increasing over the past few decades, a question of the future impact of these processes arises, notably in the temperate and cold climate zones. Using the mean, maximum and minimum daily air temperature data in the period 1951–2018 obtained from three weather stations located in the vicinity of Jeziorsko reservoir (central Poland), we have determined the mathematical correlation, described with a polynomial function, between the mean monthly air temperature and the monthly number of freeze-thaw days (FTD). A freeze-thaw day is a day when the maximum air temperature is above 0 °C while the minimum air temperature equals or is below this threshold. The number of FTDs within the study area averaged 64–71 and demonstrated a downward trend of 2–4 FTDs/10 years. The study period (1951–2018), includes a clearly marked distinct sub-period (1991–2018), when the reservoir was in operation, which experienced 58–68 FTDs. Considering the assumed rise in temperature, one should expect a further, though slightly slower, decline in the future number of FTDs. Depending on the accepted model of the temperature increase, which for the area of Poland (Central Europe) in the perspective of 30 years oscillates between +1.1 to +1.3 °C, the number of FTDs within the study area is expected to decline by −4.5 to −5.3 FTD, i.e. 6–7% and 5.4–5.5 FTD i.e. 8–9% respectively.

Radial Growth Response of Picea crassifolia to Climatic Conditions in a Dryland Forest Ecosystem in Northwest China

As an important barrier against desert invasion in Northwest China, Helan Mountains (HL), Luoshan Mountains (LS) and their natural forests have an extremely important ecological status. It is of great significance to study the relationship between forest growth and climate in this region under the background of global change. At present, relevant research mostly focuses on the Chinese pine (Pinus tabulaeformis Carr.), and little is known about how Qinghai spruce (Picea crassifolia Kom.) responds to climate change. To investigate the potential relationships between radial growth of P. crassifolia and climatic conditions in Ningxia, China, we collected tree-ring samples from P. crassifolia growing in the HL and LS and then established the standard tree-ring width chronologies for the two sites. Correlation analysis together with multivariate linear regression and relative contribution analyses were used, and results showed that radial growth in the HL was determined by the precipitation in the previous September, by the standardized evapotranspiration index (SPEI) in the current March and June, and by the maximum air temperature in the current September. The maximum air temperature in the current September contributed the most (0.348) to the radial growth in the HL. In the LS, radial growth was determined by the precipitation in the previous September and in the current March and by the minimum air temperature in the current July. The factor that made the most contribution was the precipitation in the current March (0.489). Our results suggested that in the wetting and warming future, growth of P. crassifolia in the HL will increase while that in the LS needs further investigation. Our results also provide a basis for predicting how P. crassifolia in northwest China will grow under the background of future climate change and provide a reference for formulating relevant management measures to achieve ecological protection and sustainable development policies.

GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning

An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

Comparison of reference evapotranspiration estimation methods, on days with and without precipitation

Water is gradually becoming scarcer and more expensive. Therefore, any means that aims at a more efficient use of this substance in the most diverse sectors, becomes essential. In this context, the accurate estimation of evapotranspiration is of fundamental importance. With this in mind, the objective of this work was to compare the performance of different methodologies for estimating reference evapotranspiration in relation to the FAO Penman-Monteith method on days with and without precipitation in the region of Cambará do Sul/RS. To achieve this goal, daily data on maximum air temperature (°C), minimum temperature (°C), relative air humidity (%), dew point temperature (°C), wind speed at 2 m high (m s-1), atmospheric pressure (hPa) and global solar radiation (MJ m-2 d-1), were acquired from the automatic weather station located in Cambará do Sul/RS and divided into two sets (days with and without precipitation ). The comparison between the different methodologies and the standard method, for each period mentioned above, took place through a simple linear regression analysis to obtain the regression coefficients a and b and the determination coefficient. Subsequently, Pearson's correlation coefficient, root of the mean square of the error, Willmott index and the Camargo and Sentelhas index were calculated . For the municipality of Cambará do Sul/RS to replace the Penman-Monteith method, we recommend the use of the Penman and Makkink methods, which presented satisfactory performance in all periods analyzed.

Patterns of changes in productivity of the ‘Pamyati Uchitelya’ grape variety when varying the bush loading with shoots and bunches

Приводятся экспериментальные данные по агробиологической реакции винограда сорта Памяти Учителя столового направления использования на изменение нагрузки кустов побегами и гроздями. Полевые исследования выполнены в Центральной агроэкологической зоне виноградарства Краснодарского края. Cхема посадки кустов - 3,5 × 3,5 м, формировка кустов - высокоштамбовый двуплечий кордон, подвой Берландиери × Рипариа SО4. Среднегодовая температура воздуха 12,5-13,0 °С, сумма активных температур 3900-4100 °С, максимальная температура во время вегетации - плюс 40°С, минимальная зимой опускается до минус 30 °С. Годовая сумма атмосферных осадков - 700-800 мм. Почвы малогумусные выщелоченные мощные черноземы. В таких агроэкологических условиях сорт показал высокую отзывчивость на оптимизацию нагрузки кустов побегами и гроздями. При нагрузке кустов побегами 18 шт./куст и гроздями 25 шт./куст средняя масса грозди достигает наибольшей величины и составляет 0,625 кг. Наибольшая урожайность товарного винограда (10,9 т/га) формируется при нагрузке кустов побегами и гроздями в количестве 24 и 44 шт./куст соответственно. При таких регламентах нагрузки кустов гроздь массой 0,393 кг имеет привлекательный товарный вид. Оптимизированный регламент нагрузки кустов побегами и гроздями в количестве 24 и 44 шт./куст рекомендуется применять в Центральной агроэкологической зоне виноградарства Краснодарского края для выращивания высоких урожаев сорта Памяти Учителя на подвое Берландиери × Рипариа SО4. The experimental records on agrobiological response of the ‘Pamyati Uchitelya’ table grape variety to changes in the loading of bushes with shoots and bunches are presented. Filed experiments were carried out in the Central agroecological viticultural zone of the Krasnodar Territory. The planting pattern of bushes is 3.5 × 3.5 m, the training system - a high-bole bilateral cordon, the rootstock - ‘Berlandieri × Riparia SO4’. The average annual air temperature of the zone is 12.5-13.0 °C, the sum of active temperatures is 3900-4100 °C, the maximum air temperature during the growing season is +40 °C, the minimum air temperature in winter goes to -30°C. The annual total precipitation is 700-800 mm. The soils are low-humus, leached deep chernozems. The variety showed heavy response to optimization of bush loading with shoots and bunches under these agroecological conditions. When bushes are loaded with shoots of 18 pcs/bush and bunches of 25 pcs/bush, the average bunch weight reaches the highest value of 0.625 kg. The highest cropping capacity of commercial grapes (10.9 t/ha) is achieved with loading of bushes with shoots and bunches in the amount of 24 and 44 pcs/bush, respectively. With such regulations of bush loading, a bunch of 0.393 kg has attractive marketable presentation. The optimized regulation of bush loading with shoots and bunches in the amount of 24 and 44 pcs/bush is recommended for using in the Central agroecological viticultural zone of the Krasnodar Territory for growing high yields of the ‘Pamyati Uchitelya’ variety on the ‘Berlandieri × Riparia SO4’ rootstock.