Climatic Zones Research Articles

Validation of satellite-derived solar irradiance datasets: a case study in Saudi Arabia

A robust dataset of Surface Solar Irradiance is essential for secure competitive financing for solar energy projects. Rating agencies and lenders alike require verification of the solar-resource dataset for utilizing each solar energy project, as this can be translated directly into expected electrical energy and revenues. The accuracy of the dataset and the variability of solar radiation, as recorded by historical solar data, play a significant role in estimating the future performance of the project and its budget. The historical observed solar irradiance datasets by local stations are the best and most reliable for a specific site, but they are not always available for long and continuous periods in any location, especially in arid areas. So, the importance of historical solar radiation datasets derived from satellite-based models arises here. This paper validates the historical modeled datasets of the three most famous satellite-based commercial prediction models (SolarGIS, SUNY, and Solcast) against the observed dataset by six ground stations in Saudi Arabia under different climatic zones. The validation method has been implemented using the standard error metrics: Maximum Absolute Error (MAE) and relative Maximum Bias Error (rMBE). The validation process showed that, in the case of GHI, the discrepancy between observed and predicted values is narrow, while in the case of DNI, the discrepancy is wide. Also, the predicted GHI values are more accurate than predicted DNI values, and -in general- the values predicted by the SUNY model are less accurate than those predicted by SolarGIS and Solcast models for both GHI and DNI. The resultant of this validation process could be accepted not for the six locations under study only but, also for deserts and arid areas across Saudi Arabia and might be extended to similar arid areas around the world.

Towards sustainable precipitation management in Madurai Town Planning Area (India)

This study aimed to look for efficient development strategies towards achieving sustainable precipitation management in the Madurai Town Planning Area (MTPA) in India. In the MTPA, precipitation is the predominant source of freshwater for agricultural and domestic uses in addition to water received through conventional systems. The average annual precipitation in the region is ~ 816 mm with standard deviation of 233 mm. The regression analysis showed that annual precipitation is following a decreasing trend and will be about 716 mm by 2030. The Precipitation Concentration Index (PCI) values ranging from 18 to 20 represented strong irregularities and seasonality in annual precipitation over the study area. March, July, September, and November months contributed 2.5, 6.19, 14, and 16% to annual precipitation, respectively, and all showed decreasing trends over time. Only 20% of Northeast monsoon was active over the past 44 years (1976–2019), resulting in a 15% probability of flood and drought occurrences throughout the MTPA. The long-term (1976–2019) average value of annual precipitation days was about 42. The Concentration Index (CI) showed that 60–70% of precipitation was received within 25% of precipitable days. Daily precipitation class of moderate (7.5–34 mm) and rather heavy (34–64 mm) ranges were the dominant types of precipitation, contributing 46% and 26% to total annual precipitation, respectively. Except for October, the monthly precipitation received was deficit to meet the irrigation water demand in the MTPA. However, development of precipitation harvesting systems can annually preserve about 27 Mm3 freshwater in the urban space of the MTPA.

Observed and future shifts in climate zone of Borneo based on CMIP6 models

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950–2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172–2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: ‘Wet and cold’ (WC) and ‘Wet’ (W) representing wetter zones, and ‘Wet and hot’ (WH) and ‘Dry and hot’ (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8–94.6%), MSE (57.0–99.5%), NRMSE (42.7–92.1%), PBIAS (100–108%), MD (23.0–85.3%), KGE (21.1–78.1%), and VE (5.1–9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

Precipitation Seasonality Amplifies as Earth Warms

AbstractPrecipitation exhibits a pronounced seasonal cycle, of which the phase and amplitude are closely associated with water resource management. While previous studies suggested an emerged delaying phase in the past decades, whether the amplified amplitude has emerged is controversial. Using multiple observational data sets and climate simulations, here we show that the amplification of precipitation annual cycle has emerged in most global land areas since the 1980s, especially in the tropics. These amplifications are mainly driven by anthropogenic emissions, and will be further intensified by 17.6% in the future (2081–2100) under high emission scenario (Shared Socioeconomic Pathways, SSP585), and limited to 7.2% under SSP126 scenario, relative to the historical period (1982–2014). Precipitation seasonality will be amplified by 4.2% for each 1°C of global warming, which is seen in all emission scenarios. The mitigation of lower emissions is helpful for alleviating the amplification of precipitation seasonality in a warming world.

Climate change and the built environment - a systematic review

AbstractRecent intergovernmental panel on climate change reports have once again emphasised the effective measures to reduce greenhouse gas emissions and the importance of the built environment. Historically, passive and active solutions are known for their potential to make the built environment more environmentally friendly. Recently, a significant number of studies covered the effectiveness of such solutions under distinct current and different future climate and emission predictions. Through the PRISMA framework, this paper presents a comprehensive state-of-the-art review of such studies within the last 10 years (2013–2023) to understand their impact, their tangible applications, and their empirical evidence. Local ecosystems, weather patterns, geographical and cultural challenges dictate the solutions for a warmer future. Among the solutions, as expected, passive solutions remain most effective even though a combination with active ones is necessary regardless of the context. The review in this paper is expandable beyond the effective reported solutions and it highlights the most effective solutions under different climate zones.

Annual and seasonal precipitation dynamics in the South of Russia in the context of climate change

Annual and seasonal precipitation dynamics in the South of Russia in the context of climate change

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

Seasonality of Birth Weight in Singleton Full-term Births in Finland

Seasonal birth weight has been identified across different climate zones. Finland has wide seasonal and regional variation in climate within the country. We explore the potential seasonality of birth weight in singleton full-term births in Finland, and its variations across time and within the country. We apply descriptive time-series graphs, linear regression across regions, and linear regression with mother fixed effects using Finnish register data consisting of more than 1,800,000 infants born from 1987 to 2021. The descriptive findings indicate a decline in birth weight from 1987 to 2021. A monthly seasonal trend with peaks in spring and autumn and troughs in summer and winter is observed. The pattern gets less distinct with time and shows within-country variations. The seasonal pattern is also present when applying mother fixed effects. We suggest that the seasonal variation is more related to variations in climate than stable characteristics at the family level.

A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery

Abstract. With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis.

Technical and Enviro-economic Analysis of a 0.78 kWp PV System

Aim: To determine the technical, economic and environmental performance analysis of an installed PV backup system. Study Design: To attain this goal, analysis were performed using a freely available PVSyst 6.7.0 software tool. Economic evaluation was performed using respective present worth of individual component prices. Environmental performance comparison is made between the PV system and diesel generator when each is used as an independent backup system. Input in-situ measured data were determined, measured and keyed into the tool. Place and Duration of Study: The system is installed in an institutional building in a tropical climatic zone with coordinates 0.42 N and 35.03 E. The system was studied for a period of one year where data was collected and recorded for techno-enviroeconomic analysis. Methodology: The system consists of four PV modules rated at 0.78 KWp, charge controller and an inverter unit and battery bank, and is utilized as a power backup system to supply electricity whenever power failure occurs, which is frequent and real in Kenya. The techno-enviroeconomic performances analysis of the system were evaluated using the PVSyst software, a freely available design and analytical tool, where input data were measured and recorded at the site. Results: The performance analysis of the system showed that PV array efficiency of 13.24%, FF of 0.68, CF of 21.23%, and PR of 73.63% and generated electrical energy of 1092 kWh/year. The LCOE of PV electricity was 0.059 $/kWh, while total saving of CO2 emission in tons of CO2 was 9.0 tCO2 with LCE value of 331 gCO2/ kWh. Conclusion: Techno- enviroeconomic performance results of the studied PV system show that the system can contribute significantly to the mitigation of CO2 emission. This work also could be helpful for consumers and policy makers in the choice of renewable based or fossil based backup generation systems.

PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping

Abstract. The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping using Sentinel-2 satellite imagery. The Random Forest algorithm is utilized for LCZ classification, with evaluation conducted through spectral separability analysis and accuracy assessment between PRISMA and Sentinel-2 derived LCZ maps as well as with the benchmark LCZ Generator mapping tool. An initial experiment on the effect of PRISMA image pan-sharpening on LCZ spectral separability is also presented. Results obtained for Milan (Northern Italy) demonstrate the potential of hyperspectral imagery in enhancing LCZ identification compared to multispectral data, with promising improvements in LCZ maps overall accuracy. Finally, air temperature patterns within each LCZ class are explored, qualitatively confirming the influence of urban morphology on thermal comfort.

Establishing Strategies to Improve Cracking Resistance of High Recycled Binder Ratio Asphalt Mixtures

This study aims to investigate promising mitigation strategies for improving the cracking performance of high recycled binder ratio (RBR) asphalt mixtures. Moisture-resistant aggregates that do not require anti-stripping agents, reclaimed asphalt pavement (RAP), and recycled asphalt shingles (RAS) were sampled for this study from sources in two climatic zones, south and north. The south-moisture-resistant (SR) mixtures were designed with 0.16 and 0.29 RBRs with RAP, while the north-moisture-resistant (NR) mixtures included 0.21 and 0.37 RBRs with RAP and 0.44 RBR with RAP/RAS. The mitigation strategies evaluated included a softer binder, a different binder source with higher ΔTc, a recycling agent (RA), reduced recycled binder availability (RBA), polymer-modified asphalt (PMA), and hybrid approaches including softer binder + RBA and PMA+RBA. The Indirect Tensile Asphalt Cracking Test (IDEAL-CT) and Disc-Shaped Compact Tension (DCT) test were conducted to capture intermediate-temperature and low-temperature cracking performance, respectively. In addition to the cracking tolerance index (CTindex), an IDEAL-CT interaction diagram analysis was added to further understanding the effect of mixture variables on cracking performance. The findings suggest that while no single strategy works for all RBR mixtures tested, RA, RBA, softer binder, or their combinations yield adequate cracking performance depending on factors including RAP binder stiffness and quantity, RA dose, climatic zone, and RBA.

Temperature Fluctuations in Power conductors: Analysis and Overcurrent Relay Adaptation Strategies for Mitigation

In electrical networks, the efficient flow of energy faces constraints primarily associated with the conductor’s temperature. By assessing transmission infrastructure, identifying critical geographical and climatic zones, one can compute the line’s temperature. Focusing on the importance of maintaining optimal conductor temperature for efficient power transmission, this paper evaluates the critical role of conductor temperature in overhead transmission lines and explores its dynamic variations influenced by various ambient conditions. Emphasizing the significance of maintaining optimal conductor temperature, the study delves into mitigation strategies and adaptive protection mechanism, with a focus on overcurrent relays, with specific emphasis on the efficacy of current-time graphs. By addressing these factors, the research aims to enhance the reliability and efficiency of power transmission systems, ensuring sustained performance under diverse environmental scenarios. Overall, the paper contributes valuable insights to optimize the flow of energy in overhead transmission lines, thereby enhance the robustness also, improving the resilience and functionality of electrical networks.

Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica

Remote sensing-based precipitation products face several challenges in high latitudes and specifically over frozen surfaces (i.e., snow and ice). Consequently, precipitation estimates tend to be lower in quality over these regions, including Antarctica, the coldest continent on Earth. In this study, we developed a method for adjusting precipitation estimates over Antarctica by leveraging CloudSat's ability to capture snowfall compared to other satellite products over snow and ice surfaces. We addressed limitations of CloudSat, such as poor spatiotemporal sampling, noise, and incomplete coverage near the poles. We utilized the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) to guide development, particularly during the period when the TIROS Operational Vertical Sounder (TOVS) was contributing. We adjusted monthly and seasonal TOVS and Atmospheric Infrared Sounder – Infrared (AIRS-IR) precipitation biases over Antarctica at the pixel level, as these two products are the primary inputs for the Global Precipitation Climatology Project (GPCP) in high latitudes. We assessed the adjusted TOVS and AIRS-IR through analyses of geographical maps and time series of monthly and seasonal mean precipitation rates. The results are encouraging, indicating that the proposed approach could replace the current approach used in the GPCP for adjusting precipitation estimates from AIRS-IR and TOVS over Antarctica. Adjusting precipitation estimates from TOVS and AIRS-IR using the proposed approach improves the Kling-Gupta efficiency (KGE) over their entire period by 162% and 147%, respectively. Moreover, the proposed approach can be applied to adjust other precipitation products over Antarctica at the pixel level, including satellites and reanalysis products.

Pea growth, yield, and quality affected by nitrogen fertilization to previous crop in small grain–pea rotations

AbstractStarter N fertilization at low rates may enhance pea (Pisum sativum L.) performance, but little information exists about the effect of N fertilization rate to previous crop (NRPC) on pea growth, yield, and quality in small grain–pea rotations. We examined the effect of N fertilization rates to barley (Hordeum vulgare L.) and spring wheat (Triticum aestivum L.) on succeeding pea growth, yield, and quality in barley–pea and spring wheat–pea rotations from 2006 to 2019 in the semiarid environment of US northern Great Plains. We measured pea plant density, straw and grain yields, harvest index, straw N concentration, grain protein concentration, grain test weight, straw and grain N uptake, and N harvest index. In the barley–pea rotation, pea grain yield increased with increased NRPC. Grain test weight was greater without than with NRPC in 2008 and 2009. In the spring wheat–pea rotation, straw yield was greater for 50 than 0 kg N ha−1 of NRPC in 2014, 2018, and 2019. Grain yield and N uptake were greater with than without NRPC in 2014, 2015, and 2019, but grain test weight varied with NRPC and year. Plant density, harvest index, straw N concentration, grain protein concentration, straw N uptake, and N harvest index varied with year in both barley–pea and spring wheat–pea rotations. Nitrogen fertilization to barley or spring wheat can enhance succeeding pea grain yield and N uptake during years with normal growing season precipitation in barley–pea and spring–wheat pea rotations in the semiarid region of the US northern Great Plains.

Effects of seasonal precipitation regimes on microbial biomass and extracellular enzyme activity during shrub foliar litter decomposition in a subtropical forest

Elucidating the mechanisms underlying microbial biomass and extracellular enzyme activity responses to the seasonal precipitation regime during foliar litter decomposition is highly important for understanding the material cycle of forest ecosystems in the context of global climate change; however, the specific underlying mechanisms remain unclear. Hence, a precipitation manipulation experiment involving a control (CK) and treatments with decreased precipitation in the dry season and extremely increased precipitation in the wet season (IE) and decreased precipitation in the dry season and proportionally increased precipitation in the wet season (IP) was conducted in a subtropical evergreen broad-leaved forest in China from October 2020 to October 2021. The moisture, microbial biomass, and extracellular enzyme activities of foliar litter from two dominant shrub species, Phyllostachys violascens and Alangium chinense, were measured at six stages during the dry and wet seasons. The results showed that (1) both IE and IP significantly decreased the microbial biomass carbon and microbial biomass nitrogen content and the activities of β-1,4-glucosidase, β-1,4-N-acetylglucosaminidase, acid phosphatase and cellulase in the dry season, while the opposite effects were observed in the wet season. (2) Compared with those of IE, the effects of IP on foliar litter microbial biomass and extracellular enzyme activity were more significant. (3) The results from the partial least squares model indicated that extracellular enzyme activity during foliar litter decomposition was strongly controlled by the foliar litter water content, microbial biomass nitrogen, the ratio of total carbon to total phosphorus, foliar litter total carbon, and foliar litter total nitrogen. These results provide an important theoretical basis for elucidating the microbial mechanisms driving litter decomposition in a subtropical forest under global climate change scenarios.

Does the Amount of Pre-Sowing Nitrogen Fertilization Affect Sugar Beet Root Yield and Quality of Different Genotypes?

There has always been a specific focus on nitrogen fertilization in sugar beet production due to its important effect on sugar beet root yield and quality. For stable sugar beet growth and satisfactory root yield and quality, balanced N fertilization is crucial. Thus, this study aimed to investigate spring N fertilization in two seasons as the following treatments: N0—control, N1—only pre-sowing fertilization, and N2—pre-sowing with topdressing. Four different genotypes were included in the study (Serenada, Colonia, Fred, and Danton). The experiment was set up in a plain area, belonging to the temperate climate zone in Eastern Croatia (Županja and Vrbanja), with the long-term mean (LTM) (March–October) air temperature around 16 °C and the total precipitation of 515 mm. Pre-sowing N fertilization had a smaller impact on root yield in the year with higher precipitation (31% higher than LTM). Therefore, the average yields with pre-sowing fertilization (N1) and pre-sowing fertilization with top dressing (N2) were very similar and were only 7% higher than those of the control. In a season with less rainfall (29% less than LTM), pre-sowing fertilization with top dressing (N2) had a more pronounced effect on the increase in sugar beet root yield, which was 17% higher compared to that of the control treatment. The sugar beet sucrose content and quality parameters (brei impurities, loss of sugar in molasses, extractable sugar) differed when N fertilization was applied among locations in both seasons. The white sugar yield was the highest at N2 treatment with pre-sowing and topdressing N fertilization. In general, according to the average of all locations and years of research, the Serenada hybrid achieved the highest average root yield (81.1 t ha−1), while Colonia exhibited the highest root sugar content (14.5%) and white sugar yield (9.7 t ha−1).

Urban Morphology Influencing the Urban Heat Island in the High-Density City of Xi’an Based on the Local Climate Zone

Urban form plays a critical role in enhancing urban climate resilience amidst the challenges of escalating global climate change and recurrent high-temperature heatwaves. Therefore, it is crucial to study the correlation between urban spatial form factors and land surface temperature (LST). This study utilized Landsat 8 remote sensing data to estimate LST. Random forest nonlinear analysis was employed to investigate the interaction between the urban heat island (UHI) and six urban morphological factors: building density (BD), floor area ratio (FAR), building height (BH), fractional vegetation coverage (FVC), sky view factor (SVF), and impervious surface fraction (ISF), within the framework of local climate zones (LCZs). Key findings revealed that Xi’an exhibited a significant urban heat island effect, with over 10% of the study area experiencing temperatures exceeding 40 °C. Notably, the average LST of building-class LCZs (1-6) was 3.5 °C higher than that of land cover-class LCZs (A-C). Specifically, compact LCZs (1-3) had an average LST 3.02 °C higher than open LCZs (4-6). FVC contributed the most to the variation in LST, while FAR contributed the least. ISF and BD were found to have a positive impact on LST, while FVC and BH had a negative influence. Moreover, SVF was observed to positively influence LST in the compact classes (LCZ2-3) and open low-rise class (LCZ6). In the open mid-rise class (LCZ5), SVF and LST showed a U-shaped relationship. There is an inverted U-shaped relationship between FAR and LST, with the inflection point occurring at 1.5. The results of nonlinear analysis were beneficial in illustrating the complex relationships between LST and its driving factors. The study’s results highlight the effectiveness of utilizing LCZ as a detailed approach to explore the relationship between urban morphology and urban heat islands. Recommendations for enhancing urban climate resilience include strategies such as increasing vegetation coverage, regulating building heights, organizing buildings in compact LCZs in an “L” or “I” shape, and adopting an “O” or “C” configuration for buildings in open LCZs to aid planners in developing sustainable urban environments.

Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends

Abstract. There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail.

Liebig review: The role of mineral nutrients in the development of Pseudomonas syringae diseases—Lessons learned and implications for disease control in woody plants

AbstractBackgroundThe plant pathogen Pseudomonas syringae (Ps) causes diseases in a broad range of hosts including important cash crops from several climate zones. In particular for woody crops, effective and environmentally friendly disease control strategies are not available. Although there is increasing evidence of a link between plant nutritional status and disease susceptibility, optimization of plant nutrition is often neglected in efforts to control Ps diseases. This review brings current knowledge on this topic together with the aim of facilitating the development of treatment recommendations in specific contexts.Literature reviewThe article consists of three parts. First, we compiled data on the impact of mineral nutrients on Ps disease severity in woody and herbaceous species. Next, we discuss how nutrients may be related to plant defense and/or Ps virulence. Last, we consider these findings in the context of woody hosts and give suggestions for future research.ConclusionsWe encourage a research focus on typical nutrient imbalances (deficiencies and surpluses) in specific orchard regions; testing the role of foliar fertilizers in spring (the period of highest infection risk); analyses of the interaction between nutrient supply and the microbiome in the phyllosphere; investigating the interaction between nutrient supply and other control measures, and the impact of nutrient supply on Ps diseases in the presence of other stress factors.