Average Climatic Conditions Research Articles

Evaluating the tradeoffs between water conservation, aesthetic value, evaporative cooling and CO2 emissions in St. augustinegrass and buffalograss

Urban lawns comprise a significant portion of urban greenery and provide several ecosystem services. Nevertheless, maintaining lawns comes with significant water costs in semi-arid inland southern California, as they require consistent irrigation to stay healthy and productive. The main objective of this study was to evaluate the effect of a wide range of irrigation rates and frequencies applied autonomously by a smart ET-based controller on the aesthetic value, cooling potential, and CO2 efflux of two warm-season turfgrass species. For three years, we studied the responses of Buffalograss and St. Augustinegrass to six irrigation rates and two irrigation frequencies in Riverside, California. Under historical average climate conditions, the minimum irrigation rate for Buffalograss was 93 % ETo, and for St. Augustinegrass, it was 74 % ETo. Under projected future climate conditions, the estimated minimum irrigation rate for Buffalograss did not change, but for St. Augustinegrass, it increased by 4 % and 7 % in 2100 under low emission (RCP 4.5) and high emission (RCP 8.5) scenarios, respectively. On average, canopy minus air temperature in Buffalograss was 6.2 ℃, and in St. Augustinegrass, it was 1.1 ℃. The average CO2 efflux in Buffalograss was 122.3 µg CO2-C m−2 s−1, and in St. Augustinegrass, it was 182.8 µg CO2-C m−2 s−1. Our results showed that turfgrass aesthetic values, cooling potential, and CO2 efflux diminished as the irrigation rate decreased, but at different rates for each turfgrass species.

Examining Crop Yield Losses in Iğdır Plain Irrigation Systems in Türkiye Amidst Water Constraints

Water stands as a crucial component of agricultural production. This study aims to propose water efficiency measures in agriculture as an essential element for climate change adaptation. In this study, yield losses in staple crops in agricultural regions are analyzed by using the most suitable methodologies, particularly in agriculture-reliant developing nations. Furthermore, this study seeks to determine the financial consequences of such losses. The methodology applied for this purpose was implemented in Türkiye’s Iğdır Plain, selected as the study site. As the first step, the yields of the first three most cultivated products in each product group were assessed under normal climatic conditions in terms of their crop water requirements and irrigation water requirements. Subsequently, the irrigation water supply was reduced by 10%, and the resulting yield losses were calculated per hectare. Then, the overall crop losses after applying the 10% water constraint were determined in the total cultivation area. Among the crops cultivated in the region, the analysis reveals that clover from the field crops category exhibits the highest water dependence, while apricot demonstrates the least reliance on water resources. As a result, the recommended crop rotation for the Iğdır Plain under water constraints comprises wheat, apricot, watermelon, maize, melon, apple, tomato, peach, and clover. The following measures are proposed to ensure sustainable use of water resources and reduce exposure to climate change: increasing the water transmission efficiency and water use efficiency in irrigation areas, allocating more space to water-stress-resistant crops in the crop pattern in basins, and substituting crops requiring excessive water with less water-dependent crops.

Thermal stratification characteristics and cooling water shortage risks for pumped storage reservoir–green data centers under extreme climates

Utilizing the thermal stratification of reservoirs to obtain cold water for cooling green data centers (GDCs) is a new mode of energy conservation and emission reduction. However, global warming is expected to alter this phenomenon in deep reservoirs and thus may affect the digitalization process. While the frequency and intensity of extreme climate (EC) events are increasing under a changing climate, the impacts of these highly destructive events on thermal stratification and the related cascading effects on GDC cold-water supplies are still unclear. A two-lateral-dimensional thermohydrodynamic model was established to determine the characteristics of reservoir thermal stratification changes and its potential water environment impacts based on the heat extreme experienced by the Jinshuitan Reservoir, a large, pumped storage power station (PSPS) with a GDC in southeastern China. Fourteen different PSPS inlet/outlet schemes under 2 climate scenarios were designed, and two new indexes, namely, the risk index of cold-water shortage and the index of thermal stratification, were proposed to explore the impact of extreme climates on GDC water extraction. The results showed that compared to the present climate conditions, the reservoir experienced stronger thermal stratification with a 20.6 % average increase in thermal stability, a longer thermal stratification duration with a transitional trend from seasonal to permanent stratification, and a thinner cold-water layer with a 15-m average decrease. Changes in the thermal stratification of reservoirs under EC conditions will benefit some species, such as diatoms (except for early July and the period from mid-August to early October), green algae and cyanobacteria and the migration of warm-water fish, but adversely impact dissolved oxygen, diatoms and cold-water fish. Furthermore, the risk of cold-water shortages under EC conditions was consistently greater than that under average climate (AC) conditions (traditional assessment) when the elevation of pumped storage intake/outlet was less than 145 m. According to the traditional assessment method, the intake/outlet elevation of pumped storage reservoirs resulted in a 60.27 % increase in the risk of cold-water shortages during climate extremes. Furthermore, the demand for green cold-water withdrawal and optimal pumped storage power generation benefits were met when the PSPS intake/outlet elevation was 145 m. Beyond solving this specific case, this study elucidates the importance of linking the thermal stratification and data center of cold-water withdrawal to ECs. These findings provide new insights for increasing climate change resilience.

Evaluation of a concentrated solar power plant under meteorological and climatological forcing

The performance of renewable energy-conversion systems depends strongly on the background climate and ambient meteorological conditions. However, common approaches to simulating such systems use aggregated climatic data as forcing. Given strong variability in the climate, along with possible future long-term changes, it is important to understand the trade-offs between high-resolution forcing data and representative data. Here we simulate a concentrated solar power plant driven by hourly-resolved reanalysis data using both air and water-based cooling systems. These simulations allow us to analyze plant performance under realistic meteorological variability at two locations. In addition, we simulate the plant under average climatic conditions, where all sub-seasonal and interannual variability has been removed. These simulations are cheaper to run, but do not represent extreme values in the forcing. Our analysis shows that variability in the direct normal irradiance (DNI) is a critical factor for simulating the plant realistically. We show that where (and when) DNI variability is low, average forcing provides a realistic picture of power output. Average forcing can also provide a good estimate of how the plant will perform on average. However, in cases where understanding how robustly the plant performs, it is critical to include realistic variability in the input data.

Social Work Study on the Impact of Climate Change Among Communities in Kampung Pantai Johor, Kedah

Climate change in Malaysia has led to the occurrence of flooding disasters that have a negative impact on all parties, especially the vulnerable groups, including the community in Pantai Johor Village, Kedah. Climate change refers to significant changes in average climate conditions or its variability over a long period (usually near or more). The flooding disasters have caused the Malaysian government to incur significant expenses to assist flood victims. Therefore, a qualitative study was conducted on ten respondents in Pantai Johor Village, Kedah, who were heads of households selected through purposive sampling. In-depth interviews were conducted to obtain the necessary information. All collected data were analyzed using thematic analysis methods. The study findings indicate that flooding disasters have an impact on the community's life in Pantai Johor Village, Alor Setar, Kedah. The study found that all respondents stated that climate change has an impact on their lives. Climate change in this village has led to flooding disasters, affecting the community. For the first objective of the study, the researcher successfully answered it through discussions of the study findings. The conclusions that can be drawn by the researcher based on the study's objective, which is to examine the impact of climate change on the community in Pantai Johor Village, Kedah, are that climate change brings various negative effects on the community's life in the village. The climate change that occurs has caused economic problems and disrupted the well-being of the community.

Innovative solutions for agro-industrial complexes in extreme conditions

The article discusses the spatial and architectural organization of agro-industrial complexes of different levels of equipment on the example of domestic and foreign experience. The existing technological chains of production solutions used in the following crop complexes are also analyzed. Specific examples of Russian practices in the field of agrarian economy are given, not so much in normal climatic natural conditions, but in remote harsh and remote regions of the country. The article presents foreign illustrative examples of agro-industrial complexes, unusual for Russian experience in terms of external and internal technical content.

ВЛИЯНИЕ СМАЗОЧНЫХ МАТЕРИАЛОВ НА ИЗНОСОСТОЙКОСТЬ СТАЛЬНЫХ ОЦИНКОВАННЫХ КАНАТОВ, ЭКСПЛУАТИРУЮЩИХСЯ ПРИ ОТРИЦАТЕЛЬНЫХ ТЕМПЕРАТУРАХ И В МОРСКОЙ ВОДЕ

The paper objective is to find out the effect of various lubricants on the wear resistance of galvanized steel ropes manufactured according to GOST 2688-80 Two lay rope type LK-R construction 6 19(1 +6+6/6)+ 1 o. c. Dimensions. The research was carried out on a machine manufactured according to GOST 2387-80 Stell Ropes. Method of Endurahce testify under normal conditions, at negative temperatures and under periodic exposure to seawater. It is shown that many rope lubricants used by Russian metallurgical enterprises for impregnating galvanized steel ropes, when periodically exposured to seawater, reduce their wear resistance from 16 to 34% in relation to non-lubricated ropes. Only the universal rope lubricant Rosoil-K112 showed an increase in the wear resistance of galvanized steel rope by 23%. It was found that lubricants that maximize the wear resistance of galvanized steel rope during its operation in normal climatic conditions turned out to be completely ineffective at negative temperatures or when exposured by seawater. As a result of the work carried out, it was revealed that in order to increase the wear resistance of galvanized steel rope, additional studies are needed to investigate the interaction of galvanized coating and lubricating material under conditions of periodic exposure to seawater, as well as industrial tests of galvanized steel rope lubricated with universal rope lubricant Rosoil-K112.

Drought Assessment and Monitoring for Rajshahi District Using Meteorological Drought Indices

An analysis for drought condition has been made in the Rajshahi district of Bangladesh. The assessment of drought for the country has become an urgent need. The agriculture of the country is dependent on the amount of precipitation. As drought and precipitation are inversely related so it is desired to know the condition of Rajshahi. The main characteristics that define drought are the absence of precipitation for a lengthy period and a low distribution of rainfall in contrast to the normal climatic condition. The monthly maximum average temperature data, monthly minimum average temperature data and total monthly precipitation data for the last 48 years (1971-2019) from Bangladesh Meteorological Department (BMD) are utilized for the analysis process. An online software named DrinC is preferred due to its open-source platform and its user-friendly interface. The meteorological data serve as inputs for the software. The Deciles Index (DI), the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) have been used to identify the drought condition of Rajshahi. The values of the DI and SPI are generated from the precipitation data. However, for the generation of the RDI, the precipitation data as well as the potential evapo-transpiration (PET) data, which is an intermediate data that is generated from the two different types of temperature data via DrinC software are used. The values of SPI, RDI and DI values are presented in a tabular as well as in graphical form. From the comparison of these data and from the study of the recurrence interval the state and the repetition of drought in Rajshahi district can be determined.

Comprehensive evaluation of terrestrial evapotranspiration from different models under extreme condition over conterminous United States

Terrestrial evapotranspiration (ET) is a key process in the water, energy, and carbon cycles. ET products are becoming more widely accessible with the development of remotely sensed inversions and availability of land surface assimilation information. However, the response of these products to extreme weather is not yet clear and challenging to evaluate. This study evaluated and compared the consistency and uncertainty of nine evaporation products (including GLEAM3.6b, reliability ensemble averaging (REA), CLSM, ERA5-Land (ERA5), MOD16A2, PML-V2 (PML), NOAH, FLDAS, and Synthesized (Syn)) for the period 2003–2015. Eddy covariance (EC) flux observations and the three-cornered hat (TCH) method were used to validate and assess the uncertainty of these products at both the point-scale and grid-scale under normal climatic conditions. More importantly, we conducted uncertainty assessments of ET products at daily and monthly scales under extreme climatic conditions (including high temperature (Temp), high vapor pressure deficit (VPD), and drought), referencing 50 EC measurements over the conterminous United States (COUNS). The results showed that the accuracy of different ET products varied with cover type. Compared to other products, GLEAM3.6b, REA, and Syn showed superior results for most land cover types. Specifically, GLEAM3.6b showed higher performance for the grasslands; REA showed improved performance in mixed forests, open shrubs, and wetlands, and Syn fared better in forests and croplands. The point-scale indicated that the lowest root mean square deviations (RMSD) come from Syn grid products (mean 16.13 mm/month), followed by GLEAM3.6b (mean 18.53 mm/month) and REA (mean 19.42 mm/month). The highest RMSD (mean 49.49 mm/month) originated from ERA5, while the lowest R was found in MOD16A2. The uncertainty ranking calculated using the TCH method is similar to the findings of the point-scale evaluation based on EC measurements, with Syn recording the lowest uncertainty of 5.13 mm/month, after GLEAM3.6b (5.72 mm/month), followed by REA (6.24 mm/month), and ERA5 (12.26 mm/month) with the largest uncertainty. When extreme weather conditions were encountered, the uncertainty of the nine ET products showed an increasing trend, and their consistency varied. Under high-temperature and high-VPD conditions, the consistency decreased, while during drought, the consistency showed a slight increase. Among the ET products, GLEAM3.6b performed best under high-temperature and high-VPD conditions, closely followed by REA. However, ERA5 exhibited the largest error in its estimations. On the other hand, when drought conditions occurred, GLEAM3.6b, REA, and Syn showed better performance, while CLSM, NOAH, and FLDAS ranked second, ERA5 had the largest uncertainty, and MOD16A2 and PML exhibited the worst consistency. The findings of this study offer a foundation for selecting appropriate ET products for hydrologic analysis and agricultural water resource management in the CONUS and give developers suggestions for lowering the uncertainty of ET products in the face of extreme climatic conditions.

Meteorology-driven PM2.5 interannual variability over East Asia

Atmospheric fine particulate matter (PM2.5) is a human health risk factor, but its ambient concentration depends on both precursor emissions and meteorology. While emission reductions are used to set PM2.5-related health policies, the effect of meteorology is often overlooked. To explore this aspect, we examined PM2.5 interannual variability (IAV) associated with meteorological parameters using the long-term simulation from the Community Earth System Model (CESM1), a global climate-chemistry model, with fixed emissions. The results are subsequently contrasted with the MERRA-2 reanalysis dataset, which inherently considers emission and meteorology effects. Over continental East Asia, the CESM1 domain-average PM2.5 IAV is 6.7 %, mainly attributed to humidity, precipitation, and ventilation variation. The grid-cell PM2.5 IAVs over southern East China are larger, up to 12 % due to the more substantial influence of El Niño-induced meteorological anomalies. Under such climate extreme, sub-regional PM2.5 concentration may occasionally exceed WHO air quality guideline levels despite the compliance of the long-term mean. The simulated PM2.5 IAV over continental East Asia is ~25 % of that derived from the MERRA-2 data, which highlights the influence of both emission and meteorology-driven variations and trends inherent in the latter. Although emission-driven variability is significant to PM2.5 IAV, in remote areas downwind of major source regions in East Asia, North America, and Western Europe, the MERRA-2 data revealed that meteorological variations contributed more to PM2.5 IAV than emission variations. Thus, when setting policies for complying with the WHO PM2.5-related air quality guideline levels, the highest annual PM2.5 associated with climate extremes should be considered instead of that based on average climate conditions.

Assessing the morphodynamic response of a New England beach-barrier system to an artificial reef

As a result of climate change and the ensuing accelerating sea level rise, the Ninigret Trustom Pond beach barrier system, located along the southern shore of Rhode Island, USA, has become increasingly vulnerable to tropical storms, hurricanes, and Nor’easters affecting the region. In this work, we explore the ability of a segmented artificial reef (AR) to protect the Ninigret Trustom Pond beach barrier system, by reducing the incident wave energy and ultimately the beach erosion. A set of nine AR concepts are selected, which are predicted to be accretive based on Ranasinghe et al.’s (2010) design criterion (RVB), extended by van der Baan (2013) to apply to segmented submerged breakwaters. The effectiveness of each concept is then assessed through numerical simulations, for both average wave climate conditions and storm events, with and without sea level rise, using the 2D morphodynamic model XBeach. The AR concepts are evaluated based on their ability to reduce coastal erosion during the storm events, including with a near future sea level rise of 0.31 m (scenario based on local prediction by 2050 relative to 2020 following the intermediate NOAA scenario) , while ensuring a slightly accretive behavior in average wave conditions. The reduction of the coastal erosion during the storm events is quantified by comparing the subaerial eroded volume at the site, with or without sea level rise, when implementing each of the nine pre-selected AR concepts. While all concepts are found to protect the shoreline during storm events, including with the 0.31 m sea level rise scenario, four of the nine AR concepts, pre-selected to be accretive based on the RVB criterion, are found to be erosive in average conditions. Assuming a given submergence level, the distance to the shoreline and the AR lateral confinement ratio, LC, (gap width to length ratio of the segments) are the main controlling parameter.

Investigating the effect of Manufactured sand and Silica Fume on the properties of Concrete

In the present era, concrete is one of the most commonly used construction materials worldwide. Consequently, the demand for natural river sand is increasing. Since the mining of natural sand has already been outlawed by the government in many regions, now it is essential to look into sustainable materials to control natural sand extraction. Hence, manufactured sand (MS) has been shown in numerous studies to be a more practical and environmentally responsible alternative to river sand (RS), which is normally used in the production of concrete. At the same time, the cement industry produces tons of greenhouse gases into the atmosphere, affecting atmospheric conditions. Now, it’s time to look into a suitable replacement material for cement. To investigate the feasibility of using manufactured sand & silica fume in place of river sand and cement under normal climatic conditions fresh, mechanical and durability properties were conducted in the laboratory. Twelve samples of M30 grade cubes and cylinders at desired M sand percentages of 0, 20, 40, 60, 80, and 100 & 0% and 5% variation of silica fume with cement are included in this study as a comparison to the control mix. After 28 days of water curing, a random variation in the properties of concrete was observed in the samples. In this experimental study, SF represents Silica Fume, MS represents manufactured sand, RS represents river sand, FA represents Fine aggregates and CA represents Coarse aggregates.

Climate oscillation impacts on water supply augmentation planning

Climate oscillations ranging from years to decades drive precipitation variability in many river basins globally. As a result, many regions will require new water infrastructure investments to maintain reliable water supply. However, current adaptation approaches focus on long-term trends, preparing for average climate conditions at mid- or end-of-century. The impact of climate oscillations, which bring prolonged and variable but temporary dry periods, on water supply augmentation needs is unknown. Current approaches for theory development in nature-society systems are limited in their ability to realistically capture the impacts of climate oscillations on water supply. Here, we develop an approach to build middle-range theory on how common climate oscillations affect low-cost, reliable water supply augmentation strategies. We extract contrasting climate oscillation patterns across sub-Saharan Africa and study their impacts on a generic water supply system. Our approach integrates climate model projections, nonstationary signal processing, stochastic weather generation, and reinforcement learning-based advances in stochastic dynamic control. We find that longer climate oscillations often require greater water supply augmentation capacity but benefit more from dynamic approaches. Therefore, in settings with the adaptive capacity to revisit planning decisions frequently, longer climate oscillations do not require greater capacity. By building theory on the relationship between climate oscillations and least-cost reliable water supply augmentation, our findings can help planners target scarce resources and guide water technology and policy innovation. This approach can be used to support climate adaptation planning across large spatial scales in sectors impacted by climate variability.

Soil properties zoning of agricultural fields based on a climate-driven spatial clustering of remote sensing time series data

The identification of zones within an agricultural field that respond differently to environmental factors and agronomic management is a key requirement for the adoption of more precise and sustainable agricultural practices. Several approaches based on spatial clustering methods applied to different data sources, e.g. yield maps, proximal sensors and soil surveys, have been proposed in the last decades. The current availability of a huge amount of free remote sensing data allows to apply these approaches to agricultural areas where ground or proximal data are not available. However, in order to provide useful agronomic management information, it is essential that the zoning obtained by clustering is linked to the underlying spatial variability of soil properties. In this work we explore the hypothesis that the response of crop vigor to temporal climate variability, assessed by remote sensing data time series, selected to correspond to specific growth phases and seasonal climate patterns, provides indications on the variability of soil properties within agricultural fields, for both herbaceous and tree crops. NDVI time-series for 38 years (1984–2021) were obtained for fourteen non-irrigated herbaceous and tree crop fields in Central Italy, from multispectral satellites data (Landsat 5/7/8, Sentinel 2). The Standardized Precipitation-Evapotranspiration Index (SPEI) was used to classify time series into three climatic classes (dry/normal/wet) for five different periods of the growth season, covering the main phenological phases. K-means clustering was used to identify patterns of crop growth from climatically classified image sets, as well as for all the bulked images for comparison (bulk clustering). Clustering results were compared with soil maps obtained from spatialized ground data, for soil texture (clay, silt and sand), soil organic matter and available soil water (ASW). The agreement between the different clustering results and soil maps was assessed by the Adjusted Rand Index. Agreement with soil maps varied depending on the field, the phenological phase considered and the soil property considered. Climate driven clustering from long, late growth season periods best matched soil properties, both for herbaceous and tree crops, despite being based on a limited number of images. The clustering from images spanning a longer growth period for dry years systematically outpaced the bulk clustering for silt, sand and ASW, while the clustering for normal climatic conditions was the best for organic matter. The performance of the matching between clustering and soil maps increased with soil variability significantly more (P < 0.05) than in the bulk clustering (mean slopes respectively 0.468 ± 0.167; 0.113 ± 0.270). The integration of the SPEI climatic index into the clustering procedure systematically improved the identification of zones with homogeneous soil properties, highlighting that a greater attention should be posed to the climate-crop-field interactions when using remotely sensed images.

Urbanization Heat Flux Modeling Confirms It Is a Likely Cause of Significant Global Warming: Urbanization Mitigation Requirements

Recent ground-based measurements find the magnitude of the urbanization effect on the global average annual mean surface air temperature corresponds to an urbanization contribution of 12.7%. It is important to provide modeling to help understand these results as there are conflicting concerns. This study models the global warming contribution that urbanization heat fluxes (UHF) can make due to anthropogenic heat release (AHR), and solar heating of impermeable surface areas (ISAs), with additional secondary effects. Results help explain and support ground-based observations. Climate models typically omit anthropogenic heat release (AHR) as warming estimates are below 1%. In agreement, the baseline assessment in this paper has similar findings. However, in this study, the methods of climate amplification estimates (MCAE) with data-aided physics-based amplification models are used. When the MCAE are applied at the global and microclimate levels that consider greenhouse gases (GHGs), feedback, and other secondary effects; the results show that AHR fluxes can amplify, increasing to have an estimated global warming (GW) influence of 6.5% from 1950 to 2022 yielding a 0.9% decade−1 increase. This increasing rate due to energy consumption is found as anticipated to be reasonably correlated to the increasing population growth rate over this time. Furthermore, using the MCAEs, this paper studies heat fluxes assessment due to solar heating of unshaded impermeable surfaces including likely secondary amplification effects. Impermeable surface areas (ISAs) such as asphalt roads, roofs, and building sides have been reported with high land surface contact temperatures (LSCTs) relative to non-ISAs and significantly found to contribute to urbanization warming. Results indicate that high-temperature unshaded impermeable surfaces (including building sides) are estimated to average around 10–11 °C above the earth’s ambient temperature of 14.5 °C (showing albedo ISA estimates between 0.133 and 0.115 respectively); the ISA heat fluxes with secondary effects are estimated to have about a GW influence of 6.5%. This is broken down with average contributions of 4.0% from urban ISAs and 2.5% from rural ISA heat fluxes. Asphalt road ISA heat fluxes are estimated to have about a 1.1% global warming influence. Then the total UHF effect from ISAs and AHR with secondary effects is assessed in modeling to yield a combined average GW influence of 13% helping to confirm ground-based measurement results. Several key adjustment values were used for shading, cloud coverage, and rural-to-urban ISA ratios. Microclimate GHGs and related water vapor feedback (WVF) were assessed to increase urban warming by about 50%. As well an assessment of water vapor and radiation increases from UHF is provided. This study also shows the need to incorporate urbanization heat fluxes with secondary effects into climate models and indicates the necessity for Paris Agreement urban heat flux mitigation goals. Results also found that given average climate conditions, it is possible to mitigate much of the UHI effect with an albedo increase of 0.1 that is anticipated to lower the average impermeable surface temperatures by about 9 °C. Studies show this can be accomplished with cost-effective cool roads and roofs. Although roads are only estimated to occupy 14% of ISAs, changing roads from asphalt to concrete-type surfaces would improve reflectivity by about a factor of 5 and is estimated to mitigate about 5.5% of global warming. Unfortunately, the current overuse of black asphalt on pavements and roofs is highly dangerous to our environment causing UHI increases in heatwaves, excessive temperatures, and global warming issues and should be banned. Asphalt usage also reduces opportunities for solar geoengineering of urbanization.

Impact of Heating System Local Control on Energy Consumption in Apartment Buildings

Improving the energy efficiency of residential buildings is ensured by improving the thermal protection characteristics and the wider introduction of control tools. In order to take into account the influence of individualisation of operating conditions, the study of the regulation of heating distribution of a single-pipe system with bypassed heating devices of a 5-storey typical for Ukraine mass building of the 80s after thermos-modernisation for conditions in Kyiv and Warsaw (Poland) was carried out. On the basis of the simulation model created in the Mathcad software environment for design/rated and average climatic conditions, the influence of internal heat flows into adjacent rooms on the distribution of energy consumption, changes in internal air temperature under the influence of local changes in the heating device flow coefficients were studied. The developed approaches, and the results obtained on the influence of local regulation of devices of vertical single-pipe heating systems can be used to clarify the distribution of energy consumption for heating between individual zones of apartment buildings.

Does competition management improve the ecophysiological response to water shortage of mixed woodland species of North Patagonia?

Globally, the increasing forests vulnerability and drought-induced forest mortality events, extended to a larger scale, may have the potential to rapidly alter forest functioning and the provision of ecosystem services. Patagonian forests in Southern South America constitute an extensive reservoir of wildlife, but at the same time they have high productive potential. These low-mixed forests are composed of co-dominant species that are highly disturbed by local population, both for cattle raising and wood extraction; and different efforts are being dedicated to improve their management. Very little is known about the response of their different woody species to climatic variation, particularly to severe drought in particular, that may guide management strategies to improve productivity and resilience in context of drought stress and climate change. The present study aimed at characterizing the physiological strategies of response to drought to identify the most vulnerable and most resilient species to drought and how they respond under different competition levels. The response of four species of the Andean Patagonian low mixed forest was quantified at three moments of the 2018–2019 growing season in three sites through measurements of the net carbon exchange, vulnerability to xylem cavitation, tissue water relations, and damage of the photosynthetic apparatus. Additionally, records of average monthly values of precipitation and air temperature were used to calculate a standardized precipitation-evapotranspiration index. The normal climatic conditions of NW Patagonia region are characterized by water deficits during the spring-summer growing season, but the studied season presented lower values of standardized precipitation-evapotranspiration index respect to the historical mean values, indicating drier than normal conditions. Analysis of the tissue water relations showed that, in all cases, competition had no effect over the different studied parameters. There were species-specific differences in the models fitted to the vulnerability to cavitation curves; as a whole, all the species differed from each other. Overall, for all the physiological response traits studied, the Site, Species, and Season factors were important predictors in the minimum adequate model. Although there is an effect of competition on the individual annual growth, that effect is not evidenced in physiological variables of punctual measurement. Our results suggest that the effect of summer drought cannot be modulated by density management. The productivity of the species of interest can be improved, but not their adaptability, at least to severe events such as the one of the year of study.

Impacts of Climate Variability and Change on Sorghum Crop Yield in the Babile District of Eastern Ethiopia

The impacts of various climatic conditions, such as temperature and rainfall variabilities, are very critical and sensitive to rain-fed crop production, particularly over the water stress arid and semi-arid regions of Ethiopia. This study was designed to evaluate the potential impact of climate variability and change on sorghum grain yield in the Babile district of eastern Ethiopia. The study was conducted based on observed and model-based simulated projected rainfall and temperature obtained from the Ethiopian Meteorological Institute and General Circulation Models (GCM) used by the Intergovernmental Panel on Climate Change (IPCC) of the Fifth Assessment Report CMIP5) and Agricultural Model Inter-comparison and Improvement Project (AgMIP). Three GCM models, namely GFDLESM2M, CanESM2, and HadGEM2-ES under RCP4.5, were considered to generate future climate projections for the near-term 21st century. Various univariate and multivariate statistical techniques were employed to compute and identify whether the impacts of climate variability and change on rain-fed sorghum crop performance were reasonably viable over the districts where grain yield is highly stable and productive under normal climate conditions. Our findings revealed that more stable and better rainfall performance from May to September, the season when sorghum crops are normally planted in the Babile district, was positively correlated, while the maximum and minimum temperatures of the season were negatively correlated with sorghum grain yield. A significant association has been detected between sorghum grain yield and its growing period rainfall, number of rainy days, and maximum and minimum temperature with multi-regression analysis. Thus, the variability of rainfall in August, June temperature, and the number of rainy days in September significantly impacted sorghum crop productivity. As a result, the multi-regression model adjusted R-squared indicated that 77% variance in annual sorghum yield performance was explained by rainfall and temperature conditions that prevailed during the crop growing period. During the past period, there was a significant increase in sorghum yields, which are projected to decline during the near term of the 21st century in the future. This revealed that declining and disturbed rainfall performance and increases in temperature are likely to reduce overall sorghum grain yield in the Babile district. We recommend that there is a need to enhance awareness for smallholder farmers on the adverse impact of climate variability and change on sorghum grain yield. In view of this, the farmers need to be geared toward employing climate-smart agriculture as a possible adaptation measure to reduce the negative impacts of climate variability and change on rain-fed crop production practices in the Babile district and other arid and semi-arid parts of eastern Ethiopia.

The Relationships between Climate, Tree-Ring Growth, and Cone Production in Longleaf Pine

Historically abundant longleaf pine (Pinus palustris Mill.) trees were once a leading source of profit and ecosystem services across the southeastern United States. The widespread decline in longleaf numbers following European colonization has prompted substantial restoration efforts, though much is still not understood about longleaf growth and reproductive processes. In this study, we used Pearson and regression correlation analysis to quantify the relationship between cone production, radial growth, and climate signals in longleaf pine trees at three sites across their range. We documented a high amount of intersite variability; trees at all three sites experienced significant relationships between reproduction, radial growth, and climate, though in different and sometimes contrasting ways. We found a roughly equivalent number of significant cone growth and climate correlations with extreme climate events (e.g., heat stress, hurricane frequency) as with average climate conditions, and highlight the need to consider both over multiple spans of time. This study provides a new understanding of how climate variables relate to the relationship between growth and reproduction in longleaf pine trees.

Riparian forest response to extreme drought is influenced by climatic context and canopy structure

Droughts significantly impact forest ecosystems, reducing forest health and productivity, compromising ecosystem functioning, and nature-based solutions for climate change. The response and resilience of riparian forests to drought are poorly understood despite their key role in the functioning of aquatic and terrestrial ecosystems. Here we investigate riparian forest drought responses and resilience to an extreme drought event at a regional scale. We also examine how drought event characteristics, average climate conditions, topography, soil, vegetation structure, and functional diversity shape the resilience of riparian forests to drought. We used a time series of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to calculate the resistance to and recovery after an extreme drought (2017–2018) in 49 sites across an Atlantic-Mediterranean climate gradient in North Portugal. We used generalized additive models and multi-model inference to understand which factors best explained drought responses. We found a trade-off between drought resistance and recovery (maximum r = −0.5) and contrasting strategies across the climatic gradient of the study area. Riparian forests in the Atlantic regions showed comparatively higher resistance, while Mediterranean forests recovered more. Canopy structure and climate context were the most relevant predictors of resistance and recovery. However, median NDVI and NDWI had not returned to pre-drought levels (RcNDWI mean = 1.21, RcNDVI mean = 1.01) three years after the event. Our study shows that riparian forests have contrasting drought response strategies and may be susceptible to extended legacy effects associated with extreme and/or recurring droughts, similarly to upland forests. This work highlights the drought vulnerability of riparian ecosystems and emphasises the need for further studies on long-term resilience to droughts.