Local Climate Variability Research Articles

Assessing the value of deep reinforcement learning for irrigation scheduling

Due to increasing global water scarcity pressure, researchers, policy makers and industry are looking for innovative solutions to increasing agricultural water productivity. Motivated by recent success within complex decision-making environments, Deep Reinforcement Learning (DRL) is being proposed as a method for optimizing irrigation strategies. Early research has hinted towards increased profits with DRL compared to heuristic approaches such as soil-moisture thresholds or fixed schedules. However, an assessment of the value of DRL for irrigation scheduling that incorporates local climate variability and water-use restrictions has yet to be performed. To address this gap in the literature, we created aquacrop-gym, an open-source Python framework for researchers to train and evaluate customized irrigation strategies within the crop-water model AquaCrop-OSPy. In this analysis, aquacrop-gym was used to quantify the value of DRL in comparison to conventional irrigation scheduling techniques (e.g., optimized soil-moisture heuristic) for maize production in an intensively irrigated region of the central United States. The DRL and heuristic approaches were both trained on 70 years of weather data produced from the weather generator LARS-WG, and evaluated on 30 unseen validation years of generated weather data. Findings from this analysis show that in the presence of high rainfall variability, DRL does not outperform conventional optimized heuristics. However, in the scenario where rainfall is set to zero, DRL approaches achieve higher profits on the unseen validation years. Similarly, DRL approaches also outperform optimized heuristics when severe water-use restrictions are introduced. Our analysis demonstrates that DRL approaches are a promising method of irrigation scheduling, notably in regions where farmers are faced with significant physical or regulatory water scarcity.

Impact of Local Climate Variability on Cholera Outbreaks in Bida Metropolis of Niger State, Nigeria

This study assessed local climate variability effects on cholera outbreaks in Bida Metropolis and the environs of Niger State. Bida Metropolis and Environs were purposively sampled. Climatic data (Rainfall and Temperature) of Bida and Environs between the years 2000 to 2020 were collected from the Nigerian Meteorological Agency (NiMET) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), while cases of reported cholera were collected from Umaru Saad General Hospital and Federal Medical Center Bida. Regression analysis of the collected data reveals that there is positive relationship between rainfall and cholera cases with an Fvalue of 0.637. This implies that, more cholera cases were reported during the period of high rainfall. However, there is no positive relationship between temperature and cholera cases with a t-value of 0.987. A two-factor regression analysis of the data reveals there is no significant relationship between the climatic elements combined (rainfall and temperature) and cholera cases. Most of the cases of cholera were reported at the beginning of the rainy season between March and April while the cases of cholera were low during the dry season. The study recommended that the government needs to enlighten the people on the need for adequate environmental sanitation with a high level of consciousness at the beginning of the rainy season of the relationship between climatic elements and cholera incidences. There is a need for the people to be enlightened about climate variability, its effects and the means of mitigating or moderating its impact in order to reduce the influence of climate on cholera outbreak.

Bacterial composition and physicochemical characteristics of sorghum based on environmental factors in different regions of China.

The fermentation process for Jiang-flavored baijiu using sorghum as the raw material involves a variety of microorganisms. However, the specific physicochemical characteristics of sorghum and microbial composition on its surface have not been fully elucidated. We aimed to perform a comprehensive comparative analysis of the variations in physicochemical properties and surface microflora in waxy sorghum samples from three prominent production regions in China (Renhuai, Jinsha, and Duyun). Multivariate statistical assessments were conducted that incorporated local soil and climate variables. The results showed that Cyanobacteria, unclassified bacteria, Proteobacteria, Firmicutes, and Bacteroidota were the dominant bacteria in these regions. These bacteria were associated with ethyl acetate, ethyl caprylate, ethyl lactate, and butyl groups, which synergistically produce flavorful compounds. The surface bacterial communities were affected by soil total phosphorus, altitude, diurnal temperature range, monthly mean temperature, precipitation, and effective accumulated temperature. The findings of this study provide a new perspective on microorganisms related to Jiang-flavored baijiu and can help establish a reference for the stability of liquor quality.

High-throughput phenotyping reveals multiple drought responses of wild and cultivated Phaseolinae beans.

Although drought resistance of a plant may be achieved through morphological, structural, physiological, cellular, and molecular adaptations, most studies remain limited to quantifying the effect of drought on biomass. Using a highthroughput phenotypic imaging system, we evaluated the drought resistance of 151 bean accessions (Phaseolinae; Fabaceae) in an explorative approach, by quantifying five different traits simultaneously: biomass, water use efficiency (WUE), relative water content (RWC), chlorophyll content (NDVI), and root/shoot ratio. Since crop wild relatives are important resources for breeding programs, we analyzed both wild and cultivated accessions, most of which have never been evaluated for drought resistance before. We demonstrate that the five traits are affected very differently by drought in the studied accessions, with significant correlations existing only between the biomass and WUE indicators (r=0.39), and between the RWC and NDVI indicators (r=0.40). When grouping accessions by subgenus or by species, large intraspecific and withinsubgenus variation was found. For this reason, we performed a cluster analysis, which grouped the accessions into five distinct clusters with similar response profiles. We also correlated the drought resistance for each accession to local climate variables at their original collection sites. The biomass, WUE, and RWC indicators were significantly correlated to annual precipitation (r=0.40, r=0.20, r=0.22, respectively), confirming that accessions from arid environments are generally more drought resistant. Our results demonstrate that the drought resistance of Phaseolinae beans is a multifaceted characteristic and cannot be simply quantified through biomass. Furthermore, the broader knowledge of the drought resistance of the accessions studied here may prove an invaluable resource for future crop production.

Functional leaf-trait variability and herbivory in oaks along a Mexican avocado agrosystem mosaic

Land-use changes transforming forested landscapes to avocado orchards affect abiotic conditions and ecological processes. As a consequence, such changes can also affect leaf functional traits expression of co-occurring trees in orchards and related plant–insect interactions. Here, we evaluated changes in chemical soil properties and the effects of micro-climatic variables on leaf functional traits and herbivory in Quercus castanea and Q. obtusata in fragments occurring in a mosaic with avocado orchards. We differentiate three cases: forest cover > orchards, forest cover = orchards and forest cover < orchards in Michoacan state, Mexico. In each case, at least 17 individuals of each oak species were selected to analyze leaf functional traits and herbivory. Soil samples were collected to analyze chemical properties. Soils of areas with higher proportion of forest had higher concentrations of NH4+ and K+, whereas soils with higher proportion of orchards displayed higher concentrations of Mg2+, NO3− and SO42−. Leaf thickness, fresh mass and water content were higher in areas with higher forest cover and were influenced by Mg2+, NO3−, SO42- and local climatic variables. Leaf dry weight, specific leaf area, leaf area and chlorophyll content, as well as herbivory were higher in areas with higher proportions of orchards and were affected by PO43−, total P, K+, NH4+, and by local climatic variables. These results highlight the responses and links of plant functional traits associated with soil properties and local climatic variables and their possible consequences of biotic interactions in a landscape mosaic with different covers of avocado orchards and temperate forests.

Functional traits mediate seedling survival response to climate in a temperate forest

Functional traits are increasingly used to forecast the response of plant performance to abiotic and biotic conditions. However, it remains unclear how functional traits will mediate the complex responses of plants to interactive drivers in a rapidly changing world. Here, we collected 17-year monitoring data on seedlings of 28 locally coexisting species and 8 functional traits in a temperate forest, and built multi-level regression models to evaluate (1) whether seedling survival varied among species along the acquisitive-conservative strategy gradient, and which functional trait affected seedling survival; and (2) how survival of species with different functional strategies responded to negative density dependence (NDD) and climate variation. We found that species with an acquisitive strategy had higher seedling survival. Specifically, seedling survival was positively correlated with specific root length (SRL), specific leaf area (SLA), and leaf area (LA), but inversely correlated with leaf dry mass content (LDMC). Functional strategies along the acquisitive-conservative gradient showed no significant effects on survival response to conspecific and phylogenetic negative density dependence (CNDD and PNDD). Negative effects of heterospecific negative density dependence (HNDD) on seedling survival were weaker for species with higher LDMC, but stronger for seedlings with larger SRL. In addition, species with more conservative strategies had higher resistance to drought stress and freezing damage. For example, species with lower SRL and SLA, and higher LDMC had higher survival under severer seasonal drought. Also, heavier seeds and thicker roots enhanced freezing resistance, whereas higher SRL and LA intensified the negative effects of cold weather. Our study provides strong evidence that functional traits could widely mediate the effects of local biotic context and climatic variation on plant performance dynamics, and help to infer community assemblages and species coexistence in the context of a changing world.

Land‑use and land‑cover dynamics nexus to local climate variability in Suha watershed, upper Blue Nile basin, Northwest Ethiopia

Land‑use and land‑cover dynamics nexus to local climate variability in Suha watershed, upper Blue Nile basin, Northwest Ethiopia

Natural and anthropogenic impacts on mangrove carbon dynamics: a systematic review protocol

The mangrove ecosystem serves as a vital habitat for coastal flora and fauna while playing a crucial role in storing and sequestering carbon as part of global carbon cycles. Therefore, it is imperative to evaluate the carbon dynamics, encompassing storage and sequestration, within mangrove ecosystems and their interconnectedness with natural climate fluctuations and anthropogenic influences, including land-use and land-cover changes (LULCC). Although there has been an increase in monitoring data and literature on mangrove carbon dynamics over the past two decades, there is still limited understanding regarding how climate variability, when combined with anthropogenic drivers, moderates the resilience of carbon storage and sequestration in mangroves. This study aims to build upon and enhance the previous systematic review conducted by Sasmito et al. (2019). Our specific objectives involve collating more recent literature published since 2018 and strengthening the analysis of carbon loss and recovery in tree biomass across different species, as well as its correlation with local and regional climate variations. Additionally, we will explore the impact of various types of land-use and land-cover changes on mangrove forests. Our systematic review will focus on field-based data collected from the Asia Pacific mangrove region, which represents the world’s largest and most diverse mangrove ecosystem and has been extensively studied in comparison to other regions, as indicated by previous systematic reviews. To gather relevant literature, we will conduct comprehensive searches across various databases, including Scopus, Web of Science, and Google Scholar. The structure established by Sasmito et al. (2019) for literature search, screening, and data extraction will be adopted. Data analysis will involve comparing carbon storage and sequestration under locally and regionally varying climatic conditions and anthropogenic influences. Furthermore, we will employ geographical mapping techniques to visualize species distribution and diversity within the Asia Pacific region, while also estimating carbon storage and recovery capacities.

Assessing the combined effects of local climate and mounting configuration on the electrical and thermal performance of photovoltaic systems. Application to the greater Sydney area

Extremely high urban temperatures adversely affect photovoltaic (PV) system performance. Accurate PV cell temperature assessment relies on local weather conditions, exacerbated by urban overheating, often overlooked by inadequate temperature models and non-local data. This study investigates the electrical and thermal PV performance, considering mounting configurations and local conditions. Data from ten weather stations in Greater Sydney (NSW) during 2016–2017, including a hot summer, are used. The Sandia model is used to predict cell temperatures and power output for four mounting configurations, from open rack to building-integrated (BIPV). A PV thermal model is implemented to analyse daytime convection, crucial for understanding PV impact on local climate. Results show peak cell temperatures of 60 °C (open rack) to over 90 °C (BIPV), causing up to 50% power loss and 11% reduction in monthly performance ratio. Local climate variations impact PV energy output up to 6%, with mounting configuration effects up to 11%. Daytime convective flux averages 150–180 W/m2, peaking at 700 W/m2. Convective release varies up to 22% based on local climate, generally higher for open rack than close roof mounts, with potential reversals under low wind speed conditions. These findings can improve the knowledge of PV performances in urban areas facing extreme temperatures.

Stage or Sub-Stage: The Contribution of Small Mammals to the Characterization of Middle Pleistocene Local Climate Variation

In western Europe, the Middle Pleistocene is marked by Acheulean settlement and their diversification after the MIS 12. The Arago cave recovery of numerous human settlements correlate to MIS 14, 13 and MIS 12 making it an important site for the understanding of the Lower Palaeolithic in Southwestern Europe. It is also an important site for the understanding of palaeoenvironments and palaeobiodiversity as it has yielded rich faunal associations. The faunal associations allow us to observe three climatic stages within this study: two cold ones and a mild one. Small vertebrates, with their abundance and their diversity, are particularly useful for observing these periods, which historically have been correlated to glacial or interglacial stages. If the first cold phase, dated 438 ± 31 ka, is correlated to the Marine Isotopic Stage 12 (MIS 12), the correlation of the following phases to isotopic stages can be discussed. They may correspond to climatic variations of the MIS 12. Indeed, the latest studies about palaeoclimatic reconstitution which allow us to define the evolution of the palaeo-temperature show that these differences are relatively small. Therefore, instead of a correlation to MIS 12, 13 and 14, the medium complex of the Arago cave could belong solely to MIS 12. The correlation of these environmental changes to other global data, notably the isotopic curve, is challenging because there are various local factors influencing faunal association. We propose here both hypotheses and discuss the various factors which influence the distribution and the representation of the small vertebrate species present on the site.

Local climate variability associated to sustainable agriculture at rain shadow areas of mount Rinjani and Tambora in West Nusa Tanggara

Climate is driven by solar energy received on the earth surface and local factor, such as mountains. The solar energy affects climate variables namely, temperature, atmospheric pressure, relative humidity, wind and rain. In addition, mountains perform rain shadow areas and affect variability of local climate. A descriptive research was conducted to study variability of those variables associated to direct normal solar irradiation (DNI), and its possible impact on sustainable agriculture at rain shadow areas of Mt. Rinjani in Lombok and Tambora in Sumbawa, West Nusa Tenggara. The results show that the DNI was relative similar at both sides of the Mt. Rinjani which was 13.15±2.6 MJ m−2day−1 on average. While at Mt. Tambora, the DNI at rain shadow area (southern part) was higher than northern part which were 15.6±2.07 MJ.m−2.day−1 and 13.6±3.2 MJ m−2.day−1 respectively. The DNI was relatively high in low lands and it successively decreased through medium and high lands. ETo, air and soil surface temperature were relatively high at rain shadow areas. In conclusion, the intensity of DNI was not significantly affected by aspect factor of incoming light relative to the Mt Rinjani and Mt. Tambora. The mountains had affected the variability of local climate and performed rain shadow areas to the north from Mt Rinjani and to the south from Mt. Tambora, where there had established typical dry land agriculture of semi arid tropics.

Growth dynamic and climate signals on abandoned plantation of Pinus elliottii in Southern Brazil: A dendrochronological contribution

Pinus plantation forestry in South America has expanded quickly and assumed a significant portion of land use, including in urban arborization. This invasive alien genus changes the plant community, resulting in a loss of biodiversity, and environmental issues that require public policy actions. Here, we applied dendrochronology to assess ecological growth dynamics and micro and macro scale climate effects on an abandoned Pinus elliottii trees in urban forest fragments, and provide insights into the management for public policies. We constructed a robust 41-year (1978 – 2019) chronology that showed r = 0.63, rbar = 0.47, and EPS = 0.92 with an average annual growth rate of 7.10 mm/year. Modeling tree growth of diameter, height and volume of P. elliottii based on tree-ring analysis, we assessed the average diameter without bark (42.37 cm) and height (19 m). The growth rates in the early years evidence pioneer behavior, representing rapid sexual maturity. The volume growth model indicates an average individual volume of 1.23 m³ , with maximum productivity at 16 years when the current annual volume increment peaks. This age corresponds to a diameter of 32 cm, which would be an appropriate minimum logging diameter to wood destined for a sawmill. Volumetric increment rates highlight the potential trade-in forestry sector and application in public policies that aim to control and manage P. elliotti. We evidence significant climatic signals, but with moderate influence on the variability of growth. For the local climatic variables, there was a positive influence of warm and humid summer and late spring, during the previous and current growth seasons. Furthermore, considering global climatic factors, there was only a positive influence of the Annual Southern Oscillation Index for June, representing a positive correlation for dry and cold early winter, influenced by La Niña events. This work provides an understanding of the biological growth of P. elliotti in urban forest fragments and opens a perspective to assist public policies in managing the invasion of Pinus spp. linked to the forest trade.

Integrated Multi-Head Self-Attention Transformer model for electricity demand prediction incorporating local climate variables

This paper develops a trustworthy deep learning model that considers electricity demand (G) and local climate conditions. The model utilises Multi-Head Self-Attention Transformer (TNET) to capture critical information from G, to attain reliable predictions with local climate (rainfall, radiation, humidity, evaporation, and maximum and minimum temperatures) data from Energex substations in Queensland, Australia. The TNET model is then evaluated with deep learning models (Long-Short Term Memory LSTM, Bidirectional LSTM BILSTM, Gated Recurrent Unit GRU, Convolutional Neural Networks CNN, and Deep Neural Network DNN) based on robust model assessment metrics. The Kernel Density Estimation method is used to generate the prediction interval (PI) of electricity demand forecasts and derive probability metrics and results to show the developed TNET model is accurate for all the substations. The study concludes that the proposed TNET model is a reliable electricity demand predictive tool that has high accuracy and low predictive errors and could be employed as a stratagem by demand modellers and energy policy-makers who wish to incorporate climatic factors into electricity demand patterns and develop national energy market insights and analysis systems.

Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent using Wavelet-Based Deep Learning Approach

In the present work, a wavelet-based multiscale deep learning approach is developed to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. The proposed multiscale method was tested and validated over the Krishna River basin in India. The results from the proposed methods were compared with contemporary models based on Multiple Linear Regression and Neural Networks. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The wavelet-based methods yielded results with 13–34% reduced error when compared with the best single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast rainfall with reasonable accuracy for different climate zones of the country.

The emergence of a climate change signal in long-term Irish meteorological observations

Detecting the emergence of a forced anthropogenic climate change signal from observations is critical for informing adaptation responses. By regressing local variations in climate onto annual Global Mean Surface Temperature (GMST), we track the emergence of an anthropogenic signal in long-term quality assured observations of temperature and precipitation for the island of Ireland, a sentinel location on the western European Atlantic seaboard. Analysis of station based observations, together with island scale composite series is undertaken for annual and seasonal means, together with 16 indices of extremes, with the derived signal-to-noise ratio classified as normal, unusual or unfamiliar relative to early industrial climate. More than half of indices show the emergence of at least unusual conditions relative to early industrial climate. The increase in annual mean temperature has led to the emergence of unfamiliar climate at six of eleven stations. Warming at the island scale is estimated at 0.88 °C per degree warming in GMST. While many stations show the emergence of unusual climate for spring, summer and autumn mean temperature, no forced signal of change is found for winter mean temperature. Changes in cool/warm days and nights are unfamiliar relative to early industrial climate. However, no anthropogenic signal is found for the hottest day annually or in summer – an extreme often associated with climate change in public consciousness. Increases in annual precipitation totals have emerged as unusual for western stations with large increases in winter totals per degree warming in GMST (e.g., 25.2% and 19.7% at Malin Head and Markree, respectively), indicating heightened flood risk with continued warming. By contrast, summer precipitation shows no significant relationship with GMST. Increases in rainfall intensity have emerged as unusual for 30% of stations, with increases consistent with the Clausius-Clapeyron relationship. Our analysis shows that an emerging climate change signal is discernible for Ireland, a location strongly influenced by climate variability.

Mechanism on the Short-Term Variability of the Atlantic Meridional Overturning Circulation in the Subtropical and Tropical Regions

Abstract The continuous, moored observation revealed significant variability in the strength of the Atlantic meridional overturning circulation (AMOC). The cause of such AMOC variability is an extensively studied subject. This study focuses on the short-term variability, which ranges up to seasonal and interannual time scales. A mechanism is proposed from the perspective of ocean water redistribution by layers. By offering explanations for four phenomena of AMOC variability in the subtropical and tropical oceans (seasonality, meridional coherence, layered-transport compensation as observed at 26.5°N, and the 2009/10 downturn that occurred at 26.5°N), this mechanism suggests that the short-term AMOC variabilities in the entire subtropical and tropical regions are governed by a basinwide adiabatic water redistribution process, or the so-called sloshing dynamics, rather than diapycnal processes. Significance Statement The Atlantic meridional overturning circulation (AMOC) is a key component in the global climate system due to its immense power in redistributing heat meridionally, which contributes to the hospitable climate of the United Kingdom and western Europe. Therefore, any changes in AMOC can have significant impacts on both global and local climate variability. Here I propose a mechanism to explain the short-term (up to interannual) AMOC variability in the subtropical and tropical regions from the perspective of ocean water redistribution. This mechanism suggests that the short-term variability of AMOC strength is dominated by an adiabatic process, and thus, its large-amplitude variation is mostly a reversible process. In other words, AMOC may be more resilient to short-term variability than previously believed, and it could recover autonomously from the abrupt changes.

Moisture sources and climatic effects controlling precipitation stable isotope composition in a western Mediterranean island (Pianosa, Italy)

The Mediterranean basin is indicated as a hot spot of climate change, which is an area whose climate is especially responsive to variations. The insular environment is one of the most threatened by the current climate change, especially in terms of drought events, with serious consequences for water scarcity and water stress. This issue is even enhanced in small islands, whose ecosystems are among more sensitive to climatic changes and water availability. The stable isotope composition of hydrogen (δ2H) and oxygen (δ18O) in precipitation is globally recognized as a powerful natural tracer in the water cycle and represents the starting point to investigate hydrological processes. The understanding of the prevailing factors that drive the isotopic variability of precipitation in the Mediterranean is therefore essential to unravel the hydrological processes and to ensure proper and sustainable management of potentially vulnerable resources to climate change. Here, we discuss the results of multi-year isotopic monitoring in the period 2014–2021 of monthly precipitation collected on Pianosa Island (Italy), a small island located in the northern Tyrrhenian (western Mediterranean). The lower slope and intercept of the Local Meteoric Water Line of the island compared to the Global Meteoric Water Line indicated warmer and drier climatic conditions, suggesting the existence of sub-cloud evaporation processes of raindrops during precipitation, especially in summer. The mean δ18O of precipitation was lower with respect to other sites placed at higher elevation in this Mediterranean region, due to the lack of summer precipitation which were generally enriched in heavy isotopes. Temperature and amount effects may explain part of the δ18O variability observed at the monthly and seasonal scale. An HYSPLIT-based moisture uptake analysis indicated the area between the western Mediterranean basin, Italy, and the Adriatic Sea as the region that supplied most of the humidity associated with monthly precipitation samples on Pianosa Island. Less moisture was picked from the northwestern areas of Europe, the North Atlantic Ocean, the proximal Atlantic Ocean, the Iberian Peninsula and North Africa. Consistently with the rainout effect, the higher the moisture fraction picked from the more proximal regions, the more positive the δ18O of precipitation occurring on Pianosa Island; conversely, the higher the percentage of moisture sourced from more distal regions, the more negative the δ18O. A multiple linear model was proposed to predict the δ18O of monthly precipitation from temperature, precipitation amount and moisture origin data, which explained 45% of the δ18O variability. The deuterium excess variability on the island was partly controlled by the local climatic variables, whose effect potentially modifies the original d-excess signature imprinted at the moisture source. No relationship was found between the precipitation deuterium excess and moisture sources, suggesting that more attention should be paid when using the deuterium excess as a tracer of moisture origin, especially in the Mediterranean.

Species-specific growth responses to local and regional climate variability indicate the presence of a diversity portfolio effect in mediterranean tree assemblages

Understanding the role of tree biodiversity in stabilizing ecosystem processes is of paramount importance in forest ecology, especially because of increasing temperatures and droughts. The portfolio effect is one of the main hypotheses explaining the biodiversity-stability relationship that has been defined as a variance reduction effect of biodiversity in an ecosystem process, so that fluctuations over time in highly diverse communities become significantly smaller in low-diversity communities. In this work, two main objectives were established: to analyze a potential portfolio effect in a tree assemblage through functional mechanisms of compensation, and to identify the local climatic factors and teleconnections responsible for the interannual variations in the growth of tree species. The study was carried out in a region of the Iberian Peninsula with transitional climatic characteristics that allow the coexistence of needleleaf and broadleaf tree species with a wide variety of strategies and functional traits allowing them to respond differentially to climatic conditions. A dendrochronological approach was followed to analyze tree growth in relation to climate factors. We found that the magnitude and sensitivity of the response to both local climatic elements and teleconnections were species-dependent and that during climatically extreme years, the species exhibited marked differences in tree growth and compensatory responses that contributed to an increase in temporal stability. These results point to the existence of a portfolio effect in Mediterranean woodlands that supports tree diversity as an effective driver of ecosystem stability. Although the combination of dry and hot years always has a negative effect on tree growth, the association of coniferous and broadleaved (evergreen and marcescent) species at our study site could have a significantly positive influence by increasing temporal stability at landscape level through complementary functional responses.

Long-term estimation of glacier mass balance using geospatial techniques in Western Himalayas, Ladakh, India

All glaciers are subject to mass fluctuations in the current context of climate change. For daily requirements like food, drink, irrigation, and the generation of hydroelectric power, these glaciers provide water to important basins including the Indus, Ganga, and Brahmaputra. Changes in glacier patterns are a blatant sign of local climate variability. Monitoring of glaciers requires long-term studies on glacier dynamics. Himalayan glaciers, because of their disposition in a complex topographic setting and inaccessible terrain render difficulty in the glacier observations in a continuous mode. Glacier mass fluctuations can be associated with glacier area shrinkage or expansion and concomitant snout shift. In the current study, two adjoining glaciers of different sizes, Pensilungpa and Drang Drung glaciers in the Zanskar Valley, Ladakh, India are selected. The period of the study was taken between the years 2000–2022. Earlier studies used a single day per year Accumulation Area Ratio (AAR) method to compute mass balance, which had limitations due to snow cover variability. The present study calculated and averaged all the AAR values for cloud-free images per year during the peak ablation period (mid-July to early September). Digital Elevation Model (DEM) difference technique was also employed for computing the mass budget between 2000 and 2021 b y utilizing two-time period DEMs. It was revealed that in the case of the AAR method, the Pensilungpa glacier showed 7 years of positive and 16 years of negative mass balance. The years 2003–2005 and 2011 to 2016 were depicted with negative mass balance with the highest value up to −0.752-m water equivalent (m.w.e.) for the year 2015. It has negligible areal fluctuations ranging from 0.01 to 0.6 km2. Drang Drung Glacier has shown 12 years of positive and 11 years of negative mass balance. The years 2002–2004 and 2009 to 2012 were depicted with positive mass balance with the highest value up to 0.305 m. w.e. Except, for the year 2009–2012 (areal increment ∼ 2.65 km2), years 2013–2015 showed a negative mass balance with negligible areal fluctuations. Mass balance estimation using DEM differencing method revealed an average estimated mass balance of −0.03 m. w.e. For Pensilungpa glacier though, it is 0.08 m. w.e. For Drang Drung glacier which shows good matches with the mass balance estimated using AAR method in m. w.e. Such contrasting behavior of mass balance suggests higher sensitivity of smaller glaciers to climate change.

Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity

In life-cycle impact assessment, currently available characterisation factors (CF) for climate change impacts on biodiversity are highly simplified and do not consider spatial and taxonomic differentiation of species or local climate variability. We develop the first spatially and taxonomically specific CFs for the impacts of 20 GHGs on biodiversity considering 26,648 species across terrestrial and marine ecosystems. Generally, CFs are higher in the tropics, and marine species are affected more severely than terrestrial ones. When global GHG emissions from 2020 are assessed in a scenario with a global temperature rise of 3 °C by 2100, an average of 0.25%, 0.15% and 0.03% of species are negatively affected in 2100 from CO2, CH4, and N2O emissions, respectively, across the globe. The new CFs can be used at different levels of spatial and taxonomic aggregation to quantify co-benefits for biodiversity of climate change mitigation in tools such as life-cycle assessment, input-output analyses, or integrated assessment models.