Heritable and climatic sources of variation in juvenile tree growth in an Austrian common garden experiment of Central European Norway spruce populations

We leveraged publicly available data on juvenile tree height of 299 Central European Norway spruce populations grown in a common garden experiment across 24 diverse trial locations in Austria and weather data from the trial locations and population provenances to parse the heritable and climatic components of tree height variation. Principal component analysis of geospatial and weather variables demonstrated high interannual variation among trial environments, largely driven by differences in precipitation, and separation of population provenances based on altitude, temperature, and snowfall. Tree height was highly heritable and genetic variation for tree height was strongly associated with climatic relationships among population provenances. Modeling the covariance between populations and trial environments based on climatic data increased the heritable signal for tree height.

Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads

This paper proposes an energy management strategy (EMS) for a hybrid stand-alone plant destined to supply controllable loads. The plant is composed of photovoltaic panels (PV), a wind turbine, a diesel generator, and a battery bank. The set of the power sources supplies controllable electrical loads. The proposed EMS aims to ensure the power supply of the loads by providing the required electrical power. Moreover, the EMS ensures the maximum use of the power generated by the renewable sources and therefore minimizes the use of the genset, and it ensures that the batteries bank operates into the prefixed values of state of charge to ensure their safe operation. The EMS provides the switching control of the switches that link the plant components and decides on the loads’ operation. The simulation of the system using measured climatic data of Mostoles (Madrid, Spain) shows that the proposed EMS fulfills the designed objectives.

Modelling the artificial forest (<i>Robinia pseudoacacia</i> L.) root–soil water interactions in the Loess Plateau, China

Plant root–soil water interactions are fundamental to vegetation–water relationships. Soil water availability and distribution impact the temporal–spatial dynamics of roots and vice versa. In the Loess Plateau (LP) of China, where semi-arid and arid climates prevail and deep loess soil dominates, drying soil layers (DSLs) have been extensively reported in artificial forestland. While the underlying mechanisms that cause DSLs remain unclear, they hypothetically involve root–soil water interactions. Although available root growth models are weak with respect to simulating the rooting depth, this study addresses the hypothesis of the involvement of root–soil water interactions in DSLs using a root growth model that simulates both the dynamic rooting depth and fine-root distribution, coupled with soil water, based on cost–benefit optimization. Evaluation of field data from an artificial black locust (Robinia pseudoacacia L.) forest site in the southern LP positively proves the model's performance. Further, a long-term simulation, forced by a 50-year climatic data series with varying precipitation, was performed to examine the DSLs. The results demonstrate that incorporating the dynamic rooting depth into the current root growth models is necessary to reproduce soil drying processes. The simulations revealed that the upper boundary of the DSLs fluctuates strongly with infiltration events, whereas the lower boundary extends successively with increasing rooting depth. Most infiltration was intercepted by the top 2.0 m layer, which was the most active zone of infiltration and root water uptake. Below this, the percentages of fine roots (5.0 %) and water uptake (6.2 %) were small but caused a persistently negative water balance and consequent DSLs. Therefore, the proposed root–water interaction approach succeeded in revealing the intrinsic properties of DSLs; their persistent extension and the lack of an opportunity for recovery from the drying state may adversely affect the implementation of artificial afforestation in this region as well as in other regions with similar climates and soils.

A Predictive Study of the Redistribution of Some Bread Wheat Genotypes in Response to Climate Change in Egypt

Climate change and global warming have become the most significant challenges to the agricultural production worldwide, especially in arid and semiarid areas. The main purpose of plant breeding programs now is to produce a genetically wide range of genotypes that can withstand the adverse effects of climate change. Moreover, farmers have to reallocate their cultivars due to their ability to tolerate unfavorable conditions. During this study, two field experiments and climate analysis based on 150 years of data are conducted to reallocate some genotypes of bread wheat in respect to climate change based on their performance under drought stress conditions. Climatic data indicate that there is an increase in temperature over all Egyptian sites coupled with some changes in rain amount. Among the tested cultivars, cultivar Giza 160 was the perfect one, while cultivar Masr 03 was the weakest one. Susceptibility indices are a good tool for discovering the superior genotypes under unfavorable conditions and, interestingly, some of the cultivars with high performance were among the superior cultivars in more than one of the tested traits in this study. Finally, combining the climatic data and the experimental data, we can conclude that cultivars Giza 160 and Sakha 94 are suitable for growning in zones with harsh environments, such as the eastern desert and southern Egypt, while cultivars Gemmeza 11, Sahel 01, Sakha 98, Sids 12, and Sakha 93 are suitable for growning in zones with good growing conditions, such as the Nile Delta region and northern Egypt.

Epidemiological aspects of scorpionic accidents in a municipality in Brazil’s northeastern

Scorpionic accidents are a major public health problem due to the high occurrence with potential seriousness. In this manner, the research aimed to analyze the occurrence of scorpionic accidents in a municipality in the northeastern of Brazil. An exploratory, descriptive study was made, with a quantitative approach, using secondary data which was gotten from the Notifiable Diseases Information System (SINAN), from 2008 to 2018. Data such as neighborhood, presence of street markets were also used, and the existence of sanitation and climatic data such as temperature and season. Geoprocessing was used to identify possible changes in the environment. In the analyzed period, 9,330 cases of scorpion accidents were recorded, with an average of 848 annual notifications. Scorpionic accidents occurred more frequently in women (5,686; 60.94%). Individuals aged 20 to 29 years (1.727; 18.51%) were more frequent to scorpion stings. Regarding the body parts where the stings were made, the highlights were on the foot (3.515; 37.67%) followed by the hand (2.818; 30.20%). No statistically significant relation was observed between climatic factors and scorpionic accidents. However, the high number of cases of scorpionic accidents was observed in the last 11 years studied. It was evident that during the study period there was no statistical relationship when climatic factors were correlated to scorpionic accidents. On its turn, when it was verified the results of the geoprocessing analysis, it was seen that anthropic factors have been motivating the potentiation of the occurrence of these accidents.

A simple approach towards estimating solar irradiance

Solar radiation is or vital interest in characterizing an area with respect to its agricultural potential. However, these are not readily available for a large network. An attempt. has been made to deduce solar irradiance from climatic data, such as temperature range. Based on daily data of Pune for 1986-90, a relationship has been developed between atmospheric transmittance and the daily range of air temperature. The model developed has been tested on independent data and found to give fairly accurate estimation of solar irradiance. Nearly 70% of the variation in daily solar radiation could be explained by this simple method. The effect of solar irradiance on microclimate has also been discussed. The model developed has been tested for Hyderabad and Calcutta and found to give encouraging results.

Modelling the Impacts of Climate Change on the Yield of Crops

This study aims to improve the understanding of the impact changes being experienced in our climate system will have on the level of crop productivity in the immediate period as well as in the nearest future. Nigeria was used as a case study and an observed climatic dataset was obtained and used alongside collected 20 year cassava, rice and soybean yield data to develop models that were applied to estimate future crop yield. Four statistically downscaled and bias-corrected Global Climate Models (GCMs): NOAA, MIROC5, ICHEC, and NCC performed simulations for the period 1985–2100 under the Representative Concentration Pathway RCP8.5. These were used to predict how the yields of cassava, rice and soybean will be in the years 2020-2050 and 2070-2100 for the 36 states in Nigeria and the FCT. 89 Empirical models were developed to estimate the yields of the three crops earlier mentioned across Nigeria with their coefficient of determination (R2) ranging between 15% - 99%. The result showed an increase of 3.91% (P<0.001), 0.08, 1.79 (P<0.1) and a decrease of 0.93% for cassava yield for ICHEC, MIROC, NOAA and NCC respectively. It also projected an increase in yield of 8.88% (P<0.001), 7.77% (P<0.001), 6.62% (P<0.001) and 8.85% (P<0.001) for Rice yield using climatic data from ICHEC, MIROC, NOAA and NCC respectively. Soybean, increase in yield are 2.81% (P<0.01), 5.84% (P<0.001), 11.38 (P<0.001) and 9.06% (P<0.001) for ICHEC, MIROC, NOAA and NCC respectively.

CLTS-Net: A More Accurate and Universal Method for the Long-Term Prediction of Significant Wave Height

Significant wave height (SWH) prediction plays an important role in marine engineering areas such as fishery, exploration, power generation, and ocean transportation. For long-term forecasting of a specific location, classical numerical model wave height forecasting methods often require detailed climatic data and incur considerable calculation costs, which are often impractical in emergencies. In addition, how to capture and use the dynamic correlation between multiple variables is also a major research challenge for multivariate SWH prediction. To explore a new method for predicting SWH, this paper proposes a deep neural network model for multivariate time series SWH prediction—namely, CLTS-Net. In this study, the sea surface wind and wave height in the ERA5 dataset of the relevant points P1, P2, and P3 from 2011 to 2018 were used as input information to train the model and evaluate the model’s SWH prediction performance. The results show that the correlation coefficients (R) of CLTS-Net are 0.99 and 0.99, respectively, in the 24 h and 48 h SWH forecasts at point P1 along the coast. Compared with the current mainstream artificial intelligence-based SWH solutions, it is much higher than ANN (0.79, 0.70), RNN (0.82, 0.83), LSTM (0.93, 0.91), and Bi-LSTM (0.95, 0.94). Point P3 is located in the deep sea. In the 24 h and 48 h SWH forecasts, the R of CLTS-Net is 0.97 and 0.98, respectively, which are much higher than ANN (0.71, 0.72), RNN (0.85, 0.78), LSTM (0.85, 0.78), and Bi-LSTM (0.93, 0.93). Especially in the 72 h SWH forecast, when other methods have too large errors and have lost their practical application value, the R of CLTS-Net at P1, P2, and P3 can still reach 0.81, 0.71, and 0.98. The results also show that CLTS-Net can capture the short-term and long-term dependencies of data, so as to accurately predict long-term SWH, and has wide applicability in different sea areas.

Projected Moisture Index (MI) for Tropical Sri Lanka

Atmospheric moisture loading can cause a great impact on the performance and integrity of building exteriors in a tropical climate. Buildings can be highly impacted due to the changing climate conditions over the world. Therefore, it is important to incorporate the projected changes of moisture loads in structural designs under changing climates. The moisture index (MI) is widely used in many countries as a climate-based indicator to guide the building designs for their durability performance. However, this was hardly considered in structural designs in Sri Lanka, even though the country is one of the most affected countries under climate change. Therefore, this study investigates future climate change impacts on the environmental moisture in terms of MI, which can be used in climate zoning, investigating indoor air quality, understanding thermal comfort and energy consumption, etc. The moisture index was found as a function of the drying index (DI) and wetting index (WI) to the whole country for its four rainfall seasons. The temporal and spatial distributions were plotted as MI maps and showcased under two categories; including historical MI maps (1990–2004) and future projected MI maps (2021–2040, 2041–2070, and 2071–2100). Future projected MI maps were constructed using bias-corrected climatic data for two RCP climatic scenarios (RCP4.5 and RCP8.5). Results showed that the temporal and spatial variations of MIs are justifiable to the country’s rainfall patterns and seasons. However, notable increases of MIs can be observed for future projected MIs in two seasons, and thus a careful investigation of their impacts should be assessed in terms of the construction of buildings and various agricultural activities. Therefore, the outcome of this research can be essentially used in policy implementation in adapting to the ongoing climate changes in Sri Lanka.

Investigating the Climate-Growth Response of Scots Pine (Pinus sylvestris L.) in Northern Poland

Research Highlights: This study used a 99-year time-series of daily climatic data to determine the climate-growth relationship for Scots Pine (Pinus sylvestris L.) growing in Northern Poland. The use of daily climatic data improved the calculated climatic response of the trees. Background and Objectives: It was hypothesised that daily temperature and precipitation data would more precisely identify climate–growth relationships than monthly data. We compared our results to a previous study conducted in the 1990s that utilised monthly precipitation and temperature data. Materials and Methods: The chronology construction and data analyses were performed using CooRecorder, CDendro and R packages (dplR, treeclim, dendrotools). Forty-nine cores from 31 trees were included in the final chronology. Results: The precipitation and temperature of March had the strongest influence upon ring-widths. Despite a statistically significant correlation between monthly temperature and ring-widths, reduction of error (RE) and coefficient of efficiency (CE) statistics confirmed that daily data better describe the effect of climate on tree rings width than monthly data. Conclusions: At this site, the growing season of Scots pine has changed with the observed association with precipitation now starting as early as February–March and extending to June–July.